**Intelligent Usage of Internal Combustion Engines in Hybrid Electric Vehicles**

Teresa Donateo

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/48120

## **1. Introduction**

Since early 1900s, gasoline and diesel internal combustion engines have represented the most successful automotive powering systems despite their low efficiency, their emissions issues and the increasing cost of fuel. Their main advantage over both gas engines and Battery Electric Vehicles (BEVs) is the very high energy density of liquid fuel that allows long driving ranges with small (and light-weight) storage tanks and safe and fast refueling processes. Moreover, gasoline and diesel fuels have an established infrastructure of distribution that is difficult and very expensive to replicate for other energy sources.

Environmental issues, energy crises, concerns regarding peaking oil consumption and the expected increase of number of cars in developing countries have eventually encouraged research into alternative energy sources. However, they are still unable to penetrate the market for several technological limitations.

The main drawback of BEVs resides in the batteries. They are still too expensive, too bulky and heavy (due to their low energy density). Moreover, they have an unsatisfactory life cycle and require long recharging times. Vehicles using fuel cell (FCV) a very clean fuel conversion system, have technologic drawback even higher. They add to the problems of a BEV, the use of a very light gaseous fuel that has severe limitations in terms of producing process, storing system, safety and distribution infrastructure. Thus, they are not to be considered as a viable way for eco-mobility in the next future (German, 2003).

Hybrid electric vehicles are characterized by the presence of two different typologies of energy storage systems: usually a battery and a gasoline or diesel fuel tank. HEVs have no limitation of range with respect to conventional vehicle and use the existing distribution infrastructure. The main advantages of HEVs are: the flexibility in the choice of engine operating point that allows the engine to be run in its high efficiency region and the

possibility of downsizing the ICE and so obtaining a higher average efficiency. Moreover, the engine can be turned off when the vehicle is arrested (e.g., at traffic lights) or the power request is very low (reduction of the idle losses).

PHEVs can be considered either as BEVs that can be run in hybrid mode when the state of the charge (SOC) of the batteries is low or as HEVs with batteries that can be recharged from the electricity grid. They are characterized by the use of much larger battery pack when compared with standard HEVs. The size of the battery influences the All Electric Range (AER), an important design parameters of PHEVs that is defined as the number of miles they vehicle can run in pure electric mode on the UDDS cycle. A vehicle is classified as PHEVXY if it has an AER of XY miles.

PHEVs require fewer fill-ups at the gas station than conventional cars and have the advantage, over HEV, of home recharging.

BEVs, HEVs, and PHEVs have also the capability of partially recovering energy from brakes by inverting the energy flow from batteries to wheels through the electric machine.

Simpson, 2006 presented a comparison of the costs (vehicle purchase costs and energy costs) and benefits (reduced petroleum consumption) of PHEVs relative to HEVs and conventional vehicles. On the basis of his model, Simpson found that PHEVs can reduce per-vehicle petroleum consumption. In particular, reductions higher than 45% in the petroleum consumption can be achieved using designs of PHEV20 or higher (i.e. vehicles containing enough useable energy stored in their battery to run more than 20 mi (32 km) on the UDDS cycle in electric mode according to the previous definition of AER).

The study of Simpson, 2006 underlined that from the economic point of view, the PHEVs can become a competitive technology is the cost of petroleum will continue to increase and the cost of the batteries will decrease.

Because of different characteristics of multiple energy sources, the fuel economy and the environmental impact of hybrid vehicles mainly depend on a proper power management strategy. The particular operating strategy employed in this kind of vehicles significantly influences the component attributes and the value of the PHEV technology (Gonder et al. 2007).

Generally speaking, the environmental impact of an ecologic vehicle has to be determined with a "well to wheel" (WTW) approach. From a "tank to wheel" (TTW) point of view, a BEV, or a PHEV running in electric mode do not produce either pollutant or greenhouse gases while the emissions of pollutant and CO2 in the WTW processes depend on the primary source and the technology used to generate electric energy at the grid. The well-towheel CO2 emissions of a FCV can be equal to those of a diesel engine vehicle if it uses hydrogen produced from non-renewable energies sources (Guzzella and Sciaretta, 2007).

In a hybrid vehicle, the local emissions of CO2 and pollutant strongly depend on the management strategy used for the ICE that becomes the main issue in both HEVs and PHEVs.

## **2. Classification of hybrid vehicles**

134 Internal Combustion Engines

request is very low (reduction of the idle losses).

PHEVXY if it has an AER of XY miles.

the cost of the batteries will decrease.

2007).

PHEVs.

advantage, over HEV, of home recharging.

possibility of downsizing the ICE and so obtaining a higher average efficiency. Moreover, the engine can be turned off when the vehicle is arrested (e.g., at traffic lights) or the power

PHEVs can be considered either as BEVs that can be run in hybrid mode when the state of the charge (SOC) of the batteries is low or as HEVs with batteries that can be recharged from the electricity grid. They are characterized by the use of much larger battery pack when compared with standard HEVs. The size of the battery influences the All Electric Range (AER), an important design parameters of PHEVs that is defined as the number of miles they vehicle can run in pure electric mode on the UDDS cycle. A vehicle is classified as

PHEVs require fewer fill-ups at the gas station than conventional cars and have the

BEVs, HEVs, and PHEVs have also the capability of partially recovering energy from brakes

Simpson, 2006 presented a comparison of the costs (vehicle purchase costs and energy costs) and benefits (reduced petroleum consumption) of PHEVs relative to HEVs and conventional vehicles. On the basis of his model, Simpson found that PHEVs can reduce per-vehicle petroleum consumption. In particular, reductions higher than 45% in the petroleum consumption can be achieved using designs of PHEV20 or higher (i.e. vehicles containing enough useable energy stored in their battery to run more than 20 mi (32 km) on the UDDS

The study of Simpson, 2006 underlined that from the economic point of view, the PHEVs can become a competitive technology is the cost of petroleum will continue to increase and

Because of different characteristics of multiple energy sources, the fuel economy and the environmental impact of hybrid vehicles mainly depend on a proper power management strategy. The particular operating strategy employed in this kind of vehicles significantly influences the component attributes and the value of the PHEV technology (Gonder et al.

Generally speaking, the environmental impact of an ecologic vehicle has to be determined with a "well to wheel" (WTW) approach. From a "tank to wheel" (TTW) point of view, a BEV, or a PHEV running in electric mode do not produce either pollutant or greenhouse gases while the emissions of pollutant and CO2 in the WTW processes depend on the primary source and the technology used to generate electric energy at the grid. The well-towheel CO2 emissions of a FCV can be equal to those of a diesel engine vehicle if it uses hydrogen produced from non-renewable energies sources (Guzzella and Sciaretta, 2007).

In a hybrid vehicle, the local emissions of CO2 and pollutant strongly depend on the management strategy used for the ICE that becomes the main issue in both HEVs and

by inverting the energy flow from batteries to wheels through the electric machine.

cycle in electric mode according to the previous definition of AER).

Hybrid Electric Vehicles can be classified according to their architecture, the discharge/recharge mode of batteries and the level of hybridization.

As for architecture, HEV are called "parallel" when they use a gasoline or diesel engine mechanically coupled with an electric motor at the same shaft to satisfy the power request at the wheels. A parallel HEV can be run in five modes of operation (Guzzella et al, 2007): power assist (the electric motor give the supplementary torque to the shaft when the request is higher than engine available torque), battery recharging (a part of the engine power is used to recharge the batteries), electric mode (engine turned off), conventional vehicle (electric motor turned off) and regenerative braking.

In a "series" hybrid, the power request is entirely satisfied by the electric motor. Electric current to the motor is the algebraic sum of the current form/to the batteries and the current produced by an engine-driven generator. A series HEV can be run in four modes (the same of a parallel vehicle apart from conventional mode since the engine is not connected to the shaft).

Combined hybrid that can be run either in parallel and series mode have also been developed and introduced in the automotive market.

Traditionally, series HEVs have been neglected in scientific literature since they are less efficient than parallel HEVs and require more additional weight. Moreover, their energy management was considered trivial: a simple on-off engine control was considered sufficient. However, the increasing interest in plug-in vehicles has given new impulse to the research of advanced control strategies for series architectures.

There are two possible ways to regulate the energy management of hybrid vehicles with batteries. The first one (charge depleting mode, CD) accepts the batteries to be completely discharged during the mission. In this mode, the battery SOC can increase or decrease in time but it tends to be reduced along the mission. This approach can be considered for plug-in vehicles only. The second one (charge sustaining mode, CS) tries to keep the battery always charge to not affect the vehicle autonomy. The SOC can increase or decrease in time but it tends tore main constant during the mission (for series and parallel HEVs, not possible for BEV).

A PHEVs is usually run in CD mode without using the engine until reaching a pre-assigned lower bound on the SOC, then a CS strategy is adopted. Another possibility is to discharge gradually the battery throughout the trip as in the so-called *blended mode* control (Tulpule et al., 2009).

This makes a PHEV more complex, more dependent on traffic and route information and more efficient than a standard series HEV.

Another classification of importance for hybrids is the degree of hybridization. *Micro-Hybrids* are quite similar to conventional vehicles, from that they differ for the presence of a slightly larger battery and a little more powerful electric motor that allow the engine to be turned off when the car is stopped at the cross-lights and then turned on again when the vehicles moves. This system, named Start&Stop is nowadays adopted by several automotive companies in order to fulfill the Euro V standard. It does not require an increase of the voltage of the electric systems. The increase in cost and complexity is quite small like the potentiality to decrease fuel consumption. By further increasing the electric power and the voltage, it is possible to recover the braking energy (+5-10% in fuel economy, Chan, 2007). If the power of the electric motor increases, the internal combustion engine can be downsized and the electric motor is used to increase the pick power with the Power Assist logic. This is the case of *mild hybrids* (like Honda Civic and Honda Insight), that can increase fuel economy by 20-30% (Chan, 2007) with a similar increase of cost. Mild hybrids usually are not able to be run in all electric drive like *Full Hybrids*. Full hybrids can achieve a 40-50% higher fuel economy than conventional car. (Chan, 2007) They work with very high tension in order to accept the largest electric power. They can be sub classified in Synergy Hybrids and Power Hybrids. The former are designed to maximize fuel economy (downsized engine) while the latter use the electric motor to increase the available torque (nodownsizing).

Finally, the term *Range extender* is used to define series hybrid vehicles where the small engine-alternator group is only used to recharge the battery when their SOC is too low.

## **2.1. Designing and managing internal combustion engines for hybrid applications**

The role of the internal combustion engine in hybrid electric vehicles (HEVs) is quite different from conventional vehicle. The engine has no more to be designed to fulfill the performance (maximum speed, acceleration and climb) required for the vehicle but can be downsized, thus reducing fuel consumption and greenhouse emissions. Moreover, the internal combustion engine can be better managed in order to avoid low-efficiency and high-emission operations like idling, vehicle stops and strong accelerations.

The current approach to HEV design is to use internal combustion engines developed for conventional vehicles. From this point of view, the advantage of fuel economy of HEVs can be actually defeated by the higher complexity, weight and volume of the power-train. However, many of the electronic-controlled devices used in engine to increase their efficiency and reduce emissions at idle and low speed-low torque operating mode are completely useless in HEV applications. This means that simpler, lighter and less costly engine could be developed for hybrid applications.

It is well known that internal combustion engines have poor fuel economy and larger if they work at low temperature. This is particularly important in hybrid electric vehicles since they allow the engine to be turned off for long periods during which the engine temperature decreases. This can lead to higher cold-start emissions particularly due to the poor conversion efficiency of the after-treatment devices when the light off temperature is not reached. On the other hand, hybrid electric allow either engine or after-treatment devices or both devices to be controlled to reduce the warm-up period and improve their performances in a fully integrated approach (Bayar et al., 2010). In HEV, the engine is cranked to higher speed than conventional vehicles and this makes the combustion condition during startup process quite different. Yu et al., 2006, investigated the effect of cranking speed on the start/stop operation of a gasoline engine for hybrid applications. Once again, fuel economy and emission during the engine start process depend on the control strategy used for the engine and the motor.

In order to reduce the warm-up period of the engine Lee et al., 2011 considered the recovering of exhaust gas heat exchanging system with coolant and gear box oil simultaneously. Accordingly, they developed an exhaust heat recovery device, which performs integral heat exchange of the exhaust gas heat of engine to increase the temperature of the coolant and the gear box oil, thereby reducing friction loss and improving fuel economy.

## **2.2. Approaches to the supervisory control models**

136 Internal Combustion Engines

downsizing).

**applications** 

turned off when the car is stopped at the cross-lights and then turned on again when the vehicles moves. This system, named Start&Stop is nowadays adopted by several automotive companies in order to fulfill the Euro V standard. It does not require an increase of the voltage of the electric systems. The increase in cost and complexity is quite small like the potentiality to decrease fuel consumption. By further increasing the electric power and the voltage, it is possible to recover the braking energy (+5-10% in fuel economy, Chan, 2007). If the power of the electric motor increases, the internal combustion engine can be downsized and the electric motor is used to increase the pick power with the Power Assist logic. This is the case of *mild hybrids* (like Honda Civic and Honda Insight), that can increase fuel economy by 20-30% (Chan, 2007) with a similar increase of cost. Mild hybrids usually are not able to be run in all electric drive like *Full Hybrids*. Full hybrids can achieve a 40-50% higher fuel economy than conventional car. (Chan, 2007) They work with very high tension in order to accept the largest electric power. They can be sub classified in Synergy Hybrids and Power Hybrids. The former are designed to maximize fuel economy (downsized engine) while the latter use the electric motor to increase the available torque (no-

Finally, the term *Range extender* is used to define series hybrid vehicles where the small engine-alternator group is only used to recharge the battery when their SOC is too low.

The role of the internal combustion engine in hybrid electric vehicles (HEVs) is quite different from conventional vehicle. The engine has no more to be designed to fulfill the performance (maximum speed, acceleration and climb) required for the vehicle but can be downsized, thus reducing fuel consumption and greenhouse emissions. Moreover, the internal combustion engine can be better managed in order to avoid low-efficiency and

The current approach to HEV design is to use internal combustion engines developed for conventional vehicles. From this point of view, the advantage of fuel economy of HEVs can be actually defeated by the higher complexity, weight and volume of the power-train. However, many of the electronic-controlled devices used in engine to increase their efficiency and reduce emissions at idle and low speed-low torque operating mode are completely useless in HEV applications. This means that simpler, lighter and less costly

It is well known that internal combustion engines have poor fuel economy and larger if they work at low temperature. This is particularly important in hybrid electric vehicles since they allow the engine to be turned off for long periods during which the engine temperature decreases. This can lead to higher cold-start emissions particularly due to the poor conversion efficiency of the after-treatment devices when the light off temperature is not reached. On the other hand, hybrid electric allow either engine or after-treatment devices or both devices to be controlled to reduce the warm-up period and improve their performances

**2.1. Designing and managing internal combustion engines for hybrid** 

high-emission operations like idling, vehicle stops and strong accelerations.

engine could be developed for hybrid applications.

The capability of a HEV in reducing fuel consumption and pollutant emissions strongly depends on the supervisory control strategy and the specific driving conditions. In fact, in hybrid electric vehicles a supervisor control system defines in each time the power split between the fuel conversion system (engine/alternator or fuel cell) and the electric storage systems (batteries and/or super capacitors) in order to minimize fuel consumption, sustain battery charge and reduce polluting emissions. Note that these goals are competitive and the performance of the HEV strongly depends on which goal it is given a higher importance. The optimization should be performed, ideally, over the entire life cycle of the vehicle even if a much shorter time interval (from a small number of minutes to few hours) is usually taken into account.

Several approaches for the optimization of energy management of a HEV have been presented in literature (Serrao, 2009). They can be classified in four categories: numerical optimization, analytical optimal control theory, instantaneous optimization and heuristic control techniques.

Heuristic control techniques are based on a set of rules that generate control action (i.e., the power to be delivered from the two energy sources) according to the value of some vehicle parameters like speed, acceleration, battery SOC, etc. These methods easy to implement in vehicles but they do not guarantee the minimization of either fuel consumption or emissions and the achievement of charge sustaining at the end of the mission.

Numerical optimization usually applies dynamic programming to optimize the vehicle behavior with the unrealistic assumption of perfect knowledge of the vehicle driving conditions (Lin et al, 2003).

An alternative to dynamic program is the application of the Pontrayagin's principle. This approach assumes that the power train can be described with simple analytical functions. Thus, it is often a too simplified approach and it also requires the knowledge of the driving cycle to be applied (Anatone et al. 2005, Serrao et al. 2008).

In the instantaneous optimization approach, the global minimization problem is implemented and solved as a sequence of local optimization problems. The best known of these strategies is the Equivalent Consumption Minimization Strategy for chargedsustaining vehicles. The ECMS tries to minimize the equivalent fuel consumption that is calculated as the sum, in a time interval Δt, of the actual engine fuel consumption and the fuel equivalent of the electric energy stored in/extracted from the battery in the time interval Δt. Since battery is only used as an energy buffer, its energy is produced ultimately by the fuel that the engine has consumed/saved in the past (or will consume in the future). The main drawback of the approach is that it requires the definition of equivalent factors in the conversion of fuel energy to electrical energy and vice versa (Guzzella and Sciarretta, 2007).

Recently, Millo et al. 2011 extended the ECMS technique to include engine emissions. In particular, they correlated the use of the battery with equivalent NOx emissions and compared the results of the fuel consumption-oriented optimization and the NOx optimization in terms of State of Charge history, engine operating points, etc. with respect to several standard driving cycles.

The usage of standard driving cycles in the optimization of the control strategies is a common way to obtain sub-optimal controller that, however, can give poor results in the real driving conditions.

## **2.3. Prediction of vehicle driving patterns**

As explained before, the possibility of estimating the future driving profile (speed and related power demand) is a key issue in the development of hybrid vehicles. In fact, the supervisory controller of a HEV could use the future speed profile to optimize the power split in a future time window in order to minimize fuel consumption, pollutant emission, battery usage and so on. Moreover, the information about future can be used to activate the electric warming of engine and after-treatment devices. In this way they will be at the right temperature when the engine will be turned on and the exhaust gas flow will enter the aftertreatment device.

In literature, a number of "auto-adaptive" techniques which try to predict future driving conditions based on the past ones have been defined A possible approach is to predict the future driving conditions based on the past behavior of the vehicle (Sciarretta et al, 2004) relying on the assumption that similar operating conditions will exist. But the future driving profile also depends on the instantaneous decisions which the driver will take to respond to the physical environment (driving patterns). Moreover, recent studies have shown that driver style, road type and traffic congestion levels impact significantly on fuel consumption and emissions (Ericson, 2000, Ericson, 2001). For these reasons, the control strategies proposed in some schemes (Won et al, 2005) incorporate the knowledge of the driving environment.

In the case of series HEV, the knowledge of the driving conditions have been found in literature to be less important than in the case of parallel hybrids (Barsali et al,. 2004).

In the case of plug-in hybrid electric vehicles, the control is more complex, strongly depending of the initial value of SOC and on the mission length, particularly if Blended Mode control methods are used. In fact, if the total trip was known, the best results would be obtained if the SOC would reach the lower value at the end of the trip (Karbowski et al. 2006). Gong et al. 2007, developed an Intelligent Transportation System that uses GPS information and historical traffic data do define the driving patterns to be used in the optimization. Donateo et al. 2011, have estimate numerically that the knowledge of the driving cycle in a future time window of 60s can improve fuel consumption in a series PHEV with Blended Mode control by 20%.

## **3. ICT and sustainable mobility**

138 Internal Combustion Engines

several standard driving cycles.

**2.3. Prediction of vehicle driving patterns** 

real driving conditions.

treatment device.

environment.

In the instantaneous optimization approach, the global minimization problem is implemented and solved as a sequence of local optimization problems. The best known of these strategies is the Equivalent Consumption Minimization Strategy for chargedsustaining vehicles. The ECMS tries to minimize the equivalent fuel consumption that is calculated as the sum, in a time interval Δt, of the actual engine fuel consumption and the fuel equivalent of the electric energy stored in/extracted from the battery in the time interval Δt. Since battery is only used as an energy buffer, its energy is produced ultimately by the fuel that the engine has consumed/saved in the past (or will consume in the future). The main drawback of the approach is that it requires the definition of equivalent factors in the conversion of fuel energy to electrical energy and vice versa (Guzzella and Sciarretta, 2007). Recently, Millo et al. 2011 extended the ECMS technique to include engine emissions. In particular, they correlated the use of the battery with equivalent NOx emissions and compared the results of the fuel consumption-oriented optimization and the NOx optimization in terms of State of Charge history, engine operating points, etc. with respect to

The usage of standard driving cycles in the optimization of the control strategies is a common way to obtain sub-optimal controller that, however, can give poor results in the

As explained before, the possibility of estimating the future driving profile (speed and related power demand) is a key issue in the development of hybrid vehicles. In fact, the supervisory controller of a HEV could use the future speed profile to optimize the power split in a future time window in order to minimize fuel consumption, pollutant emission, battery usage and so on. Moreover, the information about future can be used to activate the electric warming of engine and after-treatment devices. In this way they will be at the right temperature when the engine will be turned on and the exhaust gas flow will enter the after-

In literature, a number of "auto-adaptive" techniques which try to predict future driving conditions based on the past ones have been defined A possible approach is to predict the future driving conditions based on the past behavior of the vehicle (Sciarretta et al, 2004) relying on the assumption that similar operating conditions will exist. But the future driving profile also depends on the instantaneous decisions which the driver will take to respond to the physical environment (driving patterns). Moreover, recent studies have shown that driver style, road type and traffic congestion levels impact significantly on fuel consumption and emissions (Ericson, 2000, Ericson, 2001). For these reasons, the control strategies proposed in some schemes (Won et al, 2005) incorporate the knowledge of the driving

In the case of series HEV, the knowledge of the driving conditions have been found in

literature to be less important than in the case of parallel hybrids (Barsali et al,. 2004).

## **3.1. Intelligent vehicle technologies**

According to Gusikhin et al. 2008, a vehicle can be defined as intelligent if it is able to sense its own status and that of the environment, to communicate with the environment and to plan and execute appropriate maneuvers. The first application of intelligent vehicle systems has been the increase of safety by providing driver assistance in critical moments. A combination of on-board cameras, radars, lidars, digital maps, communication from other vehicles or highway systems are used to perform lane departure warning, adaptive cruise control, parallel parking assistants, crash warning, automated crash avoidance, intelligent parking systems.

Markel et al. 2008 studied the effect of integration between an electrified vehicle fleet and the electric grid in order to increase the amount of renewable energy used to power the electric vehicles by optimizing the timing and the power of the charging processes during the day. Different communication protocols have been considered and compared by Markel et al. Intelligent Transport Systems like traffic management can have a direct effect on the emissions of CO2 produced by the automotive floats (Dimitrakopoulos, 2011). According to Janota et al. 2010, Intelligent Transportation Systems can reduce consumption and emissions by acting on the vehicle (by monitoring and controlling the engine), on the infrastructure (reduction of number/duration of congestions and stoppage, optimization of intersection, cooperative systems to avoid congestions) and on the driver (planning of ecologic routes based on real-time information, support to driver for economic drive).

Recently, Information and Communication Technologies (ICT) techniques have been proposed for gathering information about the vehicle routes and road conditions that could allow the evaluation of the future power request of the vehicle over a large time window. ICT techniques can be used to estimate the future driving profile, suggest low consumption behaviors to the driver, propose alternative route, communicate the position and the status of electric recharging stations, etc. (Sciarretta et al, 2004).

Schuricht et al. 2010 analyzed two active energy management measures. The first one, uses advanced traffic light, and communication systems to support the driver during intersection approaching. The second one explores the uses of information and sensor sources from the traffic telematics for the predictive online optimal control of hybrid vehicles.

## **3.2. The CAR approach**

The role of Intelligent Transport Systems in the improvement of PHEV performance and spreading of vehicles electrification is a research issue at the Center for Automotive Research at the Ohio State University. Starting from the awareness that traffic, weather and road conditions will be available in the next future through vehicle-to-vehicle and vehicleto-infrastructure communications, the researchers at CAR emphasize the possibility this information in order to adapt the tuning of the energy management controller in HEVs, predicting the future driving profile, signaling the availability of recharge stations, predicting the route and generating statistical information for modifying pre-stored maps.

In the paper of Tulpule et al. 2011, the authors concentrated on the impact of the available data on the energy management in order to identify the most important factors on the actual fuel consumption of a PHEV. The factors analyzed in the investigation, named "Impact Factors", derive from both weather information (temperature and humidity) and traffic information (status of traffic lights, presence of pedestrian, road events in intra-city highway and inter-city highway). Their importance on the performance of the ECMS strategy were evaluated on the basis of a large amount of data acquired on a Toyota Prius converted to plug-in mode. The plug-in Prius has been run for a total of 60,000 miles in the campus area of the Ohio State University and several parameters like GPS information, temperature, fuel consumption, battery SOC, etc. were collected along with time and date.

To study the effect of the driving patterns, Gong et al. 2011 used a statistic approach to analyze real world profiles and derive information about average speed, speed limits, segment length, etc. These data were used to build a series of reference driving cycles by using the Markov chain modeling. The results of the investigation showed that the driving patterns have a relevant effect on the performance of a plug-in HEV and that the statistic values of acceleration have the largest impact of the tuning of the ECMS strategy.

## **3.3. The CREA approach**

The CREA idea of intelligent hybrid vehicle includes the possibility of sensing the traffic environment in which it moves to predict the future driving conditions (Ciccarese et al. 2010). In particular, the vehicle is assumed to receive information from GPS, on-board sensors and vehicular communications. The scheme of the intelligent HEV according to the CREA research center is shown in Figure 1.

This information can be used on-board to perform a simulation of the traffic in a pre-set time window in order to predict the power request pattern in the next future and execute on-line optimization of the energy management over the predicted power pattern. The main difference with the CAR approach is that the vehicle is assumed to be able to compute onboard a simulation of the traffic conditions by using a microscopic road traffic simulation to derive its own future power request profile and optimize fuel consumption, battery usage, emissions levels, etc. This approach requires a relevant on-board computational capability that we believe could be available in the next future for other applications like safety, entertainments and so on. Alternatively, the simulation of the traffic patterns and the calculation of the speed profiles of the vehicles in a particular urban zone could be performed by a central computational unit that could send the results to the vehicles circulating in that zone.

**Figure 1.** An intelligent hybrid vehicle according to the CREA approach

The gray area in Figure 1 represents the tools to be implemented on board. They include the prediction system, which is used to estimate the future speed profile of the vehicle, a power train simulator, which evaluates the evolution of fuel consumption and battery SOC during the prediction interval, and an optimizer, which is used to optimize the parameters of the control strategy.

## *3.3.1. The prediction block*

140 Internal Combustion Engines

**3.2. The CAR approach** 

**3.3. The CREA approach** 

CREA research center is shown in Figure 1.

The role of Intelligent Transport Systems in the improvement of PHEV performance and spreading of vehicles electrification is a research issue at the Center for Automotive Research at the Ohio State University. Starting from the awareness that traffic, weather and road conditions will be available in the next future through vehicle-to-vehicle and vehicleto-infrastructure communications, the researchers at CAR emphasize the possibility this information in order to adapt the tuning of the energy management controller in HEVs, predicting the future driving profile, signaling the availability of recharge stations, predicting the route and generating statistical information for modifying pre-stored maps.

In the paper of Tulpule et al. 2011, the authors concentrated on the impact of the available data on the energy management in order to identify the most important factors on the actual fuel consumption of a PHEV. The factors analyzed in the investigation, named "Impact Factors", derive from both weather information (temperature and humidity) and traffic information (status of traffic lights, presence of pedestrian, road events in intra-city highway and inter-city highway). Their importance on the performance of the ECMS strategy were evaluated on the basis of a large amount of data acquired on a Toyota Prius converted to plug-in mode. The plug-in Prius has been run for a total of 60,000 miles in the campus area of the Ohio State University and several parameters like GPS information, temperature, fuel

To study the effect of the driving patterns, Gong et al. 2011 used a statistic approach to analyze real world profiles and derive information about average speed, speed limits, segment length, etc. These data were used to build a series of reference driving cycles by using the Markov chain modeling. The results of the investigation showed that the driving patterns have a relevant effect on the performance of a plug-in HEV and that the statistic

The CREA idea of intelligent hybrid vehicle includes the possibility of sensing the traffic environment in which it moves to predict the future driving conditions (Ciccarese et al. 2010). In particular, the vehicle is assumed to receive information from GPS, on-board sensors and vehicular communications. The scheme of the intelligent HEV according to the

This information can be used on-board to perform a simulation of the traffic in a pre-set time window in order to predict the power request pattern in the next future and execute on-line optimization of the energy management over the predicted power pattern. The main difference with the CAR approach is that the vehicle is assumed to be able to compute onboard a simulation of the traffic conditions by using a microscopic road traffic simulation to derive its own future power request profile and optimize fuel consumption, battery usage, emissions levels, etc. This approach requires a relevant on-board computational capability that we believe could be available in the next future for other applications like safety,

consumption, battery SOC, etc. were collected along with time and date.

values of acceleration have the largest impact of the tuning of the ECMS strategy.

This block gathers status messages that surrounding vehicles and/or the infrastructure broadcast. Messages transmitted by a vehicle carry status information, such as position, speed, acceleration, etc., and, optionally, some information related to its route. Messages generated by the infrastructure, instead, carry the current status and the timing of traffic lights. Besides the status information received through vehicular communications, the system gathers the status information on the "predicting vehicle" locally obtained by a GPS receiver and/or on-board sensors and also retrieves the data on road network from the digital maps used by the GPS navigation device.

The information gathered is exploited to take, at regular intervals, a snapshot of the traffic scenario in a given area. Each snapshot is the input to a run of module which simulates the traffic dynamics over a certain time interval, whose duration is at most equal to the prediction horizon. In Ciccarese et al. 2010, a modified version of SUMO software has been considered as on-board simulator.

SUMO (Simulation of Urban MObility) is an open source microscopic road traffic simulator. The input parameters of SUMO consist of the road network, the characteristics of each vehicle, the path (route) that each vehicle follows and the timing of traffic lights.

Vehicles with the same characteristics are grouped in classes and, for each class, a set of mechanical specifications is provided maximum speed, acceleration and deceleration., vehicle length, mass, friction coefficients, etc.

The road network is represented by an oriented graph, where nodes correspond to intersections and arcs to one-way lanes. For each lane, the maximum speed, the slope and the classes of vehicles which are allowed to go along it have to be also specified. The route of a vehicle consists of a list of consecutive arcs in the graph.

Using the input data, SUMO generates a mobility trace for all vehicles according to a Car-Following model (Wang et al. 2001): each vehicle tries to hold its speed close to the maximum one allowed for the current lane and decelerates if it is approaching either to an intersection or to another vehicle on the same lane; in the latter case, its speed is adapted to that of the vehicle which moves ahead of it.

The accuracy of the proposed prediction method has been tested experimentally (Ciccarese et al. 2012) in a augmented reality environment to simulate the presence in the Ecotecke campus of a certain number of vehicles able to communicate with the target vehicle. The experimental campaign showed that the inaccuracy of the prediction method is below 4km/h. In Figure 2, a comparison is shown between the predicted and the actual speed profile of the target vehicle in a time window of 100s. More details about the experimental campaign can be found in Ciccarese et al. 2012.

**Figure 2.** Example of speed profiles obtained by the experimental environment

#### *3.3.2. The power-train simulator*

The Power train simulator block implements a model of the power-train. The block processes the output of the prediction system and calculates the related power demand of the predicting vehicle by considering aerodynamic force, inertial contribution, rolling force and grade force. Information from on board sensors (ambient temperature, asphalt conditions, tires pressure and temperature) can be used to correct the predicted load. Then, the block simulates the energy flows according to the selected energy management strategy (described above) and evaluates the evolution of fuel consumption and battery SOC during the prediction interval.

Two different paradigms are usually considered to simulate a hybrid vehicle (Guzzella and Sciaretta, 2007). In the backward paradigm, the velocity of the vehicle is an input. According to the vehicle specification and speed values, the power request at the wheel is calculated. By means of static maps, the energy consumption of both engine and batteries is calculated according to the selected energy management strategy. If the power-train is not able to meet the cycle requirements, the acceleration is reduced and the vehicle diverges from the driving cycle.

In a forward or dynamic model, the power requested by the driver through the acceleration and braking pedals is used as input to evaluate the acceleration and the vehicle speed. This kind of model is used for the development of the control systems, while the backward method is best suited for analysis and evaluation of the energy and power flow in the vehicle driveline. Thus, a backward model is considered in the proposed scheme.

If the driving cycle is predicted with a traffic model that takes into account the actual acceleration and deceleration capability of the power-train, it is not necessary to check if the vehicle is able to follow the prescribed driving cycle.

## *3.3.3. The energy management system*

This block implements the supervisor control system which defines, at each time, the power split between the fuel conversion system (engine/alternator in a series HEV) and the electric storage systems (generally batteries) with the constraints that the sum of the power extracted from each energy source must be equal to the total power requested at the wheels.

## *3.3.4. The optimizer*

142 Internal Combustion Engines

vehicle length, mass, friction coefficients, etc.

that of the vehicle which moves ahead of it.

campaign can be found in Ciccarese et al. 2012.

*3.3.2. The power-train simulator* 

**Figure 2.** Example of speed profiles obtained by the experimental environment

The Power train simulator block implements a model of the power-train. The block processes the output of the prediction system and calculates the related power demand of

a vehicle consists of a list of consecutive arcs in the graph.

SUMO (Simulation of Urban MObility) is an open source microscopic road traffic simulator. The input parameters of SUMO consist of the road network, the characteristics of each

Vehicles with the same characteristics are grouped in classes and, for each class, a set of mechanical specifications is provided maximum speed, acceleration and deceleration.,

The road network is represented by an oriented graph, where nodes correspond to intersections and arcs to one-way lanes. For each lane, the maximum speed, the slope and the classes of vehicles which are allowed to go along it have to be also specified. The route of

Using the input data, SUMO generates a mobility trace for all vehicles according to a Car-Following model (Wang et al. 2001): each vehicle tries to hold its speed close to the maximum one allowed for the current lane and decelerates if it is approaching either to an intersection or to another vehicle on the same lane; in the latter case, its speed is adapted to

The accuracy of the proposed prediction method has been tested experimentally (Ciccarese et al. 2012) in a augmented reality environment to simulate the presence in the Ecotecke campus of a certain number of vehicles able to communicate with the target vehicle. The experimental campaign showed that the inaccuracy of the prediction method is below 4km/h. In Figure 2, a comparison is shown between the predicted and the actual speed profile of the target vehicle in a time window of 100s. More details about the experimental

vehicle, the path (route) that each vehicle follows and the timing of traffic lights.

The role of the optimizer block is to adapt the parameters of the actual control strategy to the future driving conditions. This block can be implemented either as a on-line optimizer or as a memory device for loading optimized maps (Donateo et al. 2011).

## *3.3.5. Monitoring blocks*

The system also includes a block, named Energy monitoring, which monitors the energy parameters of the vehicle (engine efficiency, level of gasoline in the tank, battery SOC, etc.) and evaluates the effectiveness in optimizing the energy management. This evaluation is carried out at regular intervals of duration equal to the prediction horizon.

Another block, named Prediction accuracy, evaluates the prediction error (based on a comparison between the actual speed profile evaluated by GPS and that estimated by the prediction system). The output of the Prediction accuracy block could be used to trigger a new prediction run.

## **4. A test case: ITAN500**

In order to evaluate the effectiveness of the CREA approach in reducing fuel consumption of a plug-in HEV, a numerical investigation has been performed with respect to ITAN500.. ITAN500 a four-wheel vehicle prototype with a size comparable with that of a large scooter. ITAN500 can be classified as PHEV40 because its all-electric range is 40 miles on the UDDS cycle.

The vehicle was designed to reach a maximum speed of 90km/h in hybrid configuration with a mass of about 800 kg. By taking into account the overall transmission ratio (1/3.46) the DC motor was selected in order to generate a torque of about 27 Nm at the speed of 3560 rpm. A set of six lead acid batteries in series are used to produce the nominal voltage of 72V required to feed the electric motor. The choice of lead acid batteries was due to the need of reducing the vehicle cost. However, other kinds of batteries are currently under consideration.

A small gasoline engine with a maximum power of 9.9kW at 3600 rpm is used to extend the range of the vehicle. More details on the power-train (shown in Figure 3) can be found in a previous publication (Donateo et al. 2012).

**Figure 3.** Scheme of the ITAN500 power-train

## **4.1. The VPR power-train simulator**

VPR (Vehicle Power Request) is a backward model that uses quasi-static maps for the main power-train components (thermal engine, motor and batteries) to predict their efficiency according to the requested values of torque and speed.

The main outputs of the VPR model are the evolution of fuel consumption and battery SOC along the driving cycle. Starting from the velocity speed and grade traces, the vehicle power request is calculated by considering aerodynamic force, grade force, inertial contribution and rolling force. An example of vehicle power request trace is shown in Figure 4 together with other VPR output.

Note that during deceleration the power request is negative which means that the braking energy can be recovered and stored in the batteries. In the example shown in Figure 4, engine is turned on only in a small fraction of the vehicle missions, around 500s from the start of the cycle.

**Figure 4.** Example of VPR results

144 Internal Combustion Engines

new prediction run.

cycle.

consideration.

previous publication (Donateo et al. 2012).

**Figure 3.** Scheme of the ITAN500 power-train

**4.1. The VPR power-train simulator** 

according to the requested values of torque and speed.

**4. A test case: ITAN500** 

Another block, named Prediction accuracy, evaluates the prediction error (based on a comparison between the actual speed profile evaluated by GPS and that estimated by the prediction system). The output of the Prediction accuracy block could be used to trigger a

In order to evaluate the effectiveness of the CREA approach in reducing fuel consumption of a plug-in HEV, a numerical investigation has been performed with respect to ITAN500.. ITAN500 a four-wheel vehicle prototype with a size comparable with that of a large scooter. ITAN500 can be classified as PHEV40 because its all-electric range is 40 miles on the UDDS

The vehicle was designed to reach a maximum speed of 90km/h in hybrid configuration with a mass of about 800 kg. By taking into account the overall transmission ratio (1/3.46) the DC motor was selected in order to generate a torque of about 27 Nm at the speed of 3560 rpm. A set of six lead acid batteries in series are used to produce the nominal voltage of 72V required to feed the electric motor. The choice of lead acid batteries was due to the need of reducing the vehicle cost. However, other kinds of batteries are currently under

A small gasoline engine with a maximum power of 9.9kW at 3600 rpm is used to extend the range of the vehicle. More details on the power-train (shown in Figure 3) can be found in a

VPR (Vehicle Power Request) is a backward model that uses quasi-static maps for the main power-train components (thermal engine, motor and batteries) to predict their efficiency

The main outputs of the VPR model are the evolution of fuel consumption and battery SOC along the driving cycle. Starting from the velocity speed and grade traces, the vehicle power request is calculated by considering aerodynamic force, grade force, inertial contribution The efficiency of the electric motor according to torque and speed has been evaluated experimentally on an inertial test bench (Donateo et al. 2011).

Since the engine is run at the constant speed of 3000 rpm, its efficiency is considered as function of torque only. Literature data have been used to derive the maps of Figure 5.

The data of Figure 5 refers to a fully-warmed case, i.e. the temperature of the engine block is at the nominal temperature of 90°C. However, engine efficiency is strongly dependent on its temperature; in particular it is very low at cold start. In the VPR model, the efficiency data of Figure 5 are corrected as proposed by Guzzella et Onder, 2004 by multiplying the fullwarmed engine efficiency by a correction factor whose dependence on temperature is shown in Figure 6.

**Figure 5.** Fully-warmed engine efficiency versus torque at 3000rpm

**Figure 6.** Correction factor for engine efficiency as a function of the block temperature

Note that if VPR is run on-board, the temperature of the engine block is a measured data while in the present investigation it has to be simulated. For this reason, a thermal model based on a zero-dimensional simulation of the engine has been proposed (Donateo et al. 2012). The thermal model is able to simulate the increase of temperature when the engine is on as a function of its actual torque. When the engine is off, its temperature decreases due to the heat transfer to the surrounding air. More details on the thermal model can be found in Donateo et al. 2012.

An example of temperature trace versus time obtained from VPR with the same input conditions of Figure 4 is shown in Figure 7.

**Figure 7.** An example of temperature trace obtained with the thermal model of the engine

The overall electric efficiency between the chopper and the wheels is set constant and equal to 0.65 for the present investigation.

#### **4.2. The energy management strategy**

146 Internal Combustion Engines

Donateo et al. 2012.

conditions of Figure 4 is shown in Figure 7.

**Correction factor**

**Figure 5.** Fully-warmed engine efficiency versus torque at 3000rpm

**Figure 6.** Correction factor for engine efficiency as a function of the block temperature

Note that if VPR is run on-board, the temperature of the engine block is a measured data while in the present investigation it has to be simulated. For this reason, a thermal model based on a zero-dimensional simulation of the engine has been proposed (Donateo et al. 2012). The thermal model is able to simulate the increase of temperature when the engine is on as a function of its actual torque. When the engine is off, its temperature decreases due to the heat transfer to the surrounding air. More details on the thermal model can be found in


**Engine temperature**

An example of temperature trace versus time obtained from VPR with the same input

The energy management strategy developed for ITAN500 includes an initial Charge Depleting (CD) mode where the battery only is used until a threshold value of battery SOC is reached (*SOCCD*). Then, the vehicle can be run in three different modes.

In Mode 1, the power to the motor is supplied only by the generator/engine group.

Mode 2 uses only battery to supply power. Both engine and battery are used in the other modes. In particular, in mode 3 the engine is used both to charge battery and to supply power to the motor while in mode 4 the engine and the battery are used together to feed the motor.

According to the actual power to be supplied to the motor to move the wheels (*Pload*) and instantaneous value of SOC, the power-train is operated in one of the Areas 1-11 of the Figure 8. In particular, mode 1 is preferred in the high power region except when the battery SOC is very high (Area 5). Mode 2 is mandatory in three cases: when the battery is fully recharged (Area 3), in braking (Area 1) and when the load power is very low (Area 2). Moreover, the use of mode 2 is preferred when the SOC is reasonably high and the load power relative low with respect to the engine nominal power (Areas 4 and 7), otherwise the use of engine is preferred (Areas 8). Area 6 and 9 correspond to the use of the engine to recharge the battery (mode 3). However, this is possible only when the sum of the load power and the power request to recharge the battery is lower than PICE,max. If not, mode 1 is used.

Note that Areas 11 and 10 of Figure 8 were not taken into account because the power request is always lower than PICE,max for all the operating conditions considered in the present investigation.

The actual size of each area depends on the values of energy management parameters *SOCCD*, *SOCmin*, *k* and *PICE,min* that influences the results in terms of fuel consumption and battery usage over a specific vehicle mission. The meaning of *SOCmin*, and PICE,min is quite straightforward while some explanation has to be given for K. The K parameter was introduced to solve the dilemma between using mode 1 or mode 2 in Areas 7 and 8 since neither the engine nor the battery works at their best in that region. By using K it is possible to prefer the battery at relative low power and high SOC (area 8) and the engine otherwise .

**Figure 8.** Energy management strategy

## **4.3. The optimizer**

The optimal combination of the parameters can be easily performed off-line with a general optimization algorithm like genetic algorithms (Paladini et al. 2007).

The role of the optimizer is to find the optimal combination of parameters in Table 1 that define the size of the areas of Figure 8. For the optimization described in this paragraph, the minimum and maximum values and the steps of variation of the design variables reported in Table 1 were considered.


**Table 1.** Design variables for the optimization

In each case, the goal of the optimization was the reduction of the equivalent fuel consumption calculated in the following way:

Intelligent Usage of Internal Combustion Engines in Hybrid Electric Vehicles 149

$$
\dot{m}\_{\text{tot}} = \varpi\_{\text{FC}} \dot{m}\_{\text{ICE}} (\mathcal{G}) + \dot{m}\_{\text{eq,BAT}} \tag{1}
$$

where:

148 Internal Combustion Engines

present investigation.

**Figure 8.** Energy management strategy

**4.3. The optimizer** 

in Table 1 were considered.

**Table 1.** Design variables for the optimization

consumption calculated in the following way:

Note that Areas 11 and 10 of Figure 8 were not taken into account because the power request is always lower than PICE,max for all the operating conditions considered in the

The actual size of each area depends on the values of energy management parameters *SOCCD*, *SOCmin*, *k* and *PICE,min* that influences the results in terms of fuel consumption and battery usage over a specific vehicle mission. The meaning of *SOCmin*, and PICE,min is quite straightforward while some explanation has to be given for K. The K parameter was introduced to solve the dilemma between using mode 1 or mode 2 in Areas 7 and 8 since neither the engine nor the battery works at their best in that region. By using K it is possible to prefer the battery at relative low power and high SOC (area 8) and the engine otherwise .

The optimal combination of the parameters can be easily performed off-line with a general

The role of the optimizer is to find the optimal combination of parameters in Table 1 that define the size of the areas of Figure 8. For the optimization described in this paragraph, the minimum and maximum values and the steps of variation of the design variables reported

In each case, the goal of the optimization was the reduction of the equivalent fuel

Variable Min Max SOCCD (%) 60 80 SOCMIN (%) 20 60 K 0 1 PICE,min [W] 500 6200

optimization algorithm like genetic algorithms (Paladini et al. 2007).

*m ICE*( ) is the effective fuel consumption function of engine temperature θ;

*wFC* is the weight assigned to the level of fuel stored in the tank. It is set equal to 1 if the tank level is greater than 25% When the tank level is very low, this parameter is increased to prefer battery usage when the fuel level is low. In particular *wFC* is 1.2 for 10%<tank\_level<25% and 1.5 for tank level lower than 10%.

Note that eq. (1) has been obtained by adapting the equivalent fuel consumption defined by Sciarretta et al. 2004 for a parallel HEV to the specific power-train of ITAN500.

The equivalent fuel consumption of the battery is obtained as follows:

$$
\dot{m}\_{eq,BATT} = \frac{\eta^{\gamma} \cdot P\_{BATT}}{Q\_{LHV} \cdot \Delta t} \tag{2}
$$

where η represents the average fuel consumption of the battery which is assumed to be constant and the same in charge and discharge in the present investigation.

When the battery is in charge, *PBATT* represents the power that could be stored in the battery. Due to the battery efficiency η, the actual power stored in the battery (which define the equivalent fuel consumption) is lower than *PBATT*. This is taken into account by setting γ =1. In discharge, *PBATT* is the power requested from the battery is increased by η (γ=-1).

To complete the description of eq. (3), *QLHV* is the lower heating value of the fuel (in the present investigation gasoline is considered with *QLHV* =44MJ/kg while *Δt* is the time step of the driving cycle (*Δt* =1s).

The penalty function *fp(SOC)* takes into account the battery usage in the optimization process and has been defined according to Sciarretta et al. 2004.

### *4.3.1. Driving cycles*

In the present investigation three kinds of driving cycles were taken into account for ITAN500. The first two are standard driving cycle adopted for the registration on new cars (NEDC and UDDS). Other numerical cycles were obtained with the help of SUMO. The ITAN500 has been simulated to move in the Ecotekne campus of the University of Salento for about 10000s (2.8h) together with other vehicles that, unlike ITAN500, can enter and exit the campus area. Different driving scenarios were taken into account by changing the number and the specification of the vehicles moving in the area.

The specification of the vehicles are used in the framework of SUMO to calculate the maximum values of acceleration/deceleration allowed to each vehicle according to the difference between the actual power request (depending on aerodynamics, rolling and inertia) and the maximum traction/braking power of the vehicle. Cycles obtained in this way were named as Trace A to Trace H. More details on the procedure used to obtain the numerical cycles can be found in Donateo et al. 2011.

Another cycle named R has been taken into account. This cycle is an actual driving cycle acquired with a GPS system on board of the vehicle ITAN500 when it is run in all electric range. The cycle has been assumed to be executed for 25 times (R\*25) in order to obtain results of fuel consumption and battery usage comparable with those of cycles A and B.

The specifications of the cycles taken into account in the investigation are reported in Table 2. Note that all the cycles taken into account in the present investigation refer to a zero grade condition.


**Table 2.** Specification of the driving cycle taken into account for the creation of the maps

## *4.3.2. Full knowledge approach*

In this approach the driving cycle is assumed to be completely known and the parameters of Table 1 are optimized for each cycle of Table 2. The results of the application of this approach to cycles A, B, R, NEDC and UDDS are reported in Table 3.


**Table 3.** Results of the optimization in the case of full knowledge (initial SOC 45%)

## *4.3.3. No-knowledge approach*

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condition.

numerical cycles can be found in Donateo et al. 2011.

Cycle Total time [s] Average

*4.3.2. Full knowledge approach* 

**Cycle Duration [s]** 

were named as Trace A to Trace H. More details on the procedure used to obtain the

Another cycle named R has been taken into account. This cycle is an actual driving cycle acquired with a GPS system on board of the vehicle ITAN500 when it is run in all electric range. The cycle has been assumed to be executed for 25 times (R\*25) in order to obtain results of fuel consumption and battery usage comparable with those of cycles A and B.

The specifications of the cycles taken into account in the investigation are reported in Table 2. Note that all the cycles taken into account in the present investigation refer to a zero grade

In this approach the driving cycle is assumed to be completely known and the parameters of Table 1 are optimized for each cycle of Table 2. The results of the application of this

> **FC [l]**

**SOCCD (%)** 

**SOCMIN (%)** 

**K PICE,MIN [kW]** 

approach to cycles A, B, R, NEDC and UDDS are reported in Table 3.

**Δ SOC [%]** 

**Table 3.** Results of the optimization in the case of full knowledge (initial SOC 45%)

#A 10000 2.78 24.8 1.02 65 44.4 0.9 3.2 #B 10800 3.1 24.7 1.96 77.9 50.1 0.6 2.6 #R\*25 9550 3.38 24.8 1.91 77.3 34.8 0.98 2.4 #UDDS 1370 1.66 18.7 0.08 60.6 37.6 0.97 6.1 #NEDC 1225 2.52 17 0.16 71.4 35.6 0.26 5.9

**Equiv.fuel cons. [l/100km]** 

Cycle\_NEDC 1225 8.93 33.36 0 Cycle\_UDDS 1370 8.73 25.37 0 Cycle\_1015 661 6.90 19.45 0 Cycle\_HWFET 766 21.56 26.80 0 Trace A 10001 4.69 13.90 0 Trace B 10801 6.88 13.90 0 Trace C 9999 1.79 8.33 0 Trace D 10001 2.00 8.33 0 Trace E 10001 1.38 8.33 0 Trace F 10001 1.08 8.33 0 Trace G 10001 1.95 8.33 0 Trace H 10001 1.47 8.33 0 Cycle R 382 25.75 41.61 0.2 **Table 2.** Specification of the driving cycle taken into account for the creation of the maps

speed [m/s] Max speed [m/s] Min speed

[m/s]

The driving cycle is assumed to be completely unknown. The parameter of the control strategy are optimized for the NEDC cycle and applied to the other cycles. The results are reported in Table 4.


**Table 4.** Results of the optimization in the case of no knowledge (initial SOC 45%)

## *4.3.4. Prediction & maps supervisory control*

In order to reduce the on-board computational load required by the CREA approach, Donateo et al. 2011 proposed the use of maps that are optimized off-line with respect to reference driving conditions. They were obtained with the following procedure.

All cycles of Table 2 have been taken into account to generate one global driving cycle of 85208s (about 23 hours). Then, the VPR has been used to calculate the corresponding power request according to the specification of the vehicle and a global power request trace has been obtained. This power request trace has been divided into 1420 Mini Power Cycles (MPC) of 60s.

The 1420 MPCs have been distributed in 90 groups with the help of the K-Means clustering technique. For each group, a representative driving cycle, named Reference Mini Power Cycle has been identified and numbered.

Figure 9 shows, with different colors, five MPCs belonging to the same group. The bold blue line is the RMPC chosen with the clustering algorithm.

The off-line optimization has been performed for each of the 90 RMPCs, two levels of engine temperature (cold-hot), three levels of the initial state of charge, and three levels of the fuel tank. In this way 1620 optimized maps have been obtained. Each map contains the optimized values of SOCmin, k and PICE,min. for a particular combination of RMPC, engine temperature, initial state of charge and level of the fuel tank.

The maps could be used in an intelligent hybrid electric vehicle in the following way.

1. At any interval of 60 seconds, the predicted speed profile is obtained from the prediction block;


**Figure 9.** Example of RMPC

## **4.4. Analysis of the prediction&maps approach**

The proposed on board prediction-optimization tool has been evaluated numerically in the following way. The ITAN500 is simulated to execute one of the driving cycles of Table 2 with the assumption that they are know (by prediction) in blocks of 60s.

At any 60s, the power request versus time in the next time window of 60s is evaluated with VPR and compared with each of the RMDCs to find the most similar one. Then, the instantaneous values of engine temperature, SOC and fuel levels are set as initial values and the corresponding optimized map is loaded. The thermal model of VPR is used to predict the profile of engine temperature along the mission. The values of the energy management parameters are used to evaluate the fuel consumption and battery usage in the next 60s on the basis of the actual power request (not on the selected RMDC).

The results in terms of fuel consumption and battery usage obtained with this approach are reported in Table 5.


**Table 5.** Results of the simulation with the optimized maps (initial SOC 45%)

and road specification (es. grade) with VPR;

applied over the next 60s.

**Figure 9.** Example of RMPC

reported in Table 5.

**4.4. Analysis of the prediction&maps approach** 

with the assumption that they are know (by prediction) in blocks of 60s.

the basis of the actual power request (not on the selected RMDC).

one in terms of root mean square error is found;

state of the charge, the corresponding map is loaded;

2. The corresponding power request profile over 60s is calculated according to the vehicle

3. The power request profile is compared with each of the RMPCs and the most similar

4. According to the measured values of engine temperature, fuel tank level and battery

5. The optimized values of the energy management parameters of the selected map are

The proposed on board prediction-optimization tool has been evaluated numerically in the following way. The ITAN500 is simulated to execute one of the driving cycles of Table 2

At any 60s, the power request versus time in the next time window of 60s is evaluated with VPR and compared with each of the RMDCs to find the most similar one. Then, the instantaneous values of engine temperature, SOC and fuel levels are set as initial values and the corresponding optimized map is loaded. The thermal model of VPR is used to predict the profile of engine temperature along the mission. The values of the energy management parameters are used to evaluate the fuel consumption and battery usage in the next 60s on

The results in terms of fuel consumption and battery usage obtained with this approach are

## *4.4.1. Percentage of mission with Controlled Battery Discharge (CBD%)*

To compare the results of the three approaches, different metrics can be taken into account.

The first metric useful to compare the results of the three approaches can be derived by analyzing the typical SOC trace versus time in a plug-in hybrid electric vehicle. An example is shown in Figure 10 with respect to two different initial values of the battery SOC.

The traces of SOC show an initial zone where the results corresponding to *full knowledge*, *prediction&maps* and *no knowledge* are perfectly overlapped and the SOC decreases monotonically (Electric Mode). Of course this region is particularly evident and relevant when the initial SOC is higher (75%).

Then, there is a region in which the SOCs tends to decrease but can be kept locally constant or be increased thanks to the use of the engine (Plug-in Hybrid Mode). This region ends when the battery is fully discharged (SOC=20%). After this, the SOC remains globally constant for all cases (*full knowledge, prediction&maps* and *no knowledge*) with small variation that are not visible in the scale used for the Figures (Discharged Battery Mode). Thus, the different results in terms of fuel consumption obtained with the three methods can be accounted for with the different duration of the EM, PHM and DBM zones.

In the EM region, the fuel consumption is zero but the SOC strongly decreases due to the extensive use of the battery. In the PHM mode, the battery is the main energy source and the engine is turned on (when its efficiency is high) to decrease the slope of the SOC trace. The DBM region is the worst in terms of fuel consumption because engine has to be run also in its low efficiency region since batteries are fully discharged. A plug-in HEV is run at its best when the DBM region (SOC=20%) is reached exactly at the end of the mission and the EM region extends through as much of the mission possible. This is possible when the vehicle mission is entirely known (*full knowledge* case). The traces of Figure 10 show that the proposed method performs better than the *no knowledge* case since it allows to reduce the length of the DBM and to increase the PHM. As a consequence, the ICE is averagely run at high efficiency.

Thus, a useful metric to evaluate the performance of an energy management strategy for PHEV could be the percentage of the mission run in EM+DBM modes. This metric is named here CBD% while in a previous investigation (Donateo et al. 2012) it was referred to as Δmission.

**Figure 10.** Explanation of the meaning of CBD% for Cycle A

The value of the CBD% has been calculated for each approach with reference to cycle A, B and R\*25 of Table 2. R\*25 means that cycle R has been repeated 25 times to achieve a duration similar to that of cycle A and B.

**Figure 11.** Values of CBD% for cycles A, B and R\*25 with SOCin=45% and 75%

By analyzing the results of Figure 16 it is possible to notice that the performance of the proposed strategy is very close to that of full knowledge for cycle A (for both values of SOCin) and for cycle R with SOCin=75%. The values of *CBD*% are always slightly higher than in the *no knowledge* approach for the other cycles.

These results suggest that better performances could be obtained by increasing the duration of the prediction window used in the present investigation (60s) even if this could results in a worse accuracy of the prediction. Future development will be related to the optimization of the prediction horizon to increase the % of the mission covered by EM+DBM.

## *4.4.2. Percentage of mission with EngineON (engON%)*

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**Figure 10.** Explanation of the meaning of CBD% for Cycle A

**Figure 11.** Values of CBD% for cycles A, B and R\*25 with SOCin=45% and 75%

duration similar to that of cycle A and B.

**% of cycle in in EM+DBM** 

Δmission.

here CBD% while in a previous investigation (Donateo et al. 2012) it was referred to as

The value of the CBD% has been calculated for each approach with reference to cycle A, B and R\*25 of Table 2. R\*25 means that cycle R has been repeated 25 times to achieve a

> full knowledge prediction&maps no knowledge

Another aspect to be taken into account in evaluating the performance of the proposed energy management strategy is the usage of the internal combustion engine in terms of percentage of mission during which the engine is turned ON **(***EngON%*).

The results are shown in Figure 12 with respect to cycles A and B to understand the results of Figure 11. Note that cycle B requires the engine to be turned on for a much higher percentage of the mission with respect to cycle A. This explains why this cycle is more critical in the optimization of metric CBD%. Even if the *prediction&maps* is not much successful in optimizing CBD% , it is able to strongly reduce the usage of the engine in both cycle A and B.

**Figure 12.** Values of *EngON%*for cycles A and B (SOCin=45%)

### *4.4.3. AEE (Average Engine Efficiency)*

The average efficiency of the engine (AEE) is another important aspect to be taken into account. The results of the comparison are reported in Figure 13.

Once again, the worst performance of the *prediction&maps* method are obtained for cycle B.

**Figure 13.** Values of AEE (SOCin=45%)

## *4.4.4. Well-to-wheel emissions of CO2*

The ultimate goal of advanced power-train technologies is to reduce the overall emissions of greenhouse gases. Thus, it could be interesting to evaluate the overall well-to.-wheel (WTW) emissions of CO2 produced with the different approaches considered in this investigation.

The complete combustion of 1 liter of gasoline produces 2.4 kg of CO2. Assuming a density of 700 kg/m3, 1 kg of gasoline produces 3.42 kg of CO2 (tank to wheel emissions). Sullivan et al. 2004 consider a multiplying factor of 1.162 to pass from TTW to WTW emissions of CO2. Thus, a kg of gasoline can be assumed to produce 3.98 kg of CO2 (WTW). Using this conversion factor, the total CO2 produced along the cycles #A, #B and #R25 has been calculated from the results in Table 3 (i.e. for the full knowledge case).

As for the electric emission, the TTW contribute is obviously zero while the well-to-tank (WTT) emissions depend on the energy mixing used to generate the electricity stored in the batteries. A report from the International Energy Agency, 2011 indicates for Italy an average emission of 0.386 kg of CO2 per kWh of electric energy. Using the data about the capacity of the batteries (equivalent 1.8 kWh) and the results in terms of SOC, it is possible to evaluate the total energy used for each cycle and for each approach. Thus, the electric WTT emission of CO2 can be easily calculated.

The calculated values of CO2 emissions from engine and batteries with the *full-knowledge* approach are reported in Table 6. Note that the electric emissions are almost negligible with respect to the quantity of CO2 produced by the engine even if the engine is used only for a fraction of the mission. Moreover, they are quite the same for all cycles since the batteries are fully discharged in all cases.

The calculation of the total CO2 emissions has been repeated for the *no-knowledge* and *prediction&maps* cases. The comparison is shown in Figure 14.



**Table 6.** Well to wheel emissions of CO2 in the case of full knowledge

**Figure 13.** Values of AEE (SOCin=45%)

0.18

0.18

*4.4.4. Well-to-wheel emissions of CO2*

of CO2 can be easily calculated.

are fully discharged in all cases.

The ultimate goal of advanced power-train technologies is to reduce the overall emissions of greenhouse gases. Thus, it could be interesting to evaluate the overall well-to.-wheel (WTW) emissions of CO2 produced with the different approaches considered in this investigation. The complete combustion of 1 liter of gasoline produces 2.4 kg of CO2. Assuming a density of 700 kg/m3, 1 kg of gasoline produces 3.42 kg of CO2 (tank to wheel emissions). Sullivan et al. 2004 consider a multiplying factor of 1.162 to pass from TTW to WTW emissions of CO2. Thus, a kg of gasoline can be assumed to produce 3.98 kg of CO2 (WTW). Using this conversion factor, the total CO2 produced along the cycles #A, #B and #R25 has been

full knowledge prediction&maps no knowledge

As for the electric emission, the TTW contribute is obviously zero while the well-to-tank (WTT) emissions depend on the energy mixing used to generate the electricity stored in the batteries. A report from the International Energy Agency, 2011 indicates for Italy an average emission of 0.386 kg of CO2 per kWh of electric energy. Using the data about the capacity of the batteries (equivalent 1.8 kWh) and the results in terms of SOC, it is possible to evaluate the total energy used for each cycle and for each approach. Thus, the electric WTT emission

The calculated values of CO2 emissions from engine and batteries with the *full-knowledge* approach are reported in Table 6. Note that the electric emissions are almost negligible with respect to the quantity of CO2 produced by the engine even if the engine is used only for a fraction of the mission. Moreover, they are quite the same for all cycles since the batteries

The calculation of the total CO2 emissions has been repeated for the *no-knowledge* and

calculated from the results in Table 3 (i.e. for the full knowledge case).

0.16

0.14

cycle A cycle B

0.15 0.15

*prediction&maps* cases. The comparison is shown in Figure 14.

The results of Figure 14 reveal that complete information about the future driving mission could help to significantly reduce the overall emission of CO2 from a plug-in series HEV. The estimated reduction ranges from 12% for cycle #R\*25 to 20% for cycle #A.

**Figure 14.** Well to wheel emissions of CO2 for the proposed approaches

The results of the *prediction&maps* approach are intermediate between full-knowledge and noknowledge cases. Nevertheless, the results in terms of CO2 are not satisfactory since the proposed approach helps to reduce the greenhouse emission by only 2-4%. This results suggest the possibility to replace or integrate the goal of the optimization process (eq. 1) with a cost function that takes into account the overall well-to-wheel emission of CO2. Moreover, better results could be obtained by increasing the duration of the prediction horizon.

## **5. Summary and conclusions**

The chapter describes the optimal usage of an internal combustion engine in an intelligent hybrid electric vehicle able to sense its surrounding and adapt the energy management strategy to the actual driving conditions. After an introduction on hybrid electric vehicles and their challenges, the chapter describes the role of Information and Communication Technologies in the reduction of greenhouse emissions. Then, the chapter focuses on different approaches presented in literature on the usage of information about traffic and weather conditions for the optimal energy management of hybrid electric vehicles. In particular, the chapter describes the application of the *prediction&maps* approach developed at the University of Salento for the optimization of the engine usage in the ITAN500 plug-in hybrid electric vehicle.

Finally, the chapter proposes four metrics to evaluate the performance of the proposed method: the percentage of mission performed before reaching the lowest allowed value for battery state of charge (CBD%), the percentage of mission execute with the engine turned ON (EngON%), the average efficiency of the engine (AEE), calculated according to its actual temperature and the overall well-to-wheel emissions of CO2.

## **Author details**

Teresa Donateo *University of Salento, Italy* 

## **Acknowledgement**

The investigation was supported by the University of Salento and the Italian Ministry for Environment (MATTM), through the funding of the "P.R.I.M.E." project.

## **List of acronyms**


## **6. References**

Anatone M., Cipollone R., Sciarretta A. (2011), "Control-Oriented Modeling and Fuel Optimal control of a Series Hybrid Bus", SAE paper 2005-01-1163.

Barsali S., Miulli C., Possenti A. (2004), "A Control Strategy to Minimize Fuel Consumption of Series Hybrid Electric Vehicles", *IEEE Transactions of Energy Conversion*, Vol. 19, no. 1, pp. 187-195.

158 Internal Combustion Engines

**Author details** 

*University of Salento, Italy* 

**Acknowledgement** 

**List of acronyms** 

AEE All Electric Range BEV Battery Electric Vehicles

CD Charge Depleting CS Charge Sustaining

FCV Fuel-Cell Vehicles

SOC State of Charge TTW Tank-to-Wheel WTT Well-to-Tank WTW Well-to-Wheel

**6. References** 

GPS Global Positioning System HEV Hybrid Electric Vehicles

PHEV Plug-in Hybrid Electric Vehicles ICE Internal Combustion Engine

Teresa Donateo

presented in literature on the usage of information about traffic and weather conditions for the optimal energy management of hybrid electric vehicles. In particular, the chapter describes the application of the *prediction&maps* approach developed at the University of Salento for the

Finally, the chapter proposes four metrics to evaluate the performance of the proposed method: the percentage of mission performed before reaching the lowest allowed value for battery state of charge (CBD%), the percentage of mission execute with the engine turned ON (EngON%), the average efficiency of the engine (AEE), calculated according to its actual

The investigation was supported by the University of Salento and the Italian Ministry for

Anatone M., Cipollone R., Sciarretta A. (2011), "Control-Oriented Modeling and Fuel

Optimal control of a Series Hybrid Bus", SAE paper 2005-01-1163.

Environment (MATTM), through the funding of the "P.R.I.M.E." project.

CBD% % of mission with controlled battery discharge

ECMS Equivalent Consumption Minimization Strategy

EngON% % of mission with engine turned on

optimization of the engine usage in the ITAN500 plug-in hybrid electric vehicle.

temperature and the overall well-to-wheel emissions of CO2.


**Chapter 7** 

## **Modeling and Simulation of SI Engines for Fault Detection**

Mudassar Abbas Rizvi, Qarab Raza, Aamer Iqbal Bhatti, Sajjad Zaidi and Mansoor Khan

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/50487

## **1. Introduction**

160 Internal Combustion Engines

EVS22, Yokohama, Japan.

transport, 06-09 October 2010.

Karbowski D., Rousseau A., Pagerit S., Sharer P. (2006), "Plug-in Vehicle Control Strategy: from Global Optimization to real-time application", 22nd Electric Vehicle Symposium,

Lee J., Ohn H., Choi J-Y., Kim S. J., Min B. (2011) "Development of Effective Exhaust Gas Heat Recovery System for a Hybrid Electric Vehicle", SAE Technical Paper 2011-01-1171; Lin C.-C, Peng H., Grizzle J. W., and Kang J.-M. (2003), "Power management strategy for a parallel hybrid electric truck", *IEEE Trans. Control Syst. Technol*., vol. 11, no. 6, pp. 839–849. Markel T., Kuss M., Denholm P. (2009), "Communication and Control of Electric Drive Vehicles Supporting Renewables", Vehicle Power and Propulsion Conference, 2009. VPPC '09. IEEE. Millo F., Rolando L., Servetto E. (2011), "Development of a Control Strategy for Complex

Light-Duty Diesel-Hybrid Powertrains", SAE Technical paper 2011-24-0076.

*Management*, 48 (11), p.3001-3008, ISSN: 0196-8904.

Paladini V., Donateo T., de Risi A., Laforgia D. (2007), "Super-capacitors fuel-cell hybrid electric vehicle optimization and control strategy development", *Energy Conversion and* 

Schuricht P., Cassebaum O., Luft M., Baker B. (2010), "Methods and Algorithms in Control of Hybrid Powertraines, International congress of heavy vehicles, road trains and urban

Sciarretta A., Guzzella L., and Back M. (2004), "A real-time optimal control strategy for parallel hybrid vehicles with on-board estimation of the control parameters," in *Proc.* 

Serrao L, "A Comparative Analysis of Energy Management Strategies for Hybrid Electric

Serrao L., Rizzoni G. (2008), "Optimal Control of Power Split for a Hybrid Electric Refuse

Simpson A. (2006), "Cost-benefit Analysis of Plug-In Hybrid Electric Vehicle Technology", 22nd International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium and

Sullivan, J.L., Baker, R.E., Boyer B.A., Hammerle R.H., Kenney T.E., Muniz L., Wallington T.J. (2004), "CO2 Emission Benefit of Diesel (versus Gasoline) Powered Vehicles",

Tulpule P., Marano V., Rizzoni G. (2009), "Effects of Different PHEV Control Strategy on

Tulpule, P., Marano, V., and Rizzoni, G. (2011), "Effect of Traffic, Road and Weather Information on PHEV Energy Management," SAE Technical Paper 2011-24-0162. Weng Y., Wu T. (2011), "Car-following model of vehicular traffic", International

Won J.-S. and Langari R. (2005), "Intelligent energy management agent for a parallel hybrid vehicle—Part 2: Torque distribution, charge sustenance strategies, and performance

Yu S., Li L., Dong G. and Zhang X. (2006), "A Study of Control Strategies of PFI Engine during Cranking and Start for HEVs, *Proceedings of IEEE International Conference on* 

Zuurendonk, B. (2005), Advanced Fuel Consumption and Emission Modeling using Willans line scaling techniques for engines", traineeship report, DCT 2005.116, Technische

*IFAC Symp. Adv. Autom. Control*, Salerno, Italy, April 19–23, 2004.

Vehicles" (2009), Ph.D. Dissertation, The Ohio State University.

Vehicle Perfomance", American Control Conference DSCC 2009.

Conferences on Info-tech and Info-net 2001, ICII 2001, Oct. 2001.

results", *IEEE Trans. Veh. Technol*., vol. 54, no. 3, pp. 935–953.

Vehicle", *Proceedings of the 2008 American Control Conference*.

Exhibition, 23- 28 October,Yokohama, Japan;

*Vehicular Electronics and Safety*, ICVES 2006.

Universiteit Eindhoven.

*Environmental Science & Technology*, Vol. 38 No. 12.

During last decades of twentieth century, the basic point of concern in the development of Spark Ignition engine was the improvement in fuel economy and reduced exhaust emission. With tremendous of electronics and computer techniques it became possible to implement the complex control algorithms within a small rugged *Electronic Control Unit* (ECU) of a vehicle that are responsible to ensure the desired performance objectives. In modern vehicles, a complete control loop is present in which throttle acts as a user input to control the speed of vehicle. The throttle input acts as a manipulating variable to change the *set point* for speed. A number of sensors like *Manifold Air Pressure* (MAP), Crankshaft Speed Sensor, Oxygen sensor etc are installed in vehicle to measure different vehicle variable. A number of controllers are implemented in ECU to ensure all the desired performance objectives of vehicle. The controllers are usually designed on the basis of mathematical representation of systems. The design of controller for SI engine to ensure its different performance objectives needs mathematical model of SI engine. Mean Value Model (MVM) is one of the most important mathematical models used most frequently by the research community for the design of controllers; see for example [1], [2], [5], [7], [9], [13], [14], and [15]. The basic mean value model is based on the average behavior of SI engine in multiple ignition cycles.

Although the controllers implemented in vehicle ECU are sufficiently robust, yet introduction of fault in system significantly deteriorate the system performance. Research is now shifted to ensure the achievement of performance objectives even in case of some fault. The automotive industry has implemented some simple fault detection algorithms in ECU that identify the faults and provide their indication to a fault diagnostic kit in the form of some fault codes. The implementation is however crude as it provides fault indication only

© 2012 Rizvi et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 Rizvi et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

when the fault become significant. For incipient faults, the vehicle would keep its operation but under sub-optimal conditions till the magnitude of fault would grow to such an extent that it would become visible. Again mathematical models are used to identify the faults and develop techniques to detect the engine faults.

Different mathematical models of Spark Ignition engine proposed in literature in recent years along with the domain of their application are reviewed. The emphasis would however be given to two different mathematical models


The first section of this chapter would present different models of SI engine with only a brief description of those models. The second section would give the mathematical development of mean value model along with the simulation results of presented model and its experimental validation. The third section would give the mathematical derivation of Hybrid model along with the simulation results of model and experimental verification of simulation results. The fourth section would identify the application of these models for fault diagnosis applications.

## **2. Review of models of spark ignition engine**

The dynamic model of a physical system consists of a set of differential equations or difference equations that are developed under certain assumptions. These mathematical models represent the system with fair degree of accuracy. The main problem in development of these models is to ensure the appropriateness of modeling assumptions and to find the value of parameters that appear in those equations. However the basic advantage of this approach is that the develop model would be generic and could be applied to all systems working on that principle. Also the model parameters are associated with some physical entity that provides better reasoning. An alternate modeling technique is to represent the system using neural network that is considered to be a universal estimator. A suitably trained neural network sometime represents the system with even better degree of accuracy. The main problems associated with this approach are the lack of any physical reasoning of parameters, appropriate training of neural network and lack of generality i.e. a neural network model trained on one setup may not work properly on another setup of similar nature.

The research community working on mathematical modeling of SI engine has used both these approaches for control, state estimation and diagnostic applications. In this regard a number of different models were developed to represent the SI engine using both these approaches. Mean Value Model (MVM), Discrete Event Model (DEM), Cylinder by Cylinder Model, Hybrid Model are some of the key examples of models developed using basic laws of physics. Most neural network based models are based in one way or other on Least Square Method. A brief description of some of these models is below.

## **2.1. Mean Value Model (MVM)**

162 Internal Combustion Engines

fault diagnosis applications.

similar nature.

develop techniques to detect the engine faults.

however be given to two different mathematical models

Hybrid model proposed by the authors [21], [22].

**2. Review of models of spark ignition engine** 

The version of mean value model proposed by the authors [11], [12].

when the fault become significant. For incipient faults, the vehicle would keep its operation but under sub-optimal conditions till the magnitude of fault would grow to such an extent that it would become visible. Again mathematical models are used to identify the faults and

Different mathematical models of Spark Ignition engine proposed in literature in recent years along with the domain of their application are reviewed. The emphasis would

The first section of this chapter would present different models of SI engine with only a brief description of those models. The second section would give the mathematical development of mean value model along with the simulation results of presented model and its experimental validation. The third section would give the mathematical derivation of Hybrid model along with the simulation results of model and experimental verification of simulation results. The fourth section would identify the application of these models for

The dynamic model of a physical system consists of a set of differential equations or difference equations that are developed under certain assumptions. These mathematical models represent the system with fair degree of accuracy. The main problem in development of these models is to ensure the appropriateness of modeling assumptions and to find the value of parameters that appear in those equations. However the basic advantage of this approach is that the develop model would be generic and could be applied to all systems working on that principle. Also the model parameters are associated with some physical entity that provides better reasoning. An alternate modeling technique is to represent the system using neural network that is considered to be a universal estimator. A suitably trained neural network sometime represents the system with even better degree of accuracy. The main problems associated with this approach are the lack of any physical reasoning of parameters, appropriate training of neural network and lack of generality i.e. a neural network model trained on one setup may not work properly on another setup of

The research community working on mathematical modeling of SI engine has used both these approaches for control, state estimation and diagnostic applications. In this regard a number of different models were developed to represent the SI engine using both these approaches. Mean Value Model (MVM), Discrete Event Model (DEM), Cylinder by Cylinder Model, Hybrid Model are some of the key examples of models developed using basic laws of physics. Most neural network based models are based in one way or other on Least

Square Method. A brief description of some of these models is below.

Mean Value Model is developed on the basis of physical principles. In this model throttle position is taken as input and crankshaft speed is considered to be the output. A careful analysis indicates that MVM proposed by different researchers share same physical principles but differ from each other slightly in one way or the other [1-16], [26], and [27]. The idea behind the development of model is that the output of model represents the average response of multiple ignition cycles of an SI engine although the model could be used for cycle by cycle analysis of engine behavior. The details about the development of MVM on the basis of physical principles are provided in section 3 of this chapter.

## **2.2. Discrete Event Model (DEM)**

An SI engine work on the basis of Otto cycle in which four different processes i.e. suction, compression, expansion and exhaust take place one after the other. In a four stroke SI engine, each of these processes occurs during half revolution (180°) of engine shaft. Therefore irrespective of the engine speed it always takes two complete rotations of engine shaft to complete one engine cycle. The starting position of each of the four processes occurs at fixed crank position but depend upon certain events e.g. expansion is dependent on spark that occur slightly ahead of Top Dead Center (TDC) of engine cylinder. Also with Exhaust Gas Recycling (EGR), a portion of exhaust gases are recycled in suction. Due to EGR some delay is present in injection system to ensure overlap between openings of intake valve and closing of exhaust valve.

The working of SI engine indicates that the link of engine processes is defined accurately with crankshaft position. In discrete engine model, crankshaft position is taken as independent variable instead of time. Mathematical model based on the laws of physics is developed for air flow dynamics and fuel flow dynamics in suction and exhaust stroke, production of torque during power stroke. The crankshaft speed is estimated by solving the set of differential equations of all these processes for each cylinder. Computational cost of DEM is high but it can identify the behavior of engine within one engine cycle. Modeling the discreet event model could be seen in [1].

## **2.3. Cylinder by Cylinder Model (CCM)**

In these models, the forces acting on piston of each cylinder are modeled on the basis of laws of physics. The input to these models is the forces acting on the crankshaft assembly and output is the crankshaft speed. The forces acting on crankshaft assembly are estimated using pressure established inside the cylinder due to the burning of air fuel mixture. For a comparison of MVM and CCM, see [23].

## **2.4. Hybrid model**

Hybrid model represent the integration of continuous dynamics and discrete events in a physical system [19], [21], and [22]. In SI engine, the variables like crankshaft speed represent continuous dynamics but the spark is a discrete event. In hybrid model, the four cylinders are considered four independent subsystems and are modeled as continuous system. The cylinder in which power stroke occur is considered as the active cylinder that define the crankshaft dynamics. The sequence of occurrence of power stroke in four cylinders is defined as a series of discrete events. The behavior of SI engine is defined by the combination of both of them. The details of hybrid dynamics is provided in section 4 of this chapter.

## **3. Mean Value Model (MVM)**

In this section a simple nonlinear dynamic mathematical model of automotive gasoline engine is derived. The model is physical principle based and phenomenological in nature. Engine dynamics modeled are inlet air path, and rotational dynamics. A model can be defined as

"*A model is a simplified representation of a system intended to enhance our ability to understand, explain, change, preserve, predict and possibly, control the behavior of a system*"[25].

When modeling a system there are two kinds of objects taken into consideration


An MVM should contain relevant reservoirs only but there are no systematic rules to decide which reservoirs to include in what model. Only experience and iterative efforts can produce a good model. The studied machine is a naturally breathing four-stroke gasoline engine of a production vehicle equipped with an ECU compliant to OBD-II standard. The goal is to develop a simple system level model suitable for improvement of model-based controller design, fault detection and isolation schemes. The model developed in here has following novel features.


The model is verified with data obtained from a production vehicle engine equipped with an ECU compliant to OBD-II. Most of the models available in literature are specific to a certain brand or make because of their use of curve fittings, thus limiting their general use. Here a model is proposed which is not confined to a certain engine model and make; rather it is generic in nature. It is also adaptable to any make and model of gasoline engine without major modifications. Following are outlines of framework for deriving this model.

1. It is assumed that engine is a four stroke four cylinder gasoline engine in which each cylinder process is repeated after two revolutions.

164 Internal Combustion Engines

**3. Mean Value Model (MVM)** 

continuous dynamics but the spark is a discrete event. In hybrid model, the four cylinders are considered four independent subsystems and are modeled as continuous system. The cylinder in which power stroke occur is considered as the active cylinder that define the crankshaft dynamics. The sequence of occurrence of power stroke in four cylinders is defined as a series of discrete events. The behavior of SI engine is defined by the combination of both of them.

In this section a simple nonlinear dynamic mathematical model of automotive gasoline engine is derived. The model is physical principle based and phenomenological in nature. Engine dynamics modeled are inlet air path, and rotational dynamics. A model can be defined as

"*A model is a simplified representation of a system intended to enhance our ability to understand,* 

Flows of energy, mass, pressure and information etc flowing between reservoirs due to

An MVM should contain relevant reservoirs only but there are no systematic rules to decide which reservoirs to include in what model. Only experience and iterative efforts can produce a good model. The studied machine is a naturally breathing four-stroke gasoline engine of a production vehicle equipped with an ECU compliant to OBD-II standard. The goal is to develop a simple system level model suitable for improvement of model-based controller design, fault detection and isolation schemes. The model developed in here has

Otto (Isochoric) cycle is used for approximation of heat addition by fuel combustion

 Consequently the maximum pressure inside the cylinder and mean effective pressure (MEP) are computed using equations of Otto Cycle for prediction of indicated torque. A detailed description of Otto cycle is available in most thermodynamics and automotive

 Fitting/ regressed equations based on experimental data and constants are avoided except only for model of frictional/pumping torque which has been adapted from

The model is verified with data obtained from a production vehicle engine equipped with an ECU compliant to OBD-II. Most of the models available in literature are specific to a certain brand or make because of their use of curve fittings, thus limiting their general use. Here a model is proposed which is not confined to a certain engine model and make; rather it is generic in nature. It is also adaptable to any make and model of gasoline engine without

major modifications. Following are outlines of framework for deriving this model.

available public literature [26], and [27] and modified a little bit.

*explain, change, preserve, predict and possibly, control the behavior of a system*"[25].

Reservoirs of energy, mass, pressure and information etc

the difference of levels of reservoirs.

following novel features.

engine text books.

process.

When modeling a system there are two kinds of objects taken into consideration

The details of hybrid dynamics is provided in section 4 of this chapter.


The air dynamics are further divided into throttle flow dynamics, manifold dynamics and induction of air into the engine cylinders. These are separately treated below and then combined systematically to represent the induction manifold dynamics.

#### **3.1. Throttle flow dynamics**

Throttle flow model predicts the air flowing across the butterfly valve of throttle body. The throttle valve open area has been modeled by relationships of different levels of complexities for accuracy, see for example [2] and [16], but here it is modeled by a very simple relationship as

$$A(a) = (1 - \cos a) \frac{\pi}{4} D\_T^2 \text{ } , a\_0 \le a \le a\_{\text{max}} \tag{1}$$

Where � � �� � is cross sectional area of throttle valve plate with ��, being the diameter of plate facing the maximum opening of pipe cross section, � is the angle at which the valve is open and �(�) is the effective open area for air to pass at plate opening angle �. The angle �� is the minimum opening angle of throttle plate required to keep the engine running at a lowest speed called idle speed. At this point engine is said to be idling. The angle ���� is the maximum opening angle of throttle plate, which is 90*°* . The anomalies arising will be absorbed into the discharge coefficient(��).

Mass flow rate across this throttle valve (�� ��) is modeled with the isentropic steady sate energy flow equation of gases and the derived expression is as below.

$$\dot{m}\_{\rm ai} = A(\alpha) P\_a \mathbb{C}\_d \sqrt{\frac{2}{\beta RT\_a}} \sqrt{\left(\frac{P\_m}{P\_a}\right)^\zeta - \left(\frac{P\_m}{P\_a}\right)^\xi} \Big/ \frac{P\_m}{P\_a} < 1 \tag{2}$$

$$\text{where } \beta = \frac{\gamma - 1}{\gamma} \text{ , } \zeta = \frac{2}{\gamma} \text{ and } \zeta = \frac{\gamma + 1}{\gamma} \tag{3}$$

Here �(�) is defined in equation (1), �� is atmospheric pressure, �� is intake manifold pressure, *R* is universal gas constant, �� is ambient temperature, and � is specific heat ratio for ambient air.

**Figure 1.** Diagram showing the components of a Mean Value Model of Gasoline Engine

#### **3.2. Air induced in cylinders**

166 Internal Combustion Engines

simple relationship as

� ��

for ambient air.

**Ambient Filtered Air** 

Where �

**3.1. Throttle flow dynamics** 

Throttle flow model predicts the air flowing across the butterfly valve of throttle body. The throttle valve open area has been modeled by relationships of different levels of complexities for accuracy, see for example [2] and [16], but here it is modeled by a very

� ��

plate facing the maximum opening of pipe cross section, � is the angle at which the valve is open and �(�) is the effective open area for air to pass at plate opening angle �. The angle �� is the minimum opening angle of throttle plate required to keep the engine running at a lowest speed called idle speed. At this point engine is said to be idling. The angle ���� is the

Mass flow rate across this throttle valve (�� ��) is modeled with the isentropic steady sate

*P PP m AP*

**Figure 1.** Diagram showing the components of a Mean Value Model of Gasoline Engine

**Intake Manifold** 

**Input Throttle Angle**  <sup>2</sup> ( ) , 1 *m mm*

**Fuel Injector** 

12 1 where , and

Here �(�) is defined in equation (1), �� is atmospheric pressure, �� is intake manifold pressure, *R* is universal gas constant, �� is ambient temperature, and � is specific heat ratio

*aa a a*

 

**Fuel Film**

*Cd* (2)

 

> 

(3)

**Cylinders, Combustion, MEP** 

**Exhaust gases**

 

*RT P P P*

� is cross sectional area of throttle valve plate with ��, being the diameter of

� , �� ������� (1)

. The anomalies arising will be

**External Load** 

**Crank Shaft Flywheel** 

**Engine Torque**

�(�) � (� � ��� �) �

maximum opening angle of throttle plate, which is 90*°*

*ai a*

energy flow equation of gases and the derived expression is as below.

absorbed into the discharge coefficient(��).

The air mass induced into the cylinder (�� ��) is modeled with speed density equation of reciprocating air pumps/ compressors, because during suction stroke the engine acts like one. The expression for an ideal air pump is given by the equation:

$$
\dot{m}\_{ac} = \rho V\_d N \tag{4}
$$

Where � is the density of air, �� is swept volume of engine cylinders, and *N* is crankshaft speed in rev/min (rpm). In terms of variables easily accessible for measurement, the expression can be converted into the following using � = �� ��� and ���=��� � Here N is in rpm and � in rad/s and �� holds all the necessary conversions.

$$
\dot{m}\_{ac} = \frac{p\_m}{R T\_m} \mathcal{C}\_0 \omega \tag{5}
$$

Since air compresses and expands under varying conditions of temperature and pressure, therefore the actual air induced into the cylinder is not always as given by the equation. Hence an efficiency parameter called volumetric efficiency (��) is introduced which determines how much air goes into the engine cylinder. The equation therefore can be written as below:

$$
\hbar \dot{m}\_{ac} = \frac{p\_m}{\hbar \tau\_m} \eta\_v \mathcal{C}\_0 \omega \tag{6}
$$

#### **3.3. Intake manifold dynamics**

The intake manifold dynamics are modeled with filling and emptying of air in the intake manifold. The manifold pressure dynamics are created by filling of inlet manifold by mass flow of air entering from the throttle valve (�� ��) and emptying of the manifold by expulsion of air and flow into the engine cylinder(�� ��). Using ideal gas equation for intake manifold this can be derived as

$$\begin{aligned} PV &= mRT\\ P\_m V\_m &= m\_m RT\_m \quad \text{(Using the relationship in manifold variables)}\\ \implies P\_m &= \frac{RT\_m}{V\_m} m\_m \end{aligned} \tag{7}$$

Here *P, V, m, R* and *T* are pressure, volume, mass, Gas constant and temperature of air. It was assumed that the manifold temperature variations are small, and therefore manifold temperature is taken to be constant. To this reason, its differentiation is neglected and only variables are taken to be mass flow and manifold pressure.

$$\implies \dot{P}\_m = \frac{RT\_m}{V\_m} \dot{m}\_m \quad \text{(Differentiation w.r.t. time)}\tag{8}$$

The quantity �� � represents the instantaneous mass variation from filling and emptying of intake manifold, assuming �� � = �� �� � �� �� we can write it as

$$\dot{P}\_m = \frac{RT\_m}{V\_m} (\dot{m}\_{al} - \dot{m}\_{ac}) \tag{9}$$

$$\begin{aligned} \dot{P}\_m &= \frac{RT\_m}{V\_m} \dot{m}\_{ai} - \frac{RT\_m}{V\_m} \frac{P\_m}{RT\_m} \eta\_V C\_0 \alpha \\ \implies \dot{P}\_m &= \frac{RT\_m}{V\_m} \dot{m}\_{ai} - C\_1 \eta\_v P\_m \alpha \\ \text{where } C\_1 &= \frac{1}{V\_m} C\_0 \end{aligned} \tag{10}$$

$$\dot{P}\_m = \frac{RT\_m}{\nu\_m} A(\alpha) P\_a C\_d \sqrt{\frac{2}{\beta RT\_a}} \sqrt{\left(\left(\frac{p\_m}{p\_a}\right)^\zeta - \left(\frac{p\_m}{p\_a}\right)^\xi\right)} - C\_1 \eta\_v P\_m \omega \tag{11}$$

$$T\_{\mathfrak{b}} = f\_{\mathfrak{e}} \mathfrak{a} \quad \text{Or} \tag{12}$$

$$
\alpha = \frac{1}{l\_a} T\_b \text{ Or} \quad \dot{\omega} = \frac{1}{l\_b} T\_b \tag{13}
$$

$$T\_{\rm b} = T\_l - T\_p - T\_f - L\_T \tag{14}$$

$$T\_{\rm b} = T\_{\rm e} - L\_{T} \tag{15}$$


$$MEP = \frac{c\_r^{\gamma^2 - \nu} \{c\_r^{\gamma - 1} - 1\} (\mu\_k Q)}{(\gamma - 1)(c\_r - 1)c\_\nu r\_m (AFR)} P\_m \tag{16}$$

$$T\_l = \frac{\nu\_d}{4\pi} \eta\_{th} MEP = \frac{\nu\_d}{4\pi} \left( \frac{c\_r^{\ast 2-\nu} (c\_r^{\ast \ast 1} - 1) (H\_k Q) \left(1 - \frac{1}{c\_r^{\ast \ast 1}}\right)}{(\nu - 1)(C\_r - 1)C\_p T\_m (AFR)} \right) P\_m \tag{17}$$

The state variable in the above expression is manifold pressure (��). All the other quantities/ parameters are constants or taken to be constant usually. For example, the displacement volume of engine under consideration (��) is a strictly constant value. The same is true for compression ratio (��); this variable is a particular number for a production vehicle. For example, this number is 8.8 (�� = 8.8) for engine under study. The calorific value of fuel (�), manifold temperature (��), and �� are also taken to be fixed constants. As long as other parameters of the above expression are concerned, with the ambient air and atmospheric conditions of pressure and temperature, the variation in their values is very low but inside the cylinder, and during and after combustion, not only the composition of air changes, but also its properties may vary. The variation of index of expansion (�) with density and composition of a gas is well documented in public literature. To accommodate all the variations and some heat transfer anomalies, the entire expression is written as following, making indicated torque a function of manifold pressure with a time varying parameter (��). This parameter is called indicated torque parameter and will be estimated later in this work. In a proper way this parameter may be written as ��(�) but the brackets and variable *t* are omitted for simplicity. With all this, the expression of indicated torque in (17) becomes

$$T\_l = a\_1 P\_m \tag{18}$$

## *3.4.2. Frictional and pumping torque (*��� ��*)*

The Modeling of frictional and pumping torque has been done using a well known empirical relationship given by the equation as

$$T\_f = \frac{1}{2\pi} V\_d \{ 97000 + 15N + 5N^2 10^{-3} \} = b\_1 + a\_2 \omega + a\_3 \omega^2 \tag{19}$$

A slight variation of that can be found in [26] and [27]. The equation has been converted into the variable � with necessary conversion factors. Also the constant ��is merged into the load torque ��. Therefore the minimum value of load torque is equal to or greater than �� even when engine is idling. We can write it as

�� = �� + External Load on Engine

The relationship given in (19) represents the quantity �� for throttle positions closer to WOT (wide open throttle) and for engines up to 2000 cc [26].

## *3.4.3. Load torque (* ��*)*

Load torque �� is external load on engine. It is the load the engine has to pull/ rotate, and it includes all other than the frictional losses all around and pumping work. In case of a vehicle, all the rotating parts of engine and its driven subsystems, including electrical generating set, cam and valve timing system, air conditioner etc. and beyond the clutches, toward the differential gear assembly and wheels, the weight of vehicle and everything in it is the load while in case of electrical generating set, the generator is the load. In a production vehicle, a significant part of the load is also created at random due to driver

$$
\dot{m}\_{fc} = \dot{m}\_{ff2} + \dot{m}\_{ff3} + \dot{m}\_{fsl} \tag{20}
$$


$$
\hbar \dot{m}\_{fc} = \dot{m}\_{ff2} + \dot{m}\_{ff3} + \dot{m}\_{fsl} \tag{21}
$$

$$\dot{P}\_m = \frac{RT\_m}{V\_m} A(\alpha) P\_a C\_d \sqrt{\frac{2}{\beta RT\_a}} \left| \left( \left( \frac{p\_m}{p\_a} \right)^{\zeta} - \left( \frac{p\_m}{p\_a} \right)^{\xi} \right) - C\_1 \eta\_v P\_m \omega + \tag{22}$$

$$
\dot{\omega} = a\_1 P\_m - a\_2 \omega - a\_3 \omega^2 - L\_T \tag{23}
$$

$$a\_1 = \frac{1}{l\_c} \frac{V\_d}{4\pi} \left( \frac{c\_r^{z-\nu} (c\_r^{\nu-1} - 1) \langle H\_k Q \rangle \left(1 - \frac{1}{c\_r^{\nu-1}}\right)}{(\nu - 1)(c\_r - 1) c\_y T\_m \langle A \text{FR} \rangle} \right) 10^3 \tag{24}$$

$$a\_2 = \frac{1}{l\_d} \frac{\nu\_d}{4} \tag{25}$$

$$a\_3 = \frac{1}{l\_\ell} \frac{\nu\_{d\cdot (0.05\pi)}}{10 \times 10^4} \tag{26}$$

A detailed description of nonlinear engine models and their background can be studied in [1-16]. With fuel dynamics considered to be ideal, the model becomes a two state nonlinear model consisting of (17) and (18) only.

## **3.7. Model simulations and engine measurements**

*There is a great difference between theory and practice.* 

#### Giacomo Antonelli (1806-1876)

The manifold dynamics equation derived in earlier section is simulated on a digital computer. The simulation software used is Matlab and Matlab Simulink©. The S-function template available in Matlab is used to program the dynamic model and graphical interface of Simulink© is to run the simulations. The engine measurements are taken using an OBD-II compliant scanning hardware and windows based scanning and data storing software. The model is primed with the same input as engine was and manifold pressure measurements and model manifold pressures are plotted and compared.

A couple of set of simulations are presented for two values of discharge coefficient, and the patterns of engine measurement of manifold pressure and the output of manifold dynamic equation are compared. In first set of simulation, the discharge coefficient is taken to have its ideal value which is 1.0; and the model output manifold pressure is compared with engine measured manifold pressure. While in another simulation test, the discharge coefficient is taken to be equal to 0.5 and the experiment is repeated. As we can see from the Figure 4 and Figure 5 that the shape of trajectory of model manifold pressure and engine measurements is a large distance apart. Moreover, these trajectories do not follow the same shape and pattern. From which we can comfortably deduce that both trajectories cannot be made identical by scaling with a constant number only; thus discharge coefficient of the derived model should not be a constant number. Also, at certain points in time, the evolution of both trajectories is opposite in directions. From all of this it can be concluded that the discharge coefficient should be considered a time varying parameter.

The above figure shows the first simulation of model derived earlier in this section with constant value of discharge coefficient (in this case Cd=1). The input to engine is angle of opening of throttle valve plate. The opening angle is measured with a plane perpendicular to the axis of pipe or air flow direction. The input angle is varied with accelerator pedal for several different values. The same input is fed to the derived model as input to evaluate its behavior and compare with manifold pressure measurements. It is clear that the derived model behaves very differently than the real engine operation.

Moreover, the manifold pressure value given by the model is very high with Cd=1; almost double the measurements throughout the experiment, except for a few points. At these points the model trajectory evolves in nearly opposite direction. It should be noted that for this simulation, the measured angular velocity of engine was used in model equation, and the rotational dynamics equation was not simulated. The similar results for second simulation experiment are shown in figure 3.2. Here, the value of Cd=0.5. As we can see that the lower value of Cd has brought the model manifold pressure trajectory significant low in the plot, and it almost proceeds closer to the engine measured inlet manifold pressure. But the evolution of both trajectories is not identical, which would have been; in case of a correct value of Cd. Both simulation experiments assert that the value of Cd must not be a constant, merely scaling the trajectory. But it must be a time varying parameter to correctly match the derived model to the engine measurements. The value of volumetric efficiency was taken to be 0.8 for model simulations. If engine measurements of volumetric efficiency are used in simulations, discharge coefficient would take different values.

**Figure 2.** Throttle angle; above and manifold pressure; below with Cd=1.0 on left and Cd=0.5 on right. It is evident that the model trajectory is different than the engine measurements.

## **4. Hybrid model**

172 Internal Combustion Engines

model consisting of (17) and (18) only.

Giacomo Antonelli (1806-1876)

varying parameter.

**3.7. Model simulations and engine measurements** 

and model manifold pressures are plotted and compared.

model behaves very differently than the real engine operation.

*There is a great difference between theory and practice.* 

�� <sup>=</sup> � �� ��

A detailed description of nonlinear engine models and their background can be studied in [1-16]. With fuel dynamics considered to be ideal, the model becomes a two state nonlinear

The manifold dynamics equation derived in earlier section is simulated on a digital computer. The simulation software used is Matlab and Matlab Simulink©. The S-function template available in Matlab is used to program the dynamic model and graphical interface of Simulink© is to run the simulations. The engine measurements are taken using an OBD-II compliant scanning hardware and windows based scanning and data storing software. The model is primed with the same input as engine was and manifold pressure measurements

A couple of set of simulations are presented for two values of discharge coefficient, and the patterns of engine measurement of manifold pressure and the output of manifold dynamic equation are compared. In first set of simulation, the discharge coefficient is taken to have its ideal value which is 1.0; and the model output manifold pressure is compared with engine measured manifold pressure. While in another simulation test, the discharge coefficient is taken to be equal to 0.5 and the experiment is repeated. As we can see from the Figure 4 and Figure 5 that the shape of trajectory of model manifold pressure and engine measurements is a large distance apart. Moreover, these trajectories do not follow the same shape and pattern. From which we can comfortably deduce that both trajectories cannot be made identical by scaling with a constant number only; thus discharge coefficient of the derived model should not be a constant number. Also, at certain points in time, the evolution of both trajectories is opposite in directions. From all of this it can be concluded that the discharge coefficient should be considered a time

The above figure shows the first simulation of model derived earlier in this section with constant value of discharge coefficient (in this case Cd=1). The input to engine is angle of opening of throttle valve plate. The opening angle is measured with a plane perpendicular to the axis of pipe or air flow direction. The input angle is varied with accelerator pedal for several different values. The same input is fed to the derived model as input to evaluate its behavior and compare with manifold pressure measurements. It is clear that the derived

����������

�� <sup>=</sup> � �� � (25)

������ (26)

Although the representation of SI engine as a hybrid model is already present in literature, the main difference of the approach presented in this thesis is the manner in which the continuous states of model are being represented. The hybrid model presented by Deligiannis V. F et al (2006, pp. 2991-2996) assumed the model of four engine processes [17] i.e. suction, compression, power and exhaust as four continuous sub-systems. Similar continuous systems are also considered in DEM that can also be considered as a hybrid model. In this model, each cylinders of engine is considered as independent subsystem that takes power generated due to the burning of air fuel mixture as input and movement of piston in engine cylinder is considered as the output. These sub-systems are represented as linear systems and complete SI engine is considered as a collection of

subsystems. These subsystems are working coherently to produce the net engine output. The proposed hybrid model of SI engine can be regarded as a switched linear system. Although an SI engine is a highly nonlinear system, for certain control applications a simplified linear model is used. Li M. et al (2006, pp: 637-644) mentioned in [19] that modeling assumption of constant polar inertia for crankshaft, connecting rod and piston assemblies to develop a linear model is a reasonable assumption for a balanced engine having many cylinders. The modeling of sub-systems of proposed hybrid model would be performed under steady state conditions, when the velocity of system is fairly constant. Also the time in which the sub-system gives its output is sufficiently small. A linear approximation for modeling of sub-system can therefore be justified. Similar assumption of locally linear model is made by Isermann R et al (2001, pp: 566-582) in LOLIMOT structure [20]. The continuous cylinder dynamics is therefore represented by a second order transfer function with crankshaft speed as output and power acting on pistons of cylinder due to fuel ignition as input.

A continuous dynamic model of these sub-systems would be derived in this chapter. The timing of signals to fuel injectors, igniters, spark advance and other engine components is controlled by Electronic Control Unit (ECU) to ensure the generation of power in each cylinder in a deterministic and appropriate order. The formulation of hybrid modeling of sub-systems would be carried under the following set of assumptions:

Modeling Assumptions


The switching logic can be represented as a function of state variables of systems.

## **4.1. Framework of hybrid model**

The framework of Hybrid model for a maximally balanced SI engine with four cylinders is represented as a 5-tuple model < μ, X, Γ, Σ, ϕ >. The basic definition of model parameters is given below.

 μ = {μ�, μ�, μ�, μ�} where each element of set represents active subsystem of hybrid model.


$$\dot{\mathbf{x}}(\mathbf{t}) = \mathbf{A}\mathbf{X} + \mathbf{B}\mathbf{U} \tag{27}$$

$$\mathbf{y(t) = Cx + DU} \tag{28}$$

Where

174 Internal Combustion Engines

cylinder due to fuel ignition as input.

Modeling Assumptions

model

given below.

model.

2. Air fuel ratio is stoichiometric.

storage element (flywheel).

**4.1. Framework of hybrid model** 

subsystems. These subsystems are working coherently to produce the net engine output. The proposed hybrid model of SI engine can be regarded as a switched linear system. Although an SI engine is a highly nonlinear system, for certain control applications a simplified linear model is used. Li M. et al (2006, pp: 637-644) mentioned in [19] that modeling assumption of constant polar inertia for crankshaft, connecting rod and piston assemblies to develop a linear model is a reasonable assumption for a balanced engine having many cylinders. The modeling of sub-systems of proposed hybrid model would be performed under steady state conditions, when the velocity of system is fairly constant. Also the time in which the sub-system gives its output is sufficiently small. A linear approximation for modeling of sub-system can therefore be justified. Similar assumption of locally linear model is made by Isermann R et al (2001, pp: 566-582) in LOLIMOT structure [20]. The continuous cylinder dynamics is therefore represented by a second order transfer function with crankshaft speed as output and power acting on pistons of

A continuous dynamic model of these sub-systems would be derived in this chapter. The timing of signals to fuel injectors, igniters, spark advance and other engine components is controlled by Electronic Control Unit (ECU) to ensure the generation of power in each cylinder in a deterministic and appropriate order. The formulation of hybrid modeling of

3. Air fuel mixture is burnt inside engine cylinder at the beginning of power stroke and energy is added instantaneously in cylinder resulting in increase in internal energy. This internal energy is changed to work at a constant rate and deliver energy to a

4. At any time instant only one cylinder would receive input to become active and exerts force on piston and other cylinders being passive due to suction, compression and

5. All the four cylinders are identical and are mathematically represented by the same

The framework of Hybrid model for a maximally balanced SI engine with four cylinders is represented as a 5-tuple model < μ, X, Γ, Σ, ϕ >. The basic definition of model parameters is

μ = {μ�, μ�, μ�, μ�} where each element of set represents active subsystem of hybrid

The switching logic can be represented as a function of state variables of systems.

sub-systems would be carried under the following set of assumptions:

1. Engine is operating under steady state condition at constant load.

exhaust processes contribute to engine load torque.

$$\mathbf{U} \in \mathbb{R}, \ A \in \mathbb{R}^{2 \times 2}, \ \mathbf{B} \in \mathbb{R}^{2 \times 1}, \ \mathbf{C} \in \mathbb{R}^{1 \times 2}, \ \mathbf{D} \in \mathbb{R}$$

 Σ: μ → μ represents the generator function that defines the next transition model. For an IC engine, the piston position has a one to one correspondence with crankshaft position during an ignition cycle. The generator function is therefore defined in terms of crankshaft position as:

$$\Sigma = \begin{cases} \mu\_1 & 4\text{n}\pi \le \int \dot{\theta}\_1 \text{dt} < (4\text{n} + 1)\pi \\ \mu\_2 & (4\text{n} + 1)\pi \le \int \dot{\theta}\_1 \text{dt} < (4\text{n} + 2)\pi \\ \mu\_3 & (4\text{n} + 2)\pi \le \int \dot{\theta}\_1 \text{dt} < (4\text{n} + 3)\pi \\ \mu\_4 & (4\text{n} + 3)\pi \le \int \dot{\theta}\_1 \text{dt} < (4\text{n} + 4)\pi \end{cases} \tag{29}$$

where n=0,1,2,… and � θ� �dt represents instantaneous shaft position that identifies the output of generator function.

 ϕ: Γ × μ × X × u → X defines initial condition for the next subsystem after the occurrence of a switching event, where u represents input to subsystem. Figure 4.1 shows the subsystems and switching sequence of proposed SI engine hybrid model.

### **4.2. Modeling of sub-system**

A subsystem/cylinder is active when it contributes power to system i.e. during power stroke. When a sub-system is active its output is defined by the dynamic equations of system and its output during its inactive period is defined by its storage properties. The output of a sub-system provides initial condition to the next sub-system at the time of switching. All the subsystems are actuated sequentially during an ignition cycle. The cyclic actuation of subsystems is represented as a graph in Figure 4.1. The total output delivered by the system during complete ignition cycle would be the vector sum of outputs of all subsystems during that ignition cycle.

If T is the period of ignition cycle and u(t) is the input to system at time t within an ignition cycle and ui(t) is the input of ith subsystems; by assumption 4:

$$\mathbf{u}\_{\rm l}(\mathbf{t}) = \mathbf{u}(\mathbf{t}) \qquad \text{when} \quad \frac{(\mathbf{l} - \mathbf{1})\mathbf{T}}{4} < \mathbf{t} < \frac{\mathbf{l}\mathbf{T}}{4} \text{,} \mathbf{i} = \mathbf{1}, \mathbf{2}, \mathbf{3}, \mathbf{4} \tag{30}$$

$$\mathbf{u}\_{\mathbf{i}}(\mathbf{t}) = \mathbf{0} \qquad \text{otherwise} \tag{31}$$

**Figure 3.** Switching of subsystems (*Adopted from Rizvi (2009, pp. 1-6*)

Franco et al (2008, pp: 338-361) used mass-elastic engine crank assembly model for real time brake torque estimation [24]. In this representation of SI engine each cylinder is represented by a second order mass spring damper as shown in Figure 4.2. Consider δQ amount of energy added in system by burning air fuel mixture. The instantaneous burning of fuel increases the internal energy δU in cylinder chamber.

$$
\delta \mathbb{U} = \delta \mathbb{Q} \tag{32}
$$

At ignition time, energy is added instantaneously in engine. This will increase internal energy of system. A part of this internal energy is used to do work and rest of the energy is drained in coolant and exhaust system. If internal energy change to work with constant efficiency ηt then work δW is given by the energy balance equation as:

$$
\delta \mathcal{W} = -\eta\_{\text{ft}} \,\delta \mathcal{U} \tag{33}
$$

Using equationg (27) we get

$$
\delta \delta W = -\eta\_{\text{ft}} \,\delta Q \tag{34}
$$

If p is pressure due to burnt gases then work done during expansion stroke is given by:

$$\mathbf{W} = \int\_{\mathbf{V1}}^{\mathbf{V2}} \mathbf{p} \mathbf{dV} \tag{35}$$

$$\mathbf{p}V^{\gamma} = \mathbf{k}\_1\tag{36}$$

$$\mathbf{W} = \int\_{\text{V1}} ^{\text{V2}} \mathbf{k}\_1 \mathbf{V}^{-\text{y}} \mathbf{dV} \tag{37}$$

$$\mathcal{W} = \mathbf{k}\_1 \frac{\mathbf{v}\_2^{-\mathsf{y}+1} - \mathbf{v}\_1^{-\mathsf{y}+1}}{-\mathbf{y}+1} \tag{38}$$

$$\text{SNR} = \mathbf{k}\_1 \frac{[\text{A(x+\&x)}]^{-\gamma+1} - [\text{Ax}]^{-\gamma+1}}{-\gamma+1} \tag{39}$$

$$\delta \mathbf{W} = \mathbf{k}\_1 \frac{\mathbf{A}^{-\gamma+1}}{-\gamma+1} [ (\mathbf{x} + \delta \mathbf{x})^{-\gamma+1} - \mathbf{x}^{-\gamma+1} ] \tag{40}$$

$$\delta \mathcal{S} W = \frac{\mathbf{k}\_1 \mathbf{A}^{-\mathbf{y} + 1}}{-\mathbf{y} + 1} \left[ \mathbf{x}^{-\mathbf{y} + 1} \left( \mathbf{1} + \frac{\delta \mathbf{x}}{\mathbf{x}} \right)^{-\mathbf{y} + 1} - \mathbf{x}^{-\mathbf{y} + 1} \right] \tag{41}$$

$$\delta \mathcal{S} W = \frac{\mathbf{k}\_1 \mathbf{A}^{-\gamma+1} \mathbf{x}^{-\gamma+1}}{-\gamma+1} \left[ \left( \mathbf{1} + \frac{\delta \mathbf{x}}{\mathbf{x}} \right)^{-\gamma+1} - \mathbf{1} \right] \tag{42}$$

$$\delta \mathbf{W} = \mathbf{k}\_1 \mathbf{A}^{-\chi+1} \mathbf{x}^{-\chi} \delta \mathbf{x} \tag{43}$$

$$\delta \mathbf{Q} = -\frac{\mathbf{k}\_1 \mathbf{A}^{-\mathbf{y} + \mathbf{1}} \mathbf{x}^{-\mathbf{y}} \delta \mathbf{x}}{\eta\_{\mathbf{t}}} \tag{44}$$

$$\mathbf{m}\frac{d^2\mathbf{x}}{dt^2} = \mathbf{F} - \mathbf{k}\_2 \frac{d\mathbf{x}}{dt} - \mathbf{k}\_3 \mathbf{x} \tag{45}$$

$$\mathbf{m}\frac{d^2\mathbf{x}}{dt^2} + \mathbf{k}\_2\frac{d\mathbf{x}}{dt} + \mathbf{k}\_3\mathbf{x} = \mathbf{F} \tag{46}$$

$$\left[\mathbf{m}\frac{\mathrm{d}^2\mathbf{x}}{\mathrm{d}t^2} + \mathbf{k}\_2\frac{\mathrm{d}\mathbf{x}}{\mathrm{d}t} + \mathbf{k}\_3\mathbf{x}\right]\delta\mathbf{x} = \delta\mathbf{W} \tag{47}$$

$$\left[\mathbf{m}\frac{d^2\mathbf{x}}{dt^2} + \mathbf{k}\_2\frac{d\mathbf{x}}{dt} + \mathbf{k}\_3\mathbf{x}\right]\delta\mathbf{x} = \mathbf{k}\_1\mathbf{A}^{-\mathbf{y}+1}\mathbf{x}^{-\mathbf{y}}\delta\mathbf{x} \tag{48}$$

$$\left[\mathbf{m}\frac{\mathrm{d}^{\mathrm{g}}\mathbf{x}}{\mathrm{d}t^{3}} + \mathbf{k}\_{2}\frac{\mathrm{d}^{\mathrm{2}}\mathbf{x}}{\mathrm{d}t^{2}} + \mathbf{k}\_{3}\frac{\mathrm{dx}}{\mathrm{dt}}\right]\delta\mathbf{x} = -\mathbf{k}\_{1}\gamma\mathbf{A}^{-\gamma+1}\mathbf{x}^{-\gamma-1}\frac{\mathrm{dx}}{\mathrm{dt}}\delta\mathbf{x}\tag{49}$$

$$\mathbf{m}\frac{\mathbf{d}^3\mathbf{x}}{\mathrm{d}t^3} + \mathbf{k}\_2\frac{\mathbf{d}^2\mathbf{x}}{\mathrm{d}t^2} + \mathbf{k}\_3\frac{\mathrm{d}\mathbf{x}}{\mathrm{d}t} = -\mathbf{k}\_1\chi\mathbf{A}^{-\chi+1}\mathbf{x}^{-\chi-1}\frac{\mathrm{d}\mathbf{x}}{\mathrm{d}t} \tag{50}$$

$$\mathbf{m}\frac{d^2\mathbf{v}}{dt^2} + \mathbf{k}\_2\frac{d\mathbf{v}}{dt} + \mathbf{k}\_3\mathbf{v} = -\mathbf{\gamma}\eta\_t \frac{\mathbf{k}\_1 \mathbf{A}^{-\mathbf{y} + \mathbf{1}} \mathbf{x}^{-\mathbf{y}} \mathbf{\delta}\mathbf{x}}{\eta\_t} \frac{\mathbf{v}}{\mathbf{x}\mathbf{\delta}\mathbf{x}}\tag{51}$$

$$\mathbf{k}\,\mathbf{m}\frac{d^2\mathbf{v}}{dt^2} + \mathbf{k}\_2\frac{d\mathbf{v}}{dt} + \mathbf{k}\_3\mathbf{v} = \mathbf{\gamma}\eta\_t\delta\mathbf{Q}\frac{\mathbf{v}}{\mathbf{x}\delta\mathbf{x}}\tag{52}$$

$$\mathbf{m}\frac{\mathrm{d}^{2}\mathrm{v}}{\mathrm{d}\mathrm{t}^{2}} + \mathbf{k}\_{2}\frac{\mathrm{d}\mathrm{v}}{\mathrm{d}\mathrm{t}} + \mathbf{k}\_{3}\mathbf{v} = \frac{\mathrm{\nu}\eta\_{\mathrm{t}}\mathrm{v}}{\mathrm{x}}\frac{\mathrm{\delta Q}}{\mathrm{\delta x}}\frac{\mathrm{\delta t}}{\mathrm{\delta x}}\tag{53}$$

$$\mathbf{m}\frac{d^2\mathbf{v}}{dt^2} + \mathbf{k}\_2\frac{d\mathbf{v}}{dt} + \mathbf{k}\_3\mathbf{v} = \frac{\mathbf{v}\eta\_t\mathbf{v}}{\mathbf{x}}\mathbf{P}\{\mathbf{x}\}\frac{1}{\mathbf{v}}\tag{54}$$

$$\mathbf{m}\frac{d^2\mathbf{v}}{dt^2} + \mathbf{k}\_2\frac{d\mathbf{v}}{dt} + \mathbf{k}\_3\mathbf{v} = \frac{\mathbf{v}\eta\_t}{\mathbf{x}}\mathbf{P}(\mathbf{x})\tag{55}$$

$$\mathbf{m}\frac{d^2\mathbf{v}}{dt^2} + \mathbf{k}\_2\frac{d\mathbf{v}}{dt} + \mathbf{k}\_3\mathbf{v} = \frac{\mathbf{v}\eta\_t}{\mathbf{x}\_t}\mathbf{P}(\mathbf{x})\tag{56}$$

$$
\mathbf{k}\_3 = \boldsymbol{\omega}^2 = (2\pi \mathbf{N})^2 \tag{57}
$$

$$
\mathbf{k}\_2 = \mathbf{b} \,\,\omega^2 \mathbf{+} \mathbf{c} \tag{58}
$$



## *4.2.3. Model properties and applications*

The proposed hybrid model was used to study the properties of crankshaft speed of SI engine [Rizvi etal]. The simplicity of model also enabled to study some stochastic properties of engine variable also. An analysis of hybrid model indicates following results which are useful in statistical analysis of system.


The model was used:


## **4.3. Model input estimation**

The input to the model is the power generated inside the cylinder as a result of ignition. It is assumed that power operating on piston is coming from two sources i.e. by the ignition of fuel and by the power supplied by the engine rotating assembly due to inertia. In case of misfire, the power due to inertia of rotating assembly will maintain the movement of piston but the Power due to ignition of fuel is absent. Power can be defined as the product of force acting on piston of a cylinder and piston velocity. If F is the force acting on engine piston and v is the piston velocity, then power P acting on piston can be defined as:

Modeling and Simulation of SI Engines for Fault Detection 181

$$\mathbf{P} = \mathbf{F}\mathbf{v} \tag{59}$$

$$\mathbf{P} = \mathbf{p}. \mathbf{A}. \mathbf{v} \tag{60}$$

Where p is the pressure inside the cylinder, A is the surface area of piston which is known. The only unknown variable is the cylinder pressure that can be estimated using observer or an estimator. One such technique of cylinder pressure estimation was proposed by Yaojung S. and Moskwa J. (1995, pp: 70-78) in [28]. However for simulation purpose typical values can be used. Under idle conditions the typical value of peak pressure inside the cylinders is 25 bars. If engine is running at 15 revolutions per second i.e. idle speed, and cylinder stroke is 75mm, then average speed of piston can be easily estimated. This pulse would be provided once in each ignition cycle i.e. in 720. The time to traverse the complete stroke is 1/30 seconds or nearly 0.03 seconds. The average power provided by the fuel can now be estimated as:

180 Internal Combustion Engines

Parameter Value Description

γ 1.4 Cp / Cv

η 0.3 Efficiency

**Table 1.** Parameter values used in simulation

*4.2.3. Model properties and applications* 

useful in statistical analysis of system.

The model was used:

**4.3. Model input estimation** 

b 0.2 Friction Coefficient

k3 10000 Elasticity Coefficient

P 3 hp Power generated in cylinder

ω 100 rad/s Engine operating speed

The proposed hybrid model was used to study the properties of crankshaft speed of SI engine [Rizvi etal]. The simplicity of model also enabled to study some stochastic properties of engine variable also. An analysis of hybrid model indicates following results which are

1. Four peaks would be observed in one ignition cycle of a four cylinder SI engine.

3. Crankshaft speed is proportional to input power. (Due to linear model of subsystems)

The input to the model is the power generated inside the cylinder as a result of ignition. It is assumed that power operating on piston is coming from two sources i.e. by the ignition of fuel and by the power supplied by the engine rotating assembly due to inertia. In case of misfire, the power due to inertia of rotating assembly will maintain the movement of piston but the Power due to ignition of fuel is absent. Power can be defined as the product of force acting on piston of a cylinder and piston velocity. If F is the force acting on engine piston

2. Amplitude of four observed peaks represents four independent events.

and v is the piston velocity, then power P acting on piston can be defined as:

4. Crankshaft speed is proportional to amount of intake air.

2. To detect and isolate the misfire fault in SI engine [22].

1. To develop state observer for estimation of angular acceleration

m 20 Kg Mass of Engine moving assembly

$$\text{Power} = \left(\frac{2500000}{12} \times \text{pi} \times .075 \times .075 / 4\right) \times \left(\frac{075}{.03}\right) \tag{61}$$

$$\text{Power} = 2301 \text{ Watt} = 3.1 \text{ hp}$$

A pulse with average value of power equivalent to 3.1 hp would then be used in simulations.

**Figure 5.** Switched linear system used for simulation purpose

## **4.4. Model simulation and experimental verification**

The block diagram of switched linear system used for simulation purpose is shown in Figure 4.3. Input is provided as a periodic pulse train and three shifted versions of the same pulse train so that addition of all the four signals would also result in a periodic pulse train. H is a multiplier and represents health of a cylinder. H=1 represents a healthy cylinder that contribute to system output. H=0 represents faulty cylinder that does not contribute to system output.

## *4.4.1. Simulation results*

For simulation computer program was written to implement the block diagram shown in Figure 4.3 in Matlab. This gain of all elements was given a value equal to 1 for no-misfire simulation. To simulate the misfire situation, the gain of the corresponding sub-system was set to zero so that its output did not participate in the net system output. The nominal values of model parameters/ constants used in simulation are provided in Table 1. Under no misfire condition, the model was tuned to match its output with the experimental results. Using same parameter values, the misfire situation was simulated. The simulation results of hybrid model for both healthy and faulty conditions are shown in Figure 4.4.

**Figure 6.** Simulation Results: The waveforms representing fully balanced engine operation (left) one cylinder misfiring (right)

The simulation results were then validated by conducting an experiment. In the crankshaft position was observed using crankshaft position sensor. The output of sensor is in the form of pulses. The data was logged using a data acquisition card from National Instrument Inc. on an analog channel with a constant data acquisition rate. Engine speed is estimated using crankshaft position data and experimental setup data:

182 Internal Combustion Engines

system output.

*4.4.1. Simulation results* 

cylinder misfiring (right)

15

15.2

15.4

15.6

Speed (rps)

15.8

16

**4.4. Model simulation and experimental verification** 

The block diagram of switched linear system used for simulation purpose is shown in Figure 4.3. Input is provided as a periodic pulse train and three shifted versions of the same pulse train so that addition of all the four signals would also result in a periodic pulse train. H is a multiplier and represents health of a cylinder. H=1 represents a healthy cylinder that contribute to system output. H=0 represents faulty cylinder that does not contribute to

For simulation computer program was written to implement the block diagram shown in Figure 4.3 in Matlab. This gain of all elements was given a value equal to 1 for no-misfire simulation. To simulate the misfire situation, the gain of the corresponding sub-system was set to zero so that its output did not participate in the net system output. The nominal values of model parameters/ constants used in simulation are provided in Table 1. Under no misfire condition, the model was tuned to match its output with the experimental results. Using same parameter values, the misfire situation was simulated. The simulation results of

**Figure 6.** Simulation Results: The waveforms representing fully balanced engine operation (left) one

11 11.5 12 12.5 13 13.5 14

Speed (rps)

3.4 3.6 3.8 4 4.2 4.4

Time (sec)

4.55 4.6 4.65 4.7 4.75

Time (sec)

hybrid model for both healthy and faulty conditions are shown in Figure 4.4.

Number of Teeth in gear = 13 Angular spacing between normal Teeth = 30° Angular spacing between double Teeth = 15° Reference indication by Double teeth Data Acquisition Rate = 50000 samples/second

The reference was first searched by finding the double teeth. The number of samples polled in the time interval of passing of two consecutive gear teeth in front of magnetic sensor was observed. The number of samples polled was converted to time as Time = ������ �� ������� ������ ���� ����������� ���� .

Using angular displacement between two consecutive teeth and time to traverse that angular displacement, crankshaft speed was estimated. Crankshaft speed was finally plotted as a function of time. The experiment for the measurement of speed was conducted both under no-misfire condition and misfire condition. During experiment some load was kept on engine by application of brake. The value of applied load was however unknown but an effort was made to keep load similar in both experiments by retaining the brake paddle at the same position during both experiments. The experimental results are shown in Figure 7.

**Figure 7.** Experimental Results: The waveforms representing fully balanced engine operation (left) one cylinder misfiring (right)

## **Author details**

Mudassar Abbas Rizvi and Aamer Iqbal Bhatti *Mohammed Ali Jinnah University (MAJU), Islamabad, Pakistan* 

Qarab Raza *Center for Advanced Studies in Engineering (CASE), Islamabad, Pakistan* 

Sajjad Zaidi *Pakistan Navy Engineering College, Karachi, Pakistan* 

Mansoor Khan *Jiao tong University, Shanghai, China* 

## **5. References**


[13] R. Scattolini, A. Miotti, G. Lorini, P. Bolzern, P., Colaneri, N. Schiavoni, "Modeling, simulation and control of an automotive gasoline engine" Proceedings of 2006 IEEE, International conference on control applications, Munich, Germany, October 4-6, 2006.

184 Internal Combustion Engines

**Author details** 

Qarab Raza

Sajjad Zaidi

Mansoor Khan

**5. References** 

Mudassar Abbas Rizvi and Aamer Iqbal Bhatti

*Pakistan Navy Engineering College, Karachi, Pakistan* 

*Jiao tong University, Shanghai, China* 

Meccanica 32: 387-396, 1997.

ISBN 0-7680-04440-3.

2009, Islamabad.

Page 3891-3898.

Vol.2.,Edinburgh,U.K.,March 1991.

*Mohammed Ali Jinnah University (MAJU), Islamabad, Pakistan* 

*Center for Advanced Studies in Engineering (CASE), Islamabad, Pakistan* 

*Engine Systems*" Springer-Verlag Berlin Heidelberg 2004.

Engine Control," SAE technical paper No. 800054, 1980.

Matlab/Simulink*,*" SAE Technical Paper no.950417, 1995.

Massachusetts Institute of Technology May, 1988.

Engines," SAE Technical Paper no.900616, 1990.

[1] L. Guzzella and C.H. Onder, "*Introduction to Modeling and Control of Internal Combustion* 

[2] John J. Moskwa "Automotive Engine modeling for Real time Control", PhD Thesis,

[3] Elbert Hendricks, Spencer C. Sorenson, "Mean Value Modeling of Spark Ignition

[4] Elbert Hendricks, Alain Chevalier, Michael Jensen and Spencer C. Sorensen "Modeling of the Intake Manifold Filling Dynamics", 1996. SAE Technical Paper No. 960037. [5] Elbert Hendricks, "Engine Modeling for Control Applications: A Critical Survey*,*"

[6] Crossly, P., R., Cook, J., A.,"A Nonlinear Engine Model for Drive train System Development," IEEE Int. Conf. 'Control 91',Conference Publication No.332,

[7] Dobner, Donald J., "A Mathematical Engine Model for Development of Dynamic

[8] Gordon P. Blair, *"Design and Simulation of Four Stroke Engines*", SAE International, 1999.

[9] Weeks, R, W, Moskwa, J, J, "Automotive Engine Modeling for Real-Time Control Using

[10] Robert Todd Chang, "A Modeling Study of the Influence of Spark-Ignition Engine Design Parameters on Engine Thermal Efficiency and Performance", Master's Thesis, Department of Mechanical Engineering, Massachusetts Institute of Technology, 1988. [11] Q. R. Butt, A. I. Bhatti, M. Iqbal, M. A. Rizvi, R. Mufti, I. H. Kazmi, "Estimation of Gasoline Engine Parameters Part I: Discharge Coefficient of Throttle Body", IBCAST

[12] Q. R. Butt, A. I. Bhatti, "Estimation of Gasoline Engine Parameters using Higher Order Sliding Mode", IEEE Transaction on Industrial Electronics *Vol* 55 Issue 11 Nov 2008

