2. EV conversion prototyping development

#### 2.1. EV system modeling

of batteries and also how many of them are required to meet the driving demand. Moreover, selection of different types of motor is also presented as the main requirement [1]. Normal design process would require high-end expensive software to model the EV system. Furthermore, building the EV without the knowledge of the parameters within the system could

In addition, poor vehicle performance safety and reliability might occur when new electric propulsion characteristics do not match with the characteristics of replaced engine sharing the

Therefore, a sub-ECU must be developed to harmonize EV propulsion dynamics and existing vehicle chassis characteristics called drive-by-wire (DBW) [2]. DBW functionality can then improve EV drivability by providing power demand to the electric motor drive according to the driver preference. However, installation of the DBW ECU without appropriate functional safety design and evaluation could induce such system failures or component malfunctions due to unpredicted behaviors during actual driving situations. Therefore, during the initial development process, ECU functions are needed to be established and evaluated against

To improve EV conversion development process, model-based design process is shown in Figure 1. The method would benefit the design engineer in making better decision for the conversion and also saving time and cost by reducing error during the design process [5–7]. The process can be employed to perform system simulation based on different scenarios and technical specification. Embedded system and DBW ECU can be realized by software rapid auto coding to shorten error correction and debugging time. Virtual prototyping test can be employed to validate design requirement and EV conversion specification. The in-the-loop tests can ensure accurate implementation of both software and hardware ECU for the conver-

costly lead to the failure of the design.

4 New Trends in Electrical Vehicle Powertrains

design and functional safety aspects beforehand [3, 4].

sion using real-time verification methodology.

Figure 1. Model-based design process for EV conversion.

same chassis.

In order to set up the simulation of EV, mathematical models have to be generated first from the engineering principles and theories. The four core models are traction model, motor model, battery model, and power flow model as follows.

#### 2.1.1. Traction model

Forces acting on the vehicle govern the equation for vehicle traction as seen in Figure 2. Those forces comprised of tractive forces ð Þ Fte , rolling resistance force ð Þ Frr , aerodynamic force ð Þ Fad , lateral acceleration force ð Þ Fla , wheel acceleration force ð Þ Fwa , hill climbing force ð Þ Fhc [or component force of vehicle weight which depend on grade ð Þ θ ], and the gross weight of the co EV ð Þ mg .

The governing relation can be found in Eq. (1) where traction needs to overcome the load that is equal to five other forces:

$$F\_{\text{te}} = F\_{\text{rr}} + F\_{\text{ad}} + F\_{\text{hc}} + F\_{\text{la}} + F\_{\text{wa}} \tag{1}$$

where equation for each force components can be employed from many sources such as reference [2, 4] and other automotive textbooks.

#### 2.1.2. Motor efficiency model

In the EV conversion system, the motor replaces the internal combustion engine (ICE) in providing the torque to drive the wheel as shown in Figure 3, which also affects the traction

Figure 2. The force components involved in the vehicle traction.

Figure 3. The EV motor provides the traction to the vehicle through transmission.

of the vehicle. The motor torque, speed, and efficiency equation are presented in Eqs. (2), (3), and (4), respectively.


$$T = \frac{F\_w r}{G} \quad \text{N.m.} \tag{2}$$


(nominal cell voltage = 3.3 V up to 80% DoD)



discharge as seen in Eqs. (8) and (9).

where is the number of cells and is depth of discharge (0–1).



as following:



<sup>E</sup> <sup>¼</sup> <sup>n</sup> � �8:2816DoD<sup>7</sup> <sup>þ</sup> <sup>23</sup>:5749DoD<sup>6</sup> � <sup>23</sup>:7053DoD<sup>4</sup> � <sup>12</sup>:5877DoD<sup>3</sup>

The open-circuit voltage also affects the battery current ð Þ IB in both states of charge and

where is the battery resistance. Due to Peukert phenomenon [6], therefore it is necessary to take into account such effect by adding the power to the value, such as lead acid battery (k ≈ 1.12) and Lithium ion (k ≈ 1), when simulation of battery discharge is performed. Battery capacity is updated for each time step as shown in Eq. (10) and then used to update the depth of discharge in Eq. (11) for discharging state and in Eq. (12) for charging state

<sup>4</sup>:1315DoD<sup>2</sup> � <sup>8</sup>:65DoD <sup>þ</sup> <sup>1</sup>:37 ! (6)

þ

Model Based System Design for Electric Vehicle Conversion

http://dx.doi.org/10.5772/intechopen.77265

ð7Þ

7

ð8Þ

ð9Þ

ð10Þ

ð11Þ

ð12Þ


$$
\rho \rho = G \frac{\nu}{r} \text{ rad.s}^{\circ} \tag{3}
$$


$$\eta\_m = \frac{T\,\alpha}{T\,\alpha + k\_c T^2 k\_i \alpha + k\_m \alpha^3 + C} \tag{4}$$

where is copper losses coefficient, is iron losses coefficient, is windage loss coefficient, and is constant loss applied at any speed [6].

#### 2.1.3. Battery discharge model

Battery's dynamic behavior does have a great effect on EV performance and range. Three common types of batteries, which are lead acid, nickel cadmium, and lithium ion batteries, are governed by Eqs. (5), (6), and (7) here, respectively. As seen in [4, 6], open-circuit voltage ð Þ E of the batteries is changed as the state of charge changes and is calculated for each battery type below:


$$E = n \cdot \left( 2.15 - DoD \cdot \left[ 2.15 - 2.00 \right] \right) \tag{5}$$

Model Based System Design for Electric Vehicle Conversion http://dx.doi.org/10.5772/intechopen.77265 7


$$E = n \cdot \begin{pmatrix} -8.2816 D o D^7 + 23.5749 D o D^6 - 23.7053 D o D^4 - 12.5877 D o D^3 + \\ 4.1315 D o D^2 - 8.65 D o D + 1.37 \end{pmatrix} \tag{6}$$


ð2Þ

ð3Þ

ð4Þ

ð5Þ

of the vehicle. The motor torque, speed, and efficiency equation are presented in Eqs. (2), (3),

Figure 3. The EV motor provides the traction to the vehicle through transmission.

where is copper losses coefficient, is iron losses coefficient, is windage loss coefficient,

Battery's dynamic behavior does have a great effect on EV performance and range. Three common types of batteries, which are lead acid, nickel cadmium, and lithium ion batteries, are governed by Eqs. (5), (6), and (7) here, respectively. As seen in [4, 6], open-circuit voltage ð Þ E of the batteries is changed as the state of charge changes and is calculated for each battery

and (4), respectively.

6 New Trends in Electrical Vehicle Powertrains



2.1.3. Battery discharge model

type below: - Lead acid:

and is constant loss applied at any speed [6].


$$E = \mathbf{n} \cdot \mathbf{3}.\mathbf{3}\tag{7}$$

(nominal cell voltage = 3.3 V up to 80% DoD)

where is the number of cells and is depth of discharge (0–1).

The open-circuit voltage also affects the battery current ð Þ IB in both states of charge and discharge as seen in Eqs. (8) and (9).


$$I\_B = \frac{E - \sqrt{E^2 - 4RP\_{bar}}}{2R} \tag{8}$$


$$I\_B = \frac{-E + \sqrt{E^2 + 4RP\_{bar}}}{2R} \tag{9}$$

where is the battery resistance. Due to Peukert phenomenon [6], therefore it is necessary to take into account such effect by adding the power to the value, such as lead acid battery (k ≈ 1.12) and Lithium ion (k ≈ 1), when simulation of battery discharge is performed. Battery capacity is updated for each time step as shown in Eq. (10) and then used to update the depth of discharge in Eq. (11) for discharging state and in Eq. (12) for charging state as following:


$$CR\_{n \leftrightarrow 1} = CR\_n + \frac{\delta t \cdot I\_n^{\ \ k}}{3600} \tag{10}$$


$$DoD\_n = \frac{CR\_n}{C\_p} \tag{11}$$


$$CR\_{n+1} = CR\_n - \frac{\delta t \cdot I\_B}{3600} \tag{12}$$

#### 2.1.4. EV conversion system power flow model

To complete the simulation, the integrated power flow model is necessary to compute and update the rate of energy going in and out of battery cells, accessories, the motor, gearing components, and wheel to the road and back. Therefore, the model needs to be capable of mathematically simulating the power flow in both driving and braking as shown in Figure 4.

Traction model provides the power flow between the vehicle and the road ð Þ Pte as shown in Eq. (13). Furthermore, the motor model provides the power going in for both driving and braking mode at the motor/battery connection ð Þ Pmot\_in and at the motor/transmission connection ð Þ Pmot\_out as indicated in Eqs. (14) and (15). The power parameters are affected by the motor efficiency η<sup>m</sup> and the gearing efficiency <sup>η</sup><sup>g</sup> . The battery power is also computed and updated (Eq. (16)) during charge and discharge operation using the battery model. Power ð Þ Pac is constantly drawn out of battery due to the use of accessories, such as car stereo and light, which is accounted in Eq. (16) [6, 8].


$$P\_{te} = F\_{te} \cdot \upsilon \tag{13}$$


2.2. EV conversion simulation test

2.2.1. Programming for simulation

2.2.2. EV driving simulation

vehicle to accelerate from 0 to 100 km/h.

2.2.3. EV conversion design parameter simulation


ð15Þ

9

Pbat ¼ Pmot\_in þ Pac (16)

Model Based System Design for Electric Vehicle Conversion

http://dx.doi.org/10.5772/intechopen.77265

Models described in the previous section, especially traction model, are employed to simulate the electric vehicle conversion (EVC) performance by obtaining the velocity plot. The vehicle model specifications are approximately used as the input for the simulation. Other inputs are motor specification and road condition where Refs. [2, 4, 8] explained this specification in details.

The traction model is reduced to nonlinear first-order differential forms in [6, 8] when all inputs are substituted. Then, differential equation of velocity is numerically solved using the MATLAB script (.m) file for each time step and updates the values in the program arrays. The out velocity can be plotted against time. The EVC performance here is specified as the time for

The other important piece of information for the EV design is range per charge, which tells us how far the vehicle can travel before it needs to be recharged again. In order to obtain such information, the motor model, battery model, and power flow model introduced in the previous section are applied here along with additional inputs. Driving cycle needs to be reasonably selected to simulate the driving dynamics. For present simulation, simplified federal urban driving cycle (SFUD) in [6] is chosen since the vehicle is expected to be driven in the urban area most of the time. The main program [6, 8] is employed to call inputs, including vehicle specification and driving cycle, and then execute the power flow model and battery model for each driving cycle and update parameters, such as range and DoD simultaneously. The range per charge then can be plotted when the program is done executing the program. Scenarios for

EV design parameters shown in the list below can be easily obtained using the simulation done earlier. To obtain such information, we need to simply write the MATLAB commands in EV

An example of such torque speed map plots is shown in Figure 5, and the vehicle is still operated within the motor power range and maximum torque of 250 Nm. The constant torque

main program to update our interested parameters and then write the plot command.

EV range design can be explored using this simulation procedure [6, 8].


$$\begin{aligned} P\_{\text{not\\_int}} &= \frac{P\_{\text{not\\_out}}}{\eta\_{\text{m}}}, \\ P\_{\text{not\\_out}} &= \frac{P\_{te}}{\eta\_{\text{\\_}}} \end{aligned} \tag{14}$$

Figure 4. Diagram show power flow in/out components within the EV system for both normal forward driving and regenerative braking operations [6].


$$\begin{aligned} P\_{\text{mo\\_in}} &= P\_{\text{mo\\_out}} \cdot \eta\_m, \\ P\_{\text{mo\\_out}} &= P\_{\text{te}} \cdot \eta\_g \end{aligned} \tag{15}$$


2.1.4. EV conversion system power flow model

8 New Trends in Electrical Vehicle Powertrains

motor efficiency η<sup>m</sup>

which is accounted in Eq. (16) [6, 8].



regenerative braking operations [6].

To complete the simulation, the integrated power flow model is necessary to compute and update the rate of energy going in and out of battery cells, accessories, the motor, gearing components, and wheel to the road and back. Therefore, the model needs to be capable of mathematically simulating the power flow in both driving and braking as shown in Figure 4.

Traction model provides the power flow between the vehicle and the road ð Þ Pte as shown in Eq. (13). Furthermore, the motor model provides the power going in for both driving and braking mode at the motor/battery connection ð Þ Pmot\_in and at the motor/transmission connection ð Þ Pmot\_out as indicated in Eqs. (14) and (15). The power parameters are affected by the

updated (Eq. (16)) during charge and discharge operation using the battery model. Power ð Þ Pac is constantly drawn out of battery due to the use of accessories, such as car stereo and light,

Pmot\_in <sup>¼</sup> Pmot\_out

Pmot\_out <sup>¼</sup> Pte

ηm ,

ηg

Figure 4. Diagram show power flow in/out components within the EV system for both normal forward driving and

. The battery power is also computed and

(14)

Pte ¼ Fte � v (13)

and the gearing efficiency <sup>η</sup><sup>g</sup>

$$P\_{\text{but}} = P\_{\text{mot\\_in}} + P\_{\text{ac}} \tag{16}$$

#### 2.2. EV conversion simulation test

Models described in the previous section, especially traction model, are employed to simulate the electric vehicle conversion (EVC) performance by obtaining the velocity plot. The vehicle model specifications are approximately used as the input for the simulation. Other inputs are motor specification and road condition where Refs. [2, 4, 8] explained this specification in details.

#### 2.2.1. Programming for simulation

The traction model is reduced to nonlinear first-order differential forms in [6, 8] when all inputs are substituted. Then, differential equation of velocity is numerically solved using the MATLAB script (.m) file for each time step and updates the values in the program arrays. The out velocity can be plotted against time. The EVC performance here is specified as the time for vehicle to accelerate from 0 to 100 km/h.

#### 2.2.2. EV driving simulation

The other important piece of information for the EV design is range per charge, which tells us how far the vehicle can travel before it needs to be recharged again. In order to obtain such information, the motor model, battery model, and power flow model introduced in the previous section are applied here along with additional inputs. Driving cycle needs to be reasonably selected to simulate the driving dynamics. For present simulation, simplified federal urban driving cycle (SFUD) in [6] is chosen since the vehicle is expected to be driven in the urban area most of the time. The main program [6, 8] is employed to call inputs, including vehicle specification and driving cycle, and then execute the power flow model and battery model for each driving cycle and update parameters, such as range and DoD simultaneously. The range per charge then can be plotted when the program is done executing the program. Scenarios for EV range design can be explored using this simulation procedure [6, 8].

#### 2.2.3. EV conversion design parameter simulation

EV design parameters shown in the list below can be easily obtained using the simulation done earlier. To obtain such information, we need to simply write the MATLAB commands in EV main program to update our interested parameters and then write the plot command.

An example of such torque speed map plots is shown in Figure 5, and the vehicle is still operated within the motor power range and maximum torque of 250 Nm. The constant torque

Figure 5. Torque speed map of EVC with SFUD driving cycle and no regenerative braking mode.

region is quite small compared to the field weakening region. The plot also reveals that low motor speed is mostly required when driving in the urban area.
