Energy Storage Systems and Their Applications in Smart Grids

### **Chapter 5**

## Energy Storage Systems and Their Role in Smart Grids

*Désiré Rasolomampionona and Mariusz Kłos*

### **Abstract**

Energy storage systems play an essential role in today's production, transmission, and distribution networks. In this chapter, the different types of storage, their advantages and disadvantages will be presented. Then the main roles that energy storage systems will play in the context of smart grids will be described. Some information will be given on interactions between energy storage systems and renewables. The emphasis will be on the problems that these storage systems will have to deal with and the possible means that can be used for this purpose. Also the battery management system will be presented as a general concept. The different types of regulation that take place in smart electrical systems (also called smart grids) and the role of energy storage systems will also be discussed. In the end, we will also present one of the biggest weaknesses of storage systems, among others, the degradation of batteries with their use.

**Keywords:** electric vehicles (EV), energy storage systems (ESS), battery energy storage systems (BESS), wind farms (WF), vehicle-to-grid (V2G), photovoltaic (PV)

### **1. Introduction**

Electrical energy in an alternating current (AC) system cannot be stored electrically. However, there are several methods of its storage by converting AC energy into electromagnetic energy storage systems such as superconducting magnetic energy storage (SMES), electrochemical such as various types of batteries (accumulators), kinetically (flywheels), or even as potential energy (hydropower plants) or as compressed air [compressed air energy storage (CAES)]. The energy storage devices currently available on the market are: battery energy storage systems (BESS), energy capacitor systems (ECS), flywheel energy storage systems (FESS). ESSs in an alternating current (AC) grid cannot store electrical energy directly. **Figure 1** depicts the most important storage technologies for the power grid. Among the devices listed above, the BESS is the most commonly used, but it has drawbacks, such as limited lifetime, current and voltage restrictions, and environmental hazards [1]. As a result of the intensive development of renewable energy sources (RES), the development of electromobility, the need to improve the functioning of the existing power grids, the importance of electricity storage has increased in recent years.

**Figure 1.** *Storage technologies for the power grid [2].*

The superconducting energy storage systems are in the process of moving from their prototype stages to practical applications, which recently also receive special attention for utility applications. The latest technological developments are at such an advanced stage that practically we are now just addressing the performance analyses and the aspect of construction and operating costs. Several articles, among others [3], focus on the performance benefits of adding energy storage to power electronic compensators for utility applications.

Energy storage technologies do not in themselves represent sources of energy. However, they offer significant additional benefits to improve stability, transmission enhancement, power oscillation damping, dynamic voltage stability, tie line control, short-term spinning reserve, load leveling, under-frequency load shedding reduction, circuit break reclosing, subsynchronous resonance damping, power quality improvement, and reliability of supply.

Energy storage systems play a significant role in both distributed power systems and utility power systems. There are many benefits of energy storage systems, including improving the cost-effectivity of the power system and voltage profile. These two features are the most important specifications for storage systems.

Because of the recent development of power electronics, superconductivity, and computer science, the SMES system has received a great attention in the power systems applications. The SMES is notably used in distributed energy storage, spinning reserve, load following, automatic generation control, power quality improvement, reactive power flow control voltage control, and transient stability enhancement [4].

*Energy Storage Systems and Their Role in Smart Grids DOI: http://dx.doi.org/10.5772/intechopen.103945*

The BESSs have limited lifetime and voltage and current limitations. The FESSs involve other rotating machinery, which is not a preferable, and standby loss is high. Also, the charging method of ECS and its control scheme is not easy.

The fastest-growing power generation technology remains grid-connected solar photovoltaic (PV) power. There was a 70% increase in existing capacity to 13 GW in 2008, while for wind farms, the growth in existing capacity was 29% in 2008 to reach 121 GW, more than double the 48 GW that existed in 2004 [5, 6]. However, like all other renewable energy sources, the main disadvantage of solar and wind energy is their instability. These energy sources depend on natural and meteorological conditions [7]. Great technical challenges related to grid interconnection, power quality, reliability, protection, generation dispatch, and control are to be overcome with higher penetration of intermittent renewable resources [8].

In addition to pumped storage power plants used for years in power systems, other technologies are currently being tested and introduced to enable the storage of electric energy in the form of various energy media.

Electricity storage technologies can be broadly divided into two main categories under the angle of the energy storage form:



### **Table 1.**

*Characteristic parameters of different energy storage technologies.*

A detailed overview of various energy storage technologies is presented later in this chapter.

There are several parameters justifying the choice of ESS for an application. One can enumerate the rated power and energy of the application, its response time, its weight, its volume, and its operating temperature. The characteristic parameters of the various energy storage technologies are presented in **Table 1**. These values have been extracted from [9, 10].

### **2. Different types of energy storage**

### **2.1 Batteries**

### *2.1.1 Lead acid*

Lead-acid batteries, commercialized in 1859, are the oldest technology among all batteries that enable the storage of electricity with the use of electrochemical phenomena. Due to its simple structure, the ability to generate high currents, resistance to overcharging, and low price, this technology has become the most common option in DC systems in practically all sectors of the economy. Batteries are used, among others, in automotive starting, lighting and ignition (SLI) and uninterruptible power supplies (UPS), small electric vehicles (e.g., forklifts), or for storing electricity generated in small and medium-sized RES power plants, in the energy and telecommunications sectors.

Due to the maturity of this technology, many new solutions have been developed over the years to optimize the operation of lead-acid batteries, including maintenance-free batteries with liquid electrolyte, with regulated valve (VRLA), with liquid electrolyte absorbed in a separator made of glass mat (AGM), or with gel electrolyte. Despite the design measures that streamline the operation and define new application areas from the point of view of the basic technical parameters, this technology is a technology "leaving" the market. Recent research leads to the conclusion that it is possible to increase power and energy density by replacing lead with lighter materials such as carbon.

### *2.1.2 Li-ion*

Lithium-ion batteries have been used commercially since 1991, primarily to power small electronic devices. In recent years, largely due to the intensive development of electromobility and photovoltaic power plants, the importance of energy storage based on Li-ion cells has increased. Currently, lithium-ion batteries are used both in domestic storage tanks with a capacity of several kilowatt hours and system storage tanks with a capacity of up to several dozen megawatt hours.

Lithium-ion energy storage is characterized by a high voltage of a single battery (usually 3.6 or 3.7 V) and a high energy density. The "power" and "capacity" scaling of the battery tank (as in the case of other battery technologies) consists in combining lithium-ion batteries into series-parallel systems, forming the so-called battery strings.

This type of battery has several advantages; we can list among others the high energy/weight ratios, the absence of memory effect, and the low self-discharge. These batteries find their uses primarily in portable equipment such as laptops, cameras, cell phones, and portable tools. Thanks to its high energy density, Li-ion is also one of the most promising technologies to be used in the power supply of hybrid and rechargeable electric vehicles. However, the start-up costs of the technology remain a fairly significant barrier to its large-scale use.

### *2.1.3 NiCd/NiMH*

The technology of energy storage in nickel-cadmium batteries is known from the beginning of the twentieth century and for many years was the only alternative to lead-acid batteries. Nickel-cadmium batteries are characterized by a short charging time and resistance to ambient temperature fluctuations (from −40°C to +60°C). NiCd batteries were the chemistry of choice for a wide range of high-performance applications between 1970 and 1990. NiCd cells allowed for significant development of portable devices such as radios, camera flashlights, and power tools. The operation of nickel-cadmium batteries is similar to the previously described lithium-ion batteries. In 2006, the Parliament of the European Union approved directives that significantly limit the use of nickel-cadmium batteries. A significant disadvantage of this technology is the occurrence of the so-called memory effect, which causes a decrease in the capacity of the cells during operation. NiCd batteries have also the following disadvantages compared with NiMH batteries: first of all, their life cycle is more expensive. Secondly, in the 1990s, along with the development of lithium-ion and nickel-metal hydride batteries, their role decreased significantly also due to the difficult process of disposal of used batteries, which requires a complex recycling procedure because the batteries contain toxic compounds. This toxicity of Cd, in addition to the lower energy density, and finally the flat discharge curve and negative temperature coefficient could cause thermal runaway during voltage-controlled charging.

For these reasons, nickel metal hydride batteries (NiMH) have gained prominence over NiCd batteries in the recent past. Nickel oxyhydroxide is used by NiMH batteries for the positive electrode and metallic cadmium for the negative electrode. Research on nickel-metal hydride cells began as early as 1967, but initial problems with metal hydride instability led to a greater focus on developing nickel-hydrogen (NiH) technologies. New metal alloys developed in the 1980s allowed for the optimization of NiMH cells and are now widely used as an alternative to disposable alkaline batteries and nickel-cadmium cells, which are characterized by a much lower energy density (about 40% compared with NiMH cells). NiHM batteries have been the chemistry of choice for EV and hybrid EV (HEV) applications due to their relatively high power density, proven safety, good abuse tolerance, and very long life at a partial state of charge.

In the 1990s and 2000s, NiMH was the most popular and mature chemical technology for battery production. Batteries for EVs and hybrid EVs (HEVs) were produced on the basis of NiMH in the 1990s and 2000s. NiMH batteries had relatively high power density, proven safety, good tolerance to abuse, and a very long life at a partial state of charge. The weak point of these batteries was the relatively high self-discharge rate, up to 20% of energy is lost during the first 24 hours after charging, and then 10% during each subsequent month, although the introduction of novel separators has mitigated this problem.

NiMH batteries can also be used in uninterruptible power supply systems (UPS) and in storage tanks cooperating with RES installations. An additional advantage is the possibility of effective recycling and the lack of highly toxic compounds inside the cells, which makes the technology relatively environmentally friendly.

When overcharged, NiMH batteries use excess energy to separate and recombine water. There is then no need to maintain them. However, they should not be charged at such a rate of charge, or cell rupture may occur due to the accumulation of hydrogen. On the other hand, if the battery is overdischarged, the cell may polarize in the opposite direction, which could affect its capacity.

### *2.1.4 NaS*

In sodium-sulfur solid beta alumina, the cathode is made of molten sulfur, the anode is molten sodium, and the electrolyte is a nonporous, solid beta alumina ceramic material (**Figure 2**). As energy is drawn from the energy storage, sodium ions penetrate the solid electrolyte layer toward the cathode, causing the current to flow through the powered circuit. The process is reversed during charging. The battery cells are used to operate in high temperatures (from 300 to 350°C). In 2011–2015, at the University of Kyoto, work was carried out on solutions enabling the operation of sodium sulfur batteries at a much lower temperature of about 100°C, for use in electric vehicles and installations supplying residential buildings.

Due to the possibility of quick entry into operation, high energy density, high efficiency, and long service life, the main area of application of sulfur-sodium batteries is energy storage with very high power and capacity used to optimize the operation of power grids and RES power plants.

### *2.1.5 FBs*

Flow batteries (FBs) production technology is very promising. FBs are produced in such a way that the total energy stored is decoupled from the nominal power. The size of the reactor and the volume of the auxiliary tank are the main elements on which the nominal power and the stored capacity of the battery depend. Thanks to these characteristics, the FB is able to supply large amounts of power and energy required by electric utilities.

One of the most popular FB technologies is the iron-chromium flow batteries (ICBs). This technology was developed in the 1980s by NASA research teams and the Japanese company Mitsui. Thanks to high efficiency of energy exchange (over 80%), easy scaling, and high reliability, ICB cells are a suitable solution for multi-megawatt system energy storage and smaller uninterruptible power supply systems.

**Figure 2.** *NaS battery cell and package [11].*

### *Energy Storage Systems and Their Role in Smart Grids DOI: http://dx.doi.org/10.5772/intechopen.103945*

ICBs achieve the highest efficiency at relatively high ambient temperatures (in the range from 40°C to 60°C), thanks to which they can be successfully used in regions of hot climatic zones (unlike most electrochemical tanks that require continuous cooling). An additional advantage is the use of common, inexpensive materials chromium and iron, which are low-toxic, hence safe and environmentally friendly technology.

Energy storage based on ICB cells with a capacity of up to several megawatt hours can be used to secure electricity supplies (e.g., on continental islands or in military bases) and to optimize the operation of RES power plants.

The main disadvantage of ICB cells is the relatively low voltage of a single cell (1.18 V), which results in low energy density and large size of energy reservoirs based on this technology. An additional challenge is posed by the parasitic chemical reactions of chromium with hydrogen, which shorten the life of the cells and cause instability of the liquid electrolyte. It is possible to reduce these phenomena at the expense of lowering the efficiency.

The FB diagram is shown in **Figure 3**. Flow reactants and membrane area define the nominal power, while the total stored energy depends on the capacity of the electrolyte reservoir. It must be remembered that in a conventional battery, the cell itself stores the electrolyte, so there is a strong connection between the power and the nominal energy. A reversible electrochemical reaction takes place in the cell (flow reactor) and produces (or consumes) direct electric current. FB technology is currently used in several large and small-scale demonstration and commercial products.

### *2.1.6 EDLCs*

Supercapacitors, also known as ultracapacitors or electrochemical double-layer capacitors (EDLCs), are characterized by high power density—which translates into short charge and discharge times, high efficiency, and durability. In the case of a supercapacitor, the possibility of quick charging and discharging with high efficiency

results from the direct storage of electric energy, because the energy carrier in them is an electric field. Supercapacitors can be used in active filters, improving the quality of electricity, in distribution networks as a tool for energy balancing, and in electric vehicles and trains, popularized, e.g., through Formula 1 racing with KER (kinetic energy recovery systems) systems. There is no faradic process in EDLC, therefore no ionic or electronic transfer results in a chemical reaction. A simple charge separation causes energy to be stored in the electrochemical capacitor.

### **2.2 Fuel cells**

Water and electricity can be produced using FC using electrochemical conversion taking place in special devices that use hydrogen and oxygen. Thanks to the use of FCs, a "hydrogen economy," which is an increasingly popular concept according to which hydrogen is produced by a chemical process, can be ensured. For example, the electrolysis of water, having for objective, among others, the obtaining of hydrogen, which can be used as fuel [12]. Special devices can be used, which combine the function of the FC and the electrolyzer in a single device called regenerative FCs or unitized regenerative FCs. These devices operate as follows: Electricity is produced from hydrogen stored in the form of gaseous fuel, which will later be used for this purpose. FCs are generally optimized to perform only one function, while theoretically they can function as regenerative FCs. By combining the two functions, the size of the system can be reduced for applications requiring both energy storage (hydrogen production) and energy production (electricity production).

### *2.2.1 Alkaline fuel cells*

Alkaline fuel cells (so-called Bacon cells, from the inventor's name—F.T. Bacon) use a liquid alkaline electrolyte (most often potassium hydroxide KOH, which, depending on the type of construction, circulates inside the cell or is contained in an asbestos membrane between the electrodes). An additional advantage of alkaline fuel cells is their resistance to harsh conditions—ambient temperatures below 0°C, high humidity or salt content in the air. AFC cells are currently used primarily as energy sources in uninterruptible power supply systems (UPS), in the own needs of telecommunications and as batteries for electric busses. The disadvantage of AFCs is their low tolerance to carbon monoxide, which reacts undesirably with the electrolyte, making these cells impractical for years.

### *2.2.2 Phosphoric acid fuel cell*

Phosphoric acid fuel cells (PAFCs), developed in the 1960s, were the first commercially produced technology of this type. Since then, PAFC cells have been significantly improved in terms of operational stability, efficiency, and reduction of production costs. In PAFC cells, the electrolyte is gel orthophosphoric acid, placed in a porous layer made of Teflon silicon carbide. The electrodes, on the other hand, are made of porous graphite with an admixture of platinum.

Phosphoric acid fuel cells operate at relatively high temperatures (from 150 to 200°C), which makes them highly resistant to carbon monoxide contamination. Hot water, which is a product of reactions taking place inside the cells, can be used in cogeneration systems for electricity and heat (achieving a high process efficiency of 80%). An additional advantage is their lifetime reaching 40,000 h.

The disadvantage of the PAFC technology is the high corrosivity of the electrolyte, which entails the need to use expensive acid-resistant materials and a relatively low efficiency (30–40%). PAFC cells are used primarily in RES power plants and uninterruptible power systems (e.g., UPS) with installed powers from 50 to 400 kW.

### *2.2.3 Direct methanol fuel cell*

Fuel cells fed directly with methanol are a relatively new solution in the field of electricity storage. The technology was developed by NASA in the 1990s of the last century. DMFC cells use the advantages of methanol as a fuel: high energy density (250–800 Wh/kg), relatively low production costs, and easy transport and storage. This technology is relatively easy to use, because the methanol supplied to the cells can be stored in appropriate tanks located near the bunkers or in replaceable cartridges attached to DMFC cells.

The main area of application of fuel cells directly fed with methanol is loads with relatively low powers, e.g., portable electronic devices or power banks. In recent years, there has been an intensive development of DMFC cells adapted to power small crane vehicles used in large warehouses. Thanks to this, it is possible to shorten the charging time to a few minutes and avoid the costs associated with the installation of battery charging systems for used vehicles.

There are a few other technologies of fuel cell, i.e., Molten Carbonate Fuel Cell, Proton Exchange Membrane Fuel Cell, Solid Oxide Fuel Cell, but their detailed description will be omitted.

### **2.3 Solar energy and ESS**

The annual amount of solar energy received by the earth represents the equivalent of 120,000 TW. Less number of these available solar resources are in a condition to fully replace all nuclear energy and fossil fuels as an energy source [13, 14]. The main obstacles to the further development of solar generation are, among others: the high cost of manufacturing solar cells, dependence on weather conditions, and ultimately, storage and grid connection problem.

Utilities and system operators face some pretty serious challenges due to the integration of significant amounts of solar photovoltaic (PV) generation into the electrical grid. Grid-connected solar photovoltaic units generate and then deliver power to power grids at the distribution level. Installed systems are often designed for one-way power flow from the substation to the customer. The main technical challenges are as follows: transient and steady-state issues due to the widespread adoption of solar generation by customers on the distribution system, voltage variations, sudden weather-induced changes in output, and legacy protection devices designed with power flow in mind [15].

In the case of solar-based electricity generation, weather events such as thunderstorms can have a detrimental effect on solar production—it can range from maximum production to negligible levels in the shortest amount of time. These large-scale weather-related generation fluctuations can be highly correlated within a given geographic area, meaning that the array of solar PV panels on feeder lines downstream of the same substation has the potential to reduce its production considerably in the face of an average meteorological event taking place, for example, on the same day. These disturbances can cause power fluctuations, which can also negatively affect the electrical network in the form of voltage sags if prompt action is not taken to

counteract the change in generation. A frequency disturbance can also occur in small electrical systems, resulting from sudden changes in PV generation.

The use of battery energy storage systems (BESS) can provide power quickly in such scenarios to minimize customer interruptions [16] regardless of their location, whether in the center of the substation or distributed along a supply line. Grid-scale BESSs can mitigate the above challenges while improving system reliability and renewable resource economics. This can of course be achieved provided that adequate control schemes are installed.

Regarding the deployment of BESS technologies on the electrical power distribution system, there are two main schools of thought. Centralized storage at the MW level at the distribution station is recommended by a group of scientists. On the other hand, there is a group of people who argue that smaller energy storage systems should be distributed across distribution feeders, networked, and remotely controlled at the substation level.

Each approach has its advantages and disadvantages. Centralized storage has the following advantages in particular—easy access to electrical and Supervisory Control and Data Acquisition (SCADA) equipment of the substation, simplified control schemes, economies of scale on the one hand, and on the other hand because there is already utility-owned land available behind the substation fence. One of the solutions to the problems of deployment of BESS is appropriate sizing and location of the BESS. The ideal sizing and location will depend on the type of site. In the case of large photovoltaic solar installations, preferably a battery system of comparable size connected to the grid is installed in the same substation.

### *2.3.1 Ramp rate control*

One of the main problems with renewable energies is the lack of inertia components. In the case of photovoltaic solar production installations, the inertial components are completely absent. Additionally, the generated power can change very quickly when the sun is obscured by cloud cover. In the case of small electrical systems with high penetration of photovoltaic production, the consequences of this situation could be serious problems of energy supply, since traditional thermal units will have problems compensating for the lack of energy, and hence, maintaining the power balance in the face of rapid changes would be compromised.

As it has been written before, the BESS is used to compensate for the lack of energy in renewable energy installations. In this case where the BESS is coupled to solar power, the BESS must counteract rapid changes in output power to ensure that the installation provides ramp rates deemed acceptable by the system operator. Allowable ramp rates are among the common features of new solar and wind power purchase agreements between utilities and independent power producers. They are usually expressed by the utility in kilowatts per minute (kW/min).

The patent presented in [17] defines the Ramp Rate Control algorithm used in the Xtreme Power - Dynamic Power Resource (XP-DPR) system [18]. This algorithm continuously monitors the actual power output of the solar array and commands the unit to charge or discharge so that the total system power output is within limits set by utility requirements. The operation of an XP-DPR BESS smoothing the volatile power output of a 1 MW solar farm is depicted **Figure 4** [18].

A BESS can be used to discharge when the energy from the solar installation begins to drop in the afternoon. This can be done by charging from the grid at night or from a certain percentage of solar generation during the day. Thanks to this operation, *Energy Storage Systems and Their Role in Smart Grids DOI: http://dx.doi.org/10.5772/intechopen.103945*

### **Figure 4.**

*Ramp rate control to 50 kW/min for a 1 MW photovoltaic installation and a 1.5 MW/1 MWh BESS. (a) Full day. (b) Detail of largest event [18].*

the reduction of solar energy at a time when energy is expensive is compensated. This operation is illustrated in **Figure 5** [18].

An excessive part of the electricity produced during the day can be stored in the ESS. On the other hand, during the night, it can be released to complete the energy consumption of a household. Using this method, the total amount of electricity drawn from the power grid can be reduced. Unfortunately, discharging and recharging batteries impact their operational life.

A controller is needed to regulate the charging and discharging process of a battery to protect against overcharging and overdischarging [17]. Despite the use of this controller, some of the energy generated by the PV could still be lost due to the limited energy storage capacity. Apart from this, an inverter must be used to transform the direct current (DC) generated at the PV level into alternating current (AC) to be able to transmit it to the grid. Otherwise, the electrical energy obtained from PV could not be used by household appliances.

In the case of smart grids, it is possible to perform this combination as one of the tasks performed by the main fusion box. Customers choose and register their electricity tariffs in real time, where the price of electricity varies over time [19]. They can also recharge their battery, thereby storing energy in real time from the electricity drawn from the electricity grid, with the aim of reusing it later. The customer can choose the tariff that suits him in order to optimize his bill—for example, by

**Figure 5.** *Full-day output of the solar time-shift application [18].*

recharging the battery from the electricity grid when the electricity price is low while discharging it during the period of high electricity prices.

### *2.3.2 PV and DSM*

The scientific literature relevant to energy management in PV-equipped homes mainly focuses on demand-side management (DSM), so how to react to shape the household electrical load during periods of high PV production and to minimize network energy consumption [20].

The local consumption is normally managed in such a way that the ESS battery charge is activated as soon as the PV output power is greater than the electrical load of the house, **Figure 6**. However, this strategy is not able to combat overvoltages that may occur during peak PV production hours (12:00 p.m. to 2:00 p.m.) as the ESS battery is fully charged during the morning hours of sunny days, well before the maximum PV generation period [20].

**Figure 6** presents a choice of strategy to be able to move the battery charging period from the "conventional" range to the "proposed" range. This can be achieved by an optimization based on 1-day solar irradiance predictions, described, for example, in [22].

A host of electrical configuration hardware including smart meters, smart sockets, to realize load transfer of different appliances, and main controller to realize load management (i.e., load shift) [23, 24] is used for power management of energy storage systems (ESS) in houses equipped with PV.

### **2.4 Hydrogen energy storage**

The conversion of hydrogen to heat or electricity is fairly easy to accomplish using the popular equation "hydrogen plus air produces electricity and drinking water." Other than that, hydrogen, as the most common chemical element on the planet, is considered an eternal source of energy [14].

In 2004, two institutions, the U.S. National Research Council [25] and the American Physical Society [26], published two comprehensive studies analyzing the technical options concerning the use of hydrogen, including the problem of the cost

**Figure 6.** *Conventional storage strategy and proposed strategy compared [21].*

of hydrogen obtained from various sources. The only thing missing from these studies is the key question of the overall energy balance of a hydrogen economy.

In fact, there is a whole plethora of processes to be set in motion to obtain hydrogen. We need energy to produce, compress, liquefy, transport, transfer, and store hydrogen. We also lose energy without hope of recovery for its reconversion into electricity with fuel cells [27]. The analysis on the actual energy content in accordance with the law of conservation of energy was analyzed on the basis of the heat of formation or HHV (Higher Heating Value).

### *2.4.1 How is the hydrogen produced*

### *2.4.1.1 Hydrogen from electrolysis*

One of the best-known methods for producing hydrogen is the transformation of water (or rather the dissociation of its molecules) by electrolysis. However, this process is very energy intensive. We envision that in a sustainable energy future, priority will be given to the direct route, i.e., the transformation of renewable electricity into a chemical energy vector. According to [12], the standard water formation potential is 1.48 V, which would correspond to the heat of formation or higher calorific value HHV of hydrogen. The authors [12] also claim that for advanced solid or alkaline polymer electrolyzers, about 0.1 V is lost through biasing, while 0.2 Ω cm<sup>2</sup> is typical for area-specific resistance.

### *2.4.1.2 Hydrogen generation (PEM electrolyzer) system*

The production of hydrogen can be carried out in an efficient manner using the electrolysis of water using polymer electrolyte membrane (PEM) cells. This means of obtaining hydrogen is quite simple to implement. PEM electrolyzers are compact and the current capacity is higher.

The following four auxiliaries are used in the dynamic model of a PEM electrolyzer [28]: the anode, the cathode, the membrane, and the voltage auxiliary (**Figure 7**).

**Figure 7.** *Electrolyzer modeling block diagram [28].*

The flow rates of oxygen and water and their partial pressures are calculated at the auxiliary anode calculated. The calculation of the partial pressures of hydrogen and water as well as their flow rates is carried out by the cathodic system. Water content, electro-osmotic drag, water diffusion, and membrane conductivity are calculated by the membrane auxiliary. The voltage auxiliary calculates the voltage of the electrolyzer by incorporating the Nernst equation, the ohmic bias, and the activation bias.

### *2.4.1.3 Hydrogen consumption (fuel cell) system*

The PEM fuel cell is the inverse equivalent of a PEM electrolyzer. It is modeled similarly to the PEM electrolyzer described in the previous section. A chemical reaction with oxygen is carried out in order to obtain the chemical energy of the hydrogen fuel, which will then be converted into electricity.

Water and heat are the by-products of this reaction. The authors [29] developed the dynamic fuel cell model shown here. This model is made up of four main auxiliaries: the anode, the cathode, the membrane, and the voltage (**Figure 8**).

### *2.4.1.4 Hydrogen from biomass*

It is also possible to produce hydrogen from biomass. However, it seems that this option does not really have a future because, first of all, the process is quite complex: Biomass must be converted into biomethane by aerobic fermentation or gasification before it can produce hydrogen. And secondly, we know, however, that natural-gasgrade biomethane (more than 96% CH4) is already a perfect fuel for transport and stationary applications. Why turn it into hydrogen? There is already a biomethane supply system from waste water digesters in many European countries, the finished

**Figure 8.** *Full cell modeling block diagram [29].*

product of which is already being sold at petrol (fueling) stations to an increasing number of satisfied drivers.

The high energy losses may be tolerated for some niche markets, but it is unlikely that hydrogen will ever become an important energy carrier in a sustainable energy economy built on renewable sources and efficiency.

Moreover, the delivered hydrogen must be converted to a motion for all transport applications. IC engines convert hydrogen within 45% efficiency directly into mechanical motion, while equally efficient fuel cells systems produce DC electricity for traction motors. Further losses may occur in transmissions, etc. All in all, hardly 50% of the hydrogen energy contained in a vehicle tank is converted to motion of a car. The overall efficiency between electricity from renewable sources and wheel motion is only 20–25%.

### *2.4.2 Hydrogen transformation: fuel-cell-powered vehicles*

The most efficient way to use a fuel, in particular hydrogen, in a vehicle is to convert the fuel's energy directly into electricity in a fuel cell. The hybrid design consists of realizing a corresponding illustrated drive train in which the charge of the fuel cell can be leveled using a small battery or an ultracapacitor, much like in a hybrid vehicle with a maintenance electric motor dump. The energy in the battery in question is much smaller than the energy stored in hydrogen. For example, if we store 3 kg of hydrogen, it would be equivalent to three gallons of gasoline or about 100 kWh. This would correspond to more energy than that in the battery of a passenger car.

### *2.4.2.1 Hydrogen production in micro fuel cell applications*

The successful commercialization of miniature fuel cells presents a huge constraint as an alternative to conventional rechargeable batteries for supplying electricity to portable electronic devices such as laptops and mobile phones.

Unfortunately, serious difficulties and significant risks are linked to the storage and handling of hydrogen, whether in the form of compressed gas or liquid, which is used as fuel [30]. Furthermore, compared with storage in the form of liquid hydrocarbons such as methanol, the stored density of hydrogen in compressed or liquid form is significantly lower. This hydrogen can later be reformed to generate the gas when needed. Other methods of hydrogen storage such as in the form of metal hydrides [31, 32] have been discussed extensively in the literature.

There are, however, a number of disadvantages of using hydrides to store hydrogen. These include loss of hydrogen storage capacity after repeated use (limited service life of the alloy), higher weight per unit amount of hydrogen stored (hydrides have the weight of the added metal to the total weight of the storage tank), and the difficulty in extracting all the stored hydrogen due to hysteresis.

### *2.4.3 Microgrid and hydrogen-based ESS*

Most of the projects launched related to wind/solar hydrogen power plant systems [34, 35] and whose results are presented in various scientific articles show the need to introduce greater optimization of the operation of the electrical energy production facility. Although autonomous operation is achieved, several articles report technical problems during operation and serious shortcomings such as electrolyzer breakdowns, high inefficiency in the hydrogen loop, loss of fuel cell performance, breakdowns of pump, etc. It was therefore concluded that the technical problems

related to the design and operation of power plants are not yet fully resolved and that an in-depth study is recommended to achieve more reliable operation.

It is also necessary to take into account the fairly complex management of energy production in microgrids (MGs). Energy management in MG is a big challenge to face due to the need to integrate generation, storage systems, and different types of loads, while controlling while the demand is satisfied [36].

In general, two timescales are taken into consideration for MG energy management, as shown in [37]:


The process of management of energy production in microgrids is shown in **Figure 9**. The ON-OFF switching thresholds for the electrolyzer and the fuel cell are indicated there. In addition, a protection system against overcharging (high state of charge (SOC)) or undercharging (low SOC) is incorporated into the battery bank.

### *2.4.4 Control of a grid-connected hybrid system integrating RE, hydrogen, and batteries*

There are two uncontrollable but equally essential parameters for the production of RES. These are solar irradiance and wind speed. Therefore, a supporting power source is needed to increase the degree of controllability and operability of the HRES. In almost all solutions for the production and control of renewable energy sources, DC/DC power converters are used to connect them to a central DC bus. In order to coordinate energy use in microgrids, different optimization methods can be used. The use of the supervisory control system based on ANFIS is presented and demonstrated in [38] in order to manage the power of the microgrid.

An example of a grid-connected hybrid system is shown in **Figure 10**. The system is composed of WT and PV panels (renewable and primary energy sources) and a hydrogen subsystem and a battery (SSE).

A three-phase inverter is used to connect the whole system to the grid. Primary renewable sources are generated whenever there is wind or solar radiation. As far as

**116 Figure 9.** *Scheme of energy management control strategy [33].*

*Energy Storage Systems and Their Role in Smart Grids DOI: http://dx.doi.org/10.5772/intechopen.103945*

```
Figure 10.
Grid-connected hybrid system under study [38].
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possible, renewable energy is stored in the battery and/or in the form of hydrogen using the electrolyzer. This stored energy will be retrieved at the appropriate time to support renewable generation when needed.

The supervisory control system shown in **Figure 11** is used to determine the power generated by/stored in the hydrogen and the battery. The energy management is carried out taking into account the power requested by the grid, the available power, the level of the hydrogen tank, and the SOC of the battery.

### **2.5 CAES**

In a compressed air energy storage facility (CAES), the surplus energy is used to compress the air for later use. The compressed air is then stored in a cavern as

**Figure 11.** *Scheme of the supervisory control system based on states [38].*

potential energy. During the energy demand period, it is expanded back in the turbine. As the energy demand increases, the compressed air is heated and expanded by a gas turbine generator to generate electricity.

CAES replaces the compression ratio of air in the turbine, eliminating the use of fuel gas to compress air. Compression and expansion of air are respectively exothermic and endothermic processes, which in fact makes the design of the system quite complicated. With this in mind, three types of systems are considered to manage heat exchange:


Many applications are planned for CAES. Among other things, we can cite the use of CAES as a support for the electrical network for load leveling applications [40–42]. In this type of application, energy is stored during periods of low demand and then converted back into electricity when the demand for electricity is high. Natural caverns are used as air reservoirs in commercial systems.

### **2.6 Flywheel**

Flywheel ESS (FESS) is a system for storing energy in a rotating mass, [43].

Flywheel systems are capable of delivering very high peak power. In fact, given recent advances in power electronics and engineering materials, only the power converter is able to limit the input/output peak power. The number of charge-discharge cycles of the FESS is practically infinite. Their power and energy density are very high. Thanks to these characteristics, FESSs are generally used in transmission and power quality applications that require a large number of charge-discharge cycles [44, 45]. This solution is used in particular in synchronous generators to stabilize the output voltage. Lately this technology has become increasingly attractive for a number of other applications such as transmission and improving power quality. FESSs also allow for relatively simple state monitoring, as "state of charge" is a function of easily measurable parameters such as flywheel inertia and speed [46].

The flywheel's maximum rotational speed is the key factor that determines the technology used to build each component. The FESS is classified as low- or highthroughput FESS depending on this speed. The boundary between the two systems is around 10,000 rpm. Not only the material, geometry, and length of the flywheel, but also the type of electric machine and the type of bearing are determined by the rotational speed of the flywheel [40, 47]. High-speed systems are more complex due to technological requirements. However, since the total energy stored in the flywheel depends on the square of the rotational speed, high-speed flywheels provide a higher energy density. Other design considerations such as system performance, security, and reliability are also taken into account [41, 42, 48].

### **3. Battery management systems**

Energy storage systems should intervene in situations where the variation in demand must be taken into consideration. Applications that could benefit from energy storage within the power grid have a wide range of requirements.

There are isolated regions where seasonal energy storage is needed. Megawatthours of capacity is stored for months at a time [49]. On the other hand, the stabilization of transport and distribution networks requires that energy can only be stored for a few minutes before being returned to the network or locally. At these precise moments, we are obliged to have energy capacities on the watt-hour scale [50]. Many different forms of energy storage have been developed to operate on all of these time and energy scales. It is also necessary to have an effective management system to maintain safe operation and optimal performance due to the high demands placed on these energy storage systems.

In order to overcome all the different requirements, not only regarding the reaction time of the BESS, a battery management system (BMS) is used to monitor and maintain safe and optimal operation of each battery pack. Additionally, a Supervisory Control System (SSC) must be installed to monitor the entire system.

During their normal operating period, the batteries are permanently in a charge/ discharge cycle, therefore in a permanent state of nonequilibrium. Moreover, the situation worsens for the case of storage systems based on intercalation (e.g., Li chemistry), making it difficult to properly monitor battery status and maintain safe operation.

Batteries in a BESS will degrade during cycling, even during normal operation. In extreme load periods, this degradation can even accelerate, especially with the increase in temperature (both ambient and operating). The main role of the basic BMS is to control the batteries only to meet the power demand.

It is possible to reduce the causes of battery degradation and improve system performance by using BMS based on smarter models. There are predictive and adaptive models of BMS, which are particularly useful for large battery packs used in applications such as electric vehicles and grid integration [51–53].

**Figure 12** depicts a general BESS-BMS structure for implementing a particular solution used to solve the complex problem of BESS control [54]. The BMS can accurately estimate many internal variables that allow it to gain an in-depth understanding of the battery's state of charge (SOC) and state of health (SOH). This task is carried out using physics-based models.

The tasks for which the BMS is responsible are: operational safety (thermal management, operation between safety current and voltage limits, shutdown on fault detection, etc.), state estimation (determination of the SOC), the estimation of the parameters (determination of the SOH), the remaining time (tr) (according to the load profile applied), and other miscellaneous functions.

For BESSs with Li-ion batteries and other closed-cell systems, the BMS must also perform inter-cell load balancing. For RFBs (redox flow batteries), the BMS must control electrolyte flow based on power demand. Many battery packs with individual BMS will be combined to create a large capacity BESS in large systems. Battery information is transmitted from the BMS to the SSC, which is the interface between the network and the BMS.

**Figure 12.** *Schematic for the implementation of a battery pack and BMS into a BESS [54].*

The intervention of the BSSs proceeds in the following way—when the grid needs energy from the batteries to supply the load, the SSC chooses the optimal protocol to release the load from a pack (or battery packs) by taking into account both the current state of the batteries and the demand of the network. In order to meet the final power demand, this SSC protocol will call for power to individual packs.

There are times when the required battery power profiles will be more flexible and the BESS may have more control over the charging pattern. For example, the discharge power is severely limited in a peak-shaving application, while the charge power can be chosen according to the needs of the BESS. Here, the best load profiles can be determined by running routines on individual BMSs. The determined load profiles are then transmitted to the SSCs, which then take over the control of the input power of the network.

### **3.1 BMS architecture**

To implement advanced BMS in a grid-scale application requires advanced architecture and a mix of power electronics to connect the battery and BMS within the larger grid. In addition, detailed modeling is extremely useful to predict SOC and SOH as accurately as possible. To manage in real time the nonlinearity, the constraints and the objectives of the model have to be considered. The BMS must be very efficient thanks to the implementation of appropriate algorithms. The implementation of BMS must be done in such a way that an architecture including monitoring and control is realized at several levels [55].

A typical grid storage (GSS) solution consists of a direct current (DC) system, a power conversion system (PCS), a BMS, an SSC, and a grid connection. The DC system is composed of individual cells, which are first assembled into modules, then *Energy Storage Systems and Their Role in Smart Grids DOI: http://dx.doi.org/10.5772/intechopen.103945*

### **Figure 13.**

*Simplified illustration of GSS architecture, with a battery-based DC system, a power conversion system (PCS), and a grid connection [54].*

**Figure 14.** *Conceptual illustration of BMS control cycle [54].*

assembled into systems of sufficient capacity to support GSS application requirements. The cells are connected in different electrical configurations in series and in parallel to power a high-voltage bus, which interfaces with the PCS. The PCS is a fourquadrant DC/AC converter connecting the DC system to the grid via a transformer. An illustration of this architecture is shown in **Figure 13**.

Several independent GSSs composed of DC subsystems, PCS, and transformer combinations, called power blocks, can be used in the composition of the system (**Figure 14**). The power supplies can be composed of effectively identical elements, they can also comprise hybrid battery units of different sizes or types. A dedicated BMS manages and controls the operation of the individual power blocks. The SSC on the other hand manages and coordinates the operation of all the power blocks, it also manages the total power of the system and the allocation of this power between the power blocks.

### **4. Energy storage systems and power grid regulation**

As indicated before, high penetration of intermittent renewable resources can introduce technical challenges including grid interconnection, power quality, reliability, protection, generation dispatch, and control. Therefore, the industry will need to confront the challenges associated with higher levels of penetration.

Several articles include a simple diagram for charging and discharging the Battery Energy Storage System (BESS) in order to upgrade the intermittency of renewable energy production. To some extent, this involves storing excess energy when solar/ wind power generation exceeds a threshold and offloading it to the grid when load demand is high [56, 57].

### **4.1 Using ESS for dispatching wind generation**

In general, wind energy is considered difficult to control and therefore until now considered non-dispatchable. In conventional grid capacity calculation processes, wind energy is in most cases excluded. One of the suggested ways to overcome this drawback is the use of energy storage systems (see, e.g., [58]). An energy storage system (ESS) can play different roles in the power system—either it can be used to manage energy itself, or it can also be used for energy quality improvement [59].

The combination of energy storage and wind generation improves the availability of wind energy, which can be installed in the grid without worrying about the voltage stability of the system. This allows also to increase the capacity of the existing network infrastructure. Other additional benefits are lower system losses and improved power factor.

For example, the output power of a wind farm can generally be "smoothed" using the ESS, in order to improve the quality of the energy obtained, on the other hand insists on the energy management aspect, which for them is the main objective. In [60], the authors first presented an "*optimization design to determine the most appropriate capacity of the BESS, based on long-term wind speed statistics and maximizing service lifetime/ BESS unit cost*." This is on contrast with what is proposed in [61] where a method for determining the power output schedule of the wind farm, using shortterm wind power forecasts, was developed.

Researchers have been taking advantage of the flexible charging/discharging ability of battery energy storage system (BESS) in the design of scheduling schemes for wind farms. In [61], a control strategy for optimal use of the BESS for smoothing out the intermittent power from the wind farm is developed. The simulations the authors have carried out showed that using an actual wind farm data and a realistic BESS model, the desired dispatch set points reasonably close while keeping SOC of BESS within desired limits. The main disadvantage of this method is that it does not allow longer-term power dispatch commitment usually required from generators.

The role assumed by the ESS in wind power trading is another active topic of research (see, e.g., [62]). In some countries, renewable energy plant owners benefit from priority grid supply, where the grid operator has to take control of the energy and pay a fixed return for the energy produced. The article [62] shows how a well-designed ESS can bring additional economic benefits in a project related to energy production. Indeed, if the production of wind energy can be planned in a similar way to the management of energy production from a conventional power plant, the place of wind energy in the power industry will definitely improve significantly, because then one can think about the possibility of dispatching this energy in the power system.

The controllability of power from a wind power generating station by means of BESS is proposed in [63]. This is achieved thanks to two BESSs, one of which is charged using wind power, while the other sends its power into the network. Using statistical wind speed data, the charging characteristics of the BESS are studied and a method to determine the expected charging time of the BESS to reach the stipulated battery state of charge is developed.

A review of design and control of PV and/or wind and/or diesel hybrid systems with energy storage in batteries is presented in [64]. One of the storage technologies to be considered in the future is hybrid hydrogen systems. A particular problem for the installation of this kind of system is the cost of the technologies. Assuming that these costs will decrease over time and investments, the model developed was simulated with prospective costs for the year 2010. The authors of [65] came to the

conclusion that the storage system will emerge as the optimal solution, and therefore it is possible to work on a sample of exploitation schemes of a network dominated by renewable energies.

### **4.2 Use of optimization in designing ESS**

As it was written before, one of the main challenges of power generation from wind turbines is the high variability of the wind, which causes the power generation to be intermittent. It is therefore urgent to work on a control system to load/unload the BESS via a converter so that the production of the wind farm can be stabilized and therefore distributed on an hourly basis while taking into account, among others, the SOC and deep discharge limits of the BESS.

This type of problem, whose constraints are more or less difficult to fulfill, can be formulated as an optimal control problem. It is necessary to construct an objective function whose goal is to minimize the deviations between the wind power and the time distribution set points using the BESS. The constraints in question are mainly the constraints on the SOC and the discharge current of the battery, the value margins of which must be fulfilled at all times.

Optimal control, as its name suggests, considers the problem to be solved in such a way as to find a control law for a given system such that a certain criterion of optimality is reached. A control problem includes a cost functional, which is a function of the state and control variables. An optimal control is a set of differential equations describing the trajectories that control variables must follow in order to minimize the cost functional.

Optimal control problems are of different types that can be classified according to (i) the performance index (PI), (ii) the type of time domain (continuous and discrete), (iii) the presence of different types of constraints, and (iv) variables free to be chosen. The optimal control problem can be formulated by considering the following [66]:


In [67], an adaptive artificial neural network (ANN)-controlled SMES is presented for enhancing the transient stability of fixed-speed wind farms connected to a multimachine power system. The control scheme of SMES depends on a sinusoidal pulse width modulation (PWM), voltage source converter (VSC), and DC-DC converter using insulated gate bipolar transistors (IGBTs). An adaptive ANN controller is introduced as the control methodology of DC-DC converter. The effectiveness of the proposed adaptive ANN-controlled SMES is then compared with that of an optimally tuned proportional-integral (PI)-controlled SMES by the response surface methodology and genetic algorithm (RSM-GA) considering both of symmetrical and unsymmetrical faults.

The authors [68] presented a new convex optimization and control method to enhance the value of the lithium-ion-based energy storage system. A novel quadratic objective convex optimization problem, aimed at obtaining an optimal schedule for the BESS, has been elaborated on the basis of technical and economic variables. The

objectives of the optimization process, according to authors [68] are: (i) obtaining significantly reduced substation transformer losses, (ii) savings on the cost of energy delivered from the grid, (iii) reducing the life cycle cost of the battery storage system, and finally, (iv) taking into account the variability of the distributed generation resources.

A new method for assessing the role of both WF and ESS is shown in [69]. The main contributions of [69] consist of proposing integrated day-ahead bidding and real-time operation strategies for wind-storage systems (abbreviated WF-ESS) as a price taker. Both the WF and ESS are considered as active actors in the energy market, and their failure to comply with the terms of the contract may result in appropriate penalties. The cooperative strategy is that ESS sets charging or discharging reserve capacities at each time interval up to which the ESS can compensate for potential imbalances from the WF. Coordinating the roles of ESS and WF is to fix the charge or discharge reserve capacity at each time interval to compensate for potential imbalances in WF power production.

### **4.3 Simulations in wind-storage system studies**

Wind systems must be analyzed in a complex way to properly model the phenomena that take place there. Offline electromagnetic transient programs are used, among other things. As a general rule, detailed models take too long when studying slow phenomena, such as the impacts of wind fluctuations on the voltage and frequency of the system, so we tend to build more simplified models, which do not have much impact on the veracity of the results obtained, but on the other hand allow rapid simulations of slow phenomena. However, there may be situations where the loss of detail of specific components in a model can lead to inaccuracy in the simulation, which in turn has significant effects on system design and control testing.

The best way to carry out studies of the ESS system integrated into the wind farm is to do real-time simulations for the following reasons [70]:


### **4.4 ESS and V2G**

According to the latest trend resulting from advances in analyzing the impact of human behavior from the perspective of environmental friendliness, electric vehicles *Energy Storage Systems and Their Role in Smart Grids DOI: http://dx.doi.org/10.5772/intechopen.103945*

can to a certain extent be considered environmentally friendly and can significantly reduce fuel consumption of gasoline. It is predicted that they will dominate the future of the automotive industry.

One of the hottest technologies right now is vehicle-to-grid (V2G) technology that allows electric vehicles to act as distributed energy storages that transfer energy back to the main grid when needed. V2G power flow is achieved by local aggregator through communication between grid and consumers/prosumers. Complete information on power exchange is sent directly from the smart meter to the data centers. The main advantages of V2G include: (a) active support of the network, (b) support of reactive power, (c) control power factor, and (d) support for the integration of RES [71–73]. This effectively achieves load flattening, peak shaving, and frequency regulation throughout a day [74]. Moreover, EVs can even be used to transport energy from remote renewable sources to loads in urgent need of power supply [75]. These services can be obtained by charging the EV during periods of inactivity and add extra EV energy in the electricity grid in peak hours. Apart from supporting the provision of effective power, bidirectional V2G has capability of providing reactive power to ensure the voltage regulation.

On the other hand, there is in inverse operation called G2V, during which the power flows from the generator to the vehicle (G2V) to charge the battery and power flow in opposite to provide peak shaving or concept of "spinning reserve." The power flow can be in any of two modes of operation, namely: (a) unidirectional and (b) bidirectional.

### **4.5 ESS and ancillary services**

V2G electric power capacity can be substantial with attractive ancillary services revenue opportunities. The batteries can act as a source of stored energy to provide a number of grid services. The most promising market for these vehicles is probably that of the ancillary services [76]. Possible services for V2G are: supply of peak power, supply of primary, secondary and tertiary control (for frequency regulation and balancing), load leveling, and voltage regulation. It is unlikely that each vehicle will be contracted separately because the maximum power output of each vehicle is too low. But a fleet manager or aggregator could conclude a contract for a fleet of PHEVs. The advantage of dealing with an aggregator or fleet manager is that a single party represents a more significant amount of power, that is, the accumulated power of the vehicles in the fleet. Moreover, the availability profile of a larger group of vehicles is much smoother. A single vehicle owner could conclude a contract with the aggregator without being concerned about the interface with the electricity markets.

### *4.5.1 Frequency regulation*

The network frequency is one of the most important parameters for evaluating the quality of energy. Frequency regulation is the measure of adjusting the frequency of the system to the nominal value by providing small injections of power (positive or negative) into the network. Many organisms, also called transmission network actors, are responsible for frequency regulation. Examples include Regional Transmission Operators (RTOs) as well as Independent System Operators (ISOs) who simply refer to this service as "regulation."

The theory of frequency control is presented in detail in many books and papers [77–81]. The balance between production and demand between control areas is

measured in terms of area control error (ACE). Each control zone generates automatic generation control (AGC) signals based on its ACE values, and the regulation resources respond to the AGC signals to perform the regulation. A complex telemetry system performs this operation in real time and is controlled by the grid operator.

As it was indicated previously, the frequency of the network one of the parameters makes it possible to evaluate the quality of energy. So it is essential to keep the frequency at appropriate levels, i.e., between 49.99 and 50.01 Hz according to the ENTSO-E, the former UCTE [82]. The second equally important parameter is the voltage. Network management consists, among other things, of providing power reserves to maintain frequency and voltage, thus facilitating effective management of imbalances or congestion.

The frequency regulation is carried out at several levels of control: primary, secondary, and tertiary control. Primary Frequency Control (PFC) kicks in during the first few seconds when the system frequency exceeds (or drops under) a preestablished dead band and quickly rebalances the generated and consumed power. In the case of the European power system, the primary reserves regulate the frequency and stabilize the European network to avoid breakdowns. Frequency control is automatically and continuously activated. Primary control can only be activated if primary reserves are available.

Primary regulation is the most demanding in terms of response time and therefore is also the most expensive. This is because PFC has traditionally been provided by thermal generators, which are designed to deliver bulk energy, but not to provide fast-acting reserves. One of the alternative or complementary solutions to the participation of thermal generators in the PFC is the active participation of the loads, which is also considered as a fast and profitable alternative. Nevertheless, a reduction in the load can limit the supply of PFC on the load side because in this case it would prove that the intervention of the load would no longer be very useful.

To complement the generation-side PFC, load-side PFC has been considered as a fast-responding and cost-effective alternative [83–89]. Nonetheless, the provision of load-side PFC is constrained by end-use disutility caused by load curtailment.

In order to balance the network, the secondary reserves are allocated the day before and are adjusted automatically and continuously. The set point is calculated upward and downward on a defined time base (i.e.,15 min) [85]. Most of the research on secondary control of frequency is focused on microgrid stand-alone operation [36, 87]. In this case, we can imagine the intervention of the ESS in the following way: if the frequency is lower than 50 Hz, the batteries could discharge (regulation up), and if the frequency is higher than 50 Hz, the batteries could charge (regulation down). These operations are described in detail in the articles [90, 91]. The frequency can be restored through the use of reserves by leveling out the imbalances between the rated and measured power injections and to restore the frequency.

With regard to tertiary reserves, there are two types: tertiary production reserves and tertiary withdrawal reserves. In both cases, the reserves are used only when major imbalances or major congestions appear. Tertiary reserves are not activated automatically as in the case of primary and secondary reserves, but manually. In practice, the tertiary reserves only intervene very rarely, about a few times a year. Their intervention time is estimated at about 15 min.

So far there is no clear position as to what type or types of ancillary services would be economically profitable for EVs. Scientific opinions differ on the subject. One can quote, for example, the position of the authors [71] who affirm that the secondary and tertiary controls are supposed to be competitive, and the primary control is supposed

### *Energy Storage Systems and Their Role in Smart Grids DOI: http://dx.doi.org/10.5772/intechopen.103945*

to be highly competitive. Other authors [92], however, state that the primary control should have the highest value for V2G. Still others say [73] peak power control might be the most economical solution, giving as an example the control system used in Japan. According to an analysis presented in [93], it would seem that the power to be delivered by the tertiary reserves would be too high and the duration too long for the vehicles. So in short, as a compromise, we could say that the possibility of using the primary and secondary controls from the point of view of interaction with the ESS is the most probable.

### *4.5.2 Voltage regulation*

The appearance of an imbalance in the production of electricity can also endanger the voltage, as well as the frequency. In particular, when distributed power generators, such as photovoltaics (PV), increase significantly, a significant increase in voltage may occur due to the reversal of current to the distribution system. In this case, we can highlight the intervention of electric vehicles in the same distribution system in order to absorb the excess electricity, thus minimizing the reverse flow, which will contribute to a balanced electrical condition and a steady voltage.

In a low-voltage grid, the cables are common and contrary to the situation in the transmission networks or the medium-voltage networks, the resistance *R* is large compared with the reactance *X*. The adjustment of the active power flow in the grid will influence the magnitude of the voltage. Voltage regulation maintains the voltage within the limits defined by the mandatory standard EN50160 [94]. This voltage control can be integrated into the electric vehicle charger. With regard to the participation of electric vehicles in the regulation of the voltage, it can be carried out in the following way: the load of the vehicles stops when the voltage on the level of the connection to the network becomes too weak. In a later step, the discharge of an active power unit can also be taken into account to increase the grid voltage.

### *4.5.3 Load leveling and peak power*

The electricity load profile generally consists of peak and off-peak loads. Usually electricity suppliers offer different types of tariffs in order to encourage consumers to use the most favorable price ranges during off-peak hours. For load leveling, demand is shifted from peak hours to off-peak hours. Therefore, dispatching is necessary. As in the case of other loads, controllable and aggregate electric vehicles can be discharged during off-peak hours (such as at night and early in the morning), therefore the total load during off-peak hours can be increased, and the gap with the peak hours can be optimized. As the difference between peak and off-peak loads is high, the operation of gensets becomes more difficult, as well as their investment and running costs. Energy stored during off-peak hours is typically released during peak hours to relieve congestion in the grid infrastructure. In this case, peak power delivery and load leveling are the same.

Providing peak power in this way would not be very easy for EVs since the power duration would be relatively long and their storage capacity limited, even in the event that aggregators come into play. On the other hand, from a battery wear point of view, providing peak power is generally not cost-effective as the cost of battery wear would be quite high [95].

Load leveling is more convenient for EVs. The vehicle in this case does not necessarily need to unload during peak hours. Total electricity consumption is simply shifted to off-peak hours of low electricity consumption, which would help minimize power losses and increase grid efficiency. In all these scenarios, the implementation of smart meters or real-time pricing and coordinated pricing is essential, as this would control the incoming and outgoing flows of energy from EVs [96, 97].

### **4.6 Aggregation of energy storage services through V2G**

V2G is defined as the provision of energy and ancillary services, such as regulation or spinning reserves, from an EV to the grid. This can be accomplished by discharging energy through bidirectional power flow or through charge rate modulation with unidirectional power flow [71, 72, 98].

For the vehicle-to-grid concept, three elements are required.


The vehicle-to-grid (V2G) estimation methods available in the literature mainly focus on determining the achievable power capacity for a group of EVs [71, 90]. However, these methods are applicable only for determining the V2G capacity and not suitable for real-time V2G capacity estimation and scheduling. Apart from the capacity estimation, other methods have been proposed for aggregating EVs and supplying V2G power to the grid. The aggregation process is also governed by the amount of power and energy that the EVs can supply during any given interval. However, none of the methods available in literature consider dynamic EV scheduling for estimating V2G capacity [90].

In cases where EVs participate in the V2G system, the management, i.e., dispatching, of PHEVs is crucial. Reliable communication must be established between the vehicles and the electrical network, because throughout the duration of the process, data exchange will take place to send the request and carry it out at the level of the EVs. There are three main ways to achieve this communication. First of all, the signal can be sent to each vehicle separately, or via a central controller supervising the EVs, this can, for example, be centralized in a car park. A third possibility is also possible one can realize the communication using a third-party aggregator, which would be responsible for the separately located vehicles.

Since the energy market system was created, a new player called fleet manager or aggregator has taken place. Its role in the new reality where electric cars are taking an increasingly important place in energy control is to help manage contract systems for a fleet of PHEVs. With vehicle-to-grid (V2G) technology, PEVs parked in a certain area can act as a PEV aggregator when connected to the grid through smart equipment. Such a PEV aggregator can represent a well-defined reactive load and provide additional generation capacity for the provision of ancillary services for power grids [100, 101].

The primary role of the aggregator, a business entity as discussed in [102, 103], is to purchase energy to satisfy transportation needs of its fleet of EVs at the minimum cost. The aggregator is a unit that acts as a mediator between the system operator and

### *Energy Storage Systems and Their Role in Smart Grids DOI: http://dx.doi.org/10.5772/intechopen.103945*

individual customers, thanks to which it is possible to coordinate the power exchange between owners of electric vehicles (EV) and the power system. The primary goal of the aggregator is to maximize profits from energy trading and regulatory reserve on wholesale markets. At the same time, the aggregator can also seek to increase their revenue by performing energy arbitrage [102, 104, 105] and/or providing ancillary services [101, 106]. The aggregator itself does not have EV batteries, as they are owned by individual customers who have EVs, therefore they should receive reimbursement of the costs of battery degradation due to their additional use beyond transport needs. The V2G capacities of many electric vehicles are combined by aggregators, then submitted to the appropriate markets [90, 102, 106, 107]. The aggregator in question can be a unit of the public service to which the electric vehicles are connected or a third-party company.

Apart from the fact that an aggregator or a fleet manager is the only interlocutor representing a greater volume of "power," therefore the cumulative power of the vehicles in the fleet is greater, there is also greater flexibility in the supply of power to manage because the greater the number of cars in the fleet, the more fluid the availability of a larger group of vehicles. A single vehicle owner can therefore enter into a contract with the aggregator on more flexible terms without worrying about the interface with the electricity markets.

### **4.7 Considering the battery degradation**

Using the batteries as storage devices for grid purposes reduces their lifetime [105]. Therefore, EV owners must be compensated for the lost utility of their batteries due to degradation when providing services, and this payment will reduce aggregator's revenues. In order for the services from EVs to be economically viable, the revenues must outweigh the cost compensation for the degradation of EV batteries.

Unfortunately, the current state of battery production technology as well as the stage at which scientific research in the field of batteries means that for the moment the use of BESS has a number of drawbacks. The main disadvantages of their use are (i) the large investments, (ii) the associated operating costs due to the degradation of their performance over time (SOH, health status problems). Depending on how BESS is managed, their degradation is increased or mitigated, forcing vehicle owners to replace them after a certain period of time. In this context, the sizing and optimal operation of the BESS are two crucial factors to ensure the extension of their life span necessary to achieve the economic viability of the system.

It has been presented before that aggregators participate in the energy market for a defined commercial purpose, that is, to make the maximum profit. For this purpose, in order to define the annual net benefit (ANP) of the system during the lifetime of a battery, for example, it is necessary to make an estimate of the lifetime of the battery. However, this life span is highly dependent on various operating conditions, and therefore, it is not easy to predict how long a battery will last.

In normal times, the cost of operating the system with a battery system includes two elements: (i) the cost of purchasing electricity (ii) the investment cost of the battery and the inverter. These parameters can be determined in an analytical way presented by example in [108]. However, there are other battery working conditions that are considered "abnormal." We can cite, among other things, the degradation of the batteries during periods when the load demand is exceptionally greater than on other days. Battery storage could increase its profitability by providing fast regulation service under a performance-based regulation mechanism, which better exploits a

battery's fast ramping capability. However, battery life might be decreased by frequent charge-discharge cycling, especially when providing fast regulation service. According to [109], it is profitable for battery storage to extend its service life by limiting its operational strategy to some degree. This is also presented analytically among others in [110]. These evaluation methods make it possible to calculate in a close way the loss of capacity of the battery taking into account the discharge rate of the demand during the lifetime of the battery.

Other algorithms were presented for different objectives in order to assure the necessary lifetime extension of ESS so that the economic viability of the system is reached. We can enumerate among others day-ahead forecast errors reduction for wind power, battery energy dispatch, peak shaving, and overvoltage prevention of LV grids, respectively [108, 111].

Authors [112, 113] have presented battery degradation costs associated with additional cycling. The main contributions of the formulation used in [112, 113] are: to simultaneously optimize bidding of V2G, it is necessary to take into consideration: energy, regulation up, regulation down, spinning reserves. They formulated the problem as a linear program, which can be quickly and efficiently solved for large groups of EVs.

Another aspect of the battery degradation is how to compensate the customer's loss. Authors [114] present their point of view concerning this matter. They proposed a bidding strategy for the aggregator to maximize its profits from participating in competitive energy and different regulating reserves markets, while compensating EV owners for degradation. According to [114], an optimal strategy for both energy and reserve markets considering their trade-offs and effect on EV battery degradation has to be taken into account. Also the realistic approach to participating in the voluntary reserve markets with price-quantity offers that are justified is of high priority. And finally assessing the expected profit the EV aggregator can collect by participating in the energy and regulation market is also important.

### **5. Conclusion**

Recent advances in electric energy storage technologies provide an opportunity of using energy storage to address intermittency of renewables. Combining energy storage with renewables improves availability, increases the amount of wind generation that may be installed on the grid without risking the system's voltage stability, increases throughput of existing grid infrastructure, and yields various ancillary benefits.

The solar energy source is the fastest-growing energy source. In small electrical systems, sudden changes in PV generation result in a frequency disturbance; hence, in order to minimize customer interruptions, the use of battery energy storage systems (BESSs) can be of great help.

Hydrogen, as the most common chemical element on the planet, is considered an eternal source of energy. One of the best-known methods for producing hydrogen is the transformation of water. The production of hydrogen can be carried out in an efficient manner using the electrolysis of water using polymer electrolyte membrane (PEM) cells. The inverse equivalent of a PEM electrolyzer is the PEM full cell. It is also possible to produce hydrogen from biomass. However, it seems that this option does not really have a future because the process is quite complex.

All the different requirements regarding among others the reaction time of the BESS can be overcome using a battery management system (BMS), which is aimed

### *Energy Storage Systems and Their Role in Smart Grids DOI: http://dx.doi.org/10.5772/intechopen.103945*

at monitoring and maintaining safe and optimal operation of each battery pack is necessary. In addition, a System Supervisory Control (SSC) must be installed to monitor the entire system. It is possible to reduce the causes of battery degradation and improve system performance by using BMS based on smarter models. The BMS can accurately estimate many internal variables that allow it to gain an in-depth understanding of the battery's state of charge (SOC) and state of health (SOH). This task is carried out using physics-based models.

An energy storage system (ESS) can be categorized in terms of the role it plays in a power system: either it is for energy management or for power quality enhancement. Because of the recent development of power electronics, superconductivity, and computer science, the SMES system has received a great attention in the power systems applications. It has been utilized in distributed energy storage, spinning reserve, load following, automatic generation control, power quality improvement, reactive power flow control, voltage control, and transient stability enhancement. As the levels of penetration of renewable energy rise, the technical impact of renewable energy on grid operation led to the application of energy storage for renewables.

Electrical vehicles are among the most popular ESSs, selling energy could be beneficial for EV. Their batteries can act as a source of stored energy to provide a number of grid services. V2G is defined as the provision of energy and ancillary services, such as regulation or spinning reserves, from an EV to the grid. Possible services for V2G are: supply of peak power, supply of primary, secondary, and tertiary control (for frequency regulation and balancing), load leveling, and voltage regulation. In order to make EVs efficiently participate in the regulation process, it is important to know when, statistically, vehicles are available for charging or discharging. The connection to the electric power grid offers opportunities for EVs for charging the vehicle but also for discharging and thus injecting energy into the grid. In order to participate in energy markets, the V2G capabilities of many EVs are combined by aggregators and then bid into the appropriate markets. However, using the batteries as storage devices for grid purposes reduces their lifetime. Therefore, EV owners must be compensated for the lost utility of their batteries due to degradation when providing services, and this payment will reduce aggregator's revenues.

### **Author note**

This chapter could be compiled and published with the financial support of the FUEL GROUP https://www.fueldigital.io/

*Smart Grids Technology and Applications*

### **Author details**

Désiré Rasolomampionona\* and Mariusz Kłos Warsaw University of Technology, Warsaw, Poland

\*Address all correspondence to: draso@pw.edu.pl

© 2022 The Author(s). Licensee IntechOpen. This chapter is 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.

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### **Chapter 6**

## Segmented Coil Design Powering the Next Generation of High-efficiency Robust Micro-implants

*Yun Tao, Rosti Lemdiasov, Arun Venkatasubramanian and Marshal Wong*

### **Abstract**

The next generation of Micro Active Implantable Medical Devices (M-AIMD) are small (< 1 cc), wireless, as well as battery-less. They are located in different parts of the body ranging from brain computer interface electrode arrays (e.g., Blackrock Neurotech Utah Array) to multi-chamber cardiac pacemakers (e.g., Abbott dual chamber Nanostim device). These devices require efficient charging and powering solutions that are very challenging to design. Such solutions require the careful balancing of multiple design parameters such as size, separation distance, orientation, and regulatory limits for emission and tissue safety. In this article, we introduce unique optimisation metrics for designing efficient transmit and receive coils for nearfield magnetics-based charging solutions. We elaborate on how the metrics need to be altered depending on the regulatory limits. We discuss the impact of body tissue loading on transmit and receive coil performance using circuit analysis. We introduce a novel "segmented" transmit coil arrangement. We discuss the physics of segmentation, and we build a full wave simulation model, with practical design procedure, which is verified with measurements. Finally, we compare the near fields with and without tissue loading to show that segmented coils offer significant improvement to the performance and robustness of a wireless power transfer system.

**Keywords:** wireless power, coil, efficiency, delivered power, figure of merit, SAR

### **1. Introduction**

Progress in semiconductor technology has led to the development of substantially miniaturised Micro Active Implantable Medical Devices (also called M-AIMD) that are significantly smaller in size and are implanted in difficult to reach interstitial spaces within the human body, thereby permitting direct interaction with organ systems. This reduction in size facilitates the use of delivery systems (e.g., via catheter or hypodermic needle) that significantly reduce procedure time and burden of care

for patients [1–4]. M-AIMDs are either battery-less or have small batteries necessitating the need for efficient charging and powering solutions [5–8]. The most common method is power transferred from an on-body transmitter to an in-body AIMD equipped with a receiver using near-field magnetic induction [9]. This is very challenging as it requires carefully balancing multiple design parameters such as size, depth of implant, orientation of implant (and associated misalignment), and regulatory limits for emission and tissue safety [10]. The need to efficiently deliver power to a small target volume (<1 cc) inside the body requires careful design of the transmit coil system and the receive coil system [11].

There is a lot of work in the literature identifying various parameters that need to be optimised to maximise power delivered to a load (therapy delivering M-AIMD) [12–15]. For example, Fu et al. [16] studied the SIMO (single input multiple output) resonant inductive system and derived an expression for the optimal load and efficiency. Monti et al. [17] concentrated on deriving the solution for the SIMO system that is not necessarily a resonant inductive system. The authors approached the problem of maximising efficiency as a generalised eigenvalue problem. Zargham and Gulak [18] focused their attention on SISO (single input single output) systems. They focused on power transfer through CMOS substrates and lossy biological tissue. Minnaert and Stevens [19] described the three optimisation approaches (efficiency, delivered power and conjugate matching) for SISO systems. Their derivation was based on a generalised 2-port system and was not specific to an inductive resonant system. They suggested that the efficiency of power transmission is a monotonic function of an "extended kQ product, α" which was first introduced by the works of Ohira [20, 21]. Cho et al. [22] studied specific coil designs for wireless power transmission and compared the performance of the designed coils by using a figure of merit defined by Shinohara et al. [23]. In [24], Sharma derived the formulas for efficiency and the figure of merit of a two-coil resonant system. While there are many more relevant articles in the literature, to the best of our knowledge, none of the articles provides metrics to efficiently design the transmitter and receiver coils independently, taking into account the most important regulatory limits for designing these coils for delivering wireless power to medical implants. This is one of the two novel contributions of this article.

This article also focuses on the design of efficient transmit and receive coils where the coil segments are separated by lumped capacitors. These coils are called segmented coils. Segmentation of coils using lumped or distributed capacitors is not new and has been heavily used in Magnetic Resonance Imaging (MRI), for reducing Specific Absorption Rate (SAR) (for transmit coils) [25] and improving coil sensitivity (for receive coils) [26]. Mirbozorgi et al. [27] mentioned that segmentation helps achieve homogeneous power transfer efficiency. Tang et al. [28, 29] stated that the segmentation can significantly reduce the power loss (including the dielectric loss) and required voltage. Stoecklin et al. [30] concluded that capacitive coil segmentation can effectively suppress dielectric losses and non-uniform current distribution. Mark et al. [31] demonstrated that the segmentation results in decrease of the electric field above the transmit coil thereby reducing SAR in the nearby tissue and permitting higher power transfer efficiency. Pokharel et al. [32] use lumped capacitors to segment printed coils and subsequently develop a stacked metamaterial inspired wireless power transfer (WPT) system for efficient and robust power delivery to M-AIMDs. Most of the literature have discussed the positive outcomes of segmentation of coils, but to the best of our knowledge, no one has provided a detailed analytical and numerical (full wave) explanation, as to why segmented coils have lower

*Segmented Coil Design Powering the Next Generation of High-efficiency Robust… DOI: http://dx.doi.org/10.5772/intechopen.105789*

dielectric losses and significant reduction in SAR when they are near (<1 cm) lossy body tissue. In this article, we address those gaps in knowledge and define our figures of merit (FoMs) to highlight the positive impact of segmenting the transmitter and receiver coils separately. To further validate our novel FoMs, we build and test the transmitter and receiver coils and compare our calculations with measurement results. This is the second novel contribution of this article.

### **2. Organisation of this work**

This article is organised into the following sections:

First, we present a brief overview of the pertinent regulations (exposure and radiation) that limit the performance of WPT systems for medical implants. For a chosen design frequency, we identify the critical parameters that bound the maximum currents that can be carried by a transmit coil and a receive coil. These maximum currents dictate the maximum power that can be delivered by a WPT system.

Second, for a two-coil system, we derive, using circuit analysis, the optimal load resistance needed to maximise (a) delivered power and (b) efficiency. For both cases, we find the receive coil current, delivered power and efficiency.

Third, we derive an optimisation metric we term, *system figure of merit*, for a two-coil WPT system and show that the popular system link efficiency used in the literature, is a monotonic function of the system figure of merit. We split the system figure of merit into two parts: transmit figure of merit (characterising the transmit coil) and receive figure of merit (characterising the receive coil). We demonstrate that an increase of any of these two figures of merit results in an increase of the overall system link efficiency.

Fourth, we identify two mechanisms that cause proximity of lossy dielectric tissue to impact the impedance of a transmit coil. We identify the first mechanism to be associated with the interaction of the coil current with the tissue and the second mechanism to be associated with the interaction of the charges accumulated in the coil with the tissue.

Fifth, we study the effects of introducing lumped capacitors in series with coil wiring to break the coil turns into segments. We study the effect of segmentation on the resistance and reactance of coils. We investigate the impact that segmentation capacitors have on the transmit and receive figure of merit of the coil.

Finally, we validate our circuit models and associated transmit and receive figures of merit with measurements and full wave simulations in HFSS. We perform measurements and full wave simulations of the electrical properties of transmit coil design to demonstrate that the introduction of the segmentation capacitors improves the figure of merit of the transmit coil when it is both unloaded (in air) and loaded with lossy tissue.

### **3. Review of regulations**

**Figure 1** diagrammatically illustrates that the regulations associated with wireless power transmission to an implant from an external transmitter can be divided into two groups: radiation (EMC) and exposure. We note that in this article we have examined the regulatory limits for USA and Europe only.

### **Figure 1.**

*Diagrammatic representation of the pertinent FCC and EU regulations.*

### **3.1 Exposure**

Ensuring the safety of the human body during exposure to electromagnetic waves is an indisputable fact. SAR is a measuring factor for electromagnetic wave absorption. SAR is calculated as

$$SAR = \frac{\sigma\_{\text{tissue}}}{\rho\_{\text{tissue}}} \left| E \right|^2 \tag{1}$$

where *σtissue* is the conductivity of the tissue in S/m, *ρtissue* is its mass density in kg/m<sup>3</sup> , and |E| is the RMS magnitude of the induced electric field in the tissue due to exposure to these EM waves. FCC [33] limits the peak average SAR to 1.6 W/kg, averaged over 1 gram of tissue. EN 1999/519/EC [34] limits SAR, as well as the volumetric current in the tissue. The actual limit values of SAR depend on the body part exposed to the RF energy. The limit on the induced current depends on the frequency.

### **3.2 Radiation**

The FCC rules and regulations are presented in Title 47 of the Code of Federal Regulations (CFR). Part 15 [35] covers the radio frequency devices. Part 18 [36] covers the Industrial, Scientific and Medical Equipment (ISM). Part 15 and Part 18 limit the radiated electric field at 3 m or 30 m depending on frequency.

EN 300330 [37] covers Short Range Devices (SRD) in the frequency range 9 kHz to 25 MHz and inductive loop systems in the frequency range 9 kHz to 30 MHz. It is a harmonised standard covering the essential requirements of article 3.2 of Directive 2014/53/EU. The standard limits the magnetic field at 10 m from the device. The most generous H-field limits are in three frequency bands containing 6.78 MHz, 13.56 MHz, 27.12 MHz.

EN 303417 [38] covers the wireless power transmission systems, using technologies other than radio frequency beam in the 19–21 kHz, 59–61 kHz, 79–90 kHz, 100– 300 kHz, 6765–6795 kHz ranges. It is harmonised standard covering the essential requirements of article 3.2 of Directive 2014/53/EU.

*Segmented Coil Design Powering the Next Generation of High-efficiency Robust… DOI: http://dx.doi.org/10.5772/intechopen.105789*

EN 300220–2 [39] covers SRDs operating in the frequency range 25–1000 MHz. for non-specific radio equipment. The most generous H-field limits are in two frequency bands containing 27.12 MHz, 40.68 MHz.

EN 2013/572/EU [40] covers SRDs too. The emphasised frequency bands having higher limits are centred at 6.78 MHz, 13.56 MHz, 27.12 MHz, 40.68 MHz.

EN 55014–1 ("CISPR 14") [41] covers household appliances, electric tools and similar apparatus. This regulation is very restrictive (3 dBμA/m at 3 m in 4–30 MHz range) when applied to the inductive loops and WPT devices.

EN 55011 ("CISPR 11") [42] covers ISM equipment. The devices are sorted into two groups (Non-ISM and ISM equipment) and two classes (non-residential environment and residential environment).

### **4. Derivation for delivered power and efficiency**

Most commonly, WPT circuits use electromagnetic coupling between coils. These WPT circuits use capacitors to reduce reactive power. **Figure 2** is a commonly chosen series–series capacitor representation which has been widely used because the capacitances can be chosen independent of the load and coupling conditions.

At resonance, *<sup>ω</sup>*<sup>2</sup> <sup>¼</sup> <sup>1</sup> *LtCt* <sup>¼</sup> <sup>1</sup> *LrCr* , the equation that links the currents in the transmit and in the receive coil is:

$$j a \text{MI}\_l + (R\_r + R\_L)I\_r = \mathbf{0} \tag{2}$$

where *It* is the current in the transmit coil, *Ir* is the current in the receive coil, *Rr* is the resistance of the receive coil, *RL* is the resistance of the load, and *M* is the mutual inductance. In our derivations, we assume that the voltages and currents are strictly sinusoidal. Therefore, we replace the time derivatives *∂=∂t* by *jω* (multipliers).

From Eq. (2) we can see that there is a 90-degree phase shift between the transmit and receive currents. The *It*,*limit* and *Ir*,*limit* are the maximum allowed currents in the transmit coil and in the receive coil, correspondingly.

If we ignore the phase shift and redefine *jIr* as new *Ir*, then we get the following expression for the load resistance:

**Figure 2.** *Schematic of the WPT system.*

$$R\_L = \frac{a\mathcal{M}I\_l}{I\_r} - R\_r \tag{3}$$

Additionally, the delivered power is:

$$P\_L = \frac{1}{2} R\_L I\_r^2 = \frac{1}{2} \left( a M I\_t I\_r - R\_r I\_r^2 \right) \tag{4}$$

The power loss in the receive coil is:

$$P\_r = \frac{1}{2} R\_r I\_r^2 \tag{5}$$

The power loss in the transmit coil is:

$$P\_t = \frac{1}{2} R\_t I\_t^2 \tag{6}$$

The efficiency is:

$$\eta = \frac{P\_L}{P\_L + P\_r + P\_t} = \frac{\mathbf{1} - \frac{R\_r}{a\mathbf{M}}\frac{I\_r}{I\_t}}{\mathbf{1} + \frac{R\_r}{a\mathbf{M}}\frac{I\_t}{I\_r}}\tag{7}$$

We assume that the current in the transmit coil is fixed at its maximum value of *It* ¼ *It*,*limit*.We can proceed with two ways: (a) to maximise the delivered power and (b) to maximise the efficiency.

(a) Maximising the delivered power.

We differentiate the delivered power with respect to *Ir*, equate it to zero and obtain the optimal current in the receive coil:

$$I\_{r,opt} = \min\left(\frac{\alpha \mathcal{M}}{2\mathcal{R}\_r} I\_l, I\_{r,limit}\right) \tag{8}$$

where the receive current is limited by *Ir*,*limit*.

(b) Maximising the efficiency.

We differentiate the efficiency, equate it to zero and obtain the optimal current in the receive coil:

$$I\_{r,opt} = \min\left(I\_t \cdot \frac{R\_t}{o\mathcal{M}} \left(\sqrt{1 + Q\_M^2} - \mathbf{1}\right), I\_{r,limit}\right) \tag{9}$$

where

$$Q\_M = \frac{aM}{\sqrt{R\_t R\_r}},\tag{10}$$

which we call a mutual quality factor.

We use the optimal receive current to obtain the expressions for the optimal delivered power *PL* by using Eq. (4) and efficiency *η* by using Eq. (7).

If the current limits are high (infinite), then both cases can be elaborated further:

a. Maximising the delivered power, with high current limits.

Optimal current is:

*Segmented Coil Design Powering the Next Generation of High-efficiency Robust… DOI: http://dx.doi.org/10.5772/intechopen.105789*

$$I\_{r,opt} = \frac{\alpha \mathbf{M}}{2\mathbf{R}\_r} I\_t \tag{11}$$

Optimal load resistance is:

$$R\_{L,opt} = R\_r \tag{12}$$

Delivered power is:

$$P\_L = \frac{1}{8} \frac{\left(\rho \mathcal{M}\right)^2}{R\_r} I\_t^2 \tag{13}$$

Efficiency is:

$$\eta = \frac{1}{2 + \frac{4R\_l R\_r}{\left(\alpha M\right)^2}} = \frac{1}{2 + \frac{4}{Q\_M^2}}\tag{14}$$

b. Maximising the efficiency, with high current limits.

Optimal current is:

$$I\_{r,opt} = \frac{R\_t}{o\mathcal{M}} \left(-\mathbf{1} + \sqrt{\mathbf{1} + \mathbf{Q}\_{\mathcal{M}}^2}\right) I\_t \tag{15}$$

Optimal load resistance is:

$$R\_{L,opt} = R\_r \sqrt{\mathbf{1} + \mathbf{Q}\_M^2} \tag{16}$$

Delivered power is:

$$P\_L = \frac{1}{2} \frac{R\_l R\_r}{a\mathcal{M}} I\_t^2 \left(\mathbf{1} + \mathbf{Q}\_{\mathcal{M}}^2 - \sqrt{\mathbf{1} + \mathbf{Q}\_{\mathcal{M}}^2} \right) \tag{17}$$

Efficiency is:

$$\eta = \frac{\sqrt{\mathbf{1} + \mathbf{Q}\_{\mathcal{M}}^2} - \mathbf{1}}{\sqrt{\mathbf{1} + \mathbf{Q}\_{\mathcal{M}}^2} + \mathbf{1}} \tag{18}$$

From the above formulas we see that *QM* serves as the system figure of merit. Increase of the *QM* leads to the increase of the efficiency of the transfer in both cases.

### **5. Figures of merit for transmit and receive coils**

### **5.1 From efficiency perspective**

For some WPT systems the magnetic field of the transmit coil is not changing significantly in the region of space that contains the receive coil. This happens if the receive coil is much smaller than the transmit coil and/or it is located far enough from the transmit coil. The induced voltage of the receive coil is *jωMIt* (where *M* is the mutual inductance). However, according to Faraday's law of induction, the induced voltage is *jωBtAr*, where *Bt* is the magnetic field of the transmit coil, *Ar* is the area of the receive coil.

The expression for the mutual quality factor can be expressed as follows:

$$Q\_M = \frac{\alpha \mathbf{M}}{\sqrt{R\_l R\_r}} = \alpha \frac{\mathbf{B}\_t / I\_t \cdot \mathbf{A}\_r}{\sqrt{R\_l R\_r}} = \alpha \frac{\mathbf{B}\_t / I\_t}{\sqrt{R\_l}} \cdot \frac{\mathbf{A}\_r}{\sqrt{R\_r}} \tag{19}$$

We observe that the values of the transmit and receive coil can be separated. We can define the figures of merit for the transmit coil *Ft* and the receive coil *Fr*

$$F\_t \equiv \frac{\mathcal{B}\_t / I\_t}{\sqrt{\mathcal{R}\_t}} = \frac{\mathcal{B}\_t}{\sqrt{2P\_{\text{in}}}} \tag{20}$$

$$F\_r \equiv \frac{A\_r}{\sqrt{R\_r}}\tag{21}$$

where *Pin* <sup>¼</sup> <sup>1</sup> <sup>2</sup> *RtI* 2 *<sup>t</sup>* is an input (transmitted) power into the transmit coil.

The expression for the receive figure of merit *Fr* can be represented differently in the following way. From the expression for the delivered power (13):

$$P\_L = \frac{1}{8} \frac{\left(\alpha B\_t / I\_t \cdot A\_r\right)^2}{R\_r} I\_t^2 = \frac{1}{8} \alpha^2 B\_t^2 \cdot \frac{A\_r^2}{R\_r} \tag{22}$$

we get:

$$F\_r \equiv \frac{A\_r}{\sqrt{R\_r}} = \frac{\sqrt{8P\_L}}{aB\_l} \tag{23}$$

From Eq. (19) we get the expression for the mutual inductance as:

$$Q\_M = a \cdot F\_t F\_r \tag{24}$$

Increase in any of these two figures of merit (*Ft* and *Fr*) leads to an increase in the efficiency. The mutual quality factor *QM* can be seen as a figure of merit for the transmit-receive coil system. The system with higher *QM* is more efficient.

The expressions for the transmit figures of merit defined as *Ft* <sup>¼</sup> *Bt* ffiffiffiffiffiffi <sup>2</sup>*Pin* <sup>p</sup> and *Fr* <sup>¼</sup> ffiffiffiffiffiffi 8*PL* p *ωBt* are more general than those defined using the transmit resistance *Rt*, receive resistance *Rr* and receive coil area *Ar*. These definitions apply not only to coils, but also to any "structure" that can perform the following tasks: (a) generate magnetic field (if transmit structure), (b) harvest RF energy (if receive structure). The transmit and receive figures of merit are defined as follows:


It is worth mentioning that the *Ft* and *Fr* figures of merit are not the properties solely of the transmit coil and receive coil correspondingly. The coil resistances (and consequently the figures of merit) are affected by the nearby tissue. The coil-tissue separation distance clearly affects these figures of merit. These figures of merit also depend on frequency.

### **5.2 From delivered power perspective**

The expression for the delivered power Eq. (22) can be modified as follows:

$$P\_L = \frac{1}{8} \boldsymbol{\alpha}^2 \cdot \boldsymbol{B}\_t^2 \cdot \boldsymbol{F}\_r^2 \tag{25}$$

The delivered power is proportional to the square of the receive figure of merit *Fr*. It is also proportional to the square of the magnetic field *Bt* of the transmit coil. This seems to be an intuitive result: the higher the magnetic field is, the more power we can harvest from it.

### *5.2.1 Considering SAR limit*

The magnetic field that we are able to generate at the location of the receive coil cannot be arbitrarily high: the current in the coil is limited by exposure and radiation limits. SAR limit is one of these limits. One can define a SAR figure of merit as a ratio of the magnetic field of the transmit coil to the square root of SAR:

$$F\_{t,SAR} \equiv \frac{B\_t}{\sqrt{SAR}}\tag{26}$$

By defining the SAR figure of merit using Eq. (26) the maximum achievable magnetic field would be calculated as *Ft*,*SAR* � ffiffiffiffiffiffiffiffiffi *SAR* <sup>p</sup> . The FCC limit of SAR is 1.6 W/kg.

It is worth saying that the *Ft*,*SAR* figure of merit is not a property solely of the transmit coil. It is a property of the combination of the transmit coil and the nearby tissue. The coil-tissue separation clearly affects the *Ft*,*SAR*. This figure of merit is also a function of frequency.

This figure of merit can also be used to compare the competing designs of the transmit coils. The transmit coil with higher *Ft*,*SAR* can deliver more power to the receive coil.

### *5.2.2 Considering other limits*

Apart from SAR, there are other regulations that limit the transmit coil current and the transmit coil magnetic field. For each one of them one can establish the corresponding figure of merit in the following way:

a. Volumetric current *J*, according to EN 1999/519/EC, if below 10 MHz. The corresponding figure of merit would be:

$$F\_{tJ} \equiv \frac{B\_t}{J} \tag{27}$$

**Figure 3.** *Figures of merit diagram.*

b. Electric field at the certain distance from the coil (*d* = 3 m, 30 m, 300 m). The corresponding figure of merit would be:

$$F\_{t,E} \equiv \frac{B\_t}{E\_d} \tag{28}$$

c. Magnetic field at the certain distance from the coil (*d* = 10 m). The corresponding figure of merit would be:

$$F\_{t,H} \equiv \frac{B\_t}{H\_d} \tag{29}$$

**Figure 3** provides a visual representation of the development of figures of merit from the WPT formulas and the regulations.

### **6. Impact of tissue loading on the transmit and receive coils**

The electric field of the transmit coil can be separated into two parts: the "current" electric field and the "charge" electric field:

$$\mathbf{E} = \mathbf{E}\_{current} + \mathbf{E}\_{charge} = -j\alpha \mathbf{A} - \nabla \Phi \tag{30}$$

where **A** is the magnetic vector potential and Φ is the electric scalar potential. **Figure 4** shows the two components of the electric field when a WPT coil is close to lossy dielectric tissue (e.g. muscle).

The **E***charge* mostly exists between the terminals of the coil. The **E***current* electric field exists as concentric circles above the coil.

### **6.1 E***current* **electric field**

**E***current* electric field infiltrates the tissue and excites current in it. The current in the tissue flows in self-terminating lines as shown in **Figure 5**. This leads to ohmic losses

*Segmented Coil Design Powering the Next Generation of High-efficiency Robust… DOI: http://dx.doi.org/10.5772/intechopen.105789*

**Figure 4.** *Electric field of the coil.*

**Figure 5.** *Induced current in the tissue.*

in the tissue and adds to the resistance of the transmit coil. Additionally, there is some amount of inductance associated with this current flow.

The effect of the current flow in the tissue may be crudely approximated by a shorted inductance. The Kirchhoff's laws are:

$$(R + joL)I + joM\_{\text{tissue}}I\_{\text{tissue}} = V$$

$$joM\_{\text{tissue}}I + (R\_{\text{tissue}} + joL\_{\text{tissue}})I\_{\text{tissue}} = \mathbf{0} \tag{31}$$

where *Mtissue* is the mutual inductance between the transmit coil and the shorted inductance.

Solving this for impedance *Z* ¼ *V=I*:

$$Z = R + \frac{\alpha^2 M\_{\text{tissue}}^2 R\_{\text{tissue}}}{R\_{\text{tissue}}^2 + \alpha^2 L\_{\text{tissue}}^2} + j\alpha \left( L - \frac{\alpha^2 M\_{\text{tissue}}^2 L\_{\text{tissue}}}{R\_{\text{tissue}}^2 + \alpha^2 L\_{\text{tissue}}^2} \right) \tag{32}$$

The presence of the **E***current* electric field results in an increase of the resistance and a decrease of the inductance in the presence of the tissue. Generally, Eq. (32) can be written as:

$$Z = R + R\_{eddy} + jo(L - L\_{eddy}) \tag{33}$$

where the definitions of *Reddy* and *Leddy* can be inferred from the Eq. (32).

It can be observed that the tissue loading the coil leads to induced (eddy) currents in the tissue which causes power loss. This power loss in the tissue exhibits itself as an increased resistance and a decreased reactance of the transmit coil.

### **6.2 E***charge* **electric field**

When we excite the transmit coil with voltage, there are electric charges that accumulate on the wiring near the coil terminals. When the coil is in close proximity to lossy tissue it can be modelled as a lossy dielectric between the plates of a parallel plate capacitor, as shown in **Figure 6**.

### **Figure 6.** *Approaching tissue to the coil.*

The Ampere's law is:

$$\nabla \times \mathbf{H} = \mathbf{J} + j a \mathbf{D} \tag{34}$$

where **H** is the magnetic field, **J** is the current density, **D** is the electric displacement. Taking divergence on both sides of (34) we get:

$$\operatorname{div}((\sigma\_{\text{tissue}} + j\alpha \varepsilon\_0 \varepsilon\_{r,\text{tissue}}) \mathbf{E}) = \mathbf{0} \tag{35}$$

where *σtissue* is conductivity of tissue in S/m, *ε<sup>r</sup>*,*tissue* is relative electric permittivity of tissue in F/m, *ε*<sup>0</sup> is vacuum permittivity.

The normal component of the vector ð Þ *σtissue* þ *jωε*0*ε<sup>r</sup>*,*tissue E* is preserved in the lossy tissue as shown in **Figure 7**.


**Figure 7.** *Electric field inside the capacitor.*

The electric fields inside the capacitor and outside of the tissue are related as follows:

$$E\_{\text{tissue}} = \frac{E\_0}{\varepsilon\_{r,\text{tissue}} + \frac{\sigma\_{\text{irvar}}}{j\_{\text{joc}\_0}}} \tag{36}$$

where *E*<sup>0</sup> is the electric field in air.

We denote the thickness of the tissue as *l* and the remaining free space between the plates of the capacitor as *h*. Voltage across the capacitor plates is:

$$\mathbf{V} = h\mathbf{E}\_0 + l\mathbf{E}\_{\text{tissue}} = \mathbf{E}\_0 \left( h + \frac{l}{\varepsilon\_{r,\text{tissue}} + \frac{\sigma\_{\text{tissue}}}{j\omega\epsilon\_0}} \right) \tag{37}$$

*Segmented Coil Design Powering the Next Generation of High-efficiency Robust… DOI: http://dx.doi.org/10.5772/intechopen.105789*

The electrical field in empty space between capacitor plates is:

$$E\_0 = \frac{Q}{\varepsilon\_0 A} \tag{38}$$

where *Q* is the charge on the capacitor plates and *A* is the area of the capacitor plates. Capacitance is:

$$C = \frac{Q}{V} = \frac{\varepsilon\_0 A}{h + \frac{l}{\varepsilon\_{r, time} + \frac{\varepsilon\_{\text{pump}}}{j\_{\text{av}\_0}}}} \tag{39}$$

We note that the capacitance has an imaginary component. The impedance associated with this capacitance is calculated as:

$$Z\_{\varepsilon} = \frac{1}{j\alpha \mathcal{C}} = \frac{1}{j\alpha \varepsilon\_0 A} \left( h + \frac{l}{\varepsilon\_{r, \text{tissue}} + \frac{\sigma\_{\text{inner}}}{j\alpha \varepsilon\_0}} \right) \tag{40}$$

This can be elaborated as:

$$Z\_c = \frac{1}{j\alpha c\_0 A} \left( h + \frac{l}{\varepsilon\_{r, \text{tissue}}} \frac{\mathbf{1}}{\mathbf{1} + \frac{\sigma\_{\text{time}}}{a^2 c\_0^2 c\_{r, \text{time}}^2}} \right) + \frac{l}{A \sigma\_{\text{tissue}}} \frac{\mathbf{1}}{\mathbf{1} + \frac{a^2 c\_0^2 c\_{r, \text{time}}^2}{\sigma\_{\text{time}}^2}} = \frac{\mathbf{1}}{j\alpha \mathbf{C}\_p} + R\_p \tag{41}$$

where *Cp* and *Rp* are the effective capacitance and resistance and take the form:

$$C\_p = \frac{\varepsilon\_0 A}{h + \frac{l}{v\_{r, \text{time}}} \cdot \frac{\alpha^2 v\_0^2 c\_{r, \text{time}}^2}{\sigma\_{\text{time}}^2 + \alpha^2 v\_0^2 c\_{r, \text{time}}^2}} \tag{42}$$

$$R\_p = \frac{l}{A\sigma\_{\text{tissue}}} \cdot \frac{\sigma\_{\text{tissue}}\,^2}{\sigma\_{\text{tissue}}\,^2 + \,\alpha^2 \epsilon\_0^2 \epsilon\_{r,\text{tissue}}^2} \tag{43}$$

As we see from these formulas, the presence of the tissue between the capacitor plates leads to an increase of the effective capacitance *Cp* and the appearance of the effective resistance *Rp*. In the absence of tissue *Rp* ¼ 0. When the coil is closer to a lossy dielectric medium like body tissue (e.g. muscle), we observe that the resonance frequency of the coil drops (detuning) and the ohmic losses increase.

To determine the resistance and reactance of a coil in close proximity to lossy tissue we develop an equivalent circuit shown in **Figure 8**. The impedance of the circuit in **Figure 8** is:

$$Z = \frac{1}{j\omega C\_p + \frac{1}{R + j\omega L}}\tag{44}$$

$$\begin{array}{c|c} & \text{(44)}\\ \hline & \begin{array}{c} R\_P \\ \hline \\ C\_P = \end{array} \end{array} \qquad \begin{array}{c|c} & R, L \\ & \begin{array}{c} \\ \hline \\ \\ \hline \end{array} \end{array} \qquad \begin{array}{c|c} & R, L \\ & \begin{array}{c} \\ \hline \\ \\ \\ \hline \end{array} \end{array} \qquad \begin{array}{c|c} & R, L \\ & \begin{array}{c} & \begin{array}{c} \\ \hline \\ \\ \\ \\ \\ \hline \end{array} \end{array} \qquad \begin{array}{c|c} & R, L \\ & \begin{array}{c} & \begin{array}{c} \\ \hline \\ \\ \\ \\ \\ \\ \\ \end{array} \end{array} \qquad \begin{array}{c|c} & R, L \\ & \begin{array}{c} & \begin{array}{c} & \begin{array}{c} & R \\ \hline \\ & & \begin{array}{c} & R \\ & & \begin{array}{c} & R \end{array} \end{array} \end{array} \end{array} \tag{44}$$

**Figure 8.** *Coil model with shunt capacitor and resistor.*

We assume that the capacitive reactance <sup>1</sup> *<sup>ω</sup>Cp* far exceeds the resistance *Rp*, and we neglect the resistance *Rp*. To further simplify this expression, we assume that the quality factor of the coil is much higher than unity (*R* ≪ *jωL*). The self-resonance frequency of the coil is defined as:

$$\alpha\_t = \frac{1}{\sqrt{LC\_p}}\tag{45}$$

We assume that the capacitive reactance 1*=ωCp* of the coil is much higher than the inductive reactance *ωL*. This implies that the Self Resonance Frequency (SRF) of the coil is much higher than the operating frequency (*ω* ≪ *ωs*), which is considered favourable for most practical coil designs.

With the aforementioned assumptions, the impedance of the coil simplifies to:

$$Z \approx \frac{R}{\left(1 - \frac{\alpha^2}{\alpha\_i^2}\right)^2} + j\alpha \frac{L}{1 - \frac{\alpha^2}{\alpha\_i^2}}\tag{46}$$

From the above equation it can be observed that the proximity of lossy dielectric tissue results in an increase of the parasitic capacitance *Cp* and lowers the SRF of the coil *ω<sup>s</sup>* due to an appearance of the parasitic resistance in series with the parasitic capacitance. This always results in an increase in the resistance of the coil. Depending on the coil geometry, dielectric properties of tissue near the coil and frequency of operation, the reactance may either decrease or increase when the coil is near lossy tissue.

### **7. Segmentation**

Segmentation is a process of inserting additional capacitors in between the coil windings (see **Figure 9**). The capacitor placement is roughly equidistant throughout the windings of the coil. The purpose of the segmentation capacitors is to decrease the voltages between the terminals of the coil and between the turns of the coil.

The values of the segmentation capacitors are chosen to significantly decrease the visible inductance of the coil. There is no exact formula for the values of the segmentation capacitors, but our recommendation is as follows:

**Figure 9.** *Schematic of non-segmented and segmented loaded coils.*

*Segmented Coil Design Powering the Next Generation of High-efficiency Robust… DOI: http://dx.doi.org/10.5772/intechopen.105789*

$$\mathcal{C}\_{\text{seg}} = \frac{N}{\alpha^2 L} \tag{47}$$

where *N* is the number of segments and *L* is the coil inductance. If we have *N* segments, then we have *N*-1 segmentation capacitors. The cumulative effect of *N*-1 segmentation capacitors placed in series is represented as the cumulative segmentation capacitance:

$$\mathcal{C}\_{\text{seg},\varepsilon} = \frac{N}{(N-1)a^2L} \tag{48}$$

### **7.1 Effect of segmentation on the coil resistance and inductance**

**Figure 10** shows the equivalent circuit of a non-segmented and segmented coil when the coil is loaded by body tissue. The segmentation affects the coil impedance by reducing the electric charges on the wiring of the coil. Mathematically, the effect of segmentation capacitors can be introduced by modifying Eq. (46) as follows:

$$Z \approx \frac{R + R\_{\rm eddy}}{\left(1 - \frac{\alpha^2}{\alpha\_i^2} \left(1 - \frac{1}{\alpha^2 LC\_{\rm reg}}\right)\right)^2} + j\alpha \left(\frac{L\left(1 - \frac{1}{\alpha^2 LC\_{\rm reg}}\right)}{1 - \frac{\alpha^2}{\alpha\_i^2} \left(1 - \frac{1}{\alpha^2 LC\_{\rm reg}}\right)} - L\_{\rm effdy}\right) \tag{49}$$

The SRF *ω<sup>s</sup>* depends on whether the coil is loaded or not: loaded value *ω<sup>s</sup>*,*loaded* is smaller than the unloaded value *ω<sup>s</sup>*,*unloaded*. We consider the ratio *ω*<sup>2</sup>*=ω*<sup>2</sup> *<sup>s</sup>*,*loaded* much less than unity, otherwise the coil would not be functioning correctly.

We will now study the effect of tissue loading on both the non-segmented and the segmented coils. For the non-segmented coil, the expression 1 � <sup>1</sup> *ω*<sup>2</sup>*LCseg*,*<sup>c</sup>* � � is unity. The coil is tuned under unloaded condition by placing a tuning capacitor *Ctune* in series with it. So, the reactance of the unloaded coil is zero.

For the tuned non-segmented coil, the unloaded and loaded impedances are:

$$Z\_{\text{unloaded}} \approx \frac{R}{\left(1 - \frac{\alpha^2}{\alpha^2\_{\text{s,unloaded}}}\right)^2} + j\alpha \left(\frac{L}{1 - \frac{\alpha^2}{\alpha^2\_{\text{s,unloaded}}}} - \frac{1}{\alpha^2 \mathcal{C}\_{\text{tunze}}}\right) \tag{50}$$

$$Z\_{loaded} \approx \frac{R + R\_{eddy}}{\left(1 - \frac{\alpha^2}{\alpha\_{\text{ }l,load}^2}\right)^2} + j\alpha \left(\frac{L}{1 - \frac{\alpha^2}{\alpha\_{\text{ }l,load}^2}} - L\_{eddy} - \frac{1}{\alpha^2 C\_{\text{tunc}}}\right) \tag{51}$$

$$\underbrace{\begin{array}{c} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p}} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p}} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c\_p} \square\_{c$$

**Figure 10.** *Non-segmented and segmented loaded coils.* The difference between these values is:

$$\begin{aligned} Z\_{\text{loaded}} &- Z\_{\text{unloaded}} \approx R \left( \frac{2\alpha^2}{\alpha\_{s,\text{loaded}}^2} - \frac{2\alpha^2}{\alpha\_{s,\text{unloaded}}^2} \right) + R\_{\text{eddy}} \left( 1 + \frac{2\alpha^2}{\alpha\_{s,\text{loaded}}^2} \right) \\ &+ j\alpha L \left( \frac{\alpha^2}{\alpha\_{s,\text{landdd}}^2} - \frac{\alpha^2}{\alpha\_{s,\text{unloaded}}^2} \right) \end{aligned} \tag{52}$$

For the segmented coil, the expression 1 � <sup>1</sup> *ω*<sup>2</sup>*LCseg*,*<sup>c</sup>* � � simplifies to 1*=<sup>N</sup>* for a coil with *N* segments. Again, we tune the coil when it is not loaded, so the reactance of the unloaded coil is zero.

For the tuned segmented coil, the unloaded and loaded impedances are:

$$Z\_{\text{unloaded}} \approx \frac{R}{\left(1 - \frac{\alpha^2}{\alpha\_{\text{s,unloaded}}^2} \cdot \frac{1}{N}\right)^2} + jo\left(\frac{\frac{1}{N} \cdot L}{1 - \frac{\alpha^2}{\alpha\_{\text{s,unloaded}}^2} \cdot \frac{1}{N}} - \frac{1}{\alpha^2 C\_{\text{tunec}}}\right) \tag{53}$$

$$Z\_{\text{loaded}} \approx \frac{R + R\_{\text{eddy}}}{\left(1 - \frac{\alpha^2}{\alpha\_{\text{'},\text{land}}} \cdot \frac{1}{N}\right)^2} + j\omega \left(\frac{\frac{1}{N} \cdot L}{1 - \frac{\alpha^2}{\alpha\_{\text{'},\text{land}}} \cdot \frac{1}{N}} - L\_{\text{eddy}} - \frac{1}{\alpha^2 C\_{\text{tame}}}\right) \tag{54}$$

The difference between these values is:

$$\begin{aligned} Z\_{\text{loaded}} - Z\_{\text{unloaded}} &\approx \frac{R}{N} \left( \frac{2\alpha^2}{\alpha\_{s,\text{loaded}}^2} - \frac{2\alpha^2}{\alpha\_{s,\text{unloaded}}^2} \right) + R\_{\text{eddy}} \left( 1 + \frac{2\alpha^2}{N\alpha\_{s,\text{loaded}}^2} \right) \\ \text{seg} &\quad \text{seg} \\ + j\alpha \frac{L}{N^2} \left( \frac{\alpha^2}{\alpha\_{s,\text{loaded}}^2} - \frac{\alpha^2}{\alpha\_{s,\text{unloaded}}^2} \right) \end{aligned} \tag{55}$$

Comparing Eqs. (52) and (55) we observe that for a segmented coil: (a) the resistance increase due to proximity of lossy tissue is lower than that for unsegmented coil, (b) the reactance increase due to the proximity of lossy tissue is lower than that for unsegmented coil. This is clearly due to the 1*=N* and1*=N*<sup>2</sup> factors responsible for this effect. Therefore, segmenting the coil significantly improves the robustness of the coil to the deleterious effects of the lossy body tissue.

### **7.2 Effect of segmentation on the transmit coil figure of merit**

**Figure 11** shows the equivalent circuit for non-segmented and segmented loaded transmit coils.

The input current splits into two branches: current *I* that flows through the ideal inductor *L* and parasitic current *Ip* that flows through the capacitor *Cp* and resistor *Rp*

These two currents are related as follows:

*Segmented Coil Design Powering the Next Generation of High-efficiency Robust… DOI: http://dx.doi.org/10.5772/intechopen.105789*

**Figure 11.**

*Non-segmented and segmented loaded transmit coils.*

$$I\_p \approx -\frac{\alpha^2}{\alpha\_s^2} \left( 1 - \frac{1}{\alpha^2 L C\_{\text{seg}}, c} \right) I \tag{56}$$

where we neglected the resistances *R* and *Rp*.

In the transmit figure of merit ( *Bt* ffiffiffiffiffiffi <sup>2</sup>*Pin* <sup>p</sup> ), the magnetic flux density *Bt* depends on the current *I* in the coil. If we keep the current *I* fixed, then the magnetic flux density will also remain fixed.

The power needed to generate the current *I* (and magnetic flux density *Bt*) is:

$$P\_{in} = \frac{1}{2} \left( R + R\_{\rm eddy} \right) I^2 + \frac{1}{2} R\_p I\_p^2$$

$$= \frac{1}{2} \left( R + R\_{\rm eddy} \right) I^2 + \frac{1}{2} R\_p \frac{\alpha^4}{\alpha\_s^4} \left( 1 - \frac{1}{\alpha^2 L C\_{\rm Neg,c}} \right)^2 I^2 \tag{57}$$

The figure of merit is then:

$$F\_t = \frac{B\_t}{\sqrt{2P\_{in}}} = \frac{B\_t/I}{\sqrt{\left(R + R\_{eddy}\right) + R\_p \frac{\alpha^4}{\alpha\_i^4} \left(1 - \frac{1}{\alpha^2 LC\_{\text{avg},c}}\right)^2}}\tag{58}$$

We observe that segmentation leads to an increase in the transmit figure of merit of a coil. This is because for the segmented coil, the voltage *V* across the terminals of the coil is reduced by *<sup>I</sup> jωCseg*,*<sup>c</sup>* . This means that the current through the parasitic resistance *Rp* will be less and, therefore, the corresponding ohmic loss will be less.

### **7.3 Effect of segmentation on the receive figure of merit**

**Figure 12** shows the equivalent circuit for non-segmented and segmented loaded receive coils. In the figure, *Ar* is the effective aperture area of the receive coil, *Bt* is the incident magnetic field from the transmit coil and *ωBtAr* is the voltage appearing across the receive coil terminals.

For the non-segmented coil, the optimal loaded resistance is:

$$R\_L \approx \frac{R\_r + R\_{\text{eddy}}}{\left(1 - \frac{\alpha^2}{\alpha\_r^2} \left(1 - \frac{1}{\alpha^2 L C\_{\text{ue},c}}\right)\right)^2} \tag{59}$$

Currents through the voltage source *ωBtAr* and through the load *RL*

**Figure 12.** *Non-segmented and segmented loaded receive coils.*

$$I\_r \approx \frac{1}{2} \cdot \frac{\alpha B\_l A\_r}{R\_r + R\_{eddy}} \tag{60}$$

$$I\_L \approx \frac{1}{2} \cdot \frac{\alpha B\_l A\_r}{R\_r + R\_{eddy}} \cdot \left(1 - \frac{\alpha^2}{\alpha\_s^2} \left(1 - \frac{1}{\alpha^2 L C\_{\text{seg},c}}\right)\right) \tag{61}$$

The delivered power is:

$$P\_L \approx \frac{\left(\alpha B\_l A\_r\right)^2}{8\left(R\_r + R\_{\text{delay}}\right)} \cdot \left(1 - \frac{R\_p}{R\_r + R\_{\text{delay}}} \frac{\alpha^4}{\alpha\_s^4} \left(1 - \frac{1}{\alpha^2 L C\_{\text{reg},\varepsilon}}\right)^2\right) \tag{62}$$

Again, we observe that the receive figure of merit ffiffiffiffiffiffiffiffi 8*PL* <sup>p</sup> *<sup>=</sup>ωBt* without the segmentation capacitor (*Cseg*,*<sup>c</sup>* ! *inf*) is lower than the receive figure of merit with segmentation capacitor. Segmentation, therefore, leads to the increase of the receive figure of merit.

### **8. Full wave simulations and measurements**

To verify the theory presented in the previous sections, a PCB spiral coil is modelled in Ansys HFSS, as shown in **Figure 13**. A trace on the bottom layer is used to connect the inner terminal of the coil through a via, to form a closed loop. The

**Figure 13.**

*Top (left) and bottom (right) view of the spiral coil. (a is the tuning capacitor, b is the capacitor for 2 segments and c is the capacitors for 4 segments).*

*Segmented Coil Design Powering the Next Generation of High-efficiency Robust… DOI: http://dx.doi.org/10.5772/intechopen.105789*


**Table 1.**

*Coil dimensions.*

locations of the segmentation capacitors are indicated for different segmentation numbers. The dimensions of the coil are listed in **Table 1**. The substrate is a 1.5 mm FR-4 with 1 oz. copper.

The coil is firstly simulated without any capacitors. The inductance of the coil can be obtained as:

$$L = \frac{\text{im}(Z\_{11})}{w} \tag{63}$$

where *Z*<sup>11</sup> is the input impedance of the coil, and *ω* is the radian frequency. The tuning capacitor can be calculated as:

$$C\_{tun\epsilon} = \frac{1}{\omega \, \text{im}(Z\_{11})} \tag{64}$$

The values of the segmentation capacitors are calculated using Eq. (47). In practice, the values of the segmentation capacitors would be a little higher due to the parasitic capacitance of the coil itself.

Once the coil is tuned to resonate at the desired frequency, either with or without segmentation, the resistance can be obtained as:

$$R = \text{re}(Z\_{11})\tag{65}$$

To evaluate the effect of the segmentation on the resistance, three cases are compared by simulation and verified with measurement: (a) coil without segmentation with one series capacitor to resonate the coil; (b) coil with one segmentation capacitor splitting the coil wiring into two equal segments; (c) coil with three segmentation capacitors splitting the coil wiring into four equal segments.

### **8.1 Coil resistance**

The fabricated coils with and without segmentation are shown in **Figure 14**. All the coils are tuned to resonate at 27.12 MHz. A comparison of the simulated and the measured resistance with and without segmentation is shown in **Table 2**. Excellent

**Figure 14.** *Fabricated coils with and without segmentation capacitors.*


### **Table 2.**

*Simulated and measured resistance for segmented and non-segmented coils.*

agreement is found between the simulations and the measurements. The values of the capacitors needed to resonate the coil at 27.12 MHz are higher than the values calculated using Eq. (64). The measured resistances of the Printed Circuit Board (PCB) coils are higher than the simulated ones because of the extra capacitance and loss from the testing cable and connector which is not included in the simulations. What is clear from both simulation and measurement is that the addition of segmentation capacitors significantly reduces the coil resistance and the associated power loss in the coil.

### **8.2 Figure of merit** *Ft*

The electromagnetic (EM) fields generated from the coils can be simulated in HFSS. The FoM *Ft* is used to compare the coils with and without segmentation. For ease of comparison, the fields along the X, Y, Z directions are plotted, where the origin of the coordinate system is the center of the coil, and the coil is placed at the XY-plane, as indicated in **Figure 13**. The electric fields normalised by the input power as *<sup>E</sup>*ffiffiffiffi *Pin* <sup>p</sup> are also plotted. From **Figure 15** we can observe the following trends:


**Figure 16** plots the heat map of the magnitude of the electric field in the PCB substrate indicating that, as the electric fields are concentrated around the segmentation capacitors, the dielectric loss in the substrate is reduced.

### **8.3 Transfer efficiency**

In this section, the effect of the segmentation on the power transfer efficiency is evaluated in both simulations and measurements. The receive coil shares the same HFSS model as the transmit coil, only with different dimensions and number of turns. *Segmented Coil Design Powering the Next Generation of High-efficiency Robust… DOI: http://dx.doi.org/10.5772/intechopen.105789*

**Figure 15.**

*FoM Ft and normalised electric field plots along Z, X, and Y directions. The plots for X and Y directions are at z = 4 mm height.*


### **Table 3.** *Geometry of the receive coil.*

The parameters of the receive coil that is simulated is shown in **Table 3**. The substrate is 0.8 mm FR-4 with 1 oz. copper.

The receive coil is placed 10 mm above the transmit coil with its center aligned with the center of the transmit coil, as shown in **Figure 17**. To investigate the loading effect of the human body, a hand is placed close to the coil. To measure the transfer efficiency, we perform the following steps:


$$Z = Z\_0(U+\mathbb{S})\left(U-\mathbb{S}\right)^{-1} \tag{66}$$

where *U* is the unity matrix, *Z*0=50 Ω

f. Calculate mutual inductance using:

$$M = \frac{1}{\alpha} \text{im}(Z\_{21})\tag{67}$$


**Figure 17.**

*Measurement setup for the transfer efficiency (left: top view; middle: side view; right: a hand is close to the coils).*

*Segmented Coil Design Powering the Next Generation of High-efficiency Robust… DOI: http://dx.doi.org/10.5772/intechopen.105789*

While designing a WPT system for medical implants, care must be taken to understand the various use cases and user interactions and its implications on power delivery. An important decision that needs be made is whether a design is maximised for delivered power to an implant or efficiency of the WPT link. On one hand, if it is challenging for the receive coil inside of the implant to harvest the needed amount of power, then maximising the delivered power is preferential. On the other hand, most body worn charging systems are battery-powered and have a limited amount of available power to deliver to the implant. So, maximising the efficiency directly results in longer duration before the battery runs out on the charger and needs to be recharged by the patient or the caregiver.

As an example for this article, we chose to calculate the transfer efficiency using Eq. (18) for the coils with and without segmentation. The transfer efficiency is also simulated in HFSS for comparison. Furthermore, a 200 mm 200 mm 3-layered tissue stack model is placed 2 mm above the transmit coil and the receive coil is embedded in the fat layer with the same 10 mm distance to the transmit coil in HFSS, as shown in **Figure 18**. The thickness of the skin, fat and muscle is 2 mm, 23 mm and 20 mm, respectively.

The simulated and measured transfer efficiencies are summarised in **Table 4**. We observe that the transfer efficiency both in air and in tissue can be improved with segmentation. Although we have done the calibration to minimise the effect of the cables and connectors, the measured efficiency is still a little lower than the simulated one, which is not surprising. However, with segmentation, we can see that the measured efficiency is much closer to the simulated one. It implies that the segmentation can reduce the loading effect of the environment (e.g. cables). The measured efficiency of a coil without segmentation in the presence of body tissue (hand) shows a significant drop from 61.9% to 46.2%, while the measured efficiency of a coil with two and four segmentations shows only a drop from 65.0% to 62.3% and 67.2% to 66.5%,

### **Figure 18.**

*Transfer efficiency simulation with tissue stack.*


**Table 4.** *Transfer Efficiency (%).*

respectively. This clearly indicates that the segmented coils are more robust to the presence of lossy tissue. In case of the simulated coils, the tissue of **Figure 18** has a much larger effect on the coil, because there is large drop in efficiency when the tissue is nearby for non-segmented and segmented coils.

### **8.4 Figure of merit** *Ft***,***SAR*

The SAR value in tissue is simulated in HFSS using the same tissue model as in the previous section. For a fair comparison, the SAR is also normalised by the input power as:

$$
\overline{SAR} \equiv \frac{SAR}{P\_{in}} \tag{68}
$$

**Figure 19** compares the distribution of the peak average SAR where the SAR value has been normalised to the peak SAR value for each of the three coil designs presented. The IEC/IEEE 62704-4 method is used to calculate the peak average 1 g SAR. Without segmentation, the regions of high SAR value occur at the overlapping area between the trace on the top and bottom layers of the PCB. This is because there is high stored electric field between the layers resulting in high parasitic capacitance. For the coil with two segments, the regions of high SAR value are between the segmentation capacitor and the areas of overlap between the top and the bottom layers of the PCB. Both these regions have high parasitic capacitance. For the coil with four segments, the 3 segmentation capacitors are lined close to each other resulting in a region of high stored electric field. This results in the coil with four segments having higher peak average SAR compared to the coil with two segments, but still lower than the coil with no segmentation capacitors. The results also clearly indicate that the locations of the segmentation capacitors play a critical role in reducing the peak average SAR.

Another important advantage of introducing segmentation capacitors in the wiring of the coils (or along the coil traces) is that the distribution of the averaged SAR and its maximum value can be significantly altered by optimising the locations of the segmentation capacitors along the coil. For example, the 4-segment coil (in **Figure 14**) has the three segmentation capacitors in close proximity, all in the same sector of the circular coil. For the same coil **Figure 20** shows a significantly different SAR distribution and reduced maximum SAR value when the three segmentation capacitors are

### **Figure 19.**

*The SAR maps in the tissue. (top: coil with no segments; middle: coil with two segments; bottom: coil with four segments).*

*Segmented Coil Design Powering the Next Generation of High-efficiency Robust… DOI: http://dx.doi.org/10.5772/intechopen.105789*

### **Figure 20.**

*SAR map of the coil with spread segmentation capacitors.*

spread along the coil with 90-degree separation. The heat map shows that the regions of high SAR value are shaped like a circle and the peak value of the SAR is reduced by 40%. It should be noted that the tuning capacitor is not shown in this plot because it is placed at the far end of the coil input.

With the normalised SAR and the coil resistance, the maximum allowed current within FCC limit can be calculated as

$$I\_{\max} = \sqrt{2P\_{\max} / R\_{loaded}}\tag{69}$$

where *P max* ¼ 1*:*6*=SAR*, and *Rloaded* is the coil resistance when the coil is in close proximity to lossy tissue.

**Table 5** summarises the coil impedance, SAR, *Ft*,*SAR* and maximum current compliant to FCC limit with different segmentations. The coil without segmentation is also listed for comparison. It is noted that when the segmentation capacitors are spread along the coil, the peak SAR is significantly reduced, and the maximum current within FCC limit is increased. From the **Table 5** we observe that coil resistance decreases as


### **Table 5.**

*Comparison of coil impedance, SAR,Ft,Ft*,*SAR and the max current compliant to FCC limit with segmentations.*


### **Table 6.**

*Change in resistance.*


### **Table 7.**

*Comparing the change in reactance with the predicted one.*

we increase coil segmentation: from 0.69 Ω to 0.39 Ω if in air (44%) and from 1.88 Ω to 0.94 Ω if near tissue (50%).

When we compare the "*Z*<sup>11</sup> in air" and "*Z*<sup>11</sup> with tissue", we observe that both resistance (real part of impedance) and reactance (imaginary part of impedance) increase when the tissue is in the proximity of the transmit coil. For example, the resistance grows from 0.69 Ω to 1.88 Ω (+172%) for the non-segmented coil when the tissue is approached.

The figure of merit *Ft* grows by 16% and *Ft*,*SAR* grows by 74% as we increase coil segmentation.

Let us now compare the change in resistance Δ*R* and the change in reactance Δ*X* for the four coils as shown in **Table 6**. We observe that change in resistance Δ*R* is decreasing with the progressing segmentation, up to 56%. In the following table we show the measured change in reactance Δ*X*.

From the **Table 7** we observe that there is a decrease in the change in reactance Δ*X* as the number of segments in the transmit coil, *N* increases. Assuming that Leddy contribution is negligible, the Eq. (55) predicts that the ratio of non-segmented Δ*X* to segmented Δ*X* would grow as *N*<sup>2</sup> . For the "4-segment spread" the ratio of changes in reactance far exceeds the prediction of *N*<sup>2</sup> . Spreading the segmentation capacitors away from one another, significantly helps to stabilise the transmit coil. While the numbers do not exactly match, the trend showing the increase in the ratio is as predicted in Eq. (55).

### **9. Conclusions**

In this work, we introduced and derived unique optimisation metrics for designing efficient transmit and receive coils for magnetics based WPT solutions for medical implants. We reviewed the regulations imposed on WPT systems for medical implants in the US and EU regions and determined the most limiting parameters that place a bound on the maximum current that can be driven into a coil. We derived the expressions for delivered power and efficiency considering the identified regulatory limits for the transmit and the receive coil currents. We demonstrated that, under certain

### *Segmented Coil Design Powering the Next Generation of High-efficiency Robust… DOI: http://dx.doi.org/10.5772/intechopen.105789*

conditions, the system figure of merit can be "split" into transmit figure of merit and receive figure of merit permitting independent evaluation of transmit and receive coils.

We studied the effect of lossy tissue on the performance of transmit coils from a circuit theory perspective. We showed that the resistance of the transmit coil increases in the presence of tissue because of two types of electromagnetic phenomena: (i) increase in parasitic capacitance between the opposite charges accumulating in the surfaces of the coil (charge contribution); (ii) the eddy currents in the tissue (current distribution). We showed that the change in reactance of the coil due to the presence of lossy tissue is dependent on which contribution (charge or current) is more significant.

With this improved understanding of the effect of lossy tissue on coils we introduced the concept of segmented on-body transmit coils. We hypothesised that the resistance and reactance of a transmit coil with segmentation capacitors is less sensitive to the presence of lossy tissue. We derived the impact of segmentation on the transmit figure of merit and the receive figure of merit of a coil using circuit theory. We showed analytically that segmented coils have the potential to significantly improve both (transmit and receive) figures of merit, thereby positively affecting the efficiency of a WPT system.

To validate our hypothesis and assertions we built PCB coil prototypes at 27.12 MHz with and without segmentation. We performed full wave simulations using HFSS models of the same coils. We showed through simulations and measurements that the resistance of the transmit coil reduces substantially (as much as 50%) when we went from no segmentation to up to four segments (with three segmentation capacitors). We also confirmed that the proximity of lossy tissue has a significantly smaller effect on segmented transmit coil. We noted that, on the specific coils we built, we measured that the change in reactance of a coil between air and close proximity of tissue reduced from 4.2% (for non-segmented coil) to 0.06% (for segmented coil with capacitors uniformly spread). We also confirmed that the transmit figures of merit (*Ft* and *Ft*,*SAR*) of the segmented coil are higher than those of the nonsegmented coil. *Ft* grew by 16% and *Ft*,*SAR* grew by 74% as we increased the level of segmentation. We have found that the way the segmentation capacitors are spaced on the coil has a significant effect on coil performance and the distribution of electric field close to the wiring of the coil. This is an important result as the number of segmentation capacitors and their distribution to break up the coil wiring controls the distribution of electric field and will be very useful in controlling not just SAR but also to reduce coupling with the internal electronics of a charger.

### **Acknowledgements**

We are grateful to Mark Norris, Richard Davies, Matthew Armean-Jones, and Olympia Karadima for their useful feedback that helped in writing this chapter.

*Smart Grids Technology and Applications*

### **Author details**

Yun Tao<sup>1</sup> \*, Rosti Lemdiasov<sup>2</sup> , Arun Venkatasubramanian<sup>2</sup> and Marshal Wong<sup>1</sup>


\*Address all correspondence to: yun.tao@cambridgeconsultants.com

© 2022 The Author(s). Licensee IntechOpen. This chapter is 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.

*Segmented Coil Design Powering the Next Generation of High-efficiency Robust… DOI: http://dx.doi.org/10.5772/intechopen.105789*

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## *Edited by Lucian Mihet-Popa*

This book provides a comprehensive overview of smart grid technology. It contains six chapters organized into three sections: "AC-DC Smart Hybrid Microgrid: Modelling, Control and Applications", "Smart Distribution Systems: Methodologies, Realtime Platforms and Testing Methods", and "Energy Storage Systems and Their Applications in Smart Grids". Chapters address such topics as the advantages and disadvantages of AC/DC hybrid microgrids, DER components in an AC microgrid, methodologies for solving the reconfiguration and reactive power compensation dispatch in a smart distribution network, digital twin types, the different types of energy storage systems, and much more.

Published in London, UK © 2023 IntechOpen © zhazhin\_sergey / iStock

Smart Grids Technology and Applications

Smart Grids Technology

and Applications

*Edited by Lucian Mihet-Popa*