Control strategies for high-power electric vehicles powered by hydrogen fuel cell, battery and supercapacitor

Expert Systems with Applications 40 (2013) 4791–4804

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Control strategies for high-power electric vehicles powered by hydrogen fuel cell, battery and supercapacitor Pablo García a, Juan P. Torreglosa b, Luis M. Fernández a,⇑, Francisco Jurado b a b

Department of Electrical Engineering, University of Cádiz, 11202 EPS Algeciras, Algeciras (Cádiz), Spain Department of Electrical Engineering, University of Jaén, 23700 EPS Linares, Linares (Jaén), Spain

a r t i c l e

i n f o

Keywords: Fuel cell Energy storage system Energy management system Electric vehicle

a b s t r a c t Problems relating to oil supply, pollution, and green house effects justify the need for developing of new technologies for transportation as a replacement for the actual technology based on internal combustion engines (ICE). Fuel cells (FCs) are seen as the best future replacement for ICE in transportation applications because they operate more efficiently and with lower emissions. This paper presents a comparative study performed in order to select the most suitable control strategy for high-power electric vehicles powered by FC, battery and supercapacitor (SC), in which each energy source uses a DC/DC converter to control the source power and adapt the output voltage to the common DC bus voltage, from where the vehicle loads are supplied. Five different controls are described for this kind of hybrid vehicles: a basic control based on three operation modes of the hybrid vehicle depending on the state of charge (SOC) of the battery (operation mode control); a control strategy based on control loops connected in cascade, whose aim is to control the battery and SC SOC (cascade control); a control based on the technique of equivalent fuel consumption, called equivalent consumption minimization strategy (ECMS); and two based on control techniques very used nowadays, the first one of them is a fuzzy logic control and the second one is a predictive control. These control strategies are tested and compared by applying them to a real urban street railway. The simulation results reflect the optimal performance of the presented control strategies and allow selecting the best option for being used in this type of high-power electric vehicles. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction Nowadays, FCs are becoming a real alternative to ICEs in transport applications (Amjad, Neelakrishnan, & Rudramoorthy, 2010). Compared with ICE vehicles, FC vehicles (FCVs) present the following advantages (Emadi, Lee, & Rajashekara, 2008): (i) they have higher efficiency because the energy conversion is direct (i.e. without combustion) and there is no Carnot limitation, and (ii) they emit substantially lower emission of CO2 or even no emission at all. However, FCVs have some disadvantages derived mainly from the FC system (FCS) (Chan, Bouscayrol, & Chen, 2010; Jiang & Fahimi, 2010): (i) an FCS still has low power density (10–100 times lower) compared with ICE; (ii) it presents slow start-up and slow power response; (iii) it has relatively lower efficiency at low and high output power; (iv) its inability for energy regeneration; and (v) a pure FCV has the disadvantage of increasing the capital cost of the vehicle. ⇑ Corresponding author. Tel.: +34 956 028166; fax: +34 956 028001. E-mail addresses: [email protected] (P. García), [email protected] (J.P. Torreglosa), [email protected] (L.M. Fernández), [email protected] (F. Jurado). 0957-4174/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.eswa.2013.02.028

Nevertheless, the effect of these drawbacks can be reduced by combining an FCS with an energy store system (ESS), such as a battery, a supercapacitor (SC), or a combination of both. In general, batteries have higher specific energy than SCs and hence can provide extra power for a longer period of time. SCs have a higher specific power than batteries, are more efficient, and have a longer lifetime in terms of number of charge/discharge cycles. A review of ESS for transport applications is presented in Lukic, Cao, Bansal, Rodriguez, and Emadi (2008) and Shukla, Arico, and Antonucci (2001). In general, a hybrid propulsion system based on FC and battery is the option chosen by the few projects that have used an FC propulsion system to operate high-power vehicles (Geng, Mills, & Sun, 2012; Li, Xu, Hua, Li, & Ouyang, 2009; Liangfei, Jianqiu, Jianfeng, Xiangjun, & Minggao, 2009a), because it is the most economical choice for the power train. Nevertheless, the incorporation of SC as secondary ESS in a hybrid electric vehicle with FC and battery (Ayad, Becherif, & Henni, 2011; Na, Park, Kim, & Kwak, 2011; Thounthong, Raël, & Davat, 2009) improves the overall vehicle dynamic response. In the hybrid system, the control strategy applied for the energy management plays an important role because it is responsible for

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controlling the system operation, providing the power needed, and optimizing the energy generated. Some examples of energy management system (EMS) applied to high-power electric vehicles based on FC, battery and SC are illustrated in Erdinc, Vural, and Uzunoglu (2009), Ferreira, Pomilio, Spiazzi, and Silva (2008), Na et al. (2011) and Thounthong et al. (2009). Thounthong et al. (2009) show an EMS with several PI controllers connected in series to control the main voltages for application in hybrid vehicles. In this work, the control loops composed by PI controllers are used in order to generate the appropriate reference signal for the energy source, considering their physical constraints. Na et al. (2011) present a FC hybrid electric vehicle based on predictive controller. Erdinc et al. (2009) and Ferreira et al. (2008) use an EMS based on fuzzy logic control to control the demanded load by the hybrid vehicle. In all these cases, the selected configuration is composed by a FC, battery and SC, where the energy sources are connected to a common DC bus voltage through DC/DC converters. Other examples of EMS applied to other configurations of hybrid vehicles can be found in Li and Liu (2009), Li et al. (2009), Liangfei et al. (2009a), Xu, Li, Hua, Li, and Ouyang (2009). Li and Liu (2009) and Liangfei et al. (2009a) and use control strategies based on fuzzy logic control, which determine the operating point of the FC converter depending on the load power and the battery state of charge (SOC). The hybrid system used by Liangfei et al. (2009a) is composed of a 40-kW polymer electrolyte membrane (PEM) FC and nickel–metal hydride (Ni-MH) battery, and a 30kWPEMFC, and a lead-acid battery by Li and Liu (2009). Chan et al. (2010) propose an adaptive supervisory control strategy for a hybrid city bus powered by two PEM FC stacks with a rated power of 40 kW and a Ni-MH battery. Xu et al. (2009) describe an optimal control strategy based on a time-triggered controller area network for a hybrid city bus powered by an 80kW PEMFC and Ni-MH battery. It is composed of an ECMS and a braking energy regeneration strategy. This article presents five control strategies that can be used in high-power electric vehicles powered by FC, battery and SC. Once defined in a general way, they are characterized, tested and compared by applying them to a real urban street railway with a rated traction power of 400 kW.

The article is organized as follows. After introduction, Section 2 presents the hybrid vehicle configuration. The control strategies are developed in Section 3. Section 4 shows the application of the control strategies to a high-power electric vehicle, in this case, an urban street railway. Simulation results are shown in Section 5, in which the control strategies are tested and compared by applying them to the urban street railway. Finally, the conclusions are drawn in Section 6. 2. Hybrid vehicle configuration The selected topology for a high-power hybrid electric vehicle is shown in Fig. 1. In this topology, the hybrid vehicle is powered by FC, battery and SC, where each one of these energy sources presents a DC/DC converter, which connects it to the traction standard DC bus. Furthermore, the hybrid vehicle is composed of electric traction motor drives, an auxiliary services module independent of the traction drives (e.g. principally air conditioning), a braking resistor and an EMS. The FC is the primary energy source of the hybrid electric vehicle. It is connected to a boost-type unidirectional dc/dc converter which raises the low dc voltage delivered by the FC to the traction standard dc bus. Apart from that, a rechargeable battery and a SC are used as ESS. The battery generates an extra power during the acceleration and to recovery energy during the braking. Because of its high dynamic response (Bauman & Kazerani, 2008), the SC generate/consume the peak power that neither the battery nor the FC can generate/store, and allows the DC bus voltage control. 3. Control strategies for hybrid electric vehicles The EMS of a hybrid electric vehicle should provide the power demanded by the system, control the battery and SC SOC and make the braking resistor operate, when necessary, during regenerative braking. In this work, five control strategies that can be used in the EMS of high-power electric vehicles powered by FC, battery and SC are described: (1) a control strategy based on operating modes of the hybrid vehicle (operation mode control, OMC); (2)

Fig. 1. Configuration of the high-power hybrid electric vehicle.

P. García et al. / Expert Systems with Applications 40 (2013) 4791–4804

a control strategy based on cascade control loops (cascade control, CC); (3) a control strategy based on fuel consumption minimization (equivalent consumption minimization strategy, ECMS); (4) a control strategy based on fuzzy logic (fuzzy logic control, FLC); and (5) a control strategy based on predictive control (model predictive control, MPC). All these controls are implemented by using the same operating constraints and limitations for the energy sources of the hybrid vehicle, such as:  The power generated by the FC is kept between its maximum min and minimum allowed value (P max fc , P fc ).  The dynamic response of the FC is limited in slope according to the physical limitation of the FC.  The power and current generated by the battery is kept between its boundary values of charge and discharge max (P max bat;char , P bat;disc , Ibat,char, Ibat,disc).  The current exchanged by the SC is kept between its boundary values of charge and discharge (Isc,char, Isc,disc).  The dynamic limitation of the battery is modeled as a first order system with a time constant sbat. In the case of the SC, no dynamic limitation is considered, since it presents a very fast dynamic response.  The battery and SC SOCs are kept around their reference values in order to achieve high charge efficiency (Linden & Reddy, 2002). Otherwise, the five control strategies presented in this paper use the same control loops for controlling the FC and battery con-

Fig. 2. Operation Mode Control (OMC).

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verters. These control loops allow that the FC generates and the battery generates/consumes the power determined by each control. The SC converter is controlled in order to keep the DC bus voltage at the rated value. The five control strategies and the control of the DC/DC converters are explained below. 3.1. Operation mode control This control strategy is based on an OMC, which generates the FC and battery reference powers in order to provide the power demanded by the electric vehicle, and besides, to maintain the battery SOC around the desired value. This control strategy works as follows. The OMC generates the FC reference power. The difference between the total demanded load and the FC reference power is the ideal battery reference power (Pid bat ). This battery reference power is slightly modified by using a control loop which generates a power (Psc,soc,control) to keep the SC SOC around the desirable value. Fig. 2 shows a scheme of this EMS. The OMC determines the operation mode depending on the demanded load, hybrid vehicle speed, and battery SOC (input variables). Once the operation mode is selected, the OMC generates the FC reference power (output variable). The specification of the operation modes depends on the designer knowledge about the supplies and traction device constraints, intuitive and practical aspects regarding the propulsion mechanism dynamic behavior, and successive experiments to assure the process robustness and reliability. Thus, three battery SOC levels (high, normal and low) are considered in order to determine the operation mode: discharge mode (high battery SOC), charge mode (normal battery SOC) and fast charge mode (low battery SOC). Changes between the battery SOC levels are performed by means of two hysteresis cycles, shown in Fig. 3(a). The three operation modes considered in this control strategy are described below. Fig. 3 summarizes these operation modes.  Discharge mode: In this mode, the FC must adapt its power to the load demanded power (load following strategy) between a eff lower limit, P min fc , and two upper limits, Paux or P fc , depending on the hybrid vehicle speed. If necessary, the FC provides the auxiliary services power, Paux, when the vehicle is stopped. When the vehicle power demand is higher than P eff , the battery fc

Fig. 3. Detail of OMC. (a) Hysteresis cycles for levels battery SOC, and (b) discharge modes, (c) charge mode, and (d) fast charge mode.

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supplies the difference between it and the FC power, so that its SOC decreases.  Charge mode: In this case, the FC adopts the load following strateff egy between P eff and P max fc . The FC operates at P fc when the fc power demanded by the hybrid vehicle is lower than P eff fc , which leads to the battery charge. When the power demanded by the hybrid vehicle is higher than Pmax fc , the battery supplies the difference between it and the FC maximum power, so that its SOC decreases.  Fast charge mode: This operation mode is adopted when the hybrid vehicle has to start a new trip with a very low battery SOC. When the demanded power is lower than P min fc , the FC supplies to the auxiliary services and, provides extra power, Pextra, to charge the battery. If the demanded power is between Pmin fc and Pmax  Pextra, the FC generates the demanded power plus fc this extra power to increase the battery SOC.

C, which is calculated as function of the FC hydrogen consumption Cfc, and the battery and SC equivalent consumptions, Cbat and Csc (García, Torreglosa, Fernández, & Jurado, 2012). The mathematical problem used to minimize the fuel consumption is expressed as follows:

Pfc ¼ minðC fc þ k1  C bat þ k2  C sc Þ8t

where Pfc is the FC output power; and k1 and k2 are penalty coefficients, which modify the battery and the SC equivalent fuel consumptions up or down depending on their SOC deviation from the target (He & Yang, 2006). Since the aim of the SC is to generate the power peaks demanded by the vehicle during the accelerations or brakings that FC and battery cannot generate/absorb, its contribution will be minimum, and therefore, Csc can be neglected compared with Cfc or Cbat. For this reason, Eq. (1) can be rewritten as follow:

Pfc ¼ minðC fc þ k1  C bat Þ 8t

3.2. Cascade control This control strategy generates the FC and battery reference powers by cascade control loops. The FC reference power is determined in order to maintain the battery reference SOC by using two cascade control loops. The outer loop uses a simple proportional controller in order to generate the battery reference current, which is limited in level depending on the physical limitations of the battery. The inner loop tries to adjust the battery current to the reference value defined by the outer loop. It uses a proportional-integral (PI) controller in order to determine the FC reference power, which is limited in level according to the physical limitation of the FC. On the other hand, the ideal battery reference power is determined by a single control loop based on PI controller in order to control the SC SOC. The ideal reference power defined by the PI controller is limited in level according to the physical limitation of the battery. Fig. 4 shows the control scheme of this strategy. A similar control scheme for an electric vehicle powered by FC and battery is used in Fernandez, Garcia, Garcia, Torreglosa, and Jurado (2010).

This control strategy is based on the concept of equivalent fuel consumption proposed by Paganelli, Delprat, Guerra, Rimaux, and Santin (2002). In the hybrid electric vehicle, the hydrogen energy from the FC and electrical energy from the ESS (battery and SC) are used. To make the devices comparable, an equivalent hydrogen consumption is derived from the electrical energy consumption of the ESS, such as performed for the battery in Paganelli et al. (2002) and Pisu, Koprubasi, and Rizzoni (2005). This control focuses on calculating the optimal FC power that minimizes the hydrogen consumption of the hybrid powertrain,

ð2Þ

where Cfc and Pfc depend on the FC static curve. In general, this relationship can be approximated by the following expression:

C fc ¼ c  P2fc þ b  Pfc þ a

ð3Þ

where a, b, and c are fit coefficients which must be calculated once selected the FC for the hybrid vehicle. The battery equivalent hydrogen consumption Cbat can be calculated from the battery power Pbat and the battery SOC (Liangfei, Jianqiu, Jianfeng, Xiangjun, & Minggao, 2009b; Pisu et al., 2005). To obtain this equivalent hydrogen consumption, the average values are used, since the operation points of the FC and the battery are unknown. The battery equivalent hydrogen consumption can be expressed as:

C bat ¼ Pbat  r 

C FC;av g PFC;av g

ð4Þ

with:

(

r¼ 3.3. Equivalent consumption minimization strategy control

ð1Þ

1

gchg;av g gdis

Pbat P 0

gchg  gdis;av g Pbat < 0

ð5Þ

where CFC,avg is the average FC hydrogen consumption; PFC,avg is the average FC power; gchg and gdis are the efficiencies of the battery charging and discharging, respectively; and gchg,avg and gdis,avg are the mean efficiencies. As considered in Torreglosa, Jurado, Garcia, and Fernandez (2011), the penalty coefficient k1 is expressed by:

k1 ¼ 1  2l

ðSOC  0:5ðSOC H þ SOC L ÞÞ SOC H þ SOC L

ð6Þ

where the l constant must be adjusted to reflect properly the battery charge and discharge processes Torreglosa et al. (2011), and it is chosen to balance the battery SOC during the cycle; SOCL is the lower limit of SOC; and SOCH is the upper limit of SOC. Because Csc is neglected compared with Cfc or Cbat, the FC power used in Eq. (6) can be calculated as follows:

Pfc ¼ Pload  Pbat

ð7Þ

where the total power demanded by the hybrid vehicle Pload is calculated as the sum of the output power of the electric motors and the load demanded by the auxiliary services. If Eqs. (4), (5), and (7) are taking into account, the minimization problem can be expressed as follows:

  C FC;av g 2 Pid ¼ min cðP  P Þ þ bðP  P Þ þ a þ k P E 1 bat load bat load bat bat PFC;av g Fig. 4. Cascade control.

ð8Þ

P. García et al. / Expert Systems with Applications 40 (2013) 4791–4804

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By developing this equation and considering that Pload and a can be regarded constants in the minimization problem, the optimal solution to the previous equation can be expressed as:

Pid bat ¼

b  K1 þ Pm =gm þ Paux 2c

ð9Þ

where K1 is a new constant defined as:

K 1 ¼ k1  r 

C FC;av g P FC;av g

ð10Þ Fig. 6. Fuzzy logic control.

Finally, the FC optimized power P opt fc can be calculated by: id max min Popt fc ¼ maxðminðP m þ P aux  P bat ; P fc Þ; P fc Þ

ð11Þ

where Pmax is the FC maximum power, Pmin is the FC minimum fc fc power, and Pm is the electric motor power, which is positive working as a motor and negative as a generator. On the other hand, since the SC equivalent hydrogen consumption, Csc is considered null when compared with the hydrogen equivalent consumptions of the other energy sources, this control scheme includes a new control loop in order to control the SC SOC, which is similar to that used in OMC. This control loop composed of a PI controller generates the adequate reference power for the SC from the difference between the SC reference SOC and its actual value. This SC reference power is limited in level according to the physical limitation of SC and added to the optimum battery reference power generated by the ECMS control, and thus, modifying slightly this power, which is the final reference power to be generated by the battery, once limited in level. The control scheme of ECMS is shown in Fig. 5. 3.4. Fuzzy logic based control The proposed fuzzy logic control has three inputs and two outputs. The inputs are the battery and SC SOCs and the total power demanded by the hybrid vehicle (traction power, Pm, and auxiliary services power, Paux). The outputs of the control are the optimal FC reference power (Popt fc ) and the battery reference power variation (DPref ). Similarly to the other control strategies, the goal is to probat vide the power demanded by the load at every moment, maintaining the battery and SC SOC close to their reference values and making the FC to work in its allowable operating range. Fig. 6 shows the configuration of the fuzzy logic control proposed in this paper. The specification of the rules of the fuzzy logic controller depends on the designer knowledge about the supplies and traction device constraints; intuitive and practical aspects regarding the propulsion mechanism dynamic behavior; and successive experiments to assure the process’s robustness and reliability. Thus, depending on the hybrid vehicle where the control will be applied the rules base and the membership function must be defined. The optimal battery power is calculated by Eq. (12), which is limited in level according to the battery limitations.

Fig. 5. Equivalent consumption minimization strategy control.

opt ref Popt bat ¼ P load  P fc  DP bat

ð12Þ

3.5. Predictive control Predictive control has been recently applied to hybrid vehicles (Arce, del Real, & Bordons, 2009; Chen, Gao, Dougal, & Quan, 2009; Geng et al., 2012). In Chen et al. (2009), this control is used in a vehicle powered by two parallel FCs connected to a DC bus and a SC connected to the DC bus through a DC/DC converter. Different pairs of plant models and predictive controllers are designed for different linearization points. An algorithm is applied to choose a pair of plant-controller depending on the linearization point, which fits better with the input data. In Arce et al. (2009), a predictive control strategy is applied to a FC-battery vehicle which has two DC/DC converters (each one for each energy source). The proposed predictive controller generates the FC and battery reference powers from the demanded power and the current battery SOC subject to some constraints. Finally, in Geng et al. (2012), a predictive controller is combined with a tracking controller to manage the energy flow between the FC, the battery and the electric motor acting on the FC and battery controllers. A linear time-invariant model of the plant to control is required as first step for designing the predictive controller. This plant has two inputs, the FC optimal reference power (P opt fc ) and the battery optimal reference power (P opt ), which are generate d by the predicbat tive controller. The plant outputs are the load power generated (Pload), the battery SOC (SOCbat) and the SC SOC (SOCsc). After defining the plant inputs and outputs, the controller is designed. The main objective of the predictive controller is to hold the outputs, y, at the reference values (or setpoints), r, by adjusting the manipulated variables (or actuators) u. The predictive controller generates the manipulated variables predicting the future behavior of the system by using the plant model commented above and the data collected previously during its operation. Fig. 7 shows the overall configuration of this control. The methodology of the Model Predictive Control (MPC) is characterized below using a Single Input Single Output (SISO) plant. Fig. 8 shows the SISO MPC system state, where it is assumed that this system has been operating for many sampling instants. Integer k represents the current instant. The latest measured output, yk, and previous measurements, yk1, yk2, . . ., are known and are the filled circles shown in Fig. 8(a). If there is a measured disturbance, its current and past values would be known. Fig. 8(b) shows the previous moves of the controller, uk4, . . . , uk1, as filled circles. As is usually the case, a zero-order hold re-

Fig. 7. Predictive control.

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right operation of the system. These constrains affect the system outputs, which must be kept around their setpoints, and the system inputs generated by the predictive controller which must meet the dynamic limitations of the respective energy sources. The outputs constrains are defined as follows. The power generated by the sources must stay within specific limits which are the maximum and minimum power demanded by the hybrid vehicle, which are known (driving cycle data). On the other hand, the inputs constrains are defined. Thus, the FC power is limited in level, between a minimum FC power (P min fc ) and its maximum delivered power (Pmax ). Apart from that, the battery reference power is limited in lefc vel, depending on the maximum discharge and charge powers max (Pmax bat ;char ; P bat;disc ). 3.6. Control of the DC/DC power converters

Fig. 8. Controller state at the kth sampling instant. (a) Measured and estimated outputs, and (b) past and previous moves.

ceives each move from the controller and holds it until the next sampling instant, causing the step-wise variations shown in this figure. To calculate its next move, the uk controller operates in two phases: i. Estimation. In order to make an intelligent move, the controller needs to know the current state. This includes the true value of the controlled variable, yk, and any internal variables that influence the future trend, yk+1, . . . , yk+P. To accomplish this, the controller uses all past and current measurements and the plant model. ii. Optimization. Values of setpoints, measured disturbances, and constraints are specified over a finite horizon of future sampling instants, k + 1, k + 2, . . . , k + p, where p (a finite integer P 1) is the prediction horizon. The controller computes m moves uk, uk+1, . . . , uk+M–1, where m (1 6 m 6 p) is the control horizon. In the hypothetical example shown in the figure, the values of p and m are the following ones: p = 9 and m = 4. The moves are the solution of a constrained optimization problem. In the example, the optimal moves are the four open circles in Fig. 8(b). The controller predicts that the resulting output values will be the nine open circles in Fig. 8(a). Notice that both are within their constraints, umin 6 uk+j 6 umax and ymin 6 yk+i 6 ymax. When it has finished calculating, the controller sends move uk to the plant. The plant operates with this constant input until the next sampling instant, Dt time units later. The controller then obtains new measurements and totally revises its plan. This cycle repeats indefinitely. Reformulation at each sampling instant is essential for good control. The predictions made during the optimization stage are imperfect. Periodic measurement feedback allows the controller to correct for this error and for unexpected disturbances. After describing the predictive controller operation, the system constraints are formulated. Some constrains are designed for the

As well known, the voltage of energy sources varies depending on their demanded current. Thus, an electronic power system is needed to process the sources output power (variable voltages), providing the power demanded by the system at a constant voltage in the DC bus. Specifically, the power electronic system is composed of a PWM based DC/DC converter for each energy source (Kazimierczuk, 2008), which connects the sources with the DC bus, from where the loads of electric vehicle are supplied. This section explains the controls used for the FC and battery DC/DC converters to achieve the suitable operating point defined by the applied control strategy, and the control used for the SC converter to keep the dc bus voltage around the desirable value. Once the reference powers are defined by the controls, some constraints must be applied to them and the duty cycles of the DC/DC converters must be defined as explained below. Fig. 9 shows the control loops to generate the duty cycle of the converters. FC has a time-delayed response due to the lag between the response of the react supply system and the load applied to the FC. This lag leads to a breakdown of the chemical reaction and a rapid loss in voltage (Rodatz, Paganelli, Sciarretta, & Guzzella, 2005). In case of supplying of electric motors, large voltage fluctuations are undesirable (Dalvi & Guay, 2009). It can be avoided by limiting in slope the optimum FC power defined by the control strategy (Popt fc ). Furthermore, the FC starvation problem due to the dynamic loads is avoided by using a low-pass filter. The resulting FC reference power (P ref fc ) is divided by the FC voltage in order to obtain

Fig. 9. Control strategy for the DC/DC power converters.

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the FC reference current. The error signal error between this reference current and the FC measured current is used in a PI controller in order to determine the duty cycle of the FC unidirectional DC/DC power converter. Regarding the battery, the optimal reference power is limited in slope before obtaining the battery reference power. The limitation in slope is modeled as a first order system with time constant sbat. After this, the battery reference power is divided by the measured battery voltage to obtain a reference current, which is limited in level, depending on the physical limitations of the battery. Finally, a PI controller generates the duty cycle of the battery bidirectional DC/DC converter from the error between the resulting current and the battery measured current. The SC enables the DC bus voltage control, generating the power that the FC and/or the battery are not able to generate due to their dynamics limitations. Thus, this voltage is maintained at the reference value of the DC bus by using a PI controller, which generates the SC reference current. This current is limited in level, depending on the physical limitations of SC. And finally, a PI controller generates the duty cycle of the SC bidirectional converter from the error between the resulting current and actual current. 4. Application of control strategies to a high-power electrical vehicle: urban street railway Once defined in a general way the control strategies, they are characterized, tested and compared being applied to a high-power electric vehicle, in this case, a real urban street railway called ‘‘Urbos 3’’, which presents a rated traction power of 400 kW. 4.1. Urban street railway configuration Currently, Urbos 3, developed by the Spanish manufacturer Construcciones y Auxiliar de Ferrocarriles (CAF), uses the RCA (rapid charge accumulator) system based on SCs which allows traveling from one stop to the next without catenaries. These SCs are charged during decelerations by regenerative braking and, when the tramway is completely stopped, by a pantograph installed in an air power line in each stop. This system supplies the necessary energy to complete the SCs state of charge (SOC) in approximately 30 s. A configuration for this urban street railway based on FC, battery and SC, as considered in this work, and operated with one of the described control strategies, will allow it to work in an autonomous way, without having to be connected to the grid during the stops by using the infrastructure associated to the catenary. The actual urban street railway presents a capacity of 275 passengers, and reaches a maximum speed of 70 km/h. Its traction system consists of five articulated bodies and three bogies, although only two of them are motor bogies. Fig. 10 shows the driving cycle followed by the urban street railway during the bidirectional route, which is considered in the design of the hybrid system. The route presents four trips and four stops. During the first and last trips, the railway reaches a maximum speed of 50 km/h. Regarding the demanded power, it reaches a maximum value of 420 kW for acceleration and 420 kW for braking, which have to be absorbed by the battery and/or the SC. Moreover, the auxiliary services power (57.47 kW) must be added to that demanded power. Thus, the hybrid system has to generate approximately a maximum power of 500 kW, and a constant power demand (auxiliary services power) even when the tramway is stopped. For a correct hybrid system sizing, several premises must be considered. The FC maximum power must be higher than the average power demanded by the tramway during the driving cycle in

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Fig. 10. Drive cycling of the electric vehicle under study.

order to avoid an excessive drop of the state-of-charge (SOC) of battery and SC. Otherwise, the specific roles of the FC and ESSs (battery and SC) must be also considered, since the dynamic response and the specific energy of these energy sources are very different. At present work, the new hybrid system considered for the urban street railway presents a 150 kW PEM-FC, a 90 Ah Li-ion battery and a SC bank with a total capacity of 12.6 F. These devices were selected from commercially available components. The main components and characteristics of each control strategy applied to this high power electrical vehicle are described in the following subsections. 4.1.1. Fuel cell PEM FCs can be considered as a proper option for hybrid transport applications, due to factors such as the high-power density, low operating temperature, efficiency, and relative ability to rapidly adjust to changes in power demand (Yalcinoz & Alam, 2008). In fact, a Ballard PEM FC (Ballard., 2013) is used in this work, which presents a rated power of 150 kW–621 V. The PEM FC is modeled by a simplified model, which is derived from the complete model presented in Pukrushpan, Stefanopoulou, and Peng (2002). The right performance of this simplified model is demonstrated in Garcia, Fernandez, Garcia, and Jurado (2010a). This model was used in Fernandez et al. (2010) and Garcia, Fernandez, Garcia, and Jurado (2010b) to evaluate other control strategies. In this model, the voltage generated by the PEM FC, Vcell, is obtained from the sum of the Nernst´ s voltage, the activation voltage drop, the ohmic voltage drop and the concentration voltage drop, where all the voltage drops depend on the current density (Garcia et al., 2010a; Gencoglu & Ural, 2009). The hydrogen partial pressure in the anode and oxygen partial pressure in the cathode are calculated from the mass conservation law and ideal gas law. A hydrogen valve located upstream of the anode controls the input hydrogen flow to make equal the anode and cathode pressures (Padulles, Ault, & McDonald, 2000; Pukrushpan et al., 2002). Fig. 11(a) presents the polarization curve obtained from the model used in this work and the real curve of the commercial Ballard FC considered. In this figure, it can be observed the similarity between the curves. Furthermore, Fig. 11(b) shows the FC system efficiency. Other FC components are the compressor, humidifier, and air cooler. The compressor, whose dynamic response is modeled by a first order system, controls the incoming oxygen in order to keep constant the oxygen excess ratio, kO2 (Pukrushpan et al., 2002).

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Fig. 11. (a) Polarization curves of the Ballard FC and proposed FC model, (b) FC system efficiency, (c) Nominal current discharge curve (at 90 A) of the commercial battery and proposed battery model, and (d) Discharge curves of the commercial SC and SC model.

Furthermore, the humidifier and air cooler are considered ideal so that the FC operates at an optimum temperature (80 °C) and a constant relative humidity (Pukrushpan et al., 2002).

the SC performance during the charging and discharging process. Thus, its voltage, which depends on the demanded current and the SC SOC, SOCsc, is given by:

4.1.2. Battery Batteries have shown their utility as energy storage devices in several applications (Yang, 2008). Currently, hybrid vehicles (transit buses, light trucks, and tramways) are equipped with different types of batteries, such as Pb-Acid, Ni-Cd, Ni-MH, or Li-ion batteries. Li-ion batteries present better response than Pb-Acid batteries, Ni-Cd batteries or Ni-MH batteries. Furthermore, they are lighter, can store more energy and have more durability and security (Broussely & Pistoia, 2007; Kuperman & Aharon, 2011; Rao & Wang, 2011; Scrosati, Hassoun, & Sun, 2011). Thus, a Li-ion battery, designed especially for high power and transport applications, Thundersky’s TS-LFP90AHA 4.25 V, 90 Ah (Winston, 2013), is considered for the high- power electric vehicle. The behavior of this battery is represented by the available model in SimPowerSystems (Natick, 2012). In this model, the battery is represented by its circuit equivalent, which is composed of a voltage source in series with a resistor. Fig. 11(c) presents the nominal current discharge curve at 90 A obtained from the model used in this work and the real curve of the commercial battery. The model clearly coincides with the real data, except in the battery total discharge zone. On the other hand, the battery SOC must be kept between 40% and 80% of capacity in order to achieve high charge efficiency (Bauman & Kazerani, 2008). The control strategy is designed to make the battery work in this operating range, so that the battery model is considered to be valid for representing the battery response in the hybrid system.

SOC sc ð%Þ ¼

4.1.3. Supercapacitor An analysis of hybrid vehicles power requirements and related literature (Ehsani, Gao, Gay, & Emadi, 2005) show that the power profiles expected to be applied to SC are composed of frequent charge and discharge pulses. The charge and discharge pulses characteristics are: high current levels (up to 600 A), and a duration from tens of milliseconds to tens of seconds. In this work, a 125 V, 63 F Maxwell BMOD0063-P125 module was selected (Maxwell Technologies., 2013). This module is specifically designed for heavy transport applications such as buses, electric trains, trolleys, cranes, etc. The model of the SC is composed of a resistance, which models the SC ohmic losses, in series with a capacitor, which represents

U sc 100 U sc;full

ð13Þ

where Usc is the actual SC voltage and Usc,full is the fully charged SC voltage. Fig. 11(d) shows the SC discharge curves for different currents of the commercial SC and proposed SC model. It can be observed that the model data and the real data fit well enough, better for lower demands. However, the model does not reflect the nonlinear behavior at the end of each discharge curve. The control strategy was designed to avoid the SC total discharge so this nonlinear zone will be avoided. Therefore, the SC model is considered valid for representing the SC response in the hybrid system. 4.1.4. DC/DC converters This electric vehicle uses three DC/DC converters, one for each energy source. A unidirectional boost DC/DC converter connects the FC with the dc bus maintaining the FC plus converter system stable despite variations in load (Marquezini, Ramos, Machado, & Farret, 2008). Unidirectional means that the energy only can flow from the FC to the DC bus. This converter is composed of a high frequency inductor L1, an output filtering capacitor C1, a diode D1 and a main switch S1, as shown in Fig. 1. In the case of battery and SC, bidirectional dc/dc converters are used to connect them to the DC bus. In the case of battery, a buck converter is used and a boost converter for the SC. These configurations allow the energy transmission in both directions, from the battery or SC to the DC bus, and vice versa. In both cases, they consist of a high-frequency inductor (L2 or L3), an output filtering capacitor (C2 or C3) and two switches (S2 or S4 and S3 or S5), which allow the bidirectional current flow, as shown in Fig. 1. The two-quadrant chopper model of SimPowerSystems (Natick, 2012) is used to model the DC/DC converters. Fig. 12 shows the average-value equivalent model of the converters. The power-electronics switches are represented by current and voltage sources. In fact, the equivalent model consists of a controlled current source at the DC bus side and a controlled voltage source at the FC, battery or SC side. This converter model allows preserving the average voltage dynamics with larger sample times.

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4.2.2. ECMS In this control, as mentioned previously, the static curve which relates the FC power and the equivalent hydrogen consumption must be fixed. Fig. 13 represents the curve obtained from the FC model used in the electric vehicle and the fitting curve defined by Eq. (3), where it can be observed the accurate fitting achieved. On the other hand, the following control parameters were selected in this control strategy: (1) a l constant of 0.65 is chosen to balance the battery SOC during the cycle; (2) a lower limit of SOC (SOCL) of 50%; and (3) an upper limit of SOC (SOCH) of 80%.

Fig. 12. Average-value equivalent model of the DC/DC power converters.

The voltage and the current transfer functions for the converters in the cases of FC and battery or SC are also shown in Fig. 12. 4.1.5. Loads and braking resistor The railway loads are the traction power and the power consumed by the auxiliary services. For this study, a controlled DC current source connected to the standard traction DC bus is used to represent the power of each load. During a braking or a deceleration, most of the available energy is stored in the ESSs (battery and SC), which will be recovered when necessary. However, when the battery or SC achieves the charge limit, they cannot absorb more energy, and therefore, it must be dissipated in the braking resistor. In fact, the control system determines the resistor needed for consuming the power excess, whenever the ESS reaches their maximum charge power.

4.2.3. Fuzzy logic control Table 2 shows a summary of the logic rules considered in the fuzzy logic control applied to the electric vehicle under study in order to meet the demanded load. The five cases considered are: N, negative power demand; NS, small negative power; PS, positive small power; P, positive power; PB, positive big power. Thus, depending on the demanded load and the battery and SC SOCs, the FC reference power and battery reference power variation are determined. Fig. 14 shows the membership functions of the input and output variables of the system. In the case of the demanded load, it was chosen five membership functions which cover the full range of required power. In the case of the battery and SC SOCs three membership functions were considered: S, small; M, medium; and B, big. Regarding the FC it was considered a similar classification tak-

4.2. Railway control strategies Once described the main components of the electric vehicle propulsion system, the control strategies detailed in Section 3 are characterized for the vehicle under study. Table 1 shows the main parameters considered in the control strategies presented in this work when applied to the electric vehicle under study. Regarding FC operation, it is considered in this work that: (1) the five control strategies apply an ‘always on’ strategy to the FC in order to avoid start-up problems; (2) the FC is demanded to operate between 12.5 and 145 kW, where the FC efficiency is high; and (3) the FC can reach a quick power change, from 10% to 90% rated power, at less than 2 s.

Fig. 13. Relationship between the FC hydrogen consumption, Cfc, and FC power, Pfc.

Table 2 Rules base of the fuzzy logic controller. SOCbat

SOCsc S

M

B

Pfc

DPbat

Pfc

DPbat

Pfc

DPbat

S M B

M S S

NS NS NS

S S S

NS NS NS

S S S

NB NS NS

Pload = NS S M B

M S S

NS M M

S S S

NS NS M

S S S

NS NS NS

Pload = PS S M B

B M S

M M PS

M M S

NS M PS

M S S

NS M M

S M B

B M M

M PS PS

M M S

M M PS

M M S

M M PS

Pload = PB S M B

B B M

PS PS PS

M M S

PS PS PB

M M S

M M PS

Pload = N

4.2.1. Operation mode and cascade controls These controls are applied as described before and considering the control parameters indicated in Table 1. Table 1 Main parameters of the control strategies when applied to the electric vehicle under study. Parameter

Value

Parameter

Value

P max fc

145 kW

Isc,char

400 A

P min fc

12.5 kW

SOC ref bat

65%

P max bat ;char P max bat ;disc

400 kW

SOC ref sc

75%

425 kW

750 V

Paux

57.47 kW 65 kW

V ref bus Pextra DPfc

460 A 500 A 400 A

sbat sfc ssc

0.3 s 0.5 s 0s

P eff fc Ibat,disc Ibat,char Isc,disc

270 kW 60 kW/s

Pload = P

4800

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Fig. 14. Membership functions of the fuzzy logic controller. (a) Demanded power, (b) battery SOC, (c) SC SOC, (d) optimal FC reference power, and (e) battery reference power variation.

ing into account three membership functions: S, small; M, medium and B, big. Finally, in the case of the battery power variation, NB corresponds to a high negative power, NS refers to a small negative power variation, M is a small variation around zero (medium), PS is a small positive variation power and PB corresponds to a positive big power variation. Regarding the membership functions of the demanded load, taking into account the driving cycle shown in Fig. 10, the following powers were chosen: a PS power around the power requested by the railway with constant speed, a P power, which corresponds to the required output power during a slight acceleration, and a PB power related with the maximum demanded power. Similar reasoning was applied for the membership functions of N and NS. In the battery and SC SOC membership functions, it can be observed that it is allowed higher variation of battery SOC than SC SOC because the SCs are devices with higher transient response, but with lower specific energy than batteries. For this reason, a high power generated or absorbed by SCs affects greatly to their SOC. For the FC reference power, it was selected a trapezoidal membership function for medium power (M) in order to make to operate the FC with high efficiency. In the membership functions of the battery variation power, it was considered a positive big variation power (PB) from 145 kW (maximum output FC power), since when the demanded power by the electric vehicle is higher than 145 kW, the battery begins to work. On the contrary, the battery power variation is completely negative big (NB) from 150 kW, and thus, the SC only absorbs the power that the battery is not able to store due to its dynamic limitation. According to Table 2 and membership functions, it can be seen that, during a high acceleration or high demand power (PB), the FC has to generate its maximum power, except in cases where the battery has a high SOC. In these cases, in order to save hydrogen, the FC works at its maximum efficiency, while the battery generates the rest of the required load. The battery power variation is negative big (NB) or negative small (NS) when the battery SOC is low (S), the SC SOC is high (B), and the vehicle is decelerating or braking (Pload = N or Pload = NS). Moreover, if the demanded power is small (PS), for example, when the vehicle is stopped or when it is main-

taining a constant low speed, the FC is used as primary energy source. In these cases, if necessary, the battery may be charged by the FC power, or discharged if the FC generates its minimum power. It can be observed that the membership function of the demanded power PS has a similar range of power than the membership function N of the FC power, which ensures that the FC generates the demanded power within its limits. 4.2.4. Predictive control To use this control, a linear time-invariant model of the vehicle must be designed. The linear plant designed for this vehicle was obtained by using the Control Design ToolboxÒ. Furthermore, the input and output constrains were defined in this control according to the parameters showed in Table 1. The following control parameters were selected in this control strategy: (1) battery SOC must be kept between 50% and 80% of capacity; and (2) allowable SC SOC range is between 60% and 90%.

5. Simulation results and discussion The five control strategies applied to the urban street railway under study, powered by FC, battery and SC, are assessed for the real drive cycle of the real railway by simulation in MATLABSimulinkÒ. The railway was simulated for a large route, which consists of 8 round-trip routes with stops of 15 s among them. This large route lasts about an hour. However, to check the right performance of the control strategies, the FC, battery, SC, braking resistor and total generated power were represented only for a round-trip route (376 s), as shown in Figs. 15 and 16. The following figures show the results obtained in the simulations, where the control strategies are denoted as follows: OMC (operation mode control); CC (cascade control); ECMS (Equivalent consumption minimization strategy); FLC (fuzzy logic based control) and MPC (model predictive control). Fig. 15 shows the generated power by the energy sources of the railway (FC, battery and SC), and Fig. 16 depicts the power dissipated in the braking resistor and the total power generated with each control.

P. García et al. / Expert Systems with Applications 40 (2013) 4791–4804

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Fig. 17. FC, battery, SC and dc bus voltage during eight round-trip routes.

Fig. 15. (a) FC power; (b) battery power; and (c) SC power during a round-trip route.

Fig. 18. Battery and SC SOC during eight round-trip routes.

Fig. 16. (a) Braking resistor power; and (b) total power of the railway during a round-trip route.

In general, it can be observed that all the controls achieve similar results, so that all of them make the FC and battery operate between their operation limits. Thus, during accelerations, the FC increases the generated power, and during brakings or decelerations, the controls decrease the FC power. The most significant differences appear in the minimum power generated by the FC with min the OMC, since it is Peff fc instead of P fc , which is generated by the rest of control strategies. Otherwise, Fig. 16(a) shows that the power dissipated in the braking resistor is minimal. In fact, the highest peaks in the dissipated power appear only at the end of the drive cycling, where the demanded load changes abruptly. Fig. 16(b) depicts that all the controls present the same total power of the railway (traction and auxiliary services) during the round-trip route.

The voltages of the FC, battery, SC and DC bus during eight round-trip routes are shown in Fig. 17. As observed, all the controls achieve similar results and the SC converter control allows keeping the DC bus voltage around the desirable value (750 V). Otherwise, the FC voltage changes depending on its power and the battery and SC voltages change depending on their SOC. All the controls keep the battery and SC SOCs around the desirable values, with very similar values at the end of the simulation, as seen in Fig. 18. Table 3 shows a summary of the results obtained by each control during the simulation. The parameters included in this table and used in the comparative study are: FC hydrogen mass consumption, (MH2), equivalent hydrogen mass consumption of the railway (M), battery (Mbat) and SC (MSC); FC system and hybrid vehicle (HV) average efficiency (gFCS,avg and gHV,avg); maximum and minimum battery and SC SOCs (SOCbat,max, SOCbat,min, SOCSC,max, SOCSC,min); average value of the battery and SC SOCs (SOCbat,avg and SOCSC,avg); maximum power and total energy dissipated in the braking resistor (Pbr,max and Ebr,max) and computation time (TC). In Table 3, the best value achieved is denoted in bold. Furthermore, the difference (in percent) of each parameter and strategy with respect to the best value of the parameter is also shown in the table. In the case of the average value of the battery and SC SOCs, the difference in percent is calculated with respect to the reference SOC, 65% and 75%, respectively.

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Table 3 Summary of results obtained by each control strategy during eight round-trip routes. Parameter

OMC

CC

FC hydrogen mass consumption, MH2 (kg) Eq. hydrogen mass consump. of the hybrid powertrain, M (kg) Battery equivalent hydrogen mass consumption, Mbat (kg) SC equivalent hydrogen mass consumption, Msc (kg) HV efficiency, avg, gHV, avg (%) FC efficiency, avg, gHV, avg (%) SOCbat max (%) SOCbat min (%) SOCbat avg (%) SOCsc max (%) SOCsc min (%) SOCsc avg (%) Energy dissipation, Ebr,max (kWh) Max power dissipation, Pbr,max (kW) Tc (s)

3.90 36.55

2.0% 534.5%

4.01 15.34

4.8% 166.3%

3.82 5.76

0.0% 0.0%

3.92 17.67

2.6% 206.8%

3.89 15.37

1.8% 166.8%

5.96E02

40.7%

4.24E02

0.0%

5.12E02

20.8%

4.66E02

10.0%

4.40E02

3.7%

1.50E03 53.20% 60.65% 65.03% 62.31% 64.01% 84.34% 64.64% 75.26% 0.14 282.65 1052

152.6% 0.0% 4.5% 0.0% 1.9% 1.5% 4.7% 8.6% 0.3% 627.2% 11.9% 35.4%

5.95E04 51.47% 59.42% 65.29% 63.53% 64.75% 80.53% 70.71% 75.42% 0.02 270.50 1443

0.0% 3.3% 6.4% 0.4% 0.0% 0.4% 0.0% 0.0% 0.6% 0.0% 7.1% 85.7%

8.59E04 52.64% 60.20% 65.07% 63.22% 64.61% 81.07% 64.27% 75.42% 0.21 275.37 777

44.4% 1.1% 5.2% 0.1% 0.5% 0.6% 0.7% 9.1% 0.6% 1010.4% 9.0% 0.0%

1.53E03 52.86% 63.51% 65.29% 63.33% 64.53% 83.67% 64.32% 73.13% 0.36 282.59 7732

156.6% 0.6% 0.0% 0.4% 0.3% 0.7% 3.9% 9.0% 2.5% 1806.1% 11.8% 895.1%

9.88E04 53.12% 62.13% 65.14% 63.39% 64.67% 83.70% 68.71% 75.31% 0.56 252.66 3492

66.1% 0.2% 2.2% 0.2% 0.2% 0.5% 3.9% 2.8% 0.4% 2860.5% 0.0% 349.4%

The FC hydrogen consumption and energy efficiencies are calculated from the following expressions (Brandon et al., 2006):

M H2 ¼

Z

1 Elow;H2

PH2 dt

ð14Þ

cycle

R

gHV ¼ R cycle

P H2 dt þ

R

gFCS ¼ R cycle cycle

cycle

R cycle

P HV dt R

Pbat dt þ

cycle

Psc dt þ

R cycle

Pfc dt P H2 dt

Pbr dt

ð15Þ

ð16Þ

where Elow,H2 is the lower heating value of hydrogen (Elow,H2 = 120 MJ/kg); PHV is the traction power of the HV; Pbr is the power dissipated in the braking chopper; and PH2 is the theoretical power associated with the hydrogen flow consumption in the FC, calculated as follows:

P fc þ Paux;fc

PH2 ¼

gt henm  gutil  gfci

ð17Þ

where Paux,fc is the power demanded by the auxiliary components of the FC; gtherm is the thermodynamic efficiency (0.98 at 298 K); gutil is the fuel utilization efficiency, defined as a ratio between the mass of fuel that reacted in the FC and the mass of fuel entering the FC; and gfci is the individual efficiency of the FC, calculated as the ratio between the FC voltage, Vcell, and the standard state reversible voltage, E0cell . Moreover, the equivalent hydrogen mass consumptions (M, Mbat, and MSC) are calculated by integrating the equivalent hydrogen consumptions (C, Cbat and CSC).

M¼

Z

Cdt

ð18Þ

cycle

M bat ¼

Z

C bat dt

ð19Þ

C SC dt

ð20Þ

cycle

M SC ¼

Z cycle

where CSC is calculated in similar way to Cbat:

C SC ¼ Psc

C FC;av g PFC;av g

ECMS

FLC

MPC

hydrogen mass consumption during the eight round-trip routes is 3.82 kg with ECMS control, a 4.8% better than the highest consumption achieved by the OMC. However, the differences in the equivalent hydrogen mass consumption of the hybrid powertrain are more significant, since the equivalent hydrogen consumption with ECMS control is 5.76 kg, a 534.5% better than the worst consumption achieved by the OMC with 36.55 kg. Otherwise, similar efficiencies are achieved by the controls. The difference obtained between the best and the worst HV efficiency is 3.3%, with average value around 53%. In the case of the FC efficiency, the average value in all the controls is around 62% with a maximum difference of 6.4% between the ECMS control and the cascade control. Comparing the computational time required to simulate eight round-trip routes with each control, it can be observed that the ECMS, OMC and cascade control are the simplest controls to implement. If these control strategies are applied to another electric vehicle, only a few changes are required. The cascade control has the advantage that it only requires tuning the controllers. In case of the ECMS, it is necessary to redefine the minimization problem and calculate the new solution, since the hydrogen consumption depends on the FC used in the electric vehicle. In the OMC, the hysteresis cycles and the operation modes must be revised. Otherwise, the fuzzy logic and predictive controls require significant changes. For the fuzzy logic control, the logic rules as well as the membership functions must be changed to adapt the control to the new application or driving cycle. Furthermore, the logic rules and membership functions must be designed taking into account the new driving cycle. In the MPC, the linear time-invariant model of the plant must be recalculated and the input and output constrains must be also adjusted. Thus, the computation time with fuzzy logic control and MPC is clearly higher than the rest of controls, since the control scheme is more complex. In fact, the fuzzy logic control (MPC) presents a computation time 9 (3.5) times higher than that achieved by the ECMS. Finally, it can be concluded that all the control strategies presented in this paper allows a suitable operation of the electric vehicle. However, the ECMS can be considered as the most suitable control strategy, since it achieves a good balance among the hydrogen mass consumption, efficiency, adaptability and computational time.

ð21Þ

In general, similar results are obtained by all the controls, as seen in Table 3. However, as expected, the best results related to the hydrogen consumption are obtained by the ECMS control. In fact, the

6. Conclusions This paper has presented five control strategies that can be used for the energy management system of high-power electric vehicles

P. García et al. / Expert Systems with Applications 40 (2013) 4791–4804

powered by FC, battery and SC. Each of the five control strategies presented in this work determines, in different ways, the FC and battery reference powers, while the SC is used to control the DC bus voltage. The operation mode control is based on operation modes of the electric vehicle. The cascade control uses cascade control loops in order to generate the reference powers. The third one is based on the concept of equivalent consumption minimization strategy (ECMS). Finally, the fourth and fifth control strategies generate the reference powers from fuzzy logic and predictive control. These control strategies were characterized, tested and compared applied to a real urban street railway, Urbos 3, with a rated traction power of 400 kW. The railway propulsion system is composed by a 150 kW PEM-FC, a 90 Ah Li-ion battery and a SC bank with a total capacity of 12.6 F. Simulations were performed for the real driving cycle of this railway. The simulation results reflect the optimal performance of the presented control strategies. However, the following conclusions can be derived from the comparison:  In general, the final results achieved by all the controls are very similar, however, as expected, the ECMS achieves the lowest hydrogen consumption and equivalent hydrogen mass consumption of the powertrain.  The complexity of each control influences highly the computational time. In fact, the computational times of the fuzzy logic and predictive controls are much higher than the other controls.  The ECMS, OMC and cascade controls are the simplest control to implement, so that only a few changes are required if they have to be applied to another electric vehicle. However, the fuzzy logic and predictive controls are more complex and require significant changes in order to be applied to another electric vehicle. In conclusion, among all the compared controls, the ECMS control can be considered as the most suitable control strategy to be used in high-power hybrid electric vehicles. This control minimizes the hydrogen consumption and the equivalent consumption, which allows reducing the hydrogen volume to be stored in the electric vehicle, and thus the associated weight. Furthermore, its simplicity of implementation makes this control feasible to use in other hybrid electric vehicles. Acknowledgments This research was funded by Hynergreen Technologies S.A. and the CENIT Program from the Center for the Development of Industrial Technology (an agency of the Spanish Ministry of Science and Technology), under the ecoTRANS research project. A National Industries Consortium, led by CAF (Construcciones y Auxiliar de Ferrocarriles), in which Hynergreen Technologies is a member, is currently working on this project to develop ecological technologies for urban transport. References Amjad, S., Neelakrishnan, S., & Rudramoorthy, R. (2010). Review of design considerations and technological challenges for successful development and deployment of plug-in hybrid electric vehicles. Renewable and Sustainable Energy Reviews, 14, 2874–2884. Arce, A., del Real, A. J., & Bordons, C. (2009). MPC for battery/fuel cell hybrid vehicles including fuel cell dynamics and battery performance improvement. Journal of Process Control, 19, 1289–1304. Ayad, M. Y., Becherif, M., & Henni, A. (2011). Vehicle hybridization with fuel cell, supercapacitors and batteries by sliding mode control. Renewable Energy, 36, 2627–2634. Ballard. (2013). Ballard fuel cell power, FC velocity-HD6. Available from: http:// www.ballard.com/files/PDF/Bus/HD6.pdf.

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