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A hierarchical energy management strategy for fuel cell/battery/supercapacitor hybrid electric vehicles Zhumu Fu a,b,**, Zhenhui Li b, Pengju Si a,b, Fazhan Tao a,b,* a

Henan Key Laboratory of Robot and Intelligent Systems, Henan University of Science and Technology, Luoyang, 471023, China b School of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China

highlights Reduce stress on battery and fuel cell to lift power performances and fuel economy. Adaptive low-pass filter to allocate peak power and braking energy to supercapacitor. Minimizing hydrogen consumption while improving the working efficiency of fuel cell.

article info

abstract

Article history:

In this paper, a hierarchical energy management strategy (EMS) based on low-pass filter

Received 22 April 2019

and equivalent consumption minimization strategy (ECMS) is proposed in order to lift

Received in revised form

energy sources lifespan, power performance and fuel economy for hybrid electrical vehi-

11 June 2019

cles equipped with fuel cell, battery and supercapacitor. As for the considered powertrain

Accepted 24 June 2019

configuration, fuel cell serves as main energy source, and battery and supercapacitor are

Available online xxx

regarded as energy support and storage system. Supercapacitor with high power density and dynamic response acts during great power fluctuations, which relives stress on fuel

Keywords:

cell and battery. Meanwhile, battery is used to lift the economy of hydrogen fuel. In higher

Hierarchical energy management

layer strategy of the proposed EMS, supercapacitor is employed to supply peak power and

strategy

recycle braking energy by using the adaptive low-pass filter method. Meantime, an ECMS is

Equivalent consumption

designed to allocate power of fuel cell and battery such that fuel cell can work in a high

minimization

efficient range to minimize hydrogen consumption in lower layer. The proposed EMS for

Low-pass filter

hybrid electrical vehicles is modeled and verified by advisor-simulink and experiment

Hybrid electrical vehicles

bench. Simulation and experiment results are given to confirm effectiveness of the pro-

Fuel cell

posed EMS of this paper. © 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Introduction Fuel cell vehicles are regarded as one of the most promising new energy vehicles in future transportation system thanks to

merits of no emission, low noise and high efficiency [1e4]. However, low dynamics and power density characteristics of fuel cell lead to that the fuel cell vehicles are nowhere near the real-time and dynamic performance of conventional vehicles

* Corresponding author. ** Corresponding author. E-mail addresses: [email protected] (Z. Fu), [email protected] (F. Tao). https://doi.org/10.1016/j.ijhydene.2019.06.158 0360-3199/© 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Please cite this article as: Fu Z et al., A hierarchical energy management strategy for fuel cell/battery/supercapacitor hybrid electric vehicles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.06.158

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Nomenclature EMS UDDS HWFET EUDC ECE FCHEV SOC Fm m r Af CD Fr g v q Pdem hmotor hu hbi Pfc Pb Psc mH2 MH2 Ifc F PH2 hfc SOCb SOC0b Emax b

Energy management strategy Urban dynamometer driving schedule Highway fuel economy test Extra urban driving cycle Economic commission for europe Fuel cell hybrid electric vehicles State of charge Traction force on electric motor Vehicle mass Air density Vehicle frontal area Drag coefficient Rolling resistance coefficient Gravity acceleration Vehicle speed Road slope Demand power Efficiency of electric motor Efficiency of the unidirectional DC/DC converter Efficiency of the bidirectional DC/DC converter Fuel cell power Battery power Supercapacitor power Vehicle speed Road slope Fuel cell current Faraday constant Energy released by the complete reaction of hydrogen Efficiency of fuel cell system Battery SOC Initial battery SOC Battery nominal capacity

[5]. Therefore, auxiliary energy storage devices such as supercapacitor and battery are employed to improve dynamics performance and efficiency of fuel cell, and to recycle the energy of braking and deceleration, which have attracted much attentions of scholars and researchers [6e8]. EMS plays an important role in refining performance for hybrid electrical vehicles equipped with fuel cell, battery and supercapacitor. A high-quality EMS cannot only extend the lifespan of energy sources but also lift the economy of hydrogen. In terms of EMS of fuel cell hybrid electric vehicle(FCHEV), there exist a lot of contributions and remarkable works on EMS in the field of FCHEV [9e11], which can be classified as two types: the rules-based EMS and optimizationbased EMS. The rules-based EMS [12] mainly consists of state machine control strategy [13], stiffness coefficient model control strategy [14], operation mode control strategy [15] and fuzzy logic control strategy [16]. These EMS aren't dependent on precise performance of the system models and have strong robustness and deduction, which can effectively allocate power of energy sources in a simple and easy way. The rulesbased EMS merits lies in classification of operating models, engineering experiences of engineers and static energy

Ib hb SOCsc Esc Esc nor C Vsc Vsc max t SOCref sc fs Ts Csc SOClsc SOCrsc CM Cfc Cb Csc kb ksc LHVH2 hfc hdis hchg hdis avg hchg avg SOCmin b SOCmax b Pmin b Pmax b Pmin fc Pmax fc DPfc

Battery current charge and discharge Efficiency of battery Supercapacitor SOC Energy stored in supercapacitor Normal energy stored in supercapacitor Ideal capacitor value Voltage of supercapacitor Maximum voltage of supercapacitor Time constant Reference supercapacitor SOC Regulatory factor Sample time Normal capacity Lower limit of supercapacitor SOC Energy room of supercapacitor SOC Whole hydrogen consumption fuel cell hydrogen consumption Battery hydrogen consumption Supercapacitor hydrogen consumption Battery regulatory factor Supercapacitor regulatory factor Low heat value of hydrogen Efficiency of fuel cell Discharging efficiency of battery Charging efficiency of battery Average discharging efficiency of battery Average charging efficiency of battery Lower limit of battery SOC Upper limit of battery SOC Lower limit of battery power Upper limit of battery power Lower limit of Fuel cell power Upper limit of Fuel cell power Fluctuation slope of fuel cell power

efficiency, however, the global optimal performance of EMS is difficult to obtain by this strategy [17]. For solving this issue, a lot of studies focus on and explore the optimization-based EMS. Optimization-based EMS includes global optimal strategies and real-time optimal strategies [18,19], which has been studied by researchers and engineers. Dynamic programming and genetic algorithm are the most effective strategies to solve global optimization problem, which require drive conditions are known. It is difficult to apply these strategies on improving economy of hydrogen fuel. However, equivalent consumption minimization strategy can transform global optimization problem into instantaneous ones, instantaneously split power of energy sources. In Ref. [20], the high efficient range of energy sources of FCHEV is defined by analysing their working principle and characteristics, which is used to adjust weight coefficient of energy sources using the sequential quadratic programming method. The proposed method in Ref. [20] considers the equivalent hydrogen consumption of supercapacitor and battery. In Refs. [21,22], the optimal equivalent factor of battery is designed according to the vehicle configuration and independent of the drive cycle, which can adaptive adjust the parameters of equivalent

Please cite this article as: Fu Z et al., A hierarchical energy management strategy for fuel cell/battery/supercapacitor hybrid electric vehicles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.06.158

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consumption minimization strategy to lift economy of fuel. Considering that supercapacitor has low energy density, the proposed EMS in Refs. [23,24,34]minimize hydrogen consumption of battery and fuel cell within the flow of power and energy constraint conditions of energy sources by ignoring equivalent hydrogen consumption of supercapacitor, which reduces difficulty in designing EMS. The proposed methods of the mentioned papers [23,24,34]merely consider supercapacitor passively generates or absorbs the power that fuel cell and battery cannot supply. Supercapacitor is not fully utilized for real time EMS of FCHEV. In Ref. [25], a novel energy optimization approach for electrical vehicles in smart city is considered, which exposes a new approach to power management. In Ref. [27], functional components, supplies management, packaging technology and application are discussed and illustrated for micro direct methanol fuel cell. In Refs. [28e30], energy management of hybrid power sources comprising fuel cell/battery/supercapacitor using MPC is successfully investigated, which can maintain the DC bus voltage according to the reference value as well as limit the battery and fuel cell currents slope. The authors [31] propose an intelligent power management algorithm to guarantee the best process of power extraction and injection based on fuzzy technology. The contribution and motivation of this paper compared with [23,24,34] are summarized as follows: 1) Considering high power density of supercapacitor and high energy density of fuel cell and battery, a hierarchical energy management strategy is proposed to reduce stress on battery and fuel cell, to lift power performance and fuel economy of vehicles. The proposed strategy can reduce the difficulties in utilizing supercapacitor compared with [23,24,34]. 2) By utilizing characteristics of supercapacitor, the higher layer strategy based on the adaptive low-pass filter method is designed to allocate peak power and braking energy to supercapacitor. Meanwhile, different value of supercapacitor SOC and its reference value is employed to adjust the time constant of low-pass, which can maintain supercapacitor SOC in a proper range, which extends the application of [34]. 3) Smooth and stable part of demand power is supplied by fuel cell and battery. In order to lift the economy of hydrogen fuel, battery is used to improve the working efficiency of fuel cell by minimizing hydrogen consumption, including the hydrogen consumption of fuel cell and equivalent hydrogen consumption of battery (see Fig. 1).

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Vehicle models. In Section Energy management strategy, main results including the low-pass filter design, adaptive equivalent consumption minimization strategy is addressed to optimize output power of fuel cell, battery and supercapacitor. Simulation and experiment results are shown in Section Simulation results and Experimental validation, respectively, to confirm effectiveness of the proposed design scheme. Finally, Section Simulation results presents the conclusions of this paper.

Vehicle models Longitudinal dynamics model of the vehicle The model for FCHEV is shown in Fig. 2. Fuel cell as the main energy source is connected to DC bus via a unidirectional DC/ DC converter to supply steady power. Battery as the buffer energy source is directly connected to the DC bus to lift the work efficiency and dynamic characteristic of fuel cell. Supercapacitor serves as the fast energy source connected to the DC bus through a bidirectional DC/DC converter to provide peak power, which relives the stress on battery and fuel cell. Considering force from acceleration, drag, friction, and slope of vehicles, the traction force on electric motor under the vehicle speed v and road slope q [25,26] can be described as follows: _ þ 0:5rAf CD $vðtÞ2 þ mgcosðqÞf r þ mgsinðqÞ Fm ðtÞ ¼ mvðtÞ

(1)

where Fm is the traction force on electric motor, m is vehicle mass, r is air density, Af is vehicle frontal area, CD is drag coefficient, f r is rolling resistance coefficient, g is gravity acceleration, v is vehicle speed, q is road slope. The demand power of electric motor supplied by fuel cell, battery and supercapacitor can be calculated according to the longitudinal dynamics equation of the vehicle Eq. (1), which is shown as Eq. (2) and Eq. (3). Pdem ¼

Fm $v hmotor

Pdem ¼ Pfc $hu þ Pb þ Psc $hbi

(2)

(3)

where Pdem is vehicle demand power, hmotor is the efficiency of electric motor, hu is the efficiency of the unidirectional DC/DC converter connect to fuel cell, hbi is the efficiency of the bidirectional DC/DC converter, Pfc , Pb , Psc is the power of fuel cell, battery and supercapacitor, respectively.

Fuel cell model This paper has the following configuration. The models of the main components of the fuel cell-battery-supercapacitor powered hybrid system for vehicle are given in Section

Fuel cell as the main energy source supplies steady power for FCHEV through converting the chemical energy of hydrogen and oxygen to electrical energy. The hydrogen consumption rate of fuel cell is related to its output current [23], which can be expressed as follows. mH2 ¼

Fig. 1 e Hierarchical energy management strategy.

MH2 $Ifc 2F

(4)

where mH2 is hydrogen flow rates, F is the faraday constant, MH2 is hydrogen molar mass, Ifc is fuel cell current through the oxidizing reaction between hydrogen and oxygen.

Please cite this article as: Fu Z et al., A hierarchical energy management strategy for fuel cell/battery/supercapacitor hybrid electric vehicles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.06.158

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Fig. 2 e Topology structure of FCHEV.

In order to guarantee normal operation of fuel cell system, some auxiliary deices are provided, such as humidifier, cooling fan and air compressor. Considering that conversion energy loss of fuel cell system is inevitable, efficiency of the fuel cell system is shown in Eq. (5). hfc ¼

Pfc PH2

should be noticed that the energy stored in battery is from fuel cell system. Therefore, the energy stored in battery should maintain in a safe range. The SOC is defined as the ratio between the stored charge and the maximum charge capacity of battery or supercapacitor [11,13], which can be described as follows.

(5)

where hfc is the efficiency of fuel cell system, PH2 is the energy released by the complete reaction of hydrogen. The relationship of efficiency and power of fuel cell system employed in this paper can be obtained from Eq. (5) as shown in Fig. 3. It can be observed that the efficiency of fuel cell is low as its power in extremely low or high. In order to lift the work efficiency of fuel cell and reduce the hydrogen consumption, fuel cell is supposed to work in high efficiency area.

Battery model Battery as the buffer energy source is to lift the efficiency of fuel cell system and keep supercapacitor working properly. It

Z SOCb ðtÞ ¼ SOC0b

hb $

t

Ib ðtÞ$dt

0

Emax b

(6)

where SOCb is battery SOC, SOC0b is initial battery SOC, Emax b represents the battery nominal capacity, Ib is battery current, hb is charge and discharge efficiency.

Supercapacitor model Supercapacitor exists high power density, almost unlimited lifetime, fast charging and discharging. These existing characteristics indicate supercapacitor can discharge or charge to relive the stress on fuel cell and battery in large power fluctuations or starting vehicle. For developing the low-pass filter method, the SOC of supercapacitor [32,33] can be obtained as follows. SOCsc ¼

Esc 0:5$CV2sc V2sc ¼ ¼ Esc nor 0:5$CV2sc max V2sc max

(7)

where SOCsc is the supercapacitor SOC, Esc is the energy stored in supercapacitor, Esc nor is the normal energy stored in supercapacitor, C is ideal capacitor value, Vsc is the voltage of supercapacitor, Vsc max is the maximum voltage of supercapacitor. Based on the above models an analysis, function of different energy sources can be redefined by considering their operating characteristics. Further, we propose a hierarchical energy management strategy for FCHEV to improve lifespan of energy source and economy of hydrogen fuel in the next section.

Energy management strategy Fig. 3 e Relationship between efficiency and power of fuel cell system.

In this section, a hierarchical energy management strategy based on the low-pass filter and equivalent consumption

Please cite this article as: Fu Z et al., A hierarchical energy management strategy for fuel cell/battery/supercapacitor hybrid electric vehicles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.06.158

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minimization methods is proposed. The detailed explanations on the hierarchical energy management strategy proposed in this paper is shown in Fig. 4. The proposed strategy in this paper is divided into two layers. As for the higher layer strategy as shown in Fig. 4, considering high power density of supercapacitor, an adaptive low-pass filter method is proposed to adjust supercapacitor SOC to supply peak power and recycle braking energy. In terms of lower layer strategy as shown in Fig. 4, an equivalent consumption minimization strategy for fuel cell and battery is considered to meet the residual demand power of the whole vehicle such that fuel cell works in high efficient range to minimize fuel consumption.

Higher layer strategy The aim of higher layer of the proposed EMS is that supercapacitor can forwardly supply the peak power and recycle braking energy by the low-pass filter, which can ensure power performance of FCHEV and relive stress on fuel cell and battery. As for the low-pass filter, its time constant is regulated by the error of supercapacitor SOC value and the given reference value. When supercapacitor SOC is far from its reference value, the time constant becomes smaller, weakening the filtering ability of the low-pass filter, and fuel cell and battery

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charge supercapacitor. Vice versa. Transfer function of the low-pass filter in this paper is chosen as follows. GðsÞ ¼

1 ts þ 1

(8)

where t is the time constant of the low-pass filter. The time constant is related to the supercapacitor SOC and its reference value, which is expressed as follows: t ¼ SOCsc SOCref sc $fs

(9)

where SOCref sc is the reference supercapacitor SOC, f s is regulatory factor. As for the chosen low-pass filter, the reference SOC of supercapacitor is supposed to remain in a proper range, which is satisfied with requirement of acceleration or deceleration. In other word, energy of supercapacitor is enough to supply the peak power for reliving the stress on fuel cell and battery during acceleration. Meanwhile, when decelerating, the braking energy can be recycled by the supercapacitor. The power supplied by supercapacitor is the difference value between the demand power and the power of fuel cell and battery. The reference supercapacitor SOC in next moment can be calculated according to the supercapacitor SOC in this moment, which is shown in Eq. (10).

Fig. 4 e Hierarchical energy management strategy of this paper.

Please cite this article as: Fu Z et al., A hierarchical energy management strategy for fuel cell/battery/supercapacitor hybrid electric vehicles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.06.158

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Z SOCref sc ðt þ Ts Þ ¼ SOCsc ðtÞ

Ts

Table 2 e Supercapacitor braking energy room in different speed.

Pdem Pb Pfc $dt

0

(10)

Csc

where Ts is the sample time, Csc is the normal capacity. In addition, in order to relive stress on fuel cell and battery, energy stored in supercapacitor is higher than energy that can accelerate to the maximum speed from the current speed after the low-pass filter. Therefore, under the environment of matlab/simulink and advisor, assume that fuel cell and battery work in optimal state, the minimum energy that accelerates to the maximum speed from the current speed can be calculated. Herein, the minimum energy that accelerates to 70 mph from 10 mph, 20 mph, 30 mph, 40 mph, 50 mph and 60 mph can be calculated in Table 1. It can be observed that the supercapacitor SOC fluctuate more at high speeds, meanwhile, the minimum SOC fitting function related to the speed can be approximated by the following polynomial: SOClsc ¼ 2:161e 7$v3 1:174e 4$v2 7:694e 5$v þ 0:9953 (11) SOClsc

where is the lower limit of supercapacitor SOC. Similarly, the supercapacitor need recycle the braking energy, which can improve the efficiency of energy utilization and avoid high currents to shorten battery lifespan. Therefore, under the environment of simulink and advisor, the braking energies from 10 mph, 20 mph, 30 mph, 40 mph, 50 mph and 60 mph are calculated, which are shown in Table 2. The braking energy as speed varies can be approximated by the following polynomial: SOCrsc ¼ 3:442e 7$v3 þ 1:152e 5$v2 1:037e 4$v 6:832e 5 (12) SOCrsc

where is the energy room of supercapacitor SOC. Therefore, by combining the obtained SOClsc and SOCrsc , the reference SOC of supercapacitor can be obtained as follows, which is supposed to be higher than the minimum value and be lower than the maximum value. ref l r SOCref sc ¼ min max SOCsc ; SOCsc ; 1 SOCsc

(13)

In the higher layer strategy, the fluctuating portion of demand power is distributed to the supercapacitor by the lowpass filter, which can improve the power performance of

Speed(mph) 0 1 10 20 30 40 50 60

The supercapacitor SOC() 0 0.0009 0.0108 0.0391 0.0607 0.0864 0.1187 0.1595

vehicle and relive the stress on fuel cell and battery. The equivalent consumption minimization strategy for the lower layer EMS is addressed in next subsection.

Lower layer strategy The lower layer strategy can allocate the output power of fuel cell and battery by using the equivalent consumption minimization strategy, which improve the economy of hydrogen fuel and the lifespan of fuel cell and battery. For the hybrid electrical vehicle herein, the energy stored in battery and supercapacitor is supplied by fuel cell, in other words, batteries and supercapacitors cannot be externally charged. Based on the equivalent consumption minimization strategy, energy consumption of batteries and supercapacitor is transformed to hydrogen consumption to minimize hydrogen consumption through optimizing the power of fuel cell and battery. Hence, the instantaneous hydrogen consumption composes of direct hydrogen consumption from fuel cell system and indirect equivalent hydrogen consumption from battery and supercapacitor as shown in Eq. (14). Cm ¼ min Cfc þ kb $Cb þ ksc $Csc

(14)

where Cm is the whole hydrogen consumption, Cb and Csc are battery and supercapacitor equivalent factor for equivalent hydrogen consumption, respectively, Cfc is fuel cell hydrogen consumption, kb and ksc is battery and supercapacitor equivalent hydrogen consumption, respectively. As for Eq. (14), since supercapacitor mainly generates the power peaks, and its energy density is low, Csc can be neglected compared with Cb or Cfc . Therefore, Eq. (14) can be simplified as follows: Cm ¼ min Cfc þ kb $Cb

(15)

Cfc is related to the output power and efficiency of fuel cell, which can expressed as follow: Table 1 e Supercapacitor minimum energy in different speed. Speed(mph) 0 1 10 20 30 40 50 60

Cfc ¼

The supercapacitor SOC() 1 0.9951 0.9825 0.9451 0.8851 0.7906 0.6709 0.5214

Pfc LHVH2 $hfc

(16)

where LHVH2 represents the low heat value of hydrogen, that is the conversion relationship between hydrogen mass and energy, hfc is the efficiency of fuel cell.Cb can be obtained as follows: Cb ¼

s$Pb $Cfc avg s$Pb ¼ Pfc avg LHVH2 $hfc

(17)

Please cite this article as: Fu Z et al., A hierarchical energy management strategy for fuel cell/battery/supercapacitor hybrid electric vehicles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.06.158

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where Cfc avg is the average value of Cfc , Pfc avg is the average value of Pfc , s is the equivalent factor, which is related to the charging and discharging as follows:

s¼

8 > > > > <

hchg > > > > : hdis

1 avg $hdis

Pb 0

(18)

avg $hchg Pb < 0

where hdis and hchg is the discharging and charging efficiency of battery, hdis avg and hchg avg is the average discharging and charging efficiency of battery. Considering energy stored in battery is supplied by fuel cell, the battery SOC should maintain in a proper range. Herein, Eq. (19) is used to adjust the equivalent factor as follow: SOC 0:5$ SOCmin þ SOCmax b b kb ¼ 1 m 0:5$ SOCmin þ SOCmax b b

(19)

where the constant m is adjusted to properly reflect the battery charge and discharge processes. It is chosen to balance is the upper limit of the battery SOC during the cycle. SOCmax b is the lower limit of SOC (80%, selected in this work), SOCmin b SOC (40%).

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Meanwhile, constraints are applied in order to satisfy the physical limit of fuel cell and battery during the process of optimization, which can be expressed as follows: 8 > > > > SOCmin SOCb SOCmax > b b > > > min > max > P P P > b b < b Pmin Pfc Pmax > fc fc > > > > > > d P fc > > DPfc DPfc > > : dt

(20)

and Pmin is upper and lower bounds of battery where Pmax b b max is upper and lower bounds of fuel cell power, Pfc and Pmin fc power, DPfc is the fluctuation slope of fuel cell power. Eq. (20) is applied to further assign the power of three energy source in order to lift their economy and lifespan. For fuel cell, the electric energy is generated after the chemical reaction of hydrogen and oxygen. Fuel starvation effect that the fuel cell current lags behind the flow rate of hydrogen is obvious in high power fluctuation. Hence, DPfc should be prudently selected for lifting the durability of fuel cell. Beand Pmax sides, Pfc should operate between Pmin fc fc . For battery, the energy loss is large in extremely high and low SOC than that in proper SOC. Therefore, Eq. (19) is used to adjust the

Fig. 5 e Configuration profiles of the road condition (a) Speed, (b) Demand power.

Please cite this article as: Fu Z et al., A hierarchical energy management strategy for fuel cell/battery/supercapacitor hybrid electric vehicles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.06.158

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Table 3 e Parameter configuration of vehicle and energy source. Parameter Vehicle mass (kg) Air density(kg =m3 ) Frontal area(m2 ) Drag coefficient() Rolling resistance coefficient () Fuel cell rate power (kw)

Value

Parameter

Value

1191 1.2 2 0.335 0.6

Batteries rate energy (kw.h) Batteries rate power (kw) Battery init SOC() SCs rate energy (w.h) SCs rate power (kw)

9.25 20 0.7 350 70

30

SCs init SOC()

1

equivalent factor to maintain SOCb in range from SOCmin b to SOCmax . b

Simulation results In this section, simulation results are given based on the proposed EMS of this paper, meanwhile, the comparison results using global equivalent consumption minimization strategy(GECM) in Ref. [34] are shown for confirming effectiveness under four road conditions including UDDS, HWFET, EUDC and ECE. These road conditions represent the most

Fig. 6 e Simulation results of EMS proposed in this paper (a) Output power of fuel cell, (b) Output power of battery, (c) Output power of supercapacitor. Please cite this article as: Fu Z et al., A hierarchical energy management strategy for fuel cell/battery/supercapacitor hybrid electric vehicles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.06.158

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common road cycles, and their configuration files including speed and demand power are shown in Fig. 5. Table 3 shows the parameter configuration of vehicle and energy source. Simulation results are shown in Figs. 6e8. Under the action of the EMS proposed in this paper, the fuel cell, battery and supercapacitor powers during four road conditions are shown in Fig. 6. With regards to the supercapacitor power, it can be observed that supercapacitor work during acceleration or deceleration. When vehicle accelerates in 200s, 1650s, 2100s and 2400s, supercapacitor can supply peak power and recycle braking energy rapidly, which can relive the stress on fuel cell

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and battery. Besides, the energy stored in supercapacitor is shown in Fig. 8(a). When the speed of vehicle is low, supercapacitor SOC is higher to supply the energy of acceleration, which guarantees the dynamic performance of vehicle, Vice versa. On the contrary, fuel cell and battery supply the smooth and steady power, battery is used to lift the work efficiency of fuel cell. Fuel cell power increases and decreases within the dynamic limitations from Fig. 6(a), which extend the lifespan of fuel cell. Battery only works to lift the economy of hydrogen fuel by using ECMS. However, the fuel cell and battery power under GECMS has greater power fluctuations, which is bad for

Fig. 7 e Simulation results of GECMS (a) Output power of fuel cell, (b) Output power of battery, (c) Output power of supercapacitor. Please cite this article as: Fu Z et al., A hierarchical energy management strategy for fuel cell/battery/supercapacitor hybrid electric vehicles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.06.158

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Fig. 8 e Simulation results (a) Supercapacitor SOC, (b) Battery SOC, (c) Hydrogen consumption.

Table 4 e Detailed information of energy source under different EMS. Parameter Fuel cell average power Fuel cell power range Battery average power Battery power range Battery SOC change Fuel cell variation coefficient Battery variation coefficient Fuel consumption

Adaptive ECMS

Global ECMS

6.67 kW 18.20 kW 0.459 kW 11.42 kW 0.7e0.6338 0.4497 4.9773 3.9782gal

7.622 kW 16.629 kW 0.501 kW 32.25 kW 0.7e0.7176 0.3836 9.70 4.47gal

their lifespan in Fig. 7. Furthermore, the potential of supercapacitor is not fully realized. From the perspective of energy, battery SOC under the EMS in this paper fluctuates around 0.7, which makes battery work at high efficiency area from Fig. 8(b). Supercapacitor SOC changes with speed by using upper layer strategy, which guarantees the power performance of vehicle in Fig. 8(a). And the economy of hydrogen fuel is high from Fig. 8(c). Table 4 shows the detailed information, where calculation method of variation coefficient can be found in Ref. [33]. The average power and variation coefficient of fuel cell and battery is lower than that of the GECM strategy in Ref. [34], which

Please cite this article as: Fu Z et al., A hierarchical energy management strategy for fuel cell/battery/supercapacitor hybrid electric vehicles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.06.158

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Fig. 9 e Structure configuration model of experimental bench.

Table 5 e Batteries and SCs parameters. Parameter Vehicle mass (kg) Motor rate power (kW) Fuel cell rate power (kW) Fuel cell rate voltage (V) Unidirectional DC/DC converter rate power (kW) Battery rated capacity (kW.h) Battery rated voltage (V)

Value

Parameter

Value

1295 45 10 75 10 25.6 320

Batteries cycle life (times) Supercapacitor rate voltage (V) Supercapacitor voltage range (V) Supercapacitor capacity (F) Supercapacitor rate capacity (W.h) Bidirectional DC/DC converter rate power (kW) Bidirectional DC/DC converter peak power (kW)

> 1000 288 128e288 27.5 320 10 30

Fig. 10 e Configuration profiles of the experimental road condition (a) Speed, (b) Demand power. Please cite this article as: Fu Z et al., A hierarchical energy management strategy for fuel cell/battery/supercapacitor hybrid electric vehicles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.06.158

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Fig. 11 e Output power of energy source (a) Output power of fuel cell, (b) Output power of battery, (c) Output power of supercapacitor.

effectively extends their lifespan. The hydrogen consumption of fuel cell is also low and the economy of hydrogen fuel is improved. Based on the above analysis, the proposed EMS in this paper supercapacitor can supply the peak power and recycle braking energy in an active way. The stable and smooth part of demand power is supplied by fuel cell and battery. Besides, the efficiency of fuel cell is elevated using battery based on ECMS.

Experimental validation To further verify the effectiveness of the proposed EMS, experimental platform based on the hybrid electric vehicle

equipped with fuel cell, battery and supercapacitor is developed. It includes fuel cell, battery, supercapacitor, DC/DC converters, control bench, motor, vehicle control unit and experimental vehicle. The architecture of experimental platform is shown in Fig. 9. The parameters of the experimental platform are shown in Table 5. The configuration files of road conditions are shown in Fig. 10. It includes acceleration, constant-speed, deceleration, which can represents the most commonly used road condition. Under this road condition, the proposed EMS in this paper is used to allocate the output power of energy sources. In Figs. 10 and 11(c) and Fig. 12, supercapacitor can discharge with high power when vehicle accelerates, which can relive the stress on fuel cell and battery, extend the lifespan of

Please cite this article as: Fu Z et al., A hierarchical energy management strategy for fuel cell/battery/supercapacitor hybrid electric vehicles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.06.158

international journal of hydrogen energy xxx (xxxx) xxx

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Fig. 12 e Voltage of supercapacitor.

energy sources and improve the utilization efficiency of supercapacitor. This strategy guarantees the supercapacitor SOC higher to avoid the abrupt acceleration when the speed of vehicle is low. Vice versa. Meanwhile, the stable and smooth part of the demand power after filter is allocated to fuel cell and battery. In Fig. 11, fuel cell supplies the most of demand power. Battery is used to lift the work efficiency of fuel cell to improve the economy performance of hydrogen fuel, which benefits from the use of equivalent consumption minimization strategy.

Conclusion In this paper, the hierarchical energy management strategy for fuel cell hybrid electric vehicle equipped with battery and supercapacitor was proposed in order to lift energy sources lifespan, power performance and fuel economy of FCHEV. In higher layer strategy, the low-pass filter was applied to guarantee supercapacitor undertake peak power and recycle energy from braking. Meanwhile, supercapacitor SOC was maintained in a proper range, which improved power performance of vehicles and the utilization efficiency of supercapacitor, and relived the stress on battery and fuel cell. In lower layer strategy, the equivalent consumption minimization strategy was designed to improve the economy of hydrogen fuel and extend the lifespan of energy sources. The simulation and experiment results, compared with the GECM involved in Ref. [34], showed that the average power and power variation coefficient of fuel cell and battery was low, which could extend their lifespan. Meanwhile, the hydrogen consumption of the proposed EMS in this paper was lower and supercapacitor SOC in whole road cycle can maintain in a proper range. Both of simulation and experiment results confirmed the effectiveness of the proposed EMS for FCHEV. Considering the road conditions for the EMS proposed in this paper is preknowledge, and the driver factor is not considered. How to realize identification of road condition on real time and build the driver random model are challenging and important in EMS of FCHEV. In the future work, we would apply cluster algorithm and markov chain method to study the above topic for proposing a more comprehensive EMS.

Acknowledgment The authors would like to thank the anonymous reviewers for their constructive and insightful comments for further improving the quality of this note. This work was partially supported by National Natural Science Foundation of China (Grant Nos. 61473115, U1704157), the Scientific and Technological Innovation Leaders in Central Plains (Grant No. 194200510012), the Science and Technology Innovative Teams in University of Henan Province (Grant No. 18IRTSTHN011) and the Key Scientific Research Projects of University in Henan Province (Grant No. 19A413007).

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