ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan

ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan

international journal of hydrogen energy xxx (xxxx) xxx Available online at www.sciencedirect.com ScienceDirect journal homepage: www.elsevier.com/l...

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international journal of hydrogen energy xxx (xxxx) xxx

Available online at www.sciencedirect.com

ScienceDirect journal homepage: www.elsevier.com/locate/he

Optimization based energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan* Zhumu Fu a,b, Longlong Zhu a, Fazhan Tao a,b,*, Pengju Si a,b, Lifan Sun a,b a

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

b

highlights  Propose a frequency decoupling strategy based on fuzzy control to separate required power into three frequency ranges.  Optimize membership functions of fuzzy controllers using GA.  Improve fuel economy while reducing fluctuation of fuel cell to extend its lifespan.  Confirm the effectiveness of the proposed EMS by simulation and experiment bench results.

article info

abstract

Article history:

Optimization of energy management strategy (EMS) for fuel cell/battery/ultracapacitor

Received 11 October 2019

hybrid electrical vehicle (FCHEV) is primarily aimed on reducing fuel consumption. How-

Received in revised form

ever, serious power fluctuation has effect on the durability of fuel cell, which still remains

31 December 2019

one challenging barrier for FCHEVs. In this paper, we propose an optimized frequency

Accepted 3 January 2020

decoupling EMS using fuzzy control method to extend fuel cell lifespan and improve fuel

Available online xxx

economy for FCHEV. In the proposed EMS, fuel cell, battery and ultracapacitor are employed to supply low, middle and high-frequency components of required power,

Keywords:

respectively. For accurately adjusting membership functions of proposed fuzzy controllers,

Fuel cell electrical hybrid vehicle

genetic algorithm (GA) is adopted to optimize them considering multiple constraints on

Energy management strategy

fuel cell power fluctuation and hydrogen consumption. The proposed EMS is verified by

Frequency decoupling

Advisor-Simulink and experiment bench. Simulation and experimental results confirm

Fuzzy control

that the proposed EMS can effectively reduce hydrogen consumption in three typical drive

Genetic algorithm

cycles, limit fuel cell power fluctuation within 300 W/s and thus extend fuel cell lifespan. © 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

* This paper was supported by National Natural Science Foundation of China (Grant Nos. 61473115, U1704157), the Scientific and Technological Innovation Leaders in Central Plains (Grant No. 194200510012) and the Science, Technology Innovative Teams in University of Henan Province (Grant No. 18IRTSTHNO11) and Key Scientific Research Projects of Universities in Henan Province (Grant Nos. 19A413007, 20A120008) and the National Thirteen-Five Equipment Pre-Research Foundation of China (Grant Nos. 61403120207, 61402100203), Aeronautical Science Foundation of China (Grant No. 20185142003). * Corresponding author. School of Information Engineering, Henan University of Science and Technology, Luoyang, China. E-mail address: [email protected] (F. Tao). https://doi.org/10.1016/j.ijhydene.2020.01.017 0360-3199/© 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Please cite this article as: Fu Z et al., Optimization based energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.017

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Introduction To combat global energy crisis and global warming caused by vehicles power by fossil fuels, fuel cell vehicles have been gaining ground over conventional internal combustion thanks to its efficient energy conversion and environmental friendly characteristics [1e5]. However, considering slow response dynamics of fuel cell, a sole fuel cell system may not totally satisfy the power required of vehicle especially when the vehicle is accelerating [6e9]. Therefore, in this paper, the fuel cell vehicle consists of one auxiliary energy store system (ESS) equipped with battery and ultracapacitor. In terms of different dynamic characteristics of the three energy sources of FCHEVs, energy management strategy is critical for coordinating the power flow between energy sources, which can effectively improve the whole performance of vehicles, specially in fuel economy and fuel cell lifespan. In recent years, on these aspects of extending fuel cell lifespan and improving power performance and fuel economy of FCHEVs, a lot of EMSs are developed in recent years. In terms of the proposed EMSs, they can be classified two directions: offline energy management strategies and online energy management strategies [10,11]. Offline energy management strategies employ advanced control algorithm to achieve energy management under a known driving cycle, such as dynamic programming [12], convex optimization [13], pontryagin’s minimum principle [14], and other optimization algorithms [15,16]. However, the aforementioned control strategies for FCHEVs result into heavy computation capability in practical and strongly depend on the preknowledge of drive cycles. On this aspect, online energy management strategies like model-predictive control [17], equivalent consumption minimization strategy [18], internet of vehicles based [19e22] and fuzzy control [23] are not subject to the specific drive cycle and their calculation amount is relatively small, which can achieve real-time energy distribution effectively. Among the proposed online strategies, fuzzy control method can be tolerant of imprecise mathematical model or data and has excellent robust stability in real time control of FCHEVs [24e26]. In [24], an online control strategy based on the fuzzy controller is utilized to design relevant energy management strategy for FCHEV for the improvement of fuel economy and mileage of continuation of journey. In [25], a real-time fuzzy controller is designed to protect the battery from overcharging during the repetitive braking energy accumulation. In [26], an adaptive controller based on fuzzy control method is proposed for fuel cell/battery vehicles, where fuel cell output can catch up with load power more smoothly effectively. However, the membership functions (MFs) of proposed fuzzy controllers in [24e26] are built on top of human expertise in advance, which cannot ensure the optimality of fuzzy system including its MFs. In order to determine an optimal fuzzy controller, genetic algorithms [27,28] is used to accurately adjust control parameters of fuzzy controllers while considering target of improving fuel economy and power performance of FCHEVs. Although the fuel economy and power performance are improved to some extent in [27,28], power fluctuation of fuel cell are not fully

taken into account, which results into adverse effects on fuel cell internal electrochemical structure and further shorts its lifespan. Therefore, motivated by [24e28], in this paper, frequency decoupling strategy based on fuzzy control is designed and optimized by GA. Main contributions of this papers can be summarized as follows: (1) Considering different characteristics of three energy sources, the proposed EMS separates required power into three frequency ranges by a low-pass filter and Haar wavelet transform, which guarantees fuel cell can supply the low-frequency of required power to extend its lifespan. (2) In the proposed EMS, two fuzzy controllers are designed to be combined with low-pass filter and wavelet transform, respectively, which guarantees rapid response of power demand and maintain battery/ultracapacitor SOC in a predefined range. (3) In order to obtain the optimal solution for two fuzzy controllers, a multi-objective optimization function and GA are used to tune MF control parameters of fuzzy controllers during a driving cycle while considering fuel cell lifespan and fuel economy. The rest of this paper is organized as follows. Section System description and modeling gives the model of the fuel cell/battery/ultracapacitor and description of the hybrid vehicles. Section Optimized frequency decoupling energy management strategy based on fuzzy control gives main results of frequency decoupling strategy based on fuzzy control and its optimization by GA. Section Simulation results and experimental validation presents the simulation and experimental results to confirm effectiveness of the proposed design scheme. Conclusion is given in Section Conclusion.

System description and modeling In this paper, FCHEV structure is shown in Fig. 1. Fuel cell is the primary energy source, ESS consisting of battery and ultracapacitor provides supplemental power. A unidirectional DC/DC converter is employed to connect fuel cell and DC bus to provide fuel cell’s voltage improvement. Ultracapacitor connect DC bus by a bidirectional DC/DC converter with the purpose of charging and discharging. Battery is directly linked to the DC bus to maintain DC bus voltage.

Hybrid vehicle model Firstly, in order to split the power between three energy sources, power required of FCHEV should be calculated. The required power Preq of vehicles for the given speed v can be calculated as follows [29]:   d d dm vðtÞ þ Fw ðtÞ þ Fr ðtÞ þ Fi ðtÞ ; vðtÞ > 0 hmotor dt dt Preq ðtÞ ¼   > d d > > : v$hmotor dm vðtÞ þ Fw ðtÞ þ Fr ðtÞ þ Fi ðtÞ ; vðtÞ < 0 dt dt 8 > > > <

v

(1)

Please cite this article as: Fu Z et al., Optimization based energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.017

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Fig. 1 e Proposed FCHEV powertrain structure.

where hmotor is the efficiency of electric motor, d is conversion coefficient of vehicle rotating mass, m is the vehicle mass, Fw is the force required to overcome the aerodynamic drag, Fr is the force to overcome the rolling friction with the road surface, and Fi is the gravity force when driving on nonhorizontal roads. All forces can be described as follows:

where mH2 is hydrogen mass consumption, rH2 is the hydrogen chemical energy density and hFC is global efficiency of the fuel cell system which is defined as follows:

8 < Fr ðtÞ ¼ fr mg cos a F ðtÞ ¼ 0:5Cd Arv2 : w Fi ðtÞ ¼ mg sin a

where PH2 is the theoretical power associated with the hydrogen flow consumption in fuel cell. Fig. 2 shows the global efficiency of the fuel cell system regarding fuel cell power.

(2)

where a is the road angle. Detail parameters of the vehicle model are listed in Table 1. The power split between the fuel cell, battery and ultracapacitor is given as follows:

hFC ¼

PFC PH2

(5)

Battery model

where PFC is fuel cell power, PB is battery power and PU is ultracapacitor power.

As the secondary energy source, battery mainly provides supplemental power for a long period of time. In order to maintain DC bus voltage and recycle braking energy, battery SOC should be maintained in an appropriate range. Battery SOC can be calculated as follows [31]:

Fuel cell model

SOCB ðtÞ ¼ SOC1  hB

Fuel cell is a device of hydrogen and oxygen as fuel through electrode reaction directly convert chemical energy into electrical energy. The hydrogen mass consumption is related to fuel cell power which is given by the following equation [30]:

where SOCB is battery SOC, SOC1 is initial battery SOC, hB is charge and discharge efficiency of battery, iB is battery current and QB is the battery nominal capacity.

Preq ðtÞ ¼ PFC ðtÞ þ PB ðtÞ þ PU ðtÞ

(3)

Z

Zt mH2 ¼ 0

PFC ðtÞ dt hFC ,rH2

iB ðtÞ dt 3600QB

(6)

(4)

Table 1 e Parameters of the vehicle model. Parameter Vehicle mass, mðkgÞ Gravity constant, gðm =s2 Þ Rolling resistance coefficient, fr Aerodynamic drag coefficient, Cd Vehicle frontal area, Aðm2 Þ Air density, rðkg =m3 Þ Conversion coefficient of vehicle rotating mass, d

Value 1113 9.8 0.6 0.3 1.75 1.22 1.3

Fig. 2 e Fuel cell system efficiency versus fuel cell power.

Please cite this article as: Fu Z et al., Optimization based energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.017

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Fig. 3 e Optimized frequency decoupling strategy based on fuzzy control.

Fig. 4 e Inputs MFs and output MF of FC A.

Ultracapacitor model Ultracapacitor is used to provide/absorb peak power during vehicle short-term acceleration and brake. Ultracapacitor is modeled as a capacitor and an equivalent resistance to analyze its performance. Ultracapacitor SOC is expressed by its output voltage VU and current iC [32]:

the required power of vehicles. Due to large amount of acceleration and deceleration in actual driving, rapid transient of power requirement in vehicle operation can damage the fuel cell internal electrochemical structure and shorten its lifespan. Considering different characteristics of three energy sources, in this paper EMS shown in Fig. 3 is proposed by combining frequency decoupling techniques with GA-based

2

SOCU ðtÞ ¼

ðVU ðtÞ  2RC iC ðtÞÞ V2uc;max

(7)

where SOCU is ultracapacitor SOC, RC is ultracapacitor internal resistance and Vuc;max is ultracapacitor maximum voltage.

Table 2 e FC A rule base. uf

SOCU

Optimized frequency decoupling energy management strategy based on fuzzy control As for FCHEVs, the aim of EMS is to split the instantaneous power between fuel cell, battery and ultracapacitor based on

Preq

S M RB B

NB

NS

ZE

PS

PB

S RS M B

S RS M B

B B B B

M M RS RS

RS S S S

S: Small, ZE: Zero, M: Medium, B: Big, R: Relatively, N: Negative, P: Positive.

Please cite this article as: Fu Z et al., Optimization based energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.017

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Fig. 5 e Decomposition process of 3-order Haar wavelet transform.

fuzzy control method, which consists of three parts: ultracapacitor power allocation module A, fuel cell and battery power allocation module B and optimization of GA-based FC A and FC B module C. In the following, we will discuss the three parts of the proposed EMS in detail for developing main results on fuel cell lifespan and fuel economy.

Ultracapacitor power allocation module A In this module, in order to avoid frequently power fluctuations of fuel cell and battery, the high-frequency components of required power Preq is obtained by the proposed adaptive lowpass filter by using one GA-based fuzzy controller, which is allocated to ultracapacitor. In this paper, the following lowpass filter is chosen: GðsÞ ¼

uf s þ uf

SOC and required power, which is handled by one fuzzy controller (FC A) in this paper. FC A has two input variables and one output variable. The input variables include required power and ultracapacitor SOC, output variable is regulating frequency of low-pass filter. When FCHEV accelerates (required power is PB), decreasing uf to allow ultracapacitor to supply as much power as requested if its SOC is H; when FCHEV brakes (required power is NB), increasing uf to allow ultracapacitor to absorb as much regenerative energy as possible if its SOC is L. Moreover, in order to avoid the over discharging/charging of ultracapacitor, uf is adjusted adaptively as SOCU increases or decreases. The fuzzy MFs for SOCU , Preq and uf are shown in Fig. 4, where fuzzy partitions of MFs optimized by GA will be addressed in section Optimization of FC A for uf of filter particularly. Rule base of the FC A is listed in Table 2.

(8)

Fuel cell and battery power allocation module B

where GðsÞ is transfer function of the low-pass filter, uf is its regulating frequency. In terms of the proposed low-pass filter, the important work lies in how to adjust the parameter uf such that the filter can be automatically adaptive to ultracapacitor

After allocating ultracapacitor power, fuel cell and battery is supposed to be responsible for the rest required power, that is ref lower frequency part (PFC B ). In order to reduce power

Fig. 6 e Inputs MFs and output MF of FC B.

Please cite this article as: Fu Z et al., Optimization based energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.017

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for battery, there should exist enough energy to support fuel cell when vehicle is accelerating, and enough capacity to recycle energy during its braking. With respect to the balanced SOC stored in battery, extra benefit is that FCHEV can run an extra distance with hydrogen restrictions. Based on the above analysis, other fuzzy controller (FC B) is designed to realize trade-off between fuel cell performance and the battery SOC value to obtain the fuel cell power. Input variables of FC B are the fuel cell reference power generated by the Haar wavelet transform, and battery SOC, and output variable is the fuel cell power PFC as shown in Fig. 6, where fuzzy partitions of MFs optimized by GA will be addressed in section Optimization of FC B for fuel cell power particularly. Fuzzy rule base is clarified in Table 3.

Table 3 e FC B rule base. ref

PFC

PFC

SOCB

S RS M B

VS

S

RS

M

RB

B

VB

RS S VS VS

M RS S VS

RB M RS S

B RB M RS

VB B M M

VB VB RB RB

VB VB B B

Table 4 e Operating parameters in GA. Population Crossover Mutation size M probability Pc probability Pm 30

0.9

Terminated generation N

0.09

200

Optimization of GA-based FC A and FC B module C fluctuation of fuel cell, extend its lifespan and improve fuel ref economy, PFC B is allocated further by considering the different characteristics of fuel cell and battery. Discrete wavelet transform (DWT) method is employed and defined in Eq. (9) in this paper to decouple the high-frequency and lowref frequency components of PFC B . Z Wðl; uÞ ¼

  1 tu dt; l ¼ 2j ; u ¼ k2j ; k2Z sðtÞ pffiffiffi J l l

(9)

where sðtÞ is original signal, l is scale parameter, u is position parameter, W is wavelet coefficient as a function of l and u, and J is mother wavelet as shown in following: 8 < 1 0  t  0:5 JðtÞ ¼ 1 0:5 < t  1 : 0 otherwise

(10)

Three-level Haar wavelet transform decomposition is applied for the original signal sðnÞ as shown in Fig. 5, which decompose sðnÞ into high-frequency components (detail part of the original signal) and low-frequency components (reference part of the original signal) by a low-pass filter l0 ðzÞ and a high-pass filter h0 ðzÞ, respectively. Although the obtained low-frequent part by the proposed DWT can be supplied by fuel cell completely, battery SOC is ref not taken into account in the allocation process on PFC B for improving performance of fuel cell. In the proposed EMS in this paper, battery SOC should be satisfied with physical constraints on accelerating and braking of vehicles. That is, as

Optimization of FC A for uf of filter Firstly, gene coding is the beginning of optimization, fuzzy partition of input/output MFs in FC A as shown in Fig. 4. 24 one-dimensional decimal matrices x1 /x8 ; x9 /x16 ; x17 /x24 represent MF partition points of SOCU , Preq , and uf . Each division point of the membership function is chosen as the chromosomes of the initial population, and chromosomes use decimal encoding. Secondly, optimization effect of genetic algorithm is closely related to the selection of objective function. As for FC A, the objectives of optimization lie in minimizing power fluctuation of the fuel cell and battery to prolong its lifespan, and maintaining ultracapacitor SOC during driving. The objective function evaluated for each GA chromosome is defined as follows: J1 ¼ w1

n X

ref

DPFC B ðkÞ þ w2 DSOCU

(11)

k¼1 ref

where DPFC B ðkÞ is the variation of low-pass filter output power at k time of whole driving trip, DSOCU is the value of difference between final and initial ultracapacitor SOC, w1 and w2 are the weighting coefficient. Genetic algorithm takes the fitness function of individual population as the basis for selection, individuals with higher fitness values are more likely to inherit better gens to the next generation. It can be seen from Eq (11) that the results tend to the optimum as J1 decreases. Thus, fitness function is established as follows:

Table 5 e GA optimization process. Step1

Step2 Step3 Step4

(1) Load the drive cycle data into program. (2) Initialize population size M, crossover probability Pc , mutation probability Pm and terminated generation G. (3) Using a random method to generate chromosomes to form an initial population. Calculate the fitness value F of each chromosome, and select the one with the greatest fitness as offspring chromosomes. Execute the crossover operation with probability Pc and execute the mutation operation with probability Pm. Repeat steps 2 to 3 until the maximal evolution generation G is satisfied. And select the final best elitism individual as the MF parameters of fuzzy controllers.

Please cite this article as: Fu Z et al., Optimization based energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.017

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Fig. 7 e Configuration profiles of three drive cycles.

Fig. 8 e Evolution processes for FC A and FC B in UDDS.

FðiÞ ¼

1 J1 ðiÞ

(12)

where FðiÞ is fitness value of the ith individual, J1 ðiÞ is objective function value of the ith individual. And roulette wheel selection method is used in this paper. The probability (Pi ) that the ith individual in the population is selected as follows: FðiÞ

PðiÞ ¼ PM

(13)

i¼1 FðiÞ

where M is the population size. Thirdly, when the individual fitness degree is calculated by fitness function, the individual with optimal fitness degree is obtained by crossover and mutation. Crossover operation is the exchange of certain parts of two individuals to create a new combination based on crossover probability. Crossover operation can be expressed as follows: (

I0i ¼ aIiþ1 þ ð1  aÞIi I0iþ1 ¼ aIi þ ð1  aÞIiþ1

(14)

where Ii , Iiþ1 are ith and i þ 1 th individuals, respectively, Ii 0 and

Iiþ1 0 are offspring individuals produced after crossover operator, a is a random number between 0 and 1. In order to further obtain better individuals, the individual is mutated with probability Pm . Main operating parameters of the genetic algorithm are shown in Table 4.

Optimization of FC B for fuel cell power PFC Similar to the optimization of FC A, FC B exists 28 onedimensional decimal matrices x25 /x32 ; x33 /x42 ; x43 /x52 representing partition points of input/output MFs in FC B shown in Fig. 5. The objectives of optimization FC B lie in minimizing hydrogen consumption of fuel cell, and maintaining battery SOC during driving. The objective function evaluated for each GA chromosome for FC B is defined as follows: J2 ¼ w3 DmH2 þ w4 DSOCB

(15)

where DmH2 is the value of hydrogen mass consumption during whole during cycle, DSOCB is value of the difference between final and initial battery SOC, w3 and w4 are the weighting coefficient. Then, the selection of fitness function, mutation operation and crossover operation are the same as optimization of FC A.

GA optimization process Table 6 e Fuzzy partition of input/output MFs in FC A and FC B after optimizing. x1 /x8 ; x9 /x16 ; x17 /x24 0.48, 0.54, 0.69, 0.7, 0.75, 0.89, 0.88, 0.93; 8268.3, 6380.2, 5634.6, 328.4, 19221.3, 19328.1, 23531.5, 29328.1; 0.039, 0.044, 0.047, 0.048, 0.051, 0.0597, 0.0595, 0.064

The optimization processes by using GA are briefly concluded in Table 5.

x25 /x32 ; x33 /x42 ; x43 /x52 0.19, 0.30, 0.44, 0.61, 0.71, 0.75, 0.73, 0.80; 4658.3, 3499.9, 8224.1, 7085.3, 10603.2, 9857.3, 15381.4, 15039.3, 19139.8, 20615.1; 2412.9, 1567.9, 6628.4, 6915.5, 10520.1, 10753.8, 13440.2, 14496.8, 18402.8, 17905.5

Simulation results and experimental validation Simulation results In order to verify the effectiveness of the proposed EMS, three the typical drive cycle of traffic conditions shown in Fig. 7 Highway Fuel Economy Test (HWFET), Urban Dynamometer Driving Schedule (UDDS) and New European Drive Cycle

Please cite this article as: Fu Z et al., Optimization based energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.017

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Please cite this article as: Fu Z et al., Optimization based energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.017

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Fig. 10 e Distribution comparison of fuel cell output power fluctuation in three drive cycles (a) HWFET, (b) UDDS, (c) NEDC.

Table 7 e Comparison of hydrogen consumption and distance gain between two EMSs. Driving cycle

Running distance (km)

HWFET UDDS NEDC

16.51 11.99 10.93

Hydrogen consumption(L) Distance gain (km) EMS EMS based on proposed merely fuzzy control 6.369 5.649 5.141

6.622 5.797 5.248

0.656 0.314 0.227

The bold values means that the propsoed method can reduce the hydrogen consumption effectively compared with the former method.

(NEDC)) are used to analyze the vehicle performance and fuel economy in offline simulation before real driving by ADVISOR software. Firstly, the offline optimizations of FC A and FC B are carried out by using GA under UDDS condition. The performance of the FC A and FC B are improved gradually as shown in Fig. 8. The value of objective function decreases obviously with the iteration of genetic algebra, and finally converges to a fixed value. Table 6 lists fuzzy partition of input/output MF in FC A and FC B after they are improved by GA. After selecting parameters in Table 6, optimizations of FC A and FC B are completed. In order to show the efficiency of the proposed EMS, other EMS based on merely fuzzy control in [25] is applied to the same drive cycles. Fig. 9(a) (e) (i) present the required power of vehicle in three typical drive cycles. Test parameters of two EMSs in simulation are selected as follows: fuel cell is a 30 kW proton exchange membrane fuel cell; the battery is a 9.25 kW h, 20 kW lithium-ion battery; the ultracapacitor is a 350 W h, 70 kW ultracapacitor; the initial SOC of battery and ultracapacitor is set to 0.7 and 0.9, respectively. Then, the power distribution of fuel cell, battery and ultracapacitor for three typical drive cycles under the proposed EMS in this paper are shown in Fig. 9 (b) (f) (j), respectively. It can be seen that, with the proposed EMS in this paper, ultracapacitor supplies the peak power to response power demand rapidly and relive the stress on fuel cell/battery, especially when required power fluctuates seriously, fuel cell

is satisfied with a base portion of the required power under the constraints of its transient power as the major energy source and battery helps fuel cell to supply the steady state power required. In addition, braking energy is completely absorbed by battery and ultracapacitor, and absorbed braking energy of battery and ultracapacitor is in turn used to drive the vehicle, which is beneficial for saving the hydrogen consumption. Comparison of fuel cell power between EMS proposed in this paper and EMS based on merely fuzzy control in three typical drive cycles are shown in Fig. 9(c) (g) (k), respectively. It can be seen that, with the EMS based on merely fuzzy control, fuel cell power fluctuates seriously when the vehicle accelerates and decelerates, especially in UDDS drive cycle. While with the proposed EMS in this paper, output power of fuel cell is relatively more stable and smoother compared to the EMS based on merely fuzzy control. The stable output power of fuel cell reduces adverse effect on its internal chemical structure and further lead to a longer lifespan of fuel cell. The comparison of battery SOC between two EMSs are given in Fig. 9(d) (h) (l), both the EMSs can maintain the battery SOC between 0.6 and 0.8, which guarantees high charging and discharging efficiency of battery. And DSOCB (the difference between final and initial battery SOC in three typical cycles) of EMS based on merely fuzzy control is much larger than that of EMS proposed. Moreover, from Fig. 9(d) (h) (l), the proposed EMS, battery SOC is fluctuating by less than 3.3%. With a balance battery SOC, FCHEV can run extra distances with hydrogen restrictions. To further demonstrate the advantages of the proposed EMS in reducing fuel cell power fluctuation and prolonging fuel cell lifespan, comparison of fuel cell output power fluctuation (variation of fuel cell output power per second) are shown in Fig. 10. It can be seen that, compared to EMS based on merely fuzzy control, the proposed EMS can limit the fuel cell output power fluctuation within 250 W/s and even achieve nearly 62%, 80% and 77% fuel cell output power fluctuation points in the range of 100 W/s to 100 W/s, which greatly improves the fuel cell lifespan. Furthermore, to verify the advantages and effectiveness of the proposed EMS on improving vehicle fuel economy and extending mileage of FCHEV, comparison results of hydrogen

Fig. 9 e The simulation results in different drive cycles: (a) Required power of HWFET, (b) Different power of HWFET, (c) Comparison of fuel cell power between two EMSs in HWFET, (d) Comparison of battery SOC between two EMSs in HWFET, (e) Required power of UDDS, (f) Different power of UDDS, (g) Comparison of fuel cell power between two EMSs in UDDS, (h) Comparison of battery SOC between two EMSs in UDDS, (i) Required power of NEDC, (j) Different power of NEDC, (k) Comparison of fuel cell power between two EMSs in NEDC, (l) Comparison of battery SOC between two EMSs in NEDC. Please cite this article as: Fu Z et al., Optimization based energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.017

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Fig. 11 e Structure configuration model of experimental bench.

consumption and km gain in three drive cycles are listed in Table 7. Here, the km gain is considered to be prolonged distance by the saved hydrogen using the proposed EMS of this paper compared to the merely fuzzy-based EMS. For fairness of comparison, the initial and final SOC of battery/ultracapacitor are limited to the same value by using the “Soc Correction” program in ADVISOR. Therefore, in this case, the equivalent hydrogen consumption of battery and ultracapacitor in the whole drive cycle can be ignored. According to Table 7, it is obvious that EMS proposed in this paper saves about 4.4%, 2.6% and 2% hydrogen in comparison to the EMS based on fuzzy control under three drive cycles, respectively. In addition, compared with EMS based on merely fuzzy control, the hydrogen saved by the proposed EMS can run extra distances of 0.656 km, 0.314 km and 0.227 km in three drive

Table 8 e Detailed parameters of platform. Component Fuel cell

Battery

Ultracapacitor

Motor

DC/DC converter

Parameter

Value

Type Rated power (kW) Output voltage range (V) Output current range (A) Type Capacity (kWh) Rated voltage (V) Maximum discharging rate Type Capacity (Wh) Output voltage range (V) Output current range (A) Type Rated power (kW) Rated speed (rpm) Operating voltage range (V) Rated current (A) Rated power (kW)

PEMFC 10 40e100 0e220 Lithium battery 25.6 320 4C Maxwell 320 128e288 0  ±200 PMSM 45 1500 250e420 140 10

PEMFC: Proton exchange membrane fuel cell; PMSM:Permanent magnet synchronous motor.

cycles, respectively. Therefore, the proposed EMS of this paper can reduce hydrogen consumption to improve fuel cell economy and whole efficiency of FCHEV. It can be concluded from the simulation results that, compared to EMS based on merely fuzzy control, the EMS proposed in this paper takes advantages over reducing fuel cell power fluctuation and vehicle hydrogen consumption in three typical drive cycles. Moreover, it also realizes trade-off between saving hydrogen consumption and extending fuel cell lifespan.

Experimental validation In order to further verify the performances of FCHEV under proposed EMS, a test platform shown in Fig. 11 in the lab is prepared. The platform is based on ordinary electric vehicle, equipped with centralized control system, ultracapacitor system, vehicle controller and fuel cell system. Detail parameters are listed in Table 8. Considering the cost factor, the maximal power of the fuel cell system is limited to 10 kW, which is one third of the fuel cell in the simulation. Thus, the maximum speed of the vehicle is limited to 40 km/h to avoid overload of fuel cell. The vehicle speed and required power are shown in Fig. 12(a) (b). The initial voltage of ultracapacitor is set to 199.5 V. Then, the experimental results for given speed are shown in Fig. 12 (c)e(h). It can be seen from Fig. 12(c)e(e) that fast peak power demand is handled by the ultracapacitor, fuel cell supplies most of the sustained average power as the main energy source, and battery helps fuel cell provide residual power. To verify the effectiveness of EMS proposed in extend fuel cell lifespan, fuel cell power fluctuation (variation of fuel cell output power per second) is given in Fig. 12(f). It is obvious that about 87% of the power fluctuation points are located between 300 W/s and 300 W/s, which is beneficial for extend fuel cell lifespan. The fuel cell voltage obtained by EMS proposed is given in Fig. 12(g). It can be seen that the output voltage is limited between 47 V and 71 V, which means a flat output performance

Please cite this article as: Fu Z et al., Optimization based energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.017

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Fig. 12 e Experimental results: (a) Experimental speed, (b) Required power of vehicle, (c) Output power of fuel cell, (d) Output power of battery, (e) Output power of ultracapacitor, (f) Fuel cell power fluctuation, (g) Output voltage of fuel cell, (h) Output voltage of ultacapacitor.

of fuel cell. Fig. 12(h) shows ultracapacitor voltage, where the difference between initial and final value is below 3.5 V. With a balance of ultracapacitor voltage, FCHEV acceleration performance can be guaranteed during actual driving.

Conclusion In this paper, the optimized frequency decoupling strategy based on fuzzy control for fuel cell/battery/ultracapacitor hybrid vehicle was proposed and investigated to minimize hydrogen consumption and power fluctuation of fuel cell. The proposed EMS separated required power into three frequency ranges by an adaptive low-pass filter and Haar wavelet transform. The adaptive low-pass filter was applied to guarantee ultracapacitor supply high-frequency components of load to relive stress on fuel cell and battery. Meanwhile, the low-pass filter was adaptive to required power and ultracapacitor state of charge by using one fuzzy controller. Haar wavelet transform based on other fuzzy controller was designed to allocate low and middle-frequency components of load to fuel cell and battery, respectively. In addition, considering SOC of battery/ultracapacitor, fuel cell power fluctuation and fuel economy, two fuzzy controllers are optimized by GA. The simulation results, compared with the EMS

based on merely fuzzy control in [25], showed that power fluctuation of fuel cell was limited within 250 W/s in three typical road condition, which is beneficial to extend its lifespan. Meanwhile, the proposed EMS saves about 4.4%, 2.6% and 2% hydrogen in comparison to the EMS based on fuzzy control under three drive cycles, respectively. The experimental results also verify the remarkable effect of reducing fuel cell power fluctuation. The proposed EMS of this paper relaxes patterns of drive cycles of the vehicle, however different patterns of drive cycles possess own characteristic, which can lead different fuzzy optimization. If driving pattern factors are considered in optimization of fuzzy controllers, it will lead to a better fuel consumption and improve the whole performance of vehicles. In the future work, we would apply the driving pattern factors to the optimization of EMS for proposing a more comprehensive EMS.

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Please cite this article as: Fu Z et al., Optimization based energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan, International Journal of Hydrogen Energy, https://doi.org/10.1016/ j.ijhydene.2020.01.017