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Journal of Power Sources journal homepage: www.elsevier.com/locate/jpowsour

A novel equivalent consumption minimization strategy for hybrid electric vehicle powered by fuel cell, battery and supercapacitor

T

Huan Lia,b,∗, Alexandre Raveya,b, Abdoul N'Diayea,b, Abdesslem Djerdira,b a b

FEMTO-ST, CNRS, Univ. Bourgogne Franche-Comte, UTBM, France FCLAB, CNRS, Univ. Bourgogne Franche-Comte, France

H I GH L IG H T S

SECMS strategy for FCHEV with three power sources. • Novel two degrees of freedom energy management problem. • Solving • Experimental validation of designed SECMS strategy through test bench.

A R T I C LE I N FO

A B S T R A C T

Keywords: Fuel cell hybrid electric vehicles (FCHEVs) Equivalent consumption minimum strategy (ECMS) Three power sources Experimental test bench

The aim of this paper is to present a sequential quadratic programming (SQP) based equivalent consumption minimum strategy (ECMS) (SECMS) for fuel cell hybrid electric vehicle (FCHEV) powered by fuel cell, battery and supercapacitor. In order to decrease hydrogen consumption and increase the durability of power sources, fuel cell is chosen as the main power source and supplies steady current, battery is designed as the main energy buﬀer and the replacement of fuel cell failure and supercapacitor is operated to supply peak power. Low energy density of supercpacitor lets its equivalent hydrogen consumption be taken as zero for many ECMS researches. This simpliﬁcation leads to suboptimal fuel economy and complex of control system. SECMS considers hydrogen consumption of three power sources into objective function to solve this problem. A rule based control strategy (RBCS) and an hybrid ECMS operating mode control strategy (OMCS) (HEOS) are also designed to compare with SECMS. An experimental test bench is built to validate the comparative study of three strategies. The results show that compared with RBCS and HEOS, hydrogen consumption of SECMS decreases of 2.16% and 1,47% respectively and it also has the most smooth fuel cell current, which means a lowest fuel cell degradation.

1. Introduction Traditional gasoline and diesel vehicles have lead to many problems such as global warming, environment pollution and exhaustion of petroleum energy. Electric vehicles including pure electric vehicles, hybrid electric vehicles, and plug in hybrid electric vehicle are thought to be the best way to solve these problems [1]. Compared to traditional internal combustion engine, fuel cell has high eﬃciency and zero pollution emission, which is ideal energy source for electric vehicles [2]. Gas supply of fuel cell stack lags behind the load variation, which leads to diﬃculties to track the dynamic response of current specially related to transportation application. Consequently, at least one kinds of energy storage sources (ESS) should be added as the power sources to FCHEV. Batteries have high energy density, which are the most widely used ESS in transportation systems. But batteries also have some

∗

shortcomings like low power density, long charging time, high cost, short lifetime and seriously aﬀected by temperature. In contrast, supercapacitor has high power density, very high lifetime and are not aﬀected by temperature, which makes it suitable as device for power pulse. However low energy density, voltage balancing needed and high self-discharge are the main barrier for supercapacitor to be widely used in the hybrid electric vehicles. One of the most promised solution proposed for FCHEV supplying is the topology with fuel cell, battery and supercpacitor. This topology of power train allows the main components to give play to their advantages: fuel cell as main steady power source, battery as energy buﬀer and supercapacitor as device for power pulse. In order to achieve this hybridization and reach the above goal, an energy management strategy (EMS) is necessary. In the literature, the EMSs can be classiﬁed into rule based control strategies (RBCS) [3] and optimization based control strategies [4].

Corresponding author. FEMTO-ST, CNRS, Univ. Bourgogne Franche-Comte, UTBM, France. E-mail address: [email protected] (H. Li).

https://doi.org/10.1016/j.jpowsour.2018.05.078 Received 20 February 2018; Received in revised form 22 May 2018; Accepted 23 May 2018 0378-7753/ © 2018 Elsevier B.V. All rights reserved.

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Nomenclature

baSOC F IFC mBA mFC mSC Ncell

PBA PFC PSC R Sfc SOCbamax SOCbamin T VFC

Battery SOC value Faraday constant Fuel cell current Battery equivalent hydrogen consumption Fuel cell hydrogen consumption Supercapacitor equivalent hydrogen consumption The number of fuel cell stack current

Battery output power Fuel cell power Supercapacitor output power Ideal gas constant Fuel cell on/oﬀ state Maximum battery SOC value Minimum battery SOC value The temperature of fuel cell stack Fuel cell voltage

EMS due to the need of an additional EMS to calculate supercapacitor power demand. Thus, SECMS strategy is proposed to consider energy cost of all three power sources into the objective function to solve this problem. At the same time, a RBCS and HEOS strategies are also compared to demonstrate the superiority of SECMS in minimizing the hydrogen consumption and prolonging fuel cell lifetime. This paper is organized as following: the ﬁrst section is introduction, section two gives the vehicle architecture and the model of the power train including fuel cell, battery, supercapacitor and DC/DC converters. In the third section, SECMS, RBCS and HEOS are explained. In the fourth part, the used validating test bench is described and experiment results are compared for diﬀerent control strategies. Finally, conclusions are drawn.

RBCS uses direct rules or fuzzy rules to split power demand among diﬀerent power sources, making it simple to design and allowing real time control. State machine control strategy [5], stiﬀness coeﬃcient model control strategy [6], operation mode control strategy (OMCS) [7] and fuzzy logic control strategy [8] are kinds of RBCS which are widely used. In these strategies, the rules are designed in accordance with engineering experiences, consequently, optimal power split is diﬃcult to reach [9,10]. To consume less hydrogen, increase driving distance or extend the lifetime of fuel cell and ESSs, optimization based control strategies are used to ﬁnd the optimal result. It can be divided into global optimization strategies and local optimization strategies [11]. Dynamic programming and genetic algorithm are the most eﬀective strategies to solve global optimization problem. Prior knowledge about drive condition and long calculation time limit their application on the real time vehicle control. Pontryagins minimum principle [12] and ECMS transform global optimization problem into instantaneous ones which instantaneously calculate the optimization objective function to split power among power sources [13]. Up to now, few papers focus on building EMS which takes into account three power sources. State machine control strategy of [5] and fuzzy logic strategy of [14–16] are used to control the power split among three power sources. But they belong to RBCS which lead to suboptimal results. There are lesser researches evaluating the fuel economy potential of supercapacitor and battery combination for optimization strategy [17]. Solves this problem with dynamical programming but the proposed method cannot be used in real time. Twolevel control structure, where ﬁrst level calculates the optimal results between fuel cell and battery and the second one lets supercapacitor improve the battery performance, are widely used for three power sources like [18]. ECMSs in Refs. [19–21] are designed as two level architecture and take equivalent hydrogen consumption of supercapacitor as zero which not only counter to the aim of minimizing whole hydrogen consumption but also increase the complication of

2. Vehicle models 2.1. Power train architecture The series architecture is chosen for FCHEV, as shown in Fig. 1. Proton Exchange Membrane Fuel Cell (PEMFC) is the main energy source to supply steady state power and is connected to the DC bus via a unidirectional DC/DC power converter. Lead acid battery as main energy storage source is directly connected to DC bus to hold the bus voltage. Supercapacitor as peak power supply is connect to the DC bus through a bidirectional DC/DC power converter. The longitudinal dynamics of a road vehicle can be described in the following equation (1), through which the required power Pcycle at wheel to drive the vehicle can be calculated [22]:

d Pcycle (t ) = v ⎛m v (t ) v (t ) + Fa (t ) + Fr (t ) + Fg (t ) ⎞ dt ⎠ ⎝

(1)

where Pcycle is the power demand from drive cycle, Fa is the aerodynamic friction, Fr the rolling friction, and Fg the force caused by gravity when

Fig. 1. Powertrain architecture. 263

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Fuel cell theoretical eﬃciency is deﬁned as the ration between power generated and the power of hydrogen supplied as equation (11) [25].

driving on slope road:

1 Fa = ρ A Cx v 2 2

(2)

Fr = m v Cr g cos(α )

(3)

Fg = m v g sin(α )

ηLHV =

(4)

where ηFCS is fuel cell system eﬃciency, ηLHV is fuel cell theoretical eﬃciency, ηaux fuel cell real auxiliary eﬃciency, PAUX fuel cell power consumed by auxiliary system. According to the study of [26], the compressor power is up to 93.5% of the total auxiliary power, so a precise compressor model to calculate its power variation along with fuel cell current is built and the other auxiliary power is set as a constant value. The power consumed by the air compressor is shown as equation (13)

Pcp =

(7)

⎜

⎟

i s

∫0

(9)

δmem

γ (T , λ (z )) dz

⎜

⎟

γ−1 γ

⎟

(12)

⎞ − 1⎟ Fcp ⎟ ⎠

(13)

Ncell ∗IFC 4XO2 ∗F

(14)

where S is the stoichiometric ratio, Mair is the number of air moles, XO2 oxygen molar fraction. It should be noticed the DC/DC converter connected to fuel cell stack aﬀects the output power of fuel cell stack on DC bus. So, its efﬁciency is also included into fuel cell system. According to equation (12), the fuel cell system eﬃciency can be calculated and shown in Fig. 2. It can be observed that the maximum eﬃciency point 42.83% occurs at fuel cell current 9.5A . From current 4.5A to 20A in red color, the fuel cell system eﬃciency is above 40% which is deﬁned as a high eﬃciency zone. To reduce the ﬁnal hydrogen consumption, fuel cell should be operate to seek maximum eﬃciency point within this zone. The hydrogen consumption rate can be deﬁned by fuel cell current as the following equation (15) [27]:

(8)

where α is charge transfer coeﬃcient, I0 is the exchange current density, S is the catalyst layer section area. The cell ohmic losses Vohm can be obtained by computing the membrane resistance expression as equation (10)

Vohm = Rmem i =

CP Tair ⎛ ⎛ Pout ⎞ ηmec ηmot ⎜⎜ ⎝ Pin ⎠ ⎝

Fcp = S∗Mair

where Tc is the temperature correction oﬀset, PO2 is the oxygen pressure at the interface of cathode catalyst layer, PH2 is hydrogen pressure at the interface of anode catalyst layer. The activation losses Vact can be described using the Tafel equation (9)

RT ⎛ IFC ⎞ ln 2αF ⎝ I0 S ⎠

⎜

where Pcp is air compressor power, CP is heat capacity of air, Tair inlet air temperature, ηmec is compressor mechanical eﬃciency, ηmot is the eﬃciency of compressor motor, Pin is input air pressure, Pout is output air pressure, γ is ratio of the speciﬁc heat of air. Fcp is the compressor air ﬂow rate which can be deﬁned according to the fuel cell current as function (14)

where Ecell is the electromotive potential, Vact is the cell activation losses, Vohm is the cell ohmic losses. The cell electromotive voltage Ecell can be obtained from equation (8)

Ecell = 1.229 −

VFC ⎛ PFC − PAUX ⎞ 1.254 ⎝ PFC ⎠

(6)

PEMFC as the main power source for the FCHEV transforms the chemical energy into electrical energy through the reaction between hydrogen and oxygen [23]. A steady state model is built for the fuel cell stack. The fuel cell stack output voltage is expressed as following equation (7) [24].

RT − Tc ) + ln ( PO2 PH2) 2F

(11)

ηFCS = ηLHV *ηaux =

2.2. Fuel cell model

0.85e−3 (T

VFC 1.254

(5)

where Pdemand is power demand, ηDC / AC is converter eﬃciency of DC/AC connected to motor, ηmotor is motor eﬃciency, ηDCFC is unidirectional DC/DC converter eﬃciency connected to fuel cell, ηDCSC is bidirectional DC/DC converter eﬃciency connected to supercapacitor.

Vstack = Ncell ∗ (Ecell − Vact − Vohm)

=

2F

Pcycle ηDC / AC ∗ηmotor

Pdemand = PFC ∗ηDCFC + PSC ∗ηDCSC + PBA

Vact =

VFC IFC −ΔHLHV IFC

where ηLHV is fuel cell theoretical eﬃciency, ΔHLHV is the lower heating value of hydrogen. Some auxiliary systems are needed to make sure the normal operation of fuel cell system, such as electrical control border, cooling fan and air compressor. As the result, the eﬃciency of fuel cell system is decided by fuel cell theoretical eﬃciency and real auxiliary eﬃciency, which can be calculated as equation (12)

Variable ρ is the air density, m v the vehicle mass, A front surface of the vehicle, g gravity force, Cx the drag coeﬃcient, Cr the aerodynamic drag coeﬃcient, v the speed of the vehicle and α the angle deﬁning the slope of the road. The power demand on the DC bus from the fuel cell, battery and supercapacitor is given as equation (5) (6)

Pdemand =

PFC = PH2

mH 2 =

(10)

Rmem is the internal resistance of the cell, δmem is the membrane thickness and γ (T , λ (z )) is the membrane local resistance. According to the above described function (7) (8) (9) (10) and fuel cell parameters as Table 1, the polarization curve of the fuel cell stack can be deﬁned.

∫0

t

MH2 Ncell IFC (t ) dt 2F

(15)

Table 1 Fuel cell parameters.

2.3. Fuel cell eﬃciency model In order to minimize fuel cell hydrogen consumption, fuel cell stack should be operated to seek maximum eﬃciency point at high eﬃciency region, so a precise fuel cell eﬃciency model is needed. 264

Company

Ballard NEXA PEMFC

Cell number Rated power (W) Operating voltage range (V) Maximum current (A) Air supply Cooling Fuel supply

47 1200 [22,50] 46 Air blower + ﬁlter Air fan cooled 99.99% dry H2 @1.2 bar

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2.6. DC/DC converter model Fuel cell stack is connected to DC bus through a 1-quadrant DC/DC boost converter and supercapacitor is connected to DC bus through a 2quadrant DC/DC buck/boost converter with boost operation for discharging mode and buck operation for charging mode. Each converter consists of two IGBT transistors which are controlled by two complementary pulse width modulated (PWM) signals. Two DC/DC converters have same architecture, which are shown in Fig. 1. Diﬀerent from buck/boost converter for supercapacitor, the S1 IGBT transistors of fuel cell 1-quadrant DC/DC boost converter is always set in the state of oﬀ. The relationship between input power and output power of two converters is described in the following equation (19)

Iout = ηconv Fig. 2. Fuel cell system eﬃciency curve along with stack current.

3. Energy management strategy

2.4. Battery model

The hierarchical control strategy proposed in this paper consists of two level control loop: the high level control loop corresponding to EMS and the low level control loop corresponding to DC/DC converter control, which is shown in Fig. 3.

Lead acid battery is used in the test bench, which is connected to the DC bus to hold the bus voltage. The battery state of charge (SOC) can be calculated according to battery current as equation (16) [28] [29]:

ηb Cnom

∫0

t

Ib (t ) dt

3.1. Low level control loop (16) Once the reference currents of fuel cell and supercapacitor are deﬁned by high level control loop, two classical PI controllers are applied to adjust duty cycles of PWM signals to control the real fuel cell and supercapacitor output currents to track reference currents. The low level control loop is denoted in red zone in Fig. 3.

Cnom represents the batteries nominal capacity, SOCinit is initial battery SOC, ηb is charge and discharge eﬃciency. 2.5. Supercapacitor model Supercapacitor is modeled as a capacitor and an equivalent resistance [30] as shown in Fig. 1. Capacitor represents supercapacitor performance at discharge and charge state and resistance represents the supercapacitor ohmic losses. The open circuit voltage of supercapacitor has line relationship with its SOC, so the SOC can be described as equation (17). The supercapacitor current can be calculated through following equation (18).

Voc = SOC∗ (Vmax − Vmin) + Vmin

I=

Voc −

3.2. High level control loop Three EMSs are designed for the high level control loop. A novel SECMS is designed to operate fuel cell to seek for the maximum eﬃciency point at high eﬃciency zone. Meanwhile fuel cell dynamical current change rate is limited to increase the lifetime of fuel cell. The supercapacitor is operated to supply peak power to decrease maximum transient current of battery to increase its lifetime. SOC values of battery and supercapacitor are kept within a reasonable range and the terminal SOC of battery is close to the initial one. A RBCS is designed as a benchmark against SECMS. In order to prove that neglecting supercapacitor equivalent hydrogen consumption at ECMS objective function can not reach the optimal result for three power sources power train, a HEOS is also designed.

(17)

Voc2 − 4RSC PSC 2RSC

(19)

where Pin represents input power, Uout is output voltage and ηconv is DC/ DC converter eﬃciency.

where m H2 represents the hydrogen mass rate, MH2 is the hydrogen molar mass.

SOC (t ) = SOCinit −

Pin Uout

(18)

where Vmax is supercapacitor maximum voltage, Vmin output minimum voltage, Voc capacitor voltage, RSC equivalent resistance.

Fig. 3. Hierarchical control architecture. 265

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3.2.1. SQP based equivalent consumption minimization strategy All energy needed by vehicle is supplied indirectly by the fuel cell system at last. In order to minimize hydrogen consumption, the instantaneously consumed electrical energy from battery and supercapacitor can be equivalent to the chemical energy from fuel cell. 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 equation (20),

Supercapacitor penalty coeﬃcient Ksc is composed of SOC coeﬃcient Seff and peak power coeﬃcient Speak . Seff is familiar to Kba to restrict supercapacitor SOC value at reasonable range. Speak is used to let supercapacitor supply peak power ﬁrstly. In order to avoid the frequent on/oﬀ cycles of fuel cell and frequent charge/discharge cycles of supercapacitor due to the large amplitude changes of supercapacitor SOC in short time, supercapacitor SOC is equivalent to battery SOC to deﬁne Seff . K SC , Seff and Speak can be deﬁned as equations (26)–(28) respectively:

m w (t ) = mfc (t ) + mBA (t ) + mSC (t )

K SC = Seff ∗Speak

= mfc (t ) + λba Pba (t ) + λsc Psc (t )

(20)

where m w (t ) is the whole hydrogen consumption. λba and λsc are battery and supercapacitor equivalent factor for equivalent hydrogen consumption. In order to keep fuel cell working at high eﬃciency zone, battery SOC of end cycle same to start value, supercapacitor supplying peak power, the relevant penalty coeﬃcients are added into equation (20), and the objective function is deﬁned as equation (21),

Seff =

maverage Paverage

∗ηba

(21)

(22)

for supercapacitor as equation (23):

mSC (t ) = Psc (t ) ∗

maverage Paverage

∗ηsc

(23)

where ηba ηsc is battery and supercapacitor equivalent transform eﬃciency, maverage is fuel cell average hydrogen consumption, Paverage is fuel cell average power. Fuel cell eﬃciency penalty coeﬃcient K eff is deﬁned as equation (24):

K eff

⎧ 1 − 2∗ η − ηopt ηmax − ηmin ⎪ = η − ηopt ⎨ ⎪ 1 − 2∗ ηmax − ηmin ⎩

( (

) )

η ≥ 0.4 4

(24)

where η is the instantaneous eﬃciency, ηopt is optimal eﬃciency (0.4283), ηmax the maximum eﬃciency (0.4283), ηmin the minimum eﬃciency (0.4), which deﬁne the zone described in section 2.3. When fuel cell system eﬃciency is below than 0.4, a large penalty value K eff is calculated to shut fuel cell stack down or operate fuel cell stack to meet power demand by drive cycle when battery and supercpacitor SOC are lower than limited SOC range. Regarding battery SOC penalty coeﬃcient Kba , it is deﬁned as equation (25):

Kba

( (

) )

⎧ 1 − 2 ∗ (u − Bint ) ⎪ Bmax − Bmin = ⎨ 2 ∗ (u − Bint ) ⎪ 1 − Bmax − Bmin ⎩

4

20

Bmin ≤ u ≤ Bmax u< Bmin , u > Bmax

Smin ≤ x ≤ Smax

20

x< Smin, x > Smax

(27)

(28)

3.2.2. Rule based control strategy A RBCS is designed as the benchmark against SECMS. The RBCS is divided into two parts: load following control strategy (LFCS) to decide what current the fuel cell should be operated according to power demand by drive cycle and battery SOC, and, OMCS to calculate supercapacitor current according to supercapacitor SOC and the diﬀerence between fuel cell supplied power and load power. LFCS is a real-time rule based strategy. The main idea of LFCS is that fuel cell as the main power source is operated at high eﬃciency zone same as SECMS, and its power changes follow the load power demand on DC bus. Frequent fuel cell on/oﬀ cycles degrade fuel cell more seriously, so it is operated at on state until battery SOC is above maximum value or power demand value is very low. The conditions that set fuel cell on or oﬀ are listed as following, where Sfc = 0 means fuel cell is oﬀ, Sfc = 1 means fuel cell is on, Id current demand on the DC bus, tofftime is the fuel cell minimum oﬀ time that means since the fuel cell was last on, the restart should not be less than this time. tontime is similar to tofftime means the fuel cell minimum on time.

2

η < 0.4

) )

where x is the instantaneous supercapacitor SOC, Sopt is optimal SOC, Smax the maximum SOC, Smin the minimum SOC, Iload is load current demand on the DC bus, a and b are the transform coeﬃcients from supercapacitor SOC to equivalent battery SOC and their value are decided by battery minimum SOC and maximum SOC. As described in the above content, all the factors of the objective function fw can be decided. Some other constrains should be added to make sure the normal operation of power sources. In case fuel cell works at low eﬃciency zone with at very low current and avoids frequent on/oﬀ cycle, minimum current is set as 4.5A , when fuel cell current is lower than that value, fuel cell is shut oﬀ. In case of degradation caused by large transient current, dynamical change rate of fuel cell is limited to [-1,1] ( A/ s ). The current range of supercapacitor is also limited to make sure its normal operation. The ECMS strategy is transformed into a non-linear programming problem, which has fuel cell reference current and supercapacitor reference current as variables, equivalent hydrogen consumption as weighting function and subjects to several constrains and inequality constraints. SQP is one of the most successful methods for the numerical solution of constrained nonlinear optimization problems [32]. Therefore, SQP is programmed in C language to solve ECMS optimization problem in real time.

where Kba and Ksc are penalty coeﬃcients which limit battery and supercapacitor SOC range and variation between instantaneous SOC and initial SOC, K eff is the fuel cell eﬃciency penalty coeﬃcient that urges fuel cell to operate at maximum eﬃciency at high eﬃciency zone [31]. According to equation (15), fuel cell hydrogen consumption mfc (t ) can be computed directly. The equivalent hydrogen consumption of battery is calculated through equation (22):

mBA (t ) = Pba (t ) ∗

⎧ 1− ⎪ ax + b − Sopt ⎨ ⎪ 1 − 2 Smax − Smin ⎩

1 0 ≤ Iload ≤ 30 Speak = ⎧ − 0.01∗Iload + 1 Iload < 0, Iload > 30 ⎨ ⎩

fw (t ) = K eff mfc (t ) + Kba mBA (t ) + Ksc mSC (t ) = K eff mfc (t ) + Kba λba Pba (t ) + Ksc λsc Psc (t )

( (

(26)

ax + b − Sopt 2 2S −S max min

(25) Condition 1: if the fuel cell is oﬀ and baSOC < SOCbamin , fuel cell is turned on immediately, which is not limited by tofftime . The current of fuel cell is set as Ifcmax . Condition 2: if the fuel cell is on and baSOC > SOCbamax , fuel cell is turned oﬀ, which is not limited by tontime . The current of fuel cell is 0.

where u is the instantaneous battery SOC, Bint is battery initial SOC, Bmax the maximum SOC, Bmin the minimum SOC. Kba operates the battery SOC to return back to its initial SOC. When battery SOC reaches Bmin or Bmax , high Kba value is deﬁned as the penalty factor to avoid the battery continues to discharge and charge respectively. 266

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m w (t ) = mfc (t ) + mBA (t )

Condition 3: if the fuel cell is on and SOCbamin < baSOC < SOCbaint , the fuel cell current is adjusted according to load power. Condition 4: if the fuel cell was previously oﬀ, and the average of the last 5 s of Id is larger than fuel cell maximum eﬃciency point Iopt and the time since the fuel cell was last on is larger than tofftime , the fuel cell is started. Condition 5: if the fuel cell was previously on, and the average of the last 5 s of Id is less than fuel cell maximum eﬃciency current Iopt and the time since the fuel cell was last oﬀ is larger than fuel cell minimum on time tofftime , the fuel cell is oﬀ. Condition 6: if Id on the bus is greater than ESS maximum current on the bus Iessmax , the fuel cell stays on or is turned on.

= mfc (t ) + λba Pba (t )

Trough the simpliﬁed ECMS, the fuel cell current is calculated. The supercapacitor current is then calculated by other methods like PI based control strategy and ﬁltration based control strategy. To prove that the simpliﬁcation of ECMS can not reach optimal resolution, a HEOS strategy is designed. The ﬁrst part of HEOS is simpliﬁed ECMS to calculate fuel cell current and the second part is OMCS to calculate supercapacitor current.

4. Experiment implementation

Fuel cell current Ifc is decided by current demand on DC bus Id and battery SOC value as equation (29). Fuel cell current is limited to high eﬃciency zone (4.5 A, 20 A). Beside meeting Id , fuel cell also tries to charge the battery to its SOC initial value. The charge current is calculated according to SOC value.

Imin Ich + Id < Imin Ich (baSOC ) + Id Imin ≤ Ich + Id ≤ Imax ⎨ Imax Ich + Id > Imax ⎩

4.1. Test bench description To compare the designed SECMS, RBCS and HEOS to each other, a test bench is developed. It includes PEMFC, battery, supercapacitor, DC/DC converters, measurement instruments and sensors, power supply, power load, MicroAutoBox and PC. The architecture of test bench is shown in Fig. 5. The fuel cell stack is the main power source and its parameters is shown in Table 1. Four lead acid batteries connected in series are used as the main energy storage source in the test bench. While in realistic operation, lithium ion batteries are widely used in electric vehicles. Since this test bench relies on a reference power determined by a vehicle model emulated by an active load, only the dynamic power response can be diﬀerent when switching technologies between lead-acid and Li-ion, which because the test bench does not take into account facts such as power density or weight of the batteries. Besides, the peak power from experimental driving cycle is not big enough to clearly see a diﬀerence using lead acid or Li-ion in the experiment. Moreover, if a diﬀerence appears, it would have impact on all the three compared strategies in the same way, which would not lead to a diﬀerence of results among the three of them. The capacity and rated voltage for single battery is 90 A h and 12 V respectively. Further more, the battery could be managed to replace the fuel cell as the main power source in the case of running out of hydrogen consumption or damage of fuel cell stack. Four MAXWELL supercapacitors are operated to supply peak power, with two in series and two in parallel. Each supercapacitor rated capacitance is 58 F and each rated voltage is 16 V. So supercapacitor voltage range is limited to 15 V–30 V. A boost DC/DC converter for fuel cell and a buck/boost DC/DC

⎧ Ifc =

(30)

(29)

when fuel cell reference current is decided by equation (29), the difference current Idi between Id and Ifc is supplied by battery and supercpacitor, which is decided by OMCS. The ﬂow chart of OMCS is shown in Fig. 4. The main idea of OMCS is when Idi < 0 and supercapacitor SOC SOCsc is smaller than initial supercapacitor SOC SOCint , supercapacitor ﬁrstly be charged at maximum charge current I SCmin . When supercapcitor discharge, its current I SC > 0 , when it is charged, its current I SC < 0 . when I di > I bamin and SOCsc in the range of minimum SOC and maximum SOC, the supercapacitor supplies maximum discharge current. Otherwise supercapacitor current is zero. Battery current is passively decided by diﬀerence between current demand by drive cycle, fuel cell current and supercapacitor current on DC bus. 3.2.3. Hybrid ECMS OMCS strategy Due to low energy density and the role of supercapacitor as peak power supplier, many researches on ECMS simplify the equivalent hydrogen consumption of supercapcitor into zero. On the based of equation (21), a new objective function is deﬁned as (30),

Fig. 4. Flow chart of OMCS. 267

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Fig. 5. Test bench architecture.

Fig. 6. Experimental results of battery SOC and supercapacitor SOC.

Fig. 7. Experimental results of fuel cell, battery and supercapacitor current for three control strategies.

signals to DC/DC converters. Human machine interface (HMI) in the PC is designed using the ControlDesk 4.2 program, which can monitor the predeﬁned variable's instantaneous value, tune control parameters of whole control system and record all real time variable's value in order to analyze the ﬁnal experiment results.

converter for supercapacitor are packed into one box in parallel architecture. The DC bus voltage is decided by battery voltage. Power supply and power load are programmed to supply negative power and positive power of drive cycle respectively, which are both connected to the DC BUS directly. Some measurements instruments and sensors are used to measure current and voltage of relevant power sources. MicroAutoBox Π from dSPACE is used as the control unit, which gathers all control signals needed by EMS and outputs 20 KHz PWM 268

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consumption to charge battery SOC back to initial SOC and hydrogen consumption for the end of drive cycle is deﬁned as the equivalent hydrogen consumption. It can be observed that equivalent hydrogen consumption of SECMS is the least, RBCS is the most. Taking RBCS as the basis, the decrements of SECMS and HEOS are 2.16% and 0.69%. Compared to HEOS, ECMS decreases 1.47% . As shown in Fig. 7 (a), all fuel cells of three strategies are operated at high eﬃciency zone [0.4 0.428]. Regarding to SECMS and HEOS, fuel cell currents are around maximum eﬃciency point. The fuel cell of SECMS and HEOS start at 454.8s and 462.9s respectively and they keep working until end of drive cycle. The current of RBCS changes along with current demand at DC bus. It can be noted that the fuel cell degradation of RBCS is the highest due to its high dynamical change of current reference. In Fig. 7 (c), supercapacitor only supplies peak power conforming to original design objective. It also can be observed that Supercapacitor charge/discharge at diﬀerent time and hold diﬀerent period of time for three strategies. Battery passively supplies other DC bus current besides fuel cell current and supercapacitor current on the DC bus as Fig. 7(b). In Fig. 8, the fuel cell reference currents of SECMS and HEOS are shown. It can be observed that HEOS has more current spikes than SECMS. The current spikes of HEOS are supplied by supercapacitor of SECMS to make sure the steady of fuel cell current. In the whole, SECMS consumes the least hydrogen, has most steady current change and less on/oﬀ cycles. HEOS has similar fuel cell current as SECMS but with more spike current change and consumes more hydrogen. RBCS has highest hydrogen consumption. The fuel cell dynamical current changes much and frequent on/oﬀ cycles are included which mean that it has the most degradation for overall drive cycle.

Table 2 Experiment results. EMSs

SECMS

RBCS

HEOS

Initial supercapacitor and battery SOC Final supercapacitor SOC Final battery SOC Hydrogen consumption (L) Equivalent hydrogen consumption (L)

0.8 0.5865 0.7915 194.07 225.06

0.8 0.8304 0.7946 210.35 230.02

0.8 0.8199 0.7906 194.77 228.42

Fig. 8. Compare results of fuel cell reference current for SECMS and HEOS.

4.2. Experiment results The WVUCITY drive cycle, New York Bus drive cycle and LA92 drive cycle are connected together to test the performance of three designed EMSs under constrains condition. The whole drive cycle time is 3443s and the distance is 22.11 km. Considering the possibility of fuel cell failure or out of hydrogen in the tank, battery SOC range is set from 0.75 to 0.85, its initial SOC is set 0.8. Regarding supercapacitor, its minimum, maximum and initial SOC is set 0.1, 0.9 and 0.8 respectively. From the experimental test of the emulated driving cycle, the downsized load power proﬁle of drive cycle, the battery SOC, supercapacitor SOC, fuel cell current, battery current and supercapacitor current for three control strategies are shown in Fig. 6 and Fig. 7. The experiment comparison results are shown as Table 2. From Figs. 6 and 7, It can be observed that regarding to SCEMS, the experimental results of fuel cell current and battery current are similar to simulated results. The ﬁnal simulation result of battery SOC is 0.792 similar to the experiment value 0.7915 and this diﬀerence is negligible. Supercapcitor current is sensitive to current requirement of drive cycle, fuel cell current and battery SOC, therefore small sensor noise and process calculation noise lead to the little diﬀerence between experimental supercapacitor current and simulated one of SECMS. Considering low energy density of supercapacitor and its role as the supplier of peak power, this diﬀerence can be neglected. As shown in Fig. 6, battery SOC and supercapacitor SOC of three control strategies are in the limited range (0.75, 0.85) and (0.1, 0.9) respectively. According to Table 2, the ﬁnal battery SOC of three control strategies are almost equal to initial value. The diﬀerence between initial battery SOC and ﬁnal battery SOC of HEOS is the largest among three control strategies. The hydrogen consumption of Table 2 represent fuel cell real hydrogen consumption at the end of drive cycle. The ﬁnal battery SOC, supercapacitor SOC and hydrogen consumption at the end of drive cycle are diﬀerent for three control strategies. In order to make a fair comparison of hydrogen consumption, the ﬁnal battery and supercapacitor SOC variation should also be considered into the equivalent hydrogen consumption. Because three battery ﬁnal SOC are all less than the initial value, so when the drive cycle is over, fuel cell is operated at maximum eﬃciency point 9.5 A to charge battery until its SOC value increase to initial SOC value. Similarity for supercapacitor, it's also charged for SEMS or discharged for RBCS and HEOS till the SOC of supercpacitor back to 0.8. The sum of hydrogen

5. Conclusion A SECMS strategy is proposed for FCHEV supplied by three power sources: fuel cell, battery and supercapacitor. Fuel cell is operated to seek for the maximum eﬃciency point in the deﬁned high eﬃciency zone, while the battery assumes as the main energy storage source to buﬀer energy demand by vehicle and the supercpacitor dedicates to provide the peak power. This work originally takes into account the hydrogen consumption of all three components in the adopted objective function of energy management strategy. In order to prove the superiority of the new approach, the RBCS and HEOS have also been implemented. The WVUCITY, New York Bus and LA92 drive cycles have been emulated on experimental test bench with the three above control strategies. The experiment results show that the proposed SECMS has the least hydrogen consumption and it oﬀers a longest durability of fuel cell. Appendix A. Supplementary data Supplementary data related to this article can be found at http://dx. doi.org/10.1016/j.jpowsour.2018.05.078. References [1] H. Li, A. Ravey, A. N'Diaye, A. Djerdir, Equivalent consumption minimization strategy for hybrid electric vehicle powered by fuel cell, battery and supercapacitor, IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, 2016, pp. 4401–4406. [2] W. Zhang, J. Li, L. Xu, M. Ouyang, Optimization for a fuel cell/battery/capacity tram with equivalent consumption minimization strategy, Energy Convers. Manag. 134 (2017) 59–69. [3] J. Peng, H. He, R. Xiong, Rule based energy management strategy for a seriesparallel plug-in hybrid electric bus optimized by dynamic programming, Appl. Energy 185 (2017) 1633–1643. [4] C. Manzie, O. Grondin, A. Sciarretta, G. Zito, Ecms controller robustness in ﬂex-fuel hybrid vehicles, J. Dyn. Syst. Meas. Contr. 136 (6) (2014) 064504. [5] Q. Li, H. Yang, Y. Han, M. Li, W. Chen, A state machine strategy based on droop control for an energy management system of pemfc-battery-supercapacitor hybrid tramway, Int. J. Hydrogen Energy 41 (36) (2016) 16148–16159. [6] H. Yun, S. Liu, Y. Zhao, J. Xie, C. Liu, Z. Hou, K. Wang, Energy management for fuel

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