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Economic energy management strategy design and simulation for a dual-stack fuel cell electric vehicle
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Economic energy management strategy design and simulation for a dual-stack fuel cell electric vehicle Xu Han a,b, Feiqiang Li c, Tao Zhang a,b, Tong Zhang a,b, Ke Song a,b,* a
Clean Energy Automotive Engineering Center, Tongji University, 4800 Caoan Road, 201804 Shanghai, China School of Automotive Studies, Tongji University, 4800 Caoan Road, 201804 Shanghai, China c Zhengzhou Yutong Bus Co.,Ltd, Yutong Industrial Park, Yutong Road, 450061 Zhengzhou, China b
article info
abstract
Article history:
This paper presents the design and simulation validation of two energy management
Received 6 December 2016
strategies for dual-stack fuel cell electric vehicles. With growing concerns about
Received in revised form
environmental issues and the fossil energy crisis, finding alternative methods for vehicle
10 January 2017
propulsion is necessary. Proton exchange membrane (PEM) fuel cell systems are now
Accepted 15 January 2017
considered to be one of the most promising alternative energy sources. In this work, the
Available online xxx
challenge of further improving the fuel economy and extending the driving range of a fuel cell vehicle is addressed by a dual-stack fuel cell system with specific energy management
Keywords:
strategies. An efficiency optimization strategy and an instantaneous optimization strategy
Fuel cell
are proposed. Simulation validation for each strategy is conducted based on a dual-stack
Electric vehicle
fuel cell electric vehicle model which follows the new European driving cycle (NEDC).
Multi-stack
Simulation results show that a dual-stack fuel cell system with proposed energy man-
Energy management strategy
agement strategies can significantly improve the fuel economy of a fuel cell vehicle and thus lengthen the driving range while being able to keep the start-stop frequency of the fuel cell stack within a reasonable range. © 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Introduction The use of fossil energy in the transportation sector is considered to be one of the greenhouse gas emission sources. It is estimated that emissions from fossil energy burned in transportation (carbon dioxide, carbon monoxide, nitrogen oxides, etc.) contribute to around 20% of the world's total greenhouse gas emissions [1]. Moreover, the increasingly scarce fossil resources are another reason, which makes customers, manufacturers and governments turn to other energy sources [2,3]. In this context, electric vehicles have
become a focus of the automotive industry. With the goal of zero local-emissions, hydrogen, especially in its application to fuel cells (FCs), is now considered to be an ideal alternative energy source for electric vehicles [4e7]. Because of its zero emissions, silent operation, high power density and flexible operating range, the proton exchange membrane (PEM) fuel cell, as the most promising fuel cell type, has shown tremendous development potential [8]. With its development in the past 10e15 years, PEM fuel cell technology has significantly improved, especially in power density and in the ability to follow dynamic loads, which makes it viable for automotive applications [9]. However, technical and scientific barriers
* Corresponding author. Clean Energy Automotive Engineering Center, Tongji University, 4800 Caoan Road, 201804 Shanghai, China. Fax: þ86 21 69583894. E-mail address: [email protected] (K. Song). http://dx.doi.org/10.1016/j.ijhydene.2017.01.085 0360-3199/© 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Please cite this article in press as: Han X, et al., Economic energy management strategy design and simulation for a dual-stack fuel cell electric vehicle, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.01.085
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including electrical efficiency, durability and reliability of fuel cell systems still highly restrict the commercialization of fuel cell technology in the transportation industry [10,11]. In this context, the multi-stack fuel cell (MFC) system has been presented as a possible solution. With specific fluidic and electrical architectures, MFC systems can show better performance in operation efficiency. Also, durability could be dramatically increased through the use of a degraded mode, which is enabled by its redundancy. Finally, an MFC system consists of smaller modules, which are easier to be physically integrated into the whole automotive application [12]. MFC systems have already been used by the industry in different applications, including power supplies for space exploration vehicles [13], or as air-independent propulsion for submarines [14]. Some studies have already been published on multi-stack fuel cell systems and their components. A fuel cell system is generally composed of a fuel cell stack and multiple ancillaries [15], with which the operation of the whole system can be maintained and controlled. The ancillaries include a reactant supply system, a power converter, and a thermal management system. As for the multi-stack fuel cell system, modifications in these ancillaries need to be conducted. One parallel and two series configurations of fluidic architecture are proposed in Refs. [16] and [17]. The performance and reliability of each configuration are also illustrated. As for the electric architecture, parallel and series configurations with or without a power converter are proposed in Ref. [18]. Converter topologies for MFC have also been studied. DCeACeDC topologies are proved to be most suitable for an MFC system [19]. A fault tolerant operation mode has been another research focus of the MFC system. With implementation of a bypass system, a degraded operating mode can be realized, so that the reliability of the whole fuel cell system can be improved [20]. Power distribution methods of a parallel MFC system were also investigated in Ref. [21]. Three power splitting algorithms were proposed and their influence on FC efficiency characteristics was simulated. Only limited studies conducted on MFC systems are available at this time. About 75% of existing research on MFC systems is devoted to MFC architecture, degraded mode operation, fault tolerance and converter topologies with a main focus on improvement in reliability of FC systems [12]. The energy management strategy of an MFC system and the enhancement of fuel economy have not been given much attention until now. Very little research has focused on the application of MFC systems to vehicles under specific energy management strategies. The objective of this paper is to present a design for two novel energy management strategies for dual-stack fuel cell electric vehicles and to apply these two strategies to a parallel dual-stack fuel cell system as one possible form of an MFC system. The authors have also validated the fuel economy optimization effect on a fuel cell vehicle model. The paper is structured as follows: In Section “Dual-stack fuel cell electric vehicle model”, modeling of a dual-stack fuel cell electric vehicle is introduced, which is based on the fourth generation fuel cell vehicle (G4) developed by Tongji University. In Section “Energy management strategy design for dual-stack fuel cell
system”, two energy management strategies are proposed, which aim to minimize the fuel consumption of the fuel cell system, and thus extend the driving range of the fuel cell vehicle. In Section “Simulation results and discussion”, simulation results and a discussion on proposed power distribution strategies are presented, with a comparison of the performance of the dual-stack system under both strategies and the traditional one-stack fuel cell system. In Section “Conclusions” conclusions and final remarks are discussed.
Dual-stack fuel cell electric vehicle model In this research, the fourth-generation fuel cell vehicle (G4) developed by Tongji University is regarded as a prototype vehicle. G4 is a fuel cell vehicle powered by a 70 kW fuel cell stack and a battery with an energy capacity of 3.0 kWh. All the modeling and simulation followed G4 parameters. Some key parameters used in this paper are listed in Table 1. Fig. 1 shows the power-efficiency curve of the 70 kW fuel cell stack based on experimental data. According to the power-efficiency curve, when the power output is less than 10 kW, efficiency is relatively low. With the increase of power output, efficiency improves substantially and reaches a peak at 49.09%, then slightly decreases but still stays in a relatively high efficiency interval. To apply the multi-stack concept on G4, a parallel dualstack fuel cell system is proposed (Fig. 2) [22]. Each fuel cell stack has a power of 35 kW, and is followed up with a DC/DC converter, which ensures the connection of a battery and a fuel cell stack. Moreover, the current of each fuel cell stack can be controlled with a DC/DC converter. Here, a perfectly regulated current is considered for each converter, so that the power output of fuel cell stacks and the battery can be set by a control system according to energy management strategies.
Vehicle model Based on the vehicle speed and the vehicle parameters, the traction power (Pt ) of the vehicle is calculated as follows:
Table 1 e Some key parameters of G4. Component/Parameter Vehicle weight/kg Wheel base/m Front axle load/% Height of center of mass/m Windward area(A)/m2 Coefficient of drag (Cr) Coefficient of rolling resistance (Cd) Battery voltage/V Battery capacity/Ah Fuel cell power/kW H2 storage/g PMSM rated voltage/V PMSM rated/peak power/kW PMSM rated/peak RPM/r/min PMSM maximum torque/Nm
Value 2000 2.6 60 0.5 1.96 0.35 0.009 375 8 70 3600 375 48/110 4600/11,500 230
Please cite this article in press as: Han X, et al., Economic energy management strategy design and simulation for a dual-stack fuel cell electric vehicle, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.01.085
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Fig. 1 e Power-efficiency curve based on experimental data.
Fig. 2 e Dual-stack FC system structure.
Pt ¼
v mgCr cos q þ 0:5rCd Av2 þ ma þ mg sin q 1000
(1)
where v is the vehicle speed (m/s), Cr is the rolling resistance, Cd is the aerodynamic drag coefficient of the vehicle, A is the windward area of the vehicle (m2), r is the air mass density (kg/m3), a is the vehicle acceleration (m/s2), and q is the road angle of the road. Here the road angle q is assumed to be 0 throughout the whole driving cycle. Given v and a, the traction power (Pt ) can be calculated, which can be regarded as the power demand for the battery and the dual-stack fuel cell system.
SOCðtÞ ¼ SOCinit
Zt hIbat ðtÞdt
(2)
t0
where SOCinit is the initial value of SOC, CN represents rated capacity (the capacity of the battery in standard condition, changing with service), and h is the columbic efficiency of the battery (in which the discharging condition is assumed to be 1, and when charging, it is assumed to be 0.98). The battery current Ibat ðtÞ can be described as follows:
Ibat ðtÞ ¼
Voc ðtÞ
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi V2OC ðtÞ 4Rbat ðtÞPbat ðtÞ
Battery model Batteries and ultra-capacitors are the main types of energy storage devices employed in electric and hybrid electric vehicles. In this paper, the proposed vehicle model G4 uses lithium-ion batteries, which are widely used in electric vehicles as energy storage devices. The Battery SOC (State of charge) is one of the most important parameters of the vehicle and battery energy management system. Several SOC evaluation methods are reported, including the ampere-hour integral, open circuit voltage, neural network, and fuzzy logic methods. In this paper, the ampere-hour integral is used for SOC evaluation [23]. Thus,
1 CN
2Rbat ðtÞ
(3)
where Voc (open circuit voltage) and Rbat (internal resistance of battery pack) are a function of the battery SOC and temperature. Vbat is the battery terminal voltage, which is obtained from the open circuit voltage of the battery minus the voltage drop of internal resistance. Battery output power Pbat is defined by the following equation: Pbat ¼ Vbat ðtÞIbat ðtÞ
(4)
Fuel cell stack model The fuel cell model used in the paper is a static model based on the power vs. efficiency fuel cell model in ADVISOR, which
Please cite this article in press as: Han X, et al., Economic energy management strategy design and simulation for a dual-stack fuel cell electric vehicle, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.01.085
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neglects the dynamic behavior of a fuel cell stack, and is enough for vehicle energy analysis and fuel consumption estimation [9]. The power-efficiency curve and power-fuel consumption curve are obtained from experimental data, and are modeled as lookup table of power demand. Fuel consumption H of the fuel cell stack is calculated as follows: H¼
T X
4hðtÞ
(5)
0
where T is operating time (s), h is fuel used (g) at each operating point obtained from the lookup table, and 4 is temperature correction factor, which can be obtained as follows: 3:1 ð95 Tc Þ : 4¼1þ 75
(6)
Here Tc is coolant temperature ( C) of the fuel cell stack.
DC/DC converter model For the reason that the voltage output of the fuel cell system is not in line with the voltage range of other powertrain components and auxiliary devices, a DC/DC converter is needed to process the power output, so that a constant voltage can be ensured in the DC bus. In the case of a dual-stack fuel cell system, fuel cell association is realized with the use of power converters. In this study, two boost DC/DC converters in parallel architecture are used as power converters, which is shown in Fig. 2. This architecture provides redundancy and enables the individual control of each fuel cell stack, so that the energy management could be realized [24]. For the purpose of vehicle energy analysis and fuel consumption estimation in this work, boost DC/DC converter could be modeled as a function having constant efficiency as follows [9]:
of the FC stack, a better performance in fuel economy can be achieved by optimizing the operating point of the fuel cell stack. An MFC system provides more flexibility in power distribution between sub-stacks, so that the optimization of fuel cell stack operating points can therefore be realized. In this section, two energy management strategies are proposed based on the dual-stack fuel cell vehicle model. The objective of the energy management strategy is to optimize the fuel consumption economy and thus extend the driving range of fuel cell vehicles. The energy management strategies are designed under the following three assumptions: 1. The 35 kW FC stack has the same power-efficiency characteristic curve as the 70 kW FC stack. 2. The behavior of two sub-stacks is supposed to be ideally the same. 3. The behavior of a fuel cell is regarded to be constant (Degradation and operation condition dependence are not taking into consideration).
Efficiency optimization strategy
where Pfc is the output power, hDCDC is the efficiency of DC/DC converter, which is regarded as a constant of 0.95, Pfc is the input power.
According to fuel cell power-efficiency characteristics, low and high efficiency interval can be observed. With the introduction of a dual-stack fuel cell system, higher efficiency of the whole system can be reached through power distribution strategy, which makes sure each sub-stack can be more possible to work at high efficiency intervals, so that the economic optimization of the whole FC system can be realized. Efficiency optimization strategy aims to distribute the total power demand Preq (required power for load) between the two sub-stacks, to allow the dual stack efficiency to reach the maximum value at each moment. Based on the experimental data from the power-efficiency curve, power output (P1 , P2 ) and efficiency (h1 , h2 ) of both sub-stacks at each operating point can be obtained. Each sub-stack covers part of the total power demand, so that adequate power can be provided. The efficiency of the dual-stack fuel cell hdual is defined as follows:
Electric motor model
Preq ¼ P1 þ P2
Pfc ¼ hDCDC Pfc
(7)
(8)
and The electric motor converts the electrical power supplied by the fuel cell and battery into a rotational mechanical power (torque and speed). This conversion involves an efficiency loss hEM in the motor and controller, rotor inertia, and the motor's torque speed-dependent torque capability. In this research, the permanent magnet synchronous motor (PMSM) is considered a static model based on the EM efficiency map in ADVISOR, which is modeled as lookup table indexed by rotor speed and output torque. The static model of electric motor is precise enough for fuel consumption calculation with reduced simulation time.
Energy management strategy design for dualstack fuel cell system Considering the dynamic power demand of a vehicle in an actual driving cycle and the power-efficiency characteristics
hdual ¼
P1 h1
þ Ph22
P1 þ P2
:
(9)
Table 2 e Efficiency optimization strategy power distribution data (Part). Preq 0 500 1000 e 16,000 16,500 17,000
P1
P2
Preq
P1
P2
0 500 1000 e 16,000 16,500 9000
0 0 0 e 0 0 8000
17,500 e 40,000 40,500 41,000 41,500 e
9500 e 24,750 25,250 25,750 26,250 e
8000 e 15,250 15,250 15,250 15,250 e
The values in bold represents the required power for load Preq.
Please cite this article in press as: Han X, et al., Economic energy management strategy design and simulation for a dual-stack fuel cell electric vehicle, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.01.085
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Fig. 3 e Power output distribution with efficiency optimization strategy.
Fig. 4 e (a) Operating points of main sub-stack (efficiency optimization strategy) and (b) operating points of additional substack (efficiency optimization strategy). Please cite this article in press as: Han X, et al., Economic energy management strategy design and simulation for a dual-stack fuel cell electric vehicle, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.01.085
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An algorithm has been made to calculate all possible power distributions. At each power demand point, the pair of P1 and P2 which reaches hdual:max is regarded as the target power distribution data for the efficiency optimization strategy. Calculation results are shown in Table 2. Complete power distribution results can be found in Fig. 3. Here, P1 and P2 represent the power output of each sub-stack. According to efficiency optimization strategy, one of the sub-stacks works as a main stack (P1), which exports power throughout the whole power demand range. The other substack however works as an auxiliary one (P2), which starts to work only when the power demand is over 17 kW. Power output of the auxiliary sub-stack is designed to be restricted in high efficiency intervals (>8 kW), so that the low power output with worse efficiency can be avoided. Therefore, the optimization of fuel economy can be realized. In Fig. 4(a) and (b), all the operating points of each substack under the efficiency optimization strategy throughout the whole power demand range are marked on the powerefficiency curve. Operating points of the main stack, which serves as a normal open stack, cover the entire powerefficiency curve. While for the auxiliary stack, a low power output interval is avoided. With an efficiency optimization strategy, one of the two sub-stacks is able to work in the high efficiency interval all the time.
Fig. 5 e Power-efficiency curve with efficiency optimization strategy.
fuel consumption function was established with the introduction of equivalent fuel consumption of a battery. A simulation and dynamometer test was performed to validate the instantaneous optimization control strategy. Results showed that this control strategy can dramatically improve fuel economy while battery SOC can be maintained at a reasonable range. Instantaneous optimization strategy keeps the power loss PLOSS ðtÞ at a minimum level. PLOSS is based on the following equation:
Pb ðtÞ hfcm hfc Pfc ðtÞ hfcm hfc ðtÞ þ Pb ðtÞð1 hdis ðtÞÞ þ hDCDC hchr : PLOSS Pfc ðtÞ; Pb ðtÞ ¼ > > > : P ðtÞ h h ðtÞ þ P ðtÞð1 h ðtÞÞ þ P ðtÞh ð1 h Þ b b fc fcm fc chr chr dis 8 > > > <
Through the optimization of operating points, which is realized by the power distribution under the efficiency optimization strategy, a power-efficiency curve of the dual-stack fuel cell system can be improved (Fig. 5). Compared with the one-stack fuel cell system, a dual-stack fuel cell with efficiency optimization strategy shows a much faster efficiency ramp up when the power demand is relatively low. Accordingly, peak efficiency appears earlier with around a 10 kW lower power output.
(10)
Here, Pfc and Pb stand for output of FC and battery. hfc , hfcm and hfc stand for FC efficiency, maximum FC efficiency and average FC efficiency. hchr , hdis , hchr and hdis stand for charging and discharging efficiency of battery and the average value. hDCDC is efficiency of DC/DC (constant 0.95). To apply this equation to the dual-stack fuel cell situation, power loss of both sub-stacks need to be considered and calculated. Therefore, the power loss of a dual-stack fuel cell PLOSS:dual can be rewritten as follows:
Pb ðtÞ hfcm hfc Pfc1 ðtÞ hfcm hfc1 ðtÞ þ Pfc2 ðtÞ hfcm hfc2 ðtÞ þ Pb ðtÞð1 hdis ðtÞÞ þ hDCDC hchr : PLOSS:dual Pfc1 ðtÞ; Pfc2 ðtÞ; Pb ðtÞ ¼ > > > : P ðtÞ h h ðtÞ þ P ðtÞ h h ðtÞ þ P ðtÞð1 h ðtÞÞ þ P ðtÞh ð1 h Þ b b fc1 fc2 fcm fc1 fcm fc2 chr chr dis 8 > > > <
(11)
Instantaneous optimization strategy To take the coordination between battery and fuel cell into consideration, and to further enhance the fuel economy of the whole fuel cell system, an instantaneous strategy based on the Minimum Loss Power Algorithm (MLPA) was proposed by Ke Song and Tong Zhang in 2013 [25]. The instantaneous
Here, Pfc1 and Pfc2 stand for power output of each sub-stack, while hfc1 and hfc2 stand for the efficiency of each sub-stack. According to Eq. (10), with consideration of different SOC working conditions, all possible combinations of Pfc1 ; Pfc2 ; Pb have been calculated, and the set of data which achieves the minimum PLOSS:dual is regarded as target data for instantaneous
Please cite this article in press as: Han X, et al., Economic energy management strategy design and simulation for a dual-stack fuel cell electric vehicle, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.01.085
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optimization strategy. Three-dimensional power output control rules can be obtained for both sub-stacks (Fig. 6(a) and (b)). FC power output control rules with instantaneous optimization strategy are shown in Fig. 6(c), in which the two substacks are regarded as a whole. Similar to the efficiency optimization strategy, two substacks are also divided into a main stack and an auxiliary
7
stack under instantaneous optimization strategy. Nevertheless, with consideration of battery power output and SOC working conditions, further optimization of sub-stack operating points can be realized. With battery covering part of the power demand, operating points in a low efficiency interval can be further avoided, not only for the auxiliary stack, but also for the main stack.
Fig. 6 e (a) Power output control rules of main stack (instantaneous optimization strategy); (b) power output control rules of additional stack (instantaneous optimization strategy, and (c) FC power output control rules with instantaneous optimization strategy. Please cite this article in press as: Han X, et al., Economic energy management strategy design and simulation for a dual-stack fuel cell electric vehicle, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.01.085
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dramatic improvement in dual-stack FC efficiency throughout the entire range of power demand.
Simulation results and discussion
Fig. 7 e Power demandeefficiency curve comparison of one-stack FC and dual-stack FC under two strategies.
As is shown in Fig. 6(a) and (b), under instantaneous optimization strategies, a dual-stack FC system remains shut down when the power demand is under 0.25 kW. By that time, battery serves as the main power source. With the increase of power demand, the main stack starts to work when the power demand reaches 0.25 kW. The auxiliary stack works only when the power demand is over 14.5 kW. Power outputs of both sub-stacks are restricted in a specific range, which is from 0.9 kW to 23 kW, so that the efficient operation of fuel cell stack can be guaranteed. Besides, with an instantaneous optimization strategy, the dual-stack FC is designed to offer extra energy when the power demand is between 3e9 kW and 15e19 kW. As is marked on Fig. 6(c), in these two power demand intervals, total power output of two sub-stacks is higher than required power Preq , so that the battery can be charged with extra energy provided by FC stack, to compensate power consumption in the low power demand situation. Another interesting point, as shown in Fig. 6(a) and (b), is in many areas of the power output control rules, the power output of each sub-stack remains constant, which means the power output of the FC stack can be stabilized to some extent. Thus, frequent power output changes can be avoided, which helps protect the fuel cell stack. In view of the aforementioned instantaneous optimization strategy characteristics, a further enhanced dual-stack power efficiency curve can be expected. Fig. 7 shows the power demandeefficiency curve of the two proposed power distribution strategies. Remarkable optimization effect of instantaneous optimization strategy can be observed. Because of the postponed startup point of the dual-stack FC system, the efficiency ramp power spike no longer exists in the power demandeefficiency curve. Instead, a starting efficiency point of over 45% can be achieved. What's more, compared with the efficiency optimization strategy, efficiency improvement also appears in high power output interval. Fig. 7 shows the
With consideration of the typical usage of a vehicle and the aim of fuel economy assessment, the new European driving cycle (NEDC) is selected as test driving cycle in this work. Simulation conditions are as follows: The initial SOC value is 0.95 while depleting to the end SOC value of 0.3. Mass of hydrogen is 3600 g, total volume of hydrogen is 40.88 gal. Initial pressure and temperature of hydrogen are 35 Mpa and 298.15 K, respectively. End pressure and temperature of hydrogen are 2 Mpa and 317.15 K. Table 3 shows the simulation results of two proposed energy management strategies. Strategy 1 is the efficiency optimization strategy and Strategy 2 is the instantaneous optimization strategy. For comparison, the simulation result of a one-stack fuel cell system is also listed in the first column. Within one NEDC, fuel consumption of the dual-stack fuel cell is 190.8 g with an efficiency optimization strategy, which is 28.1% less than the fuel consumption of the one-stack fuel cell. With the same fuel consumption of 3600 g, the dual-stack fuel cell provides a 34.9% longer driving range compared with the one-stack FC. With the instantaneous optimization strategy, 35.9% of fuel consumption can be saved within one NEDC, which is 7.8% higher than that of the efficiency optimization strategy. From the driving range side, 3600 g H2 can afford 239.4 km, which is 49.8% longer than that of the one-stack fuel cell, and 14.9% better than that of the efficiency optimization strategy. Fig. 8(a) and (b) shows the power output of each sub-stack during one NEDC under efficiency optimization and instantaneous optimization strategies, respectively. By comparison, power output of the traditional one-stack FC with the same simulation condition is shown in Fig. 8(c). To simplify the analysis procedure, efficient operation points and inefficient operation points are defined as follows. Generally, fuel cell operating points with efficiency higher than 40% are regarded as efficient points here. Fuel cell operating points with an efficiency lower than 40% are defined as inefficient points. Based on the power-efficiency curve, the output cut-off point for a one-stack fuel cell would be around 12 kW, and for a dual-stack fuel cell, the cut-off point would be around 6 kW. According to the aforementioned definition, operation points analysis is done based on the simulation data and results as shown in Fig. 9(a). For a one-stack fuel cell, the fuel cell output exactly follows the power demand, which is completely decided by NEDC driving cycle. During one NEDC,
Table 3 e Simulation results of the two proposed power distribution strategies. Simulation Fuel consumption (1 NEDC driving cycle) Driving range (3600 g H2)
One-stack FC
Dual-stack FC Strategy 1
Dual-stack FC Strategy 2
265.3 g 159.8 km
190.8 g (28.1%) 215.6 km (þ34.9%)
170.0 g (35.9%) 239.4 km (þ49.8%)
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Fig. 8 e (a) Power output of each sub-stack under instantaneous optimization strategy, (b) power output of each sub-stack under efficiency optimization strategy, and (c) power output of one-stack FC.
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Fig. 9 e (a) Statistics of operating points distribution (NEDC) and (b) statistics of start-stop frequency (NEDC).
the one-stack fuel cell works at inefficient operating points for 69% of cycle time. 11% is the FC stack shutdown. However, the fuel cell stack is able to work at efficient operating points for only 20% of the cycle time. As for the dual-stack fuel cell with efficiency optimization strategy, operating conditions for the main stack undergo a remarkable improvement compared with the one-stack fuel cell. 23% of the inefficient operating points are converted into efficient operating points. The auxiliary stack works for only 16% of the NEDC, and all operating points are counted as efficient. For a dual-stack fuel cell with instantaneous optimization strategy, the operating condition for the main stack is further improved, while auxiliary stack operation remains basically unchanged. However, all inefficient operating points of the main stack are ideally avoided. Here, the main stack is either
shutdown or working at optimal efficiency. The main stack remains shutdown for 40% of the cycle time, which is 29% more than before. However, a longer fuel cell stack shutdown time leads to concerns about the increase of start-stop frequency, which may influence the performance of an FC stack in the long term. Fig. 9(b) shows the start-stop frequency of one-stack fuel cell and dual-stack fuel cell with different power distribution strategies. For a one-stack fuel cell, start-stop frequency is entirely related to the test driving cycle. Obviously, a zero power demand point may lead to a one time start-stop incident. During one NEDC, 18 start-stops appear. However, for a dual-stack fuel cell, the situation of each sub-stack depends on the power distribution strategy. For the main stack in a dual-stack fuel cell system, although the turnoff time can be longer, no extra start-stop
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Table 4 e Capital cost analysis of dual-stack system.
occurs compared with the one-stack FC. That's because the fuel cell stack turn-off only happens at a low power demand, which is only slightly above zero according to the power distribution strategies. Therefore, the start-stop frequency stays the same with a one-stack fuel cell. As for the auxiliary stack in a dual-stack fuel cell system, the start-stop frequency depends on the turn-on threshold value, which is decided by the power distribution strategy. With an efficiency optimization strategy, the turn-on threshold value for the auxiliary stack is 17 kW. Power demand fluctuation around 17 kW may lead to one start-stop incident for the fuel stack. During one NEDC, 13 start-stops occur in the auxiliary stack, which is five times less than the start-stop occurrence rate in the main stack. For instantaneous optimization strategy, the turn-on threshold value is 14.5 kW. All together, 21 start-stops appear during one NEDC cycle, which represents a slight increase but is still acceptable. Here, an interesting correlation between the start-stop frequency of the auxiliary stack and the turn-on threshold value can be observed. Therefore, the start-stop frequency can be controlled by adjusting the turn-on threshold value according to the actual driving conditions of the vehicle. Based on the obtained simulation results in this study, increase of the turn-on threshold value could lead to the decrease of startstop frequency. However, this value cannot be enlarged without limitation. To realize the optimal control of the whole fuel cell system, turn-on threshold value could be determined with an overall consideration of both durability and economy performance of fuel cell system. With the consideration of the application of dual-stack fuel cell system, a capital cost analysis of the proposed system has been accomplished. Here, the cost of gas pipe, fluid pipe and wiring harness are neglected. Table 4 shows that, the introduction of dual-stack fuel cell system would lead to a cost increase around 23,000 CYN. However, taking into account the significant enhancement in fuel consumption and driving range, also compared with the total cost of a fuel cell vehicle, dual-stack fuel cell system still shows its advantage and potential in application.
Conclusions The dual-stack fuel cell system has proven effective and capable of improving the operation efficiency of the fuel cell
system which, in turn, improves the economy performance. With the proposed efficiency optimization and instantaneous optimization strategies, fuel consumption can be dramatically reduced. With certain amount of H2, dual-stack fuel cell vehicle can afford for a remarkable extended driving distance. Besides, efficiency optimization strategy has proven capable of reducing start-stop frequency, which is beneficial to the durability of the fuel cell stack. Instantaneous optimization strategy is an ideal power distribution strategy, in which the power outputs of both sub-stacks are restricted to certain range, so that the low efficiency operating points can be completely avoided. Moreover, power output changes of fuel cell stacks can be reduced to some extent, which is favorable for the protection of the fuel cell. Although startstop frequency is slightly increased, adjustment for fuel cell stack turn-on threshold value seems to be possible for startstop frequency control. However, proposed power distribution strategies and simulations in this work are conducted under assumptions. Discussions and conclusions represent preliminary results. Further experiments and research are needed to validate the economical optimization effect of the dual-stack fuel cell system under proposed power distribution strategies.
Acknowledgements The authors would like to thank the National Key Technology R&D Program (2015BAG06B01), the National Key Scientific Instrument and Equipment Development Project (2012YQ150256), and the Education Reform project of Tongji University (4250144904/007) for their kind financial support.
references
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Economic energy management strategy design and simulation for a dual-stack fuel cell electric vehicle Xu Han a,b, Feiqiang Li c, Tao Zhang a,b, Tong Zhang a,b, Ke Song a,b,* a
Clean Energy Automotive Engineering Center, Tongji University, 4800 Caoan Road, 201804 Shanghai, China School of Automotive Studies, Tongji University, 4800 Caoan Road, 201804 Shanghai, China c Zhengzhou Yutong Bus Co.,Ltd, Yutong Industrial Park, Yutong Road, 450061 Zhengzhou, China b
article info
abstract
Article history:
This paper presents the design and simulation validation of two energy management
Received 6 December 2016
strategies for dual-stack fuel cell electric vehicles. With growing concerns about
Received in revised form
environmental issues and the fossil energy crisis, finding alternative methods for vehicle
10 January 2017
propulsion is necessary. Proton exchange membrane (PEM) fuel cell systems are now
Accepted 15 January 2017
considered to be one of the most promising alternative energy sources. In this work, the
Available online xxx
challenge of further improving the fuel economy and extending the driving range of a fuel cell vehicle is addressed by a dual-stack fuel cell system with specific energy management
Keywords:
strategies. An efficiency optimization strategy and an instantaneous optimization strategy
Fuel cell
are proposed. Simulation validation for each strategy is conducted based on a dual-stack
Electric vehicle
fuel cell electric vehicle model which follows the new European driving cycle (NEDC).
Multi-stack
Simulation results show that a dual-stack fuel cell system with proposed energy man-
Energy management strategy
agement strategies can significantly improve the fuel economy of a fuel cell vehicle and thus lengthen the driving range while being able to keep the start-stop frequency of the fuel cell stack within a reasonable range. © 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Introduction The use of fossil energy in the transportation sector is considered to be one of the greenhouse gas emission sources. It is estimated that emissions from fossil energy burned in transportation (carbon dioxide, carbon monoxide, nitrogen oxides, etc.) contribute to around 20% of the world's total greenhouse gas emissions [1]. Moreover, the increasingly scarce fossil resources are another reason, which makes customers, manufacturers and governments turn to other energy sources [2,3]. In this context, electric vehicles have
become a focus of the automotive industry. With the goal of zero local-emissions, hydrogen, especially in its application to fuel cells (FCs), is now considered to be an ideal alternative energy source for electric vehicles [4e7]. Because of its zero emissions, silent operation, high power density and flexible operating range, the proton exchange membrane (PEM) fuel cell, as the most promising fuel cell type, has shown tremendous development potential [8]. With its development in the past 10e15 years, PEM fuel cell technology has significantly improved, especially in power density and in the ability to follow dynamic loads, which makes it viable for automotive applications [9]. However, technical and scientific barriers
* Corresponding author. Clean Energy Automotive Engineering Center, Tongji University, 4800 Caoan Road, 201804 Shanghai, China. Fax: þ86 21 69583894. E-mail address: [email protected] (K. Song). http://dx.doi.org/10.1016/j.ijhydene.2017.01.085 0360-3199/© 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Please cite this article in press as: Han X, et al., Economic energy management strategy design and simulation for a dual-stack fuel cell electric vehicle, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.01.085
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including electrical efficiency, durability and reliability of fuel cell systems still highly restrict the commercialization of fuel cell technology in the transportation industry [10,11]. In this context, the multi-stack fuel cell (MFC) system has been presented as a possible solution. With specific fluidic and electrical architectures, MFC systems can show better performance in operation efficiency. Also, durability could be dramatically increased through the use of a degraded mode, which is enabled by its redundancy. Finally, an MFC system consists of smaller modules, which are easier to be physically integrated into the whole automotive application [12]. MFC systems have already been used by the industry in different applications, including power supplies for space exploration vehicles [13], or as air-independent propulsion for submarines [14]. Some studies have already been published on multi-stack fuel cell systems and their components. A fuel cell system is generally composed of a fuel cell stack and multiple ancillaries [15], with which the operation of the whole system can be maintained and controlled. The ancillaries include a reactant supply system, a power converter, and a thermal management system. As for the multi-stack fuel cell system, modifications in these ancillaries need to be conducted. One parallel and two series configurations of fluidic architecture are proposed in Refs. [16] and [17]. The performance and reliability of each configuration are also illustrated. As for the electric architecture, parallel and series configurations with or without a power converter are proposed in Ref. [18]. Converter topologies for MFC have also been studied. DCeACeDC topologies are proved to be most suitable for an MFC system [19]. A fault tolerant operation mode has been another research focus of the MFC system. With implementation of a bypass system, a degraded operating mode can be realized, so that the reliability of the whole fuel cell system can be improved [20]. Power distribution methods of a parallel MFC system were also investigated in Ref. [21]. Three power splitting algorithms were proposed and their influence on FC efficiency characteristics was simulated. Only limited studies conducted on MFC systems are available at this time. About 75% of existing research on MFC systems is devoted to MFC architecture, degraded mode operation, fault tolerance and converter topologies with a main focus on improvement in reliability of FC systems [12]. The energy management strategy of an MFC system and the enhancement of fuel economy have not been given much attention until now. Very little research has focused on the application of MFC systems to vehicles under specific energy management strategies. The objective of this paper is to present a design for two novel energy management strategies for dual-stack fuel cell electric vehicles and to apply these two strategies to a parallel dual-stack fuel cell system as one possible form of an MFC system. The authors have also validated the fuel economy optimization effect on a fuel cell vehicle model. The paper is structured as follows: In Section “Dual-stack fuel cell electric vehicle model”, modeling of a dual-stack fuel cell electric vehicle is introduced, which is based on the fourth generation fuel cell vehicle (G4) developed by Tongji University. In Section “Energy management strategy design for dual-stack fuel cell
system”, two energy management strategies are proposed, which aim to minimize the fuel consumption of the fuel cell system, and thus extend the driving range of the fuel cell vehicle. In Section “Simulation results and discussion”, simulation results and a discussion on proposed power distribution strategies are presented, with a comparison of the performance of the dual-stack system under both strategies and the traditional one-stack fuel cell system. In Section “Conclusions” conclusions and final remarks are discussed.
Dual-stack fuel cell electric vehicle model In this research, the fourth-generation fuel cell vehicle (G4) developed by Tongji University is regarded as a prototype vehicle. G4 is a fuel cell vehicle powered by a 70 kW fuel cell stack and a battery with an energy capacity of 3.0 kWh. All the modeling and simulation followed G4 parameters. Some key parameters used in this paper are listed in Table 1. Fig. 1 shows the power-efficiency curve of the 70 kW fuel cell stack based on experimental data. According to the power-efficiency curve, when the power output is less than 10 kW, efficiency is relatively low. With the increase of power output, efficiency improves substantially and reaches a peak at 49.09%, then slightly decreases but still stays in a relatively high efficiency interval. To apply the multi-stack concept on G4, a parallel dualstack fuel cell system is proposed (Fig. 2) [22]. Each fuel cell stack has a power of 35 kW, and is followed up with a DC/DC converter, which ensures the connection of a battery and a fuel cell stack. Moreover, the current of each fuel cell stack can be controlled with a DC/DC converter. Here, a perfectly regulated current is considered for each converter, so that the power output of fuel cell stacks and the battery can be set by a control system according to energy management strategies.
Vehicle model Based on the vehicle speed and the vehicle parameters, the traction power (Pt ) of the vehicle is calculated as follows:
Table 1 e Some key parameters of G4. Component/Parameter Vehicle weight/kg Wheel base/m Front axle load/% Height of center of mass/m Windward area(A)/m2 Coefficient of drag (Cr) Coefficient of rolling resistance (Cd) Battery voltage/V Battery capacity/Ah Fuel cell power/kW H2 storage/g PMSM rated voltage/V PMSM rated/peak power/kW PMSM rated/peak RPM/r/min PMSM maximum torque/Nm
Value 2000 2.6 60 0.5 1.96 0.35 0.009 375 8 70 3600 375 48/110 4600/11,500 230
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Fig. 1 e Power-efficiency curve based on experimental data.
Fig. 2 e Dual-stack FC system structure.
Pt ¼
v mgCr cos q þ 0:5rCd Av2 þ ma þ mg sin q 1000
(1)
where v is the vehicle speed (m/s), Cr is the rolling resistance, Cd is the aerodynamic drag coefficient of the vehicle, A is the windward area of the vehicle (m2), r is the air mass density (kg/m3), a is the vehicle acceleration (m/s2), and q is the road angle of the road. Here the road angle q is assumed to be 0 throughout the whole driving cycle. Given v and a, the traction power (Pt ) can be calculated, which can be regarded as the power demand for the battery and the dual-stack fuel cell system.
SOCðtÞ ¼ SOCinit
Zt hIbat ðtÞdt
(2)
t0
where SOCinit is the initial value of SOC, CN represents rated capacity (the capacity of the battery in standard condition, changing with service), and h is the columbic efficiency of the battery (in which the discharging condition is assumed to be 1, and when charging, it is assumed to be 0.98). The battery current Ibat ðtÞ can be described as follows:
Ibat ðtÞ ¼
Voc ðtÞ
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi V2OC ðtÞ 4Rbat ðtÞPbat ðtÞ
Battery model Batteries and ultra-capacitors are the main types of energy storage devices employed in electric and hybrid electric vehicles. In this paper, the proposed vehicle model G4 uses lithium-ion batteries, which are widely used in electric vehicles as energy storage devices. The Battery SOC (State of charge) is one of the most important parameters of the vehicle and battery energy management system. Several SOC evaluation methods are reported, including the ampere-hour integral, open circuit voltage, neural network, and fuzzy logic methods. In this paper, the ampere-hour integral is used for SOC evaluation [23]. Thus,
1 CN
2Rbat ðtÞ
(3)
where Voc (open circuit voltage) and Rbat (internal resistance of battery pack) are a function of the battery SOC and temperature. Vbat is the battery terminal voltage, which is obtained from the open circuit voltage of the battery minus the voltage drop of internal resistance. Battery output power Pbat is defined by the following equation: Pbat ¼ Vbat ðtÞIbat ðtÞ
(4)
Fuel cell stack model The fuel cell model used in the paper is a static model based on the power vs. efficiency fuel cell model in ADVISOR, which
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neglects the dynamic behavior of a fuel cell stack, and is enough for vehicle energy analysis and fuel consumption estimation [9]. The power-efficiency curve and power-fuel consumption curve are obtained from experimental data, and are modeled as lookup table of power demand. Fuel consumption H of the fuel cell stack is calculated as follows: H¼
T X
4hðtÞ
(5)
0
where T is operating time (s), h is fuel used (g) at each operating point obtained from the lookup table, and 4 is temperature correction factor, which can be obtained as follows: 3:1 ð95 Tc Þ : 4¼1þ 75
(6)
Here Tc is coolant temperature ( C) of the fuel cell stack.
DC/DC converter model For the reason that the voltage output of the fuel cell system is not in line with the voltage range of other powertrain components and auxiliary devices, a DC/DC converter is needed to process the power output, so that a constant voltage can be ensured in the DC bus. In the case of a dual-stack fuel cell system, fuel cell association is realized with the use of power converters. In this study, two boost DC/DC converters in parallel architecture are used as power converters, which is shown in Fig. 2. This architecture provides redundancy and enables the individual control of each fuel cell stack, so that the energy management could be realized [24]. For the purpose of vehicle energy analysis and fuel consumption estimation in this work, boost DC/DC converter could be modeled as a function having constant efficiency as follows [9]:
of the FC stack, a better performance in fuel economy can be achieved by optimizing the operating point of the fuel cell stack. An MFC system provides more flexibility in power distribution between sub-stacks, so that the optimization of fuel cell stack operating points can therefore be realized. In this section, two energy management strategies are proposed based on the dual-stack fuel cell vehicle model. The objective of the energy management strategy is to optimize the fuel consumption economy and thus extend the driving range of fuel cell vehicles. The energy management strategies are designed under the following three assumptions: 1. The 35 kW FC stack has the same power-efficiency characteristic curve as the 70 kW FC stack. 2. The behavior of two sub-stacks is supposed to be ideally the same. 3. The behavior of a fuel cell is regarded to be constant (Degradation and operation condition dependence are not taking into consideration).
Efficiency optimization strategy
where Pfc is the output power, hDCDC is the efficiency of DC/DC converter, which is regarded as a constant of 0.95, Pfc is the input power.
According to fuel cell power-efficiency characteristics, low and high efficiency interval can be observed. With the introduction of a dual-stack fuel cell system, higher efficiency of the whole system can be reached through power distribution strategy, which makes sure each sub-stack can be more possible to work at high efficiency intervals, so that the economic optimization of the whole FC system can be realized. Efficiency optimization strategy aims to distribute the total power demand Preq (required power for load) between the two sub-stacks, to allow the dual stack efficiency to reach the maximum value at each moment. Based on the experimental data from the power-efficiency curve, power output (P1 , P2 ) and efficiency (h1 , h2 ) of both sub-stacks at each operating point can be obtained. Each sub-stack covers part of the total power demand, so that adequate power can be provided. The efficiency of the dual-stack fuel cell hdual is defined as follows:
Electric motor model
Preq ¼ P1 þ P2
Pfc ¼ hDCDC Pfc
(7)
(8)
and The electric motor converts the electrical power supplied by the fuel cell and battery into a rotational mechanical power (torque and speed). This conversion involves an efficiency loss hEM in the motor and controller, rotor inertia, and the motor's torque speed-dependent torque capability. In this research, the permanent magnet synchronous motor (PMSM) is considered a static model based on the EM efficiency map in ADVISOR, which is modeled as lookup table indexed by rotor speed and output torque. The static model of electric motor is precise enough for fuel consumption calculation with reduced simulation time.
Energy management strategy design for dualstack fuel cell system Considering the dynamic power demand of a vehicle in an actual driving cycle and the power-efficiency characteristics
hdual ¼
P1 h1
þ Ph22
P1 þ P2
:
(9)
Table 2 e Efficiency optimization strategy power distribution data (Part). Preq 0 500 1000 e 16,000 16,500 17,000
P1
P2
Preq
P1
P2
0 500 1000 e 16,000 16,500 9000
0 0 0 e 0 0 8000
17,500 e 40,000 40,500 41,000 41,500 e
9500 e 24,750 25,250 25,750 26,250 e
8000 e 15,250 15,250 15,250 15,250 e
The values in bold represents the required power for load Preq.
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Fig. 3 e Power output distribution with efficiency optimization strategy.
Fig. 4 e (a) Operating points of main sub-stack (efficiency optimization strategy) and (b) operating points of additional substack (efficiency optimization strategy). Please cite this article in press as: Han X, et al., Economic energy management strategy design and simulation for a dual-stack fuel cell electric vehicle, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.01.085
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An algorithm has been made to calculate all possible power distributions. At each power demand point, the pair of P1 and P2 which reaches hdual:max is regarded as the target power distribution data for the efficiency optimization strategy. Calculation results are shown in Table 2. Complete power distribution results can be found in Fig. 3. Here, P1 and P2 represent the power output of each sub-stack. According to efficiency optimization strategy, one of the sub-stacks works as a main stack (P1), which exports power throughout the whole power demand range. The other substack however works as an auxiliary one (P2), which starts to work only when the power demand is over 17 kW. Power output of the auxiliary sub-stack is designed to be restricted in high efficiency intervals (>8 kW), so that the low power output with worse efficiency can be avoided. Therefore, the optimization of fuel economy can be realized. In Fig. 4(a) and (b), all the operating points of each substack under the efficiency optimization strategy throughout the whole power demand range are marked on the powerefficiency curve. Operating points of the main stack, which serves as a normal open stack, cover the entire powerefficiency curve. While for the auxiliary stack, a low power output interval is avoided. With an efficiency optimization strategy, one of the two sub-stacks is able to work in the high efficiency interval all the time.
Fig. 5 e Power-efficiency curve with efficiency optimization strategy.
fuel consumption function was established with the introduction of equivalent fuel consumption of a battery. A simulation and dynamometer test was performed to validate the instantaneous optimization control strategy. Results showed that this control strategy can dramatically improve fuel economy while battery SOC can be maintained at a reasonable range. Instantaneous optimization strategy keeps the power loss PLOSS ðtÞ at a minimum level. PLOSS is based on the following equation:
Pb ðtÞ hfcm hfc Pfc ðtÞ hfcm hfc ðtÞ þ Pb ðtÞð1 hdis ðtÞÞ þ hDCDC hchr : PLOSS Pfc ðtÞ; Pb ðtÞ ¼ > > > : P ðtÞ h h ðtÞ þ P ðtÞð1 h ðtÞÞ þ P ðtÞh ð1 h Þ b b fc fcm fc chr chr dis 8 > > > <
Through the optimization of operating points, which is realized by the power distribution under the efficiency optimization strategy, a power-efficiency curve of the dual-stack fuel cell system can be improved (Fig. 5). Compared with the one-stack fuel cell system, a dual-stack fuel cell with efficiency optimization strategy shows a much faster efficiency ramp up when the power demand is relatively low. Accordingly, peak efficiency appears earlier with around a 10 kW lower power output.
(10)
Here, Pfc and Pb stand for output of FC and battery. hfc , hfcm and hfc stand for FC efficiency, maximum FC efficiency and average FC efficiency. hchr , hdis , hchr and hdis stand for charging and discharging efficiency of battery and the average value. hDCDC is efficiency of DC/DC (constant 0.95). To apply this equation to the dual-stack fuel cell situation, power loss of both sub-stacks need to be considered and calculated. Therefore, the power loss of a dual-stack fuel cell PLOSS:dual can be rewritten as follows:
Pb ðtÞ hfcm hfc Pfc1 ðtÞ hfcm hfc1 ðtÞ þ Pfc2 ðtÞ hfcm hfc2 ðtÞ þ Pb ðtÞð1 hdis ðtÞÞ þ hDCDC hchr : PLOSS:dual Pfc1 ðtÞ; Pfc2 ðtÞ; Pb ðtÞ ¼ > > > : P ðtÞ h h ðtÞ þ P ðtÞ h h ðtÞ þ P ðtÞð1 h ðtÞÞ þ P ðtÞh ð1 h Þ b b fc1 fc2 fcm fc1 fcm fc2 chr chr dis 8 > > > <
(11)
Instantaneous optimization strategy To take the coordination between battery and fuel cell into consideration, and to further enhance the fuel economy of the whole fuel cell system, an instantaneous strategy based on the Minimum Loss Power Algorithm (MLPA) was proposed by Ke Song and Tong Zhang in 2013 [25]. The instantaneous
Here, Pfc1 and Pfc2 stand for power output of each sub-stack, while hfc1 and hfc2 stand for the efficiency of each sub-stack. According to Eq. (10), with consideration of different SOC working conditions, all possible combinations of Pfc1 ; Pfc2 ; Pb have been calculated, and the set of data which achieves the minimum PLOSS:dual is regarded as target data for instantaneous
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optimization strategy. Three-dimensional power output control rules can be obtained for both sub-stacks (Fig. 6(a) and (b)). FC power output control rules with instantaneous optimization strategy are shown in Fig. 6(c), in which the two substacks are regarded as a whole. Similar to the efficiency optimization strategy, two substacks are also divided into a main stack and an auxiliary
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stack under instantaneous optimization strategy. Nevertheless, with consideration of battery power output and SOC working conditions, further optimization of sub-stack operating points can be realized. With battery covering part of the power demand, operating points in a low efficiency interval can be further avoided, not only for the auxiliary stack, but also for the main stack.
Fig. 6 e (a) Power output control rules of main stack (instantaneous optimization strategy); (b) power output control rules of additional stack (instantaneous optimization strategy, and (c) FC power output control rules with instantaneous optimization strategy. Please cite this article in press as: Han X, et al., Economic energy management strategy design and simulation for a dual-stack fuel cell electric vehicle, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.01.085
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dramatic improvement in dual-stack FC efficiency throughout the entire range of power demand.
Simulation results and discussion
Fig. 7 e Power demandeefficiency curve comparison of one-stack FC and dual-stack FC under two strategies.
As is shown in Fig. 6(a) and (b), under instantaneous optimization strategies, a dual-stack FC system remains shut down when the power demand is under 0.25 kW. By that time, battery serves as the main power source. With the increase of power demand, the main stack starts to work when the power demand reaches 0.25 kW. The auxiliary stack works only when the power demand is over 14.5 kW. Power outputs of both sub-stacks are restricted in a specific range, which is from 0.9 kW to 23 kW, so that the efficient operation of fuel cell stack can be guaranteed. Besides, with an instantaneous optimization strategy, the dual-stack FC is designed to offer extra energy when the power demand is between 3e9 kW and 15e19 kW. As is marked on Fig. 6(c), in these two power demand intervals, total power output of two sub-stacks is higher than required power Preq , so that the battery can be charged with extra energy provided by FC stack, to compensate power consumption in the low power demand situation. Another interesting point, as shown in Fig. 6(a) and (b), is in many areas of the power output control rules, the power output of each sub-stack remains constant, which means the power output of the FC stack can be stabilized to some extent. Thus, frequent power output changes can be avoided, which helps protect the fuel cell stack. In view of the aforementioned instantaneous optimization strategy characteristics, a further enhanced dual-stack power efficiency curve can be expected. Fig. 7 shows the power demandeefficiency curve of the two proposed power distribution strategies. Remarkable optimization effect of instantaneous optimization strategy can be observed. Because of the postponed startup point of the dual-stack FC system, the efficiency ramp power spike no longer exists in the power demandeefficiency curve. Instead, a starting efficiency point of over 45% can be achieved. What's more, compared with the efficiency optimization strategy, efficiency improvement also appears in high power output interval. Fig. 7 shows the
With consideration of the typical usage of a vehicle and the aim of fuel economy assessment, the new European driving cycle (NEDC) is selected as test driving cycle in this work. Simulation conditions are as follows: The initial SOC value is 0.95 while depleting to the end SOC value of 0.3. Mass of hydrogen is 3600 g, total volume of hydrogen is 40.88 gal. Initial pressure and temperature of hydrogen are 35 Mpa and 298.15 K, respectively. End pressure and temperature of hydrogen are 2 Mpa and 317.15 K. Table 3 shows the simulation results of two proposed energy management strategies. Strategy 1 is the efficiency optimization strategy and Strategy 2 is the instantaneous optimization strategy. For comparison, the simulation result of a one-stack fuel cell system is also listed in the first column. Within one NEDC, fuel consumption of the dual-stack fuel cell is 190.8 g with an efficiency optimization strategy, which is 28.1% less than the fuel consumption of the one-stack fuel cell. With the same fuel consumption of 3600 g, the dual-stack fuel cell provides a 34.9% longer driving range compared with the one-stack FC. With the instantaneous optimization strategy, 35.9% of fuel consumption can be saved within one NEDC, which is 7.8% higher than that of the efficiency optimization strategy. From the driving range side, 3600 g H2 can afford 239.4 km, which is 49.8% longer than that of the one-stack fuel cell, and 14.9% better than that of the efficiency optimization strategy. Fig. 8(a) and (b) shows the power output of each sub-stack during one NEDC under efficiency optimization and instantaneous optimization strategies, respectively. By comparison, power output of the traditional one-stack FC with the same simulation condition is shown in Fig. 8(c). To simplify the analysis procedure, efficient operation points and inefficient operation points are defined as follows. Generally, fuel cell operating points with efficiency higher than 40% are regarded as efficient points here. Fuel cell operating points with an efficiency lower than 40% are defined as inefficient points. Based on the power-efficiency curve, the output cut-off point for a one-stack fuel cell would be around 12 kW, and for a dual-stack fuel cell, the cut-off point would be around 6 kW. According to the aforementioned definition, operation points analysis is done based on the simulation data and results as shown in Fig. 9(a). For a one-stack fuel cell, the fuel cell output exactly follows the power demand, which is completely decided by NEDC driving cycle. During one NEDC,
Table 3 e Simulation results of the two proposed power distribution strategies. Simulation Fuel consumption (1 NEDC driving cycle) Driving range (3600 g H2)
One-stack FC
Dual-stack FC Strategy 1
Dual-stack FC Strategy 2
265.3 g 159.8 km
190.8 g (28.1%) 215.6 km (þ34.9%)
170.0 g (35.9%) 239.4 km (þ49.8%)
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Fig. 8 e (a) Power output of each sub-stack under instantaneous optimization strategy, (b) power output of each sub-stack under efficiency optimization strategy, and (c) power output of one-stack FC.
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Fig. 9 e (a) Statistics of operating points distribution (NEDC) and (b) statistics of start-stop frequency (NEDC).
the one-stack fuel cell works at inefficient operating points for 69% of cycle time. 11% is the FC stack shutdown. However, the fuel cell stack is able to work at efficient operating points for only 20% of the cycle time. As for the dual-stack fuel cell with efficiency optimization strategy, operating conditions for the main stack undergo a remarkable improvement compared with the one-stack fuel cell. 23% of the inefficient operating points are converted into efficient operating points. The auxiliary stack works for only 16% of the NEDC, and all operating points are counted as efficient. For a dual-stack fuel cell with instantaneous optimization strategy, the operating condition for the main stack is further improved, while auxiliary stack operation remains basically unchanged. However, all inefficient operating points of the main stack are ideally avoided. Here, the main stack is either
shutdown or working at optimal efficiency. The main stack remains shutdown for 40% of the cycle time, which is 29% more than before. However, a longer fuel cell stack shutdown time leads to concerns about the increase of start-stop frequency, which may influence the performance of an FC stack in the long term. Fig. 9(b) shows the start-stop frequency of one-stack fuel cell and dual-stack fuel cell with different power distribution strategies. For a one-stack fuel cell, start-stop frequency is entirely related to the test driving cycle. Obviously, a zero power demand point may lead to a one time start-stop incident. During one NEDC, 18 start-stops appear. However, for a dual-stack fuel cell, the situation of each sub-stack depends on the power distribution strategy. For the main stack in a dual-stack fuel cell system, although the turnoff time can be longer, no extra start-stop
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Table 4 e Capital cost analysis of dual-stack system.
occurs compared with the one-stack FC. That's because the fuel cell stack turn-off only happens at a low power demand, which is only slightly above zero according to the power distribution strategies. Therefore, the start-stop frequency stays the same with a one-stack fuel cell. As for the auxiliary stack in a dual-stack fuel cell system, the start-stop frequency depends on the turn-on threshold value, which is decided by the power distribution strategy. With an efficiency optimization strategy, the turn-on threshold value for the auxiliary stack is 17 kW. Power demand fluctuation around 17 kW may lead to one start-stop incident for the fuel stack. During one NEDC, 13 start-stops occur in the auxiliary stack, which is five times less than the start-stop occurrence rate in the main stack. For instantaneous optimization strategy, the turn-on threshold value is 14.5 kW. All together, 21 start-stops appear during one NEDC cycle, which represents a slight increase but is still acceptable. Here, an interesting correlation between the start-stop frequency of the auxiliary stack and the turn-on threshold value can be observed. Therefore, the start-stop frequency can be controlled by adjusting the turn-on threshold value according to the actual driving conditions of the vehicle. Based on the obtained simulation results in this study, increase of the turn-on threshold value could lead to the decrease of startstop frequency. However, this value cannot be enlarged without limitation. To realize the optimal control of the whole fuel cell system, turn-on threshold value could be determined with an overall consideration of both durability and economy performance of fuel cell system. With the consideration of the application of dual-stack fuel cell system, a capital cost analysis of the proposed system has been accomplished. Here, the cost of gas pipe, fluid pipe and wiring harness are neglected. Table 4 shows that, the introduction of dual-stack fuel cell system would lead to a cost increase around 23,000 CYN. However, taking into account the significant enhancement in fuel consumption and driving range, also compared with the total cost of a fuel cell vehicle, dual-stack fuel cell system still shows its advantage and potential in application.
Conclusions The dual-stack fuel cell system has proven effective and capable of improving the operation efficiency of the fuel cell
system which, in turn, improves the economy performance. With the proposed efficiency optimization and instantaneous optimization strategies, fuel consumption can be dramatically reduced. With certain amount of H2, dual-stack fuel cell vehicle can afford for a remarkable extended driving distance. Besides, efficiency optimization strategy has proven capable of reducing start-stop frequency, which is beneficial to the durability of the fuel cell stack. Instantaneous optimization strategy is an ideal power distribution strategy, in which the power outputs of both sub-stacks are restricted to certain range, so that the low efficiency operating points can be completely avoided. Moreover, power output changes of fuel cell stacks can be reduced to some extent, which is favorable for the protection of the fuel cell. Although startstop frequency is slightly increased, adjustment for fuel cell stack turn-on threshold value seems to be possible for startstop frequency control. However, proposed power distribution strategies and simulations in this work are conducted under assumptions. Discussions and conclusions represent preliminary results. Further experiments and research are needed to validate the economical optimization effect of the dual-stack fuel cell system under proposed power distribution strategies.
Acknowledgements The authors would like to thank the National Key Technology R&D Program (2015BAG06B01), the National Key Scientific Instrument and Equipment Development Project (2012YQ150256), and the Education Reform project of Tongji University (4250144904/007) for their kind financial support.
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