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Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles
international journal of hydrogen energy xxx (xxxx) xxx
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Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles Sina Changizian a, Pouria Ahmadi a,*, Mehrdad Raeesi b, Nader Javani c a
School of Mechanical Engineering, Faculty of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Iran School of Automotive Engineering. Faculty of Automotive Engineering, Iran University of Science and Technology, P. O. Box 1681666110, Tehran, Iran c Faculty of Mechanical Engineering, Yildiz Technical University, Besiktas, Istanbul, 34349, Turkey b
highlights
graphical abstract
Simulation of a fuel cell hybrid vehicle with Ultra Capacitor in Amsim software. Investigation of vehicle performance
in
three
main
driving
cycles. Vehicle
performance
Improve-
ment in NEDC driving cycle via an innovative controller. Optimization
of
battery
packs
state of charge.
article info
abstract
Article history:
In this research study, a fuel cell-electric hybrid car is studied. This car includes an electric
Received 5 November 2019
motor that is connected to a fuel cell and a complex which includes a battery pack and an
Received in revised form
Ultracapacitor. The assessment of this hybrid vehicle is conducted by using various driving
21 December 2019
cycles such as FTP-75 driving cycle, NEDC driving cycle and SFTP-SC03 driving cycle. Bat-
Accepted 2 January 2020
tery state of charge (SoC) and hydrogen fuel consumption are the effective parameters
Available online xxx
influencing the vehicle performance. For analysing the performance of this vehicle, an innovative computational model is considered. In this innovative computational model, an
Keywords:
accurate control strategy is considered in order to control the power demand, staying the
Fuel cell-electric hybrid car
battery packs and the Ultracapacitor state of charge in a limited domain. Results show that
Driving cycles
in NEDC driving cycle, by means of using Ultracapacitor in this model, 3.3% reduction in
Vehicle control strategy
fuel consumption and 20.2% decrease in the difference between initial and final State of
Ultracapacitor
Charge (SoC) in battery pack can be achieved. In addition, a robust regenerative braking
Regenerative braking
control strategy is used in order to recover some parts of the wasted energy in braking driving modes. © 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
* Corresponding author. E-mail address: [email protected] (P. Ahmadi). https://doi.org/10.1016/j.ijhydene.2020.01.015 0360-3199/© 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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Introduction Global warming and air pollution have destructive effects on our lives nowadays. In this regard, the transportation system plays a major contribution to Earth’s warming. Thus, for diluting the side effects of climate changes, global warming, and air pollution, transportation which is accounted for about 30% of the total world greenhouse gas emission should be investigated. A transient phase between traditional transportation system and zero-emission transportation system could be a hybrid system like a combination of solid oxide fuel cell/stirling system [1]. Alternative fuels such as CNG, LNG, biofuel, hydrogen, and electricity are those emitting less emission. However, hybrid and full battery electric vehicles are gained more attention as they have zero-emission in their operation phase [2]. Due to limited sources of energy and preciousness of it, management in the usage of energy sources is very important. In this regard, in 2013 International Energy Agency (IEA) has estimated that 20% percent of total carbon dioxide which is produced globally is a result of the road transport system and 21% of global energy consumption is share by road transport vehicles. Although recently, new approaches in electricity as a clean and sustainable energy source has caused that in 2018 the growth of electricity usage as a power source reaches 20% [3]. However, in 2016, about 55% of the world’s energy consumption has dominated by the transportation industry and 31% of CO2 emissions is the share of the transportation industry, which is considerable [4]. Also, some car manufacturing companies focus on producing a new revolution of vehicles that have high efficiency and low emissions. In this regard, the advantages and disadvantages of using fuel cell vehicles are studied [5]. Old-fashioned cars are the main contributors to toxic emissions released from their internal combustion engines. Thus, fuel cells electric vehicles have attracted more attention for their zero-emission of polluting gases like CO2. Chemical reactions of hydrogen of fuel and oxygen of air cause producing energy in fuel cells. This considerable amount of energy releases from the electrochemical process [6]. Fuel cells and electric motors are very suitable as modern propulsion. Although high cost, low durability and hydrogen storage problems are the major obstacles for fuel cell vehicles, lower emission has caused that these type of vehicles become more attractive for automakers. PEM fuel cells are often preferred for transportation applications and other stationary applications based on their significant greenhouse gas emission reduction and renewable energy source [7]. Their low operating temperature, high power density, fast start-up, system robustness, and other considerable features convinced automobile factories turning to produce them [8]. Researches have shown that according to the type-approval fuel consumption and emissions limit, fuel cell vehicles are far better than ICEVs (Internal Combustion Engines Vehicles). In this regard, the total emission for ICEVs is five times more intensive than FCVs [9]. In addition, the well-to-wheel (WTW) efficiencies in fuel cell vehicle is about 27%. However, this amount for ordinary vehicles with internal combustion engines reaches only 17% that is less than fuel cell vehicles considerably [10]. It should be mentioned that some expert
paid attention to the hybridization of ICEVs with FCVs. Ezzat and Dincer [11] have designed a new integrated energy system that includes an ICE with a FC. The results of this study show that energy and exergy efficiencies of the system are 38.66% and 36.2% respectively. The usage of FCs in many countries where fossil fuels are used to produce electricity is under consideration especially when it is possible to produce clean energy sources like hydrogen from wasted energy sources [12]. It is because of emission limits which happen in traditional power plants. However, recent research has been conducted in this regard that shows the effect of future energy networks on the limitations of hydrogen production emissions. It is worth mentioning that by use of hydrogen energy grid, total energy consumption will be managed [13]. Although FCVs are categorized as zero-emission vehicles, fuel cell degradation should be noticed. Chen et al. [14] have considered a new method to investigate the degradation process of FCs. They considered environmental conditions on the degradation process and by means of the extreme machine learning process, they investigate the overall degradation model of a FCEV. Apart from hydrogen, ethanol also can be used as a secondary fuel in a fuel cell car. The results of Purnima and Jayanti [15] study shown that the use of methanol as a second source of fuel in FCVs, the mileage of the car will be increased. Solid oxide fuel cell stack is considered by some experts too. Scarlett Chen et al. [16] have enrolled two control strategies based on analysing solid oxide fuel cell stack performance. Lithium-ion battery packs are robust energy sources nowadays. The performance of FCV could be improved by using the robust battery pack. In this regard global dynamic optimization of energy between the battery pack and the fuel cell output, can play a major role in fuel consumption and durability of the vehicle [17]. Since the simulation of the FCV could be very effective in the investigation of vehicle performance, a robust simulation could be very important. Fathabadi [18] simulates a fuel cell vehicle in the presence of battery pack. His study shows that simulation of FC has a considerable accuracy comprise to experimental data. To find the effect of using a battery pack in a fuel cell car, Tanc et al. [19] compare a fuel cell car with a fuel cell-hybrid electric car. The results show that by means of using the battery pack in a fuel cell car, energy and hydrogen consumption are improved with 8% and 32% respectively. Even, FCEVs could be used as the main propulsion system for a bus. Hou and Song [20] study results include a novel hierarchical optimal energy management strategy with a vehicle-to-cloud connectivity. A global optimization which using dynamic programing have been used in order to minimize the battery degradation cost and hybrid energy storage system cost that contains battery/ Ultracapacitor hybrid energy storage system. In order to decrease the slow dynamic behavior of fuel cell vehicles in fast power transitions and also to store the braking energy during deceleration driving conditions, Ultracapacitor can be a suitable choice for better performance of the fuel cellhybrid electric vehicle. Also, only an innovative and robust controller can be able to control the amount of power demand in these types of vehicles [21].
Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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The total performance of a proton exchange membrane fuel cell hybrid vehicle (PEMFC-HEV) strictly relates to the energy management system and energy strategy controls. In this regard, Lu¨ et al. [22] have concluded that energy management systems should be able to respond to fast dynamic needs so long as the performance and durability of the car don’t subside. Even the stack lifespan of FCV can be affected by various control strategies. Wang et al. [23] have shown that control strategy can play a major role in stack lifespan in a fuel cell car. In this regard, the use of three stacks instead of one stack in a FCV could have better durability than one stack considering the strategic hysteresis control strategy of power management [24]. An innovative control strategy that involves Markov Chain has been mentioned by Zhou et al. [25]. Their study results indicate that Markov Chain algorithm which estimates driving patterns can achieve up to 98.16% identification accuracy. This innovative control strategy saves 3.26% hydrogen consumption respectively. Innovative optimization algorithms like Grey Wolf Optimizer (GWO) algorithm can be used to optimize the energy management in a FCEV which uses Ultracapacitor as a second energy source. Djerioui et al. [26] have used GWO method to find optimized FC reference current while the objective function minimizes supercapacitors and DC bus energies. In order to reduce fuel consumption in FCEVs strategy control and optimization process play significant roles [27]. Control strategies and investigating the performance and behavior of FCEVs can be done on a large scale. In this regard, Moghaddas-Tafreshi et al. [28] design a multi-carrier energy system in order to investigate the behavior of FCEVs in an energy grid. They use a new method which is information gap decision theory (IGDT) to predict the behavior of FCEVs in the whole energy grid system. It could be mentioned that in low scales, usage of fuel cell could be effective. The results from an experimental study of Gonzalez et al. [29] show that suitable energy management even in an unmanned modeled ground vehicle could be effective. Despite FCVs are considered as zero-emission vehicles, the procedure of vehicle product and fuel product should be considered. In this regard, the fuel cell degradation problem is studied by Ahmadi et al. [30]. Their results show that by considering various driving patterns and fuel cell degradation, the average fuel economy is affected by about 23%. In addition, fuel cell degradation can be subsided by adding a regenerative braking system to this type of vehicle. Also, a well-to-wheel (WTW) is applied in order to enhance the analysis of FCVs emissions. Liu et al. [31] have compared the WTW energy use and emissions of a HFCEV and a ICEV. Their study results show that based on WTW scenario, HFCEVs have better performance than a conventional ICEV. Dynamic loads that act to the FCVs could be harmful for the durability of the fuel cell stack. Battery pack and Ultracapacitors are suitable energy sources to prevent the degradation of fuel cell stacks. Wang et al. [32] study results indicate that The discharge and charge power capabilities of the battery and supercapacitor are important parameters in the energy management control strategy. In order to integrate FC with battery and Ultracapacitor, Fu et al. [33] analysed a hierarchical energy management strategy to study the
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performance of battery and Ultracapacitor in a FC vehicle. They found that by using battery and Ultracapacitor as a second source of energy in these types of vehicles, fuel economy, power performance and energy source life span will be optimized. Thus the purpose of this research paper is to investigate the adoption of hydrogen hybrid electric vehicles and the effect of robust control strategy at different driving cycles. It should be noted that to analyze the performance of battery pack and Ultracapacitor in different driving cycles, the state of charges (the difference between the initial state of charge and final state of charge) in battery pack and Ultracapacitor shouldn’t vary significantly. As far as discharging and charging patterns of battery pack and Ultracapacitor affect straight on their lifespan, the discharging and charging patterns should be considered. In this paper, the discharging and charging patterns have been considered in order to promote battery and Ultracapacitor lifespan. The battery provides needed energy when it is necessary and also Ultracapacitor plays its role as a huge source of energy in critical situations like fast acceleration modes. In summary, followings are the main contribution of this paper in the subject area: Overall simulation of a fuel cell hybrid-electric vehicle in presence of Ultracapacitor in Amesim software; Investigation of vehicle performance in three conventional driving cycles (NEDC, FTP-75 and SFTP-SC03 driving cycles); Improvement of vehicle performance in NEDC driving cycle by means of an innovative controller and optimization of battery pack state of charge. Reduction in hydrogen fuel consumption by means of using an Ultra capacitor pack and a robust control strategy about 3.3% in NEDC driving cycle. Four modes of regenerative braking system have been discussed in this paper and series regenerative control strategy has been chosen. Results show that in braking modes of driving cycle, regenerative braking system can save considerable amount of energy during braking modes.
Model description Vehicle specifications To dynamically model the PEM fuel cell-electric hybrid vehicle in this study, Amesim software is used. The performance of PEMFC in NEDC, FTP-75 and SFTP-SC03 driving cycles are studied. The vehicle specification for a fuel cell vehicle is given in Table 1. As it is clear, to model a fuel celle electric hybrid car, some main components are required. Some main components like fuel cell, Ultracapacitor, battery pack, electric motor, gearbox, environment conditions, driver demand conditions, electronic control unit for fuel cell and vehicle and other specific components have been considered. Main components in this regard are shown in Fig. 1.
Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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Table 1 e The Fuel cell vehicle specifications adapted from Toyota Mirai [34]. PEM fuel cell parameters Vehicle weight The maximum output power of fuel cell The heating value of hydrogen Maximum current Upper voltage limit for fuel cell Lower voltage limit for fuel cell Inferior power limit for fuel cell (Plow)
Value
Unit
1850 114 120 500 287 200 5
kg kW MJ/kg A V V KW
Fuel cell modelling Fuel cell fundamentals Fuel cells are often categorized according to the type of electrolyte being used. A common form of fuel cells which is recently used is often made of a polymer as its electrolyte and a porous carbon electrode containing platinum as its catalyst. For its fuel source, Hydrogen which is stored in a tank enters the anode part of the membrane and oxygen from air enters from the cathode side. The electrochemical reaction takes place and electricity and water are produced as illustrated in Fig. 2. Comparing hydrogen vehicles with ICEVs, we noticed that water vapor is the only by-product of these vehicles. The membrane is made in such a way to permeable to protons and not to electrons. Transferring the electrons through the electrolyte could cause the irreversibility and Ohmic potential losses in the cell. Instead, electrons pass through an external circuit which has been considered. The cathode side is designed as a region where electrons and protons combine together and produce water as the only waste substance in the cell. Although, Oxygen is injected into the cathode side from the air or in purified form. Protons membrane exchange fuel cells (PMEFC) because of their suitable advantages like low operating temperatures, high power density, fast start-up,
Fig. 2 e Schematic of a fuel cell.
system robustness and flexibility of fuel type are often preferred for transportation applications [5]. The operation of fuel cell vehicle is basically related to the chemical reactions which converts chemical energy into electrical energy respectively. Fuel and oxidant are fed into the reactant sides where the reactions occur. In the presence of electrolyte, electric current will appear under controlled conditions. This generation of electricity is zero-emission and just water and heat are the wastage of the reactions and none pollutant gas produce. Fuel cells can use different kinds of fuels but the basis of reactions that occur in their pols are similar. The reaction happens between hydrogen from fuel and oxygen from air which is injected to the cell. The reactions are below: In the cathode side:
Fig. 1 e Schematic of a simulated fuel cell hybrid-electric car in the Simcenter Amesim software [35]. Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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1 O2 þ 2Hþ þ 2e/H2 O 2
Table 2 e Specifications of the battery pack. (1)
In the anode side: H2 / 2Hþ þ 2e
(2)
So, totally reaction in fuel cells is: 2H2 ðgasÞ þ O2 ðgasÞ / H2 O þ Energy
(3)
In equation (3) the form of energy which appears in reaction is electrical power that comes with heat. The rated current which reduced during reaction in fuel cell is 0.6e0.7 A, and voltage produced by the reaction of hydrogen and oxygen is 1.23 V nominally. The absolute amount of voltage is less than 1.23 V and it is due to some loss like mass transport loss and Ohmic loss.
Fuel cell voltage The output voltage of a fuel cell strictly depends on open circuit potential of the cell which strictly depends on temperature and pressure and some reducer parameters. In the following, the polarization curve which is representing the operating voltage of a specific fuel cell has been illustrated [36]. As can be seen from Fig. 3, voltage versus current density, scaled by geometric electrode area, is typically shown in this figure. So, this curve could be useful for all the size of fuel cells. There are 5 regions illustrated in the polarization curve figure. These 5 areas have caused potential losses in output fuel cell voltage. The potential losses are as follows: activation overpotential at electrodes (1), ohmic polarization of the fuel cell (2), concentration polarization of the fuel cell (3), departure from the Nernst thermodynamic equilibrium potential (4) and departure from the maximum thermal voltage (5). The total output voltage of the fuel cell can be calculated as: Ecell ¼ E0 ðT; PÞ ha;a ha;c hr hm;a hm;c hx
(4)
Specifications
Value
Unit
Initial state of charge (SoC) Voltage Number of battery cell in parallel Number of battery cell in series Rated capacity of the battery Maximum output potential Nominal voltage
45 250.41 5 76 6.5 254 244
null Volt e e Ah Volt Volt
where in this equation, E0 ðT; PÞ is the theoretical equilibrium open-circuit potential of the cell that calculated from Nernst equation. Also, ha;a and ha;c are activation overpotentials at the anode and cathode, hr is ohmic (resistive) polarization loss, hm;a and hm;c are represented as concentration (mass transfer) polarization at anode and cathode and hx represented as the departure from the Nernst equilibrium voltage respectively. It should be noted that the Nernst equilibrium voltage calculated from waterfall analogy of voltage potential at each electrode and for the fuel cell.
Battery pack The battery pack which is used in this study is a 1.6 kWh Nickel-metal hydride battery pack which has been used in Toyota Mirai [34]. The architecture of the battery pack consists of banks in serial and parallel arrangements. Each battery bank consists of cells where the number of battery banks in series is Sbank ¼ 4; the number of battery banks in parallel is Pbank ¼ 7. The battery voltage output is calculated as follows: V ¼ V0 R:I þ V
(5)
where V is the output voltage, V0 is open-circuit voltage, and R is equivalent internal resistance, I is internal current (amp) and V is input potential (V).
Fig. 3 e Polarization curve for fuel cell with significant kinetic, ohmic, concentration, and crossover potential losses [36]. Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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140
Table 3 e Technical information of used Ultracapacitor.
Capacitance Internal resistance Initial charge Maximum charge Voltage time constant Potential at external port
Value
Unit
2610 0.000531 96 100 0.1 2.7
F Ohm null C s V
120
Velocity (Km/h)
Specifications
100 80 60 40 20
More information about battery pack is available in Table 2 [37]:
0 0
200
400
600
800
1000
1200
Time (S)
Ultracapacitor
Fig. 4 e NEDC driving cycle.
In this study, Ultracapacitor has been used in order to down size the fuel cell and battery pack indeed. In this regard, some parameters should have supplied to the model builder. These parameters are the capacitance of the Ultracapacitor (F), the internal resistance of Ultracapacitor (Ohm), Initial charge of Ultracapacitor (C), maximum charge of Ultracapacitor (C) and the voltage-time constant (s). The charge was withdrawn the Ultracapacitor is calculated by: dQ ¼ Iþ dt
(6)
The Ultracapacitor is discharging whenever the Ultracapacitor discharge terminal Iþ , is negative. And also the Ultracapacitor output voltage is calculated as follow: dVþ ¼
þ Vþ steady V
(7)
t
Electric motor output torque The amount of torque that electric motor has to deliver, is related to driver’s demand which has to be limited. Equation (5) shows the relation between limits: Tmin Tlim Tmax
(9)
Then, Tmin and Tmax the negative and positive torque corresponding either to user-defined parameters or values read in tables as a function of the operating point. The output torque Tm at port 2 is determined from the limited torque Tlim by using a first-order lag: TM ¼ 11 þ tr:s:TLim
(10)
in which, tr is a user-defined constant parameter.
and: Vþ steady ¼
Electric motor output power
Q þ R:Iþ C
(8)
The mechanical power is calculated as:
Technical information of Ultracapacitor equipment is shown in Table 3 [38].
PMec ¼ Tm :u
Electric motor
where Tm and u are respectively the torque [Nm] and rotary velocity [rad/s]. The lost power is computed either from:
The electric motor which is used in this study is a motor/ generator with its converter. Static operating conditions in the linear domain of the motor are considered. This model can be used for dynamic simulations if the establishing time of the current is fast enough compared to the dynamic of the system. More information about the motor-generator unit is in Table 4.
Plost ¼ ð1 hÞ:Pmec
(11)
(12)
The motor/generator efficiency corresponds to: Motor mode:
h ¼ 2 Pelec :Pmec
(13)
Generator mode: Table 4 e Motor/Generator description. Specifications Time constant to determine the torque Maximum positive power Minimum positive torque Maximum rotary velocity for positive torque Mean efficiency
Value
Unit
0.1
s
115 335 8000
kW N.m Rev/min
0.85
e
h ¼ Pelec :Pmec
(14)
Driving cycle To analyze the performance of an urban car, using a driving cycle is inevitable. In this regard, three driving cycle are used. SFTP-SC03 (well known in Japan), NEDC and FTP-75 driving cycles are shown in Fig. 4, Fig. 5 and Fig. 6.
Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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Sampling time: ECU state updates each sampling time. A low value will slow down the simulation and high value will lead to poor results.
100 90
Velocity (Km/h)
80 70 60
Sketches and equations
50
If the power requested by the electric motor is lower than the “inferior power limit for the fuel cell”, the power is supplied by the battery or by the Ultracapacitor that depends on their state of charges and control strategies. In this case, the fuel cell is in idle condition. When the battery SOC is lower than the “lower SOC limit”, the Fuel Cell supplies power for the electric motor and the battery charge, at its maximum efficiency operating point (if this power is large enough for the electric motor) and also for Ultracapacitor the same as battery, fuel cell supplies power requested. If the electric motor needs more power, the battery charging and/or Ultracapacitor charging will stop. The battery charge occurs until the battery SOC is greater than the “upper SOC limit” and as well as for Ultracapacitor. For better understanding Fig. 7 has been shown. It should be noticed that Ultracapacitor recharging plan is as the same as the battery pack. This ECU uses a simple control strategy to manage battery, Ultracapacitor, and fuel cell. It is presented in Fig. 8. Previously a variable Pmot0 is computed which is the power needed by the motor.
40 30 20 10 0
0
200
400
600
800
1000
1200
Time (S)
Fig. 5 e FTP-75 driving cycle. 100 90
Velocity (Km/h)
80 70 60 50 40 30 20 10 0
0
100
200
300
400
500
600
Time (S)
Fig. 6 e SFTP-SC03 driving cycle.
Strategy control This is a sub-model of the control unit for Fuel Cell. It computes the power between battery, Ultracapacitor, Fuel cell and electric motor. Parameters for the strategy: Inferior power limit for the fuel cell: if the power requested by the electric motor is under this limit, the power is supplied by the battery or Ultracapacitor that depends on control strategy and state of charge battery and Ultracapacitor. In this case, the fuel cell is in idle conditions. Lower and upper SOC limits: to control the charge of the battery and Ultracapacitor. Current and power for maximum fuel cell efficiency: for battery charging or Ultracapacitor charging, the fuel cell runs at its maximum efficiency. If the power requested by the electric motor is greater than this power, the battery charging and Ultracapacitor charging is stopped. Maximum current for fuel cell: this value limits current asked to the fuel cell if current needed by the motor is greater than this value, the battery and/or Ultracapacitor will help the fuel cell. This value should be equal to the value in the fuel cell.
Pmot ¼ jIM :VM j
(16)
where IM is motor current [A] and VM is motor voltage [V]. For different control modes, the performance of each node is mentioned in Table 5. It should be noted that in each step. Once the required current is greater than the maximum fuel cell current, the battery will in operation. Battery current is calculated as follows: IB ¼
IM ,VM IFC ,VFC VB
(17)
where IM is the motor current, VM is motor voltage, IFC is fuel cell current, VFC is fuel cell voltage and VB is the battery voltage.
Vehicle control unit strategy Vehicle Control Unit (VCU) receives information from the driver, battery and electric motor. It analyses them in order to minimize the battery consumption. . The electric motor could be used as a generator to charge the battery during braking period. The control unit sends commands to the electric motor and to the vehicle.
Parameters for time management: Voltage time constant: motor voltage is computed using a first lag order.
Fig. 7 e Control algorithm of charge and discharge of the battery.
Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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Fig. 8 e Control strategy of the motor.
Drivercommand is the torque computed using the acceleration and the braking commands from the driver, as shown in Fig. 9. Where MaxtorqME is maximum torque that electric motor can provide for traction, MintorqME is minimum torque that electric motor can provide for traction situation which is related to electric motor torque curve and Maxtorqveh is maximum braking torque for the vehicle that is demanded by the driver.
Results and discussions
As it clear, the amount of fuel consumption in all three driving cycles is closely related to the distance that the vehicle is traveled. In this regard, the amount of fuel consumption in SFTP-SC03 driving cycle is higher than others. It is necessary to mention that the traveled distance in each driving cycle is equal to 57.84 km in SFTP-SC03, 55.08 km in NEDC and 45.80 km for FTP-75 driving cycle. In Table 6 the amount of fuel consumption in each driving cycle has been shown. For a better analysis of vehicle’s performance in these driving cycles, battery state of charge and Ultracapacitor state of charge has been shown in Figs. 10 and 11, consequently.
Fuel consumption Battery state of charge (SOC) As mentioned previously, specific control strategies for better vehicle performance have been considered. The better performance of the vehicle can be shown by releasing fuel consumption. In this regard, the results of total fuel consumption in different driving cycles are shown in Fig. 10.
In addition to the fuel consumption in hybrid cars, the state of charge of the battery pack plays a major role in analyzing hybrid-electric vehicles fuel cells in each driving cycle. So, the battery state of charge in each driving cycle has been presented in Fig. 11. In all driving cycles, although battery state of
Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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Fig. 8 e (continued).
charge in SFTP-SC03 driving cycle is bigger than others, the average battery state of charge is almost constant. It means that the importance of control strategy in controlling the battery state of charge and prevention of battery pack full discharge.
innovative electronic controller has been used. As Fig. 12 shows, Ultracapacitor during the driving cycle has been used regularly. It can be deduced that in all driving cycles, the Ultracapacitor has been used where a controller will regulate its performance.
Ultracapacitor state of charge (SOC)
Power outputs
Employment of Ultracapacitors in fuel cell hybrid-electric vehicles is under consideration nowadays. Nonetheless, in this paper, an Ultracapacitor is used in order to improve the vehicle performance in each driving cycle. In this regard, an
In order to consider the impact of using the battery pack and Ultracapacitor as additional power sources, the output power curves could be a good response to prove the claim of being effective in a FCHEVs. In the following, the received power
Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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Fig. 8 e (continued).
from Ultracapacitor, battery pack and fuel cell have been illustrated respectively. As it can be seen in Fig. 13, Ultracapacitor as a backup source of energy for FCHEV, plays its role suitable an efficiently. In other words, the role of Ultracapacitor in a FCHEV is to provide a huge amount of energy when it is necessary. Afterward, depends on strategy control a driving cycle pattern, Ultracapacitor has done its role well. Also in Fig. 14, the battery output power has been illustrated clearly. The role of
the battery pack as a second energy source in FCHEV, can be so critical to provide a suitable amount of energy when necessary. It should be mentioned that a negative amount of output power that can be seen in these figures, has related to the recharging mode of battery pack and Ultracapacitor. In each period of recharging, the controller depends on battery pack SOC, Ultracapacitor SOC and the amount of power demand make a suitable decision to recharge each component. A fuel cell as the main power source in this vehicle produces
Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
international journal of hydrogen energy xxx (xxxx) xxx
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Fig. 8 e (continued).
enough amount of power to satisfy power demand. The produced amount of energy by a fuel cell has been illustrated in Fig. 15.
Regenerative braking control strategy As mentioned before, battery SOC in a hybrid electric vehicles could play a major role in better performance of the vehicle and more mileage. Especially in some cases that hybrid cars are used in an urban driving cycle and driver power demand is mostly provides by an electric motor, battery SOC is a major factor of fuel more mileage. In this regard, there are 4 regenerative braking strategies that may have effect on hydrogen consumption and battery SOC. These strategies are the following: Strategy 1 (series): The motor is used to brake the vehicle and regenerate the battery. If the motor is not able to provide the requested torque the VCU uses vehicle brakes to complete braking Strategy 2 (Fixed repartition): The motor and the vehicle brakes are used together with a fixed repartition but only if the motor can provide requested torque. Strategy 3 (Full parallel): The motor and the vehicle brakes are used together. The motor provides maximum torque when driver brake torque is maximum. Strategy 4 (Parallel): The motor and the vehicle brakes are used together. The motor provides maximum torque when driver brake torque reaches a specified value. In the following, these 4 control strategies have been shown in Fig. 16. It should be noted that in this study, the series control strategy has been used. There is some limitations that limits the usage of regenerative braking include high and low battery
SOC threshold, upper electric motor angular velocity and lower electric motor angular velocity. In this study, the high and low battery SOC thresholds are 90 and 20% respectively and upper and lower electric motor angular velocity 4500 and 200 rev/min respectively. The output power of regenerative braking system has been illustrated in Fig. 17. As it is clear in Fig. 17, in the braking phase of the driving cycle, the regenerative braking system acts as a power source for the battery pack. In these periods of time, based on the braking system control strategy, braking torque demand divided into regenerative braking torque and pedal braking torque. Braking torque demand depends on the driving cycle and its deceleration modes. In Fig. 18 the regenerative braking output power versus battery state of charge has been illustrated.
Overall effects of using ultracapacitor In this study, an Ultracapacitor has been utilized in order to improve the overall performance of the hybrid vehicle. Therefore, an Ultracapacitor pack, has been employed. The overall effects of using Ultracapacitor have been shown in Fig. 19. As it is shown in Fig. 19, the application of Ultracapacitor in the output power of the battery pack in an electric motor is significant. The maximum power output of the battery pack while Ultracapacitor is being used, is less than the situation where the ultracapacitor is not being used. The maximum amount of power that has been provided by the battery pack in the presence of Ultracapacitor is 13.95 Kilo Watt in discharging mode and 30.4 Kilo Watt in charging mode. However, in the absence of the Ultracapacitor, these amounts are equal to 17.52 Kilo Watt and 57.07 Kilo Watt respectively. The difference between using and not using an Ultracapacitor in this figure in period of [0,200] seconds, can be considered
Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
12
Condition
Mode 0
idle situation
Mode 1 Mode 2
ULa
Battery
b
Fuel cell efficiency situation
Battery charging
UL charging
Fuel cell at maximum efficiency
idle situation idle situation
Battery not charging Battery charging
UL charging UL not charging
Fuel cell at maximum efficiency Fuel cell at maximum efficiency
Mode 3 Mode 4
idle situation Power needed by the motor
Battery not charging Battery charging
UL not charging UL charging
Fuel cell at idle situation Fuel cell at maximum efficiency
Mode 5
Power needed by the motor
Battery not charging
UL charging
Fuel cell at maximum efficiency
Mode 6
Power needed by the motor
Battery charging
UL not charging
Fuel cell at maximum efficiency
Mode 7 Mode 8
Power needed by the motor Power needed by the motor
Battery not charging Battery charging
UL not charging UL charging
Fuel cell at maximum efficiency Fuel cell at maximum efficiency
Mode 9
Power needed by the motor
Battery charging
UL charging
Fuel cell at maximum efficiency
Mode 10
Power needed by the motor
Battery charging
UL charging
Mode 11
Power needed by the motor
Battery not charging
UL charging
Fuel cell supplies the power with maximum power Fuel cell supplies the power
Mode 12
Power needed by the motor
Battery not charging
UL charging
Fuel cell supplies the power
Mode 13
Power needed by the motor
Battery not charging
UL charging
Mode 14
Power needed by the motor
Battery charging
UL not charging
Mode 15
Power needed by the motor
Battery charging
UL not charging
Mode 16
Power needed by motor
Battery charging
UL not charging
Mode 17
Power needed by motor
Battery not charging
UL not charging
Mode 18
Power needed by motor
Battery not charging
UL not charging
Mode 19
Power needed by motor
Battery not charging
UL not charging
Mode Mode Mode Mode
Electric Electric Electric Electric
Battery Battery Battery Battery
UL is UL is UL is UL is
Fuel cell supplies the power maximum efficiency Fuel cell supplies the power maximum efficiency Fuel cell supplies the power maximum efficiency Fuel cell supplies the power maximum efficiency Fuel cell supplies the power maximum efficiency Fuel cell supplies the power maximum efficiency Fuel cell supplies the power maximum efficiency Fuel cell at idle situation Fuel cell at idle situation Fuel cell at idle situation Fuel cell at idle situation
20 21 22 23
motor motor motor motor
provides provides provides provides
current current current current
Note: In this table the purpose of UL means Ultracapacitor. a Ultra Capacitor. b No power needed or supplied by electric motor.
is is is is
being recharged not being recharged being recharged not being recharged
being recharged being recharged not being recharged not being recharged
Operational situations Fuel cell recharges the battery and UL Fuel cell recharges UL Fuel cell drives motor and recharge battery The battery drives the motor Fuel cell recharges battery and UL and drives motor Fuel cell recharges UL and drives motor Fuel cell recharges the battery and drives motor Fuel cell drives motor Fuel cell recharges battery and UL and drives motor Fuel cell recharges battery and UL and drives motor Fuel cell drives motor
at
Fuel cell recharges the UL and drives motor Fuel cell recharges the UL and drives motor Fuel cell recharges the UL and drives motor Fuel cell recharges the battery and drives motor Fuel cell recharges the battery and drives motor Fuel cell recharges the battery and drives motor Fuel cell drives the motor
at
Fuel cell drives the motor
at
Fuel cell drives the motor
at at at at
Power supplied by electric motor Power supplied by electric motor Power supplied by electric motor No power is being used
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Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
Table 5 e Different control modes in fuel cell ECU. Modes
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Fig. 9 e Computational pattern for braking and accelerating modes.
Fig. 10 e Total fuel consumption in driving cycles.
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Fig. 12 e Ultracapacitor state of charge percent in driving cycles.
Fig. 13 e Output power of Ultracapacitor in NEDC driving cycle.
Table 6 e Fuel consumption in driving cycles. Driving cycle NEDC FTP-75 SFTP-SC03
Total fuel consumption per cycle (g)
Fuel consumption (kgH2/km)
90.7382 102.6634 52.7894
0.1205 0.1081 0.1172
Fig. 14 e Output power of battery pack in NEDC driving cycle.
Fig. 11 e Battery state of charge (SOC) percent in driving cycles.
Fig. 15 e Received output power of fuel cell in NEDC driving cycle. Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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Fig. 16 e Regenerative braking strategies. Fig. 18 e Regenerative Output power versus Battery state of charge percent (SOC). clearly. It should be noted that in this period of time, in the presence of Ultracapacitor, fuel cell works at its maximum power efficiency that provides power demand and charges the battery pack. On the contrary in the absence of Ultracapacitor, fuel cell will work in its maximum efficiency point that provides less power than its maximum power efficiency point. Fig. 20 has been shown the output power of fuel cell in both mentioned scenarios in the proposed period of time. Furthermore, the overall effect of using an Ultracapacitor in total fuel consumption has been shown in Fig. 21. As it can be seen from Fig. 21, the total amount of fuel which has been used during simulation, decreases through using Ultracapacitor. It should be mentioned that the controller that has been used in this simulation has the major role in the vehicle power management which means that in each time step, the amount of power that has been produced or has been provided by each element should be managed in order to achieve the best performance of the vehicle. The maximum amount of total fuel consumption in NEDC driving cycle in the presence of the Ultracapacitor is 551 g and in the absence of Ultracapacitor is about 572 g. However, NEDC driving cycle had been used and the related results have been achieved after 6000 s.
Performance improvement of the vehicle based on battery SOC limit After analyzing the vehicle’s performance in all three driving cycles, NEDC driving cycle has been chosen to improve vehicle
performance by decreasing total fuel consumption. Due to the role of upper and lower band of battery state of charge in vehicle performance and total fuel consumption, these fixed limiting bands are considered in this study which has direct effect on the hydrogen fuel consumption. Based on Fig. 22, by increasing the upper limit of battery pack SOC, the total fuel consumption in the vehicle in NEDC driving cycle changes. The pattern of fuel consumption isn’t unique in all points. Based on Fig. 22, it can be concluded that in point 24, the total amount of fuel consumption has the lowest amount among all points similar to points 1, 3, 5 and 10. Additionally, the effect of upper limit of battery pack SOC on the difference between final battery SOC and initial battery SOC should be considered. For better consideration Fig. 23 has been shown. Based on Fig. 23 the initial state of charge of the battery pack in all solitary simulation is 55%. The upper band for the battery SOC has been analysed and the difference between the final amount of battery pack SOC and the initial amount of battery pack SOC (that is equal to 55) has been shown. In iteration 20, where the amount of initial battery pack SOC is 55 and the upper band for battery pack SOC is 74%, the maximum difference between the final amount of battery pack SOC (that is equal to 75.18) and initial battery pack SOC (that is equal to 55) is occurred. It should be noticed that for a better performance of the vehicle in a
Fig. 17 e Regenerative Output power versus vehicle velocity. Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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Fig. 19 e Effect of using Ultracapacitor (UL) in output power of battery pack in NEDC driving cycle.
Fig. 20 e Fuel cell output power in [0,200] period of time in NEDC driving cycle.
Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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Fig. 21 e Effect of using Ultracapacitor (UL) in the total fuel consumption in NEDC driving cycle.
Fig. 22 e Pattern of increasing battery initial SOC on fuel consumption.
Fig. 23 e Effect of Battery Initial SOC on Final battery SOC difference.
single driving cycle, the difference between initial battery SOC and final battery SOC should be minimum. In this regard, the upper band for battery SOC, 78% have been chosen.
Conclusion In the present study, an integrated model for a fuel cell hybridelectric vehicle in Simecnter Amesim software has been
modeled. Furthermore, an Ultracapacitor has been considered as a backup power source in order to improve the overall performance in driving cycles. The battery pack has been validated and designed for the best performance in the simulation. Other major elements such as electric motor, electronic control unit (ECU), environmental conditions and fuel cell have been modeled to enhance the reliability of the simulation. ECU plays a major role in controlling and management of whole system power. An innovative control unit for this purpose has been designed and considered which covers all the situations of the automotive in each driving cycle including charging and discharging of the battery pack and Ultracapacitor in each point of all driving cycles. Furthermore, in order to enhance vehicle performance, the simulation has been done in three different driving cycles. In each driving cycle, the performance ofthe vehicle with Ultracapacitor has been considered and its effects on the NEDC driving cycle have been shown. Based on the effect of charging and discharging of Ultracapacitor on its lifespan, in this control strategy in each driving cycle only one time Ultracapacitor discharges and provides a huge amount of power for the vehicle to satisfy driver power demand. Ultracapacitor power in a critical situations of driving cycle like fast acceleration has caused that the output power of fuel cell limits to 100 Amper respectively. Results show that for maximum battery pack output power, 20.2% decrease in output power has been reached that could be a considerable amount for this battery pack. Also, to reduce fuel consumption, by means of Ultracapacitor, 3.3% decrease in the amount of fuel consumption has been obtained. Considering more than 4 driving cycles (6000 s), the decrease in fuel consumption would be significant. The performance of the vehicle for each driving cycle can be different, depending on strategy controls and vehicle velocity. The application of Ultracapacitor has a positive effect on performance improvement. Also, the upper band limit for battery pack state of charge has a role in decreasing or increasing the amount of fuel consumption. The results for NEDC driving cycle show that 78% for the upper band of battery state of charge gives the best performance in reducing the amount of fuel consumption. Although battery aging will be affected by this limit, in this study, this optimized amount has been reached.
Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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Available online at www.sciencedirect.com
ScienceDirect journal homepage: www.elsevier.com/locate/he
Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles Sina Changizian a, Pouria Ahmadi a,*, Mehrdad Raeesi b, Nader Javani c a
School of Mechanical Engineering, Faculty of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Iran School of Automotive Engineering. Faculty of Automotive Engineering, Iran University of Science and Technology, P. O. Box 1681666110, Tehran, Iran c Faculty of Mechanical Engineering, Yildiz Technical University, Besiktas, Istanbul, 34349, Turkey b
highlights
graphical abstract
Simulation of a fuel cell hybrid vehicle with Ultra Capacitor in Amsim software. Investigation of vehicle performance
in
three
main
driving
cycles. Vehicle
performance
Improve-
ment in NEDC driving cycle via an innovative controller. Optimization
of
battery
packs
state of charge.
article info
abstract
Article history:
In this research study, a fuel cell-electric hybrid car is studied. This car includes an electric
Received 5 November 2019
motor that is connected to a fuel cell and a complex which includes a battery pack and an
Received in revised form
Ultracapacitor. The assessment of this hybrid vehicle is conducted by using various driving
21 December 2019
cycles such as FTP-75 driving cycle, NEDC driving cycle and SFTP-SC03 driving cycle. Bat-
Accepted 2 January 2020
tery state of charge (SoC) and hydrogen fuel consumption are the effective parameters
Available online xxx
influencing the vehicle performance. For analysing the performance of this vehicle, an innovative computational model is considered. In this innovative computational model, an
Keywords:
accurate control strategy is considered in order to control the power demand, staying the
Fuel cell-electric hybrid car
battery packs and the Ultracapacitor state of charge in a limited domain. Results show that
Driving cycles
in NEDC driving cycle, by means of using Ultracapacitor in this model, 3.3% reduction in
Vehicle control strategy
fuel consumption and 20.2% decrease in the difference between initial and final State of
Ultracapacitor
Charge (SoC) in battery pack can be achieved. In addition, a robust regenerative braking
Regenerative braking
control strategy is used in order to recover some parts of the wasted energy in braking driving modes. © 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
* Corresponding author. E-mail address: [email protected] (P. Ahmadi). https://doi.org/10.1016/j.ijhydene.2020.01.015 0360-3199/© 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
2
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Introduction Global warming and air pollution have destructive effects on our lives nowadays. In this regard, the transportation system plays a major contribution to Earth’s warming. Thus, for diluting the side effects of climate changes, global warming, and air pollution, transportation which is accounted for about 30% of the total world greenhouse gas emission should be investigated. A transient phase between traditional transportation system and zero-emission transportation system could be a hybrid system like a combination of solid oxide fuel cell/stirling system [1]. Alternative fuels such as CNG, LNG, biofuel, hydrogen, and electricity are those emitting less emission. However, hybrid and full battery electric vehicles are gained more attention as they have zero-emission in their operation phase [2]. Due to limited sources of energy and preciousness of it, management in the usage of energy sources is very important. In this regard, in 2013 International Energy Agency (IEA) has estimated that 20% percent of total carbon dioxide which is produced globally is a result of the road transport system and 21% of global energy consumption is share by road transport vehicles. Although recently, new approaches in electricity as a clean and sustainable energy source has caused that in 2018 the growth of electricity usage as a power source reaches 20% [3]. However, in 2016, about 55% of the world’s energy consumption has dominated by the transportation industry and 31% of CO2 emissions is the share of the transportation industry, which is considerable [4]. Also, some car manufacturing companies focus on producing a new revolution of vehicles that have high efficiency and low emissions. In this regard, the advantages and disadvantages of using fuel cell vehicles are studied [5]. Old-fashioned cars are the main contributors to toxic emissions released from their internal combustion engines. Thus, fuel cells electric vehicles have attracted more attention for their zero-emission of polluting gases like CO2. Chemical reactions of hydrogen of fuel and oxygen of air cause producing energy in fuel cells. This considerable amount of energy releases from the electrochemical process [6]. Fuel cells and electric motors are very suitable as modern propulsion. Although high cost, low durability and hydrogen storage problems are the major obstacles for fuel cell vehicles, lower emission has caused that these type of vehicles become more attractive for automakers. PEM fuel cells are often preferred for transportation applications and other stationary applications based on their significant greenhouse gas emission reduction and renewable energy source [7]. Their low operating temperature, high power density, fast start-up, system robustness, and other considerable features convinced automobile factories turning to produce them [8]. Researches have shown that according to the type-approval fuel consumption and emissions limit, fuel cell vehicles are far better than ICEVs (Internal Combustion Engines Vehicles). In this regard, the total emission for ICEVs is five times more intensive than FCVs [9]. In addition, the well-to-wheel (WTW) efficiencies in fuel cell vehicle is about 27%. However, this amount for ordinary vehicles with internal combustion engines reaches only 17% that is less than fuel cell vehicles considerably [10]. It should be mentioned that some expert
paid attention to the hybridization of ICEVs with FCVs. Ezzat and Dincer [11] have designed a new integrated energy system that includes an ICE with a FC. The results of this study show that energy and exergy efficiencies of the system are 38.66% and 36.2% respectively. The usage of FCs in many countries where fossil fuels are used to produce electricity is under consideration especially when it is possible to produce clean energy sources like hydrogen from wasted energy sources [12]. It is because of emission limits which happen in traditional power plants. However, recent research has been conducted in this regard that shows the effect of future energy networks on the limitations of hydrogen production emissions. It is worth mentioning that by use of hydrogen energy grid, total energy consumption will be managed [13]. Although FCVs are categorized as zero-emission vehicles, fuel cell degradation should be noticed. Chen et al. [14] have considered a new method to investigate the degradation process of FCs. They considered environmental conditions on the degradation process and by means of the extreme machine learning process, they investigate the overall degradation model of a FCEV. Apart from hydrogen, ethanol also can be used as a secondary fuel in a fuel cell car. The results of Purnima and Jayanti [15] study shown that the use of methanol as a second source of fuel in FCVs, the mileage of the car will be increased. Solid oxide fuel cell stack is considered by some experts too. Scarlett Chen et al. [16] have enrolled two control strategies based on analysing solid oxide fuel cell stack performance. Lithium-ion battery packs are robust energy sources nowadays. The performance of FCV could be improved by using the robust battery pack. In this regard global dynamic optimization of energy between the battery pack and the fuel cell output, can play a major role in fuel consumption and durability of the vehicle [17]. Since the simulation of the FCV could be very effective in the investigation of vehicle performance, a robust simulation could be very important. Fathabadi [18] simulates a fuel cell vehicle in the presence of battery pack. His study shows that simulation of FC has a considerable accuracy comprise to experimental data. To find the effect of using a battery pack in a fuel cell car, Tanc et al. [19] compare a fuel cell car with a fuel cell-hybrid electric car. The results show that by means of using the battery pack in a fuel cell car, energy and hydrogen consumption are improved with 8% and 32% respectively. Even, FCEVs could be used as the main propulsion system for a bus. Hou and Song [20] study results include a novel hierarchical optimal energy management strategy with a vehicle-to-cloud connectivity. A global optimization which using dynamic programing have been used in order to minimize the battery degradation cost and hybrid energy storage system cost that contains battery/ Ultracapacitor hybrid energy storage system. In order to decrease the slow dynamic behavior of fuel cell vehicles in fast power transitions and also to store the braking energy during deceleration driving conditions, Ultracapacitor can be a suitable choice for better performance of the fuel cellhybrid electric vehicle. Also, only an innovative and robust controller can be able to control the amount of power demand in these types of vehicles [21].
Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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The total performance of a proton exchange membrane fuel cell hybrid vehicle (PEMFC-HEV) strictly relates to the energy management system and energy strategy controls. In this regard, Lu¨ et al. [22] have concluded that energy management systems should be able to respond to fast dynamic needs so long as the performance and durability of the car don’t subside. Even the stack lifespan of FCV can be affected by various control strategies. Wang et al. [23] have shown that control strategy can play a major role in stack lifespan in a fuel cell car. In this regard, the use of three stacks instead of one stack in a FCV could have better durability than one stack considering the strategic hysteresis control strategy of power management [24]. An innovative control strategy that involves Markov Chain has been mentioned by Zhou et al. [25]. Their study results indicate that Markov Chain algorithm which estimates driving patterns can achieve up to 98.16% identification accuracy. This innovative control strategy saves 3.26% hydrogen consumption respectively. Innovative optimization algorithms like Grey Wolf Optimizer (GWO) algorithm can be used to optimize the energy management in a FCEV which uses Ultracapacitor as a second energy source. Djerioui et al. [26] have used GWO method to find optimized FC reference current while the objective function minimizes supercapacitors and DC bus energies. In order to reduce fuel consumption in FCEVs strategy control and optimization process play significant roles [27]. Control strategies and investigating the performance and behavior of FCEVs can be done on a large scale. In this regard, Moghaddas-Tafreshi et al. [28] design a multi-carrier energy system in order to investigate the behavior of FCEVs in an energy grid. They use a new method which is information gap decision theory (IGDT) to predict the behavior of FCEVs in the whole energy grid system. It could be mentioned that in low scales, usage of fuel cell could be effective. The results from an experimental study of Gonzalez et al. [29] show that suitable energy management even in an unmanned modeled ground vehicle could be effective. Despite FCVs are considered as zero-emission vehicles, the procedure of vehicle product and fuel product should be considered. In this regard, the fuel cell degradation problem is studied by Ahmadi et al. [30]. Their results show that by considering various driving patterns and fuel cell degradation, the average fuel economy is affected by about 23%. In addition, fuel cell degradation can be subsided by adding a regenerative braking system to this type of vehicle. Also, a well-to-wheel (WTW) is applied in order to enhance the analysis of FCVs emissions. Liu et al. [31] have compared the WTW energy use and emissions of a HFCEV and a ICEV. Their study results show that based on WTW scenario, HFCEVs have better performance than a conventional ICEV. Dynamic loads that act to the FCVs could be harmful for the durability of the fuel cell stack. Battery pack and Ultracapacitors are suitable energy sources to prevent the degradation of fuel cell stacks. Wang et al. [32] study results indicate that The discharge and charge power capabilities of the battery and supercapacitor are important parameters in the energy management control strategy. In order to integrate FC with battery and Ultracapacitor, Fu et al. [33] analysed a hierarchical energy management strategy to study the
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performance of battery and Ultracapacitor in a FC vehicle. They found that by using battery and Ultracapacitor as a second source of energy in these types of vehicles, fuel economy, power performance and energy source life span will be optimized. Thus the purpose of this research paper is to investigate the adoption of hydrogen hybrid electric vehicles and the effect of robust control strategy at different driving cycles. It should be noted that to analyze the performance of battery pack and Ultracapacitor in different driving cycles, the state of charges (the difference between the initial state of charge and final state of charge) in battery pack and Ultracapacitor shouldn’t vary significantly. As far as discharging and charging patterns of battery pack and Ultracapacitor affect straight on their lifespan, the discharging and charging patterns should be considered. In this paper, the discharging and charging patterns have been considered in order to promote battery and Ultracapacitor lifespan. The battery provides needed energy when it is necessary and also Ultracapacitor plays its role as a huge source of energy in critical situations like fast acceleration modes. In summary, followings are the main contribution of this paper in the subject area: Overall simulation of a fuel cell hybrid-electric vehicle in presence of Ultracapacitor in Amesim software; Investigation of vehicle performance in three conventional driving cycles (NEDC, FTP-75 and SFTP-SC03 driving cycles); Improvement of vehicle performance in NEDC driving cycle by means of an innovative controller and optimization of battery pack state of charge. Reduction in hydrogen fuel consumption by means of using an Ultra capacitor pack and a robust control strategy about 3.3% in NEDC driving cycle. Four modes of regenerative braking system have been discussed in this paper and series regenerative control strategy has been chosen. Results show that in braking modes of driving cycle, regenerative braking system can save considerable amount of energy during braking modes.
Model description Vehicle specifications To dynamically model the PEM fuel cell-electric hybrid vehicle in this study, Amesim software is used. The performance of PEMFC in NEDC, FTP-75 and SFTP-SC03 driving cycles are studied. The vehicle specification for a fuel cell vehicle is given in Table 1. As it is clear, to model a fuel celle electric hybrid car, some main components are required. Some main components like fuel cell, Ultracapacitor, battery pack, electric motor, gearbox, environment conditions, driver demand conditions, electronic control unit for fuel cell and vehicle and other specific components have been considered. Main components in this regard are shown in Fig. 1.
Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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Table 1 e The Fuel cell vehicle specifications adapted from Toyota Mirai [34]. PEM fuel cell parameters Vehicle weight The maximum output power of fuel cell The heating value of hydrogen Maximum current Upper voltage limit for fuel cell Lower voltage limit for fuel cell Inferior power limit for fuel cell (Plow)
Value
Unit
1850 114 120 500 287 200 5
kg kW MJ/kg A V V KW
Fuel cell modelling Fuel cell fundamentals Fuel cells are often categorized according to the type of electrolyte being used. A common form of fuel cells which is recently used is often made of a polymer as its electrolyte and a porous carbon electrode containing platinum as its catalyst. For its fuel source, Hydrogen which is stored in a tank enters the anode part of the membrane and oxygen from air enters from the cathode side. The electrochemical reaction takes place and electricity and water are produced as illustrated in Fig. 2. Comparing hydrogen vehicles with ICEVs, we noticed that water vapor is the only by-product of these vehicles. The membrane is made in such a way to permeable to protons and not to electrons. Transferring the electrons through the electrolyte could cause the irreversibility and Ohmic potential losses in the cell. Instead, electrons pass through an external circuit which has been considered. The cathode side is designed as a region where electrons and protons combine together and produce water as the only waste substance in the cell. Although, Oxygen is injected into the cathode side from the air or in purified form. Protons membrane exchange fuel cells (PMEFC) because of their suitable advantages like low operating temperatures, high power density, fast start-up,
Fig. 2 e Schematic of a fuel cell.
system robustness and flexibility of fuel type are often preferred for transportation applications [5]. The operation of fuel cell vehicle is basically related to the chemical reactions which converts chemical energy into electrical energy respectively. Fuel and oxidant are fed into the reactant sides where the reactions occur. In the presence of electrolyte, electric current will appear under controlled conditions. This generation of electricity is zero-emission and just water and heat are the wastage of the reactions and none pollutant gas produce. Fuel cells can use different kinds of fuels but the basis of reactions that occur in their pols are similar. The reaction happens between hydrogen from fuel and oxygen from air which is injected to the cell. The reactions are below: In the cathode side:
Fig. 1 e Schematic of a simulated fuel cell hybrid-electric car in the Simcenter Amesim software [35]. Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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1 O2 þ 2Hþ þ 2e/H2 O 2
Table 2 e Specifications of the battery pack. (1)
In the anode side: H2 / 2Hþ þ 2e
(2)
So, totally reaction in fuel cells is: 2H2 ðgasÞ þ O2 ðgasÞ / H2 O þ Energy
(3)
In equation (3) the form of energy which appears in reaction is electrical power that comes with heat. The rated current which reduced during reaction in fuel cell is 0.6e0.7 A, and voltage produced by the reaction of hydrogen and oxygen is 1.23 V nominally. The absolute amount of voltage is less than 1.23 V and it is due to some loss like mass transport loss and Ohmic loss.
Fuel cell voltage The output voltage of a fuel cell strictly depends on open circuit potential of the cell which strictly depends on temperature and pressure and some reducer parameters. In the following, the polarization curve which is representing the operating voltage of a specific fuel cell has been illustrated [36]. As can be seen from Fig. 3, voltage versus current density, scaled by geometric electrode area, is typically shown in this figure. So, this curve could be useful for all the size of fuel cells. There are 5 regions illustrated in the polarization curve figure. These 5 areas have caused potential losses in output fuel cell voltage. The potential losses are as follows: activation overpotential at electrodes (1), ohmic polarization of the fuel cell (2), concentration polarization of the fuel cell (3), departure from the Nernst thermodynamic equilibrium potential (4) and departure from the maximum thermal voltage (5). The total output voltage of the fuel cell can be calculated as: Ecell ¼ E0 ðT; PÞ ha;a ha;c hr hm;a hm;c hx
(4)
Specifications
Value
Unit
Initial state of charge (SoC) Voltage Number of battery cell in parallel Number of battery cell in series Rated capacity of the battery Maximum output potential Nominal voltage
45 250.41 5 76 6.5 254 244
null Volt e e Ah Volt Volt
where in this equation, E0 ðT; PÞ is the theoretical equilibrium open-circuit potential of the cell that calculated from Nernst equation. Also, ha;a and ha;c are activation overpotentials at the anode and cathode, hr is ohmic (resistive) polarization loss, hm;a and hm;c are represented as concentration (mass transfer) polarization at anode and cathode and hx represented as the departure from the Nernst equilibrium voltage respectively. It should be noted that the Nernst equilibrium voltage calculated from waterfall analogy of voltage potential at each electrode and for the fuel cell.
Battery pack The battery pack which is used in this study is a 1.6 kWh Nickel-metal hydride battery pack which has been used in Toyota Mirai [34]. The architecture of the battery pack consists of banks in serial and parallel arrangements. Each battery bank consists of cells where the number of battery banks in series is Sbank ¼ 4; the number of battery banks in parallel is Pbank ¼ 7. The battery voltage output is calculated as follows: V ¼ V0 R:I þ V
(5)
where V is the output voltage, V0 is open-circuit voltage, and R is equivalent internal resistance, I is internal current (amp) and V is input potential (V).
Fig. 3 e Polarization curve for fuel cell with significant kinetic, ohmic, concentration, and crossover potential losses [36]. Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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140
Table 3 e Technical information of used Ultracapacitor.
Capacitance Internal resistance Initial charge Maximum charge Voltage time constant Potential at external port
Value
Unit
2610 0.000531 96 100 0.1 2.7
F Ohm null C s V
120
Velocity (Km/h)
Specifications
100 80 60 40 20
More information about battery pack is available in Table 2 [37]:
0 0
200
400
600
800
1000
1200
Time (S)
Ultracapacitor
Fig. 4 e NEDC driving cycle.
In this study, Ultracapacitor has been used in order to down size the fuel cell and battery pack indeed. In this regard, some parameters should have supplied to the model builder. These parameters are the capacitance of the Ultracapacitor (F), the internal resistance of Ultracapacitor (Ohm), Initial charge of Ultracapacitor (C), maximum charge of Ultracapacitor (C) and the voltage-time constant (s). The charge was withdrawn the Ultracapacitor is calculated by: dQ ¼ Iþ dt
(6)
The Ultracapacitor is discharging whenever the Ultracapacitor discharge terminal Iþ , is negative. And also the Ultracapacitor output voltage is calculated as follow: dVþ ¼
þ Vþ steady V
(7)
t
Electric motor output torque The amount of torque that electric motor has to deliver, is related to driver’s demand which has to be limited. Equation (5) shows the relation between limits: Tmin Tlim Tmax
(9)
Then, Tmin and Tmax the negative and positive torque corresponding either to user-defined parameters or values read in tables as a function of the operating point. The output torque Tm at port 2 is determined from the limited torque Tlim by using a first-order lag: TM ¼ 11 þ tr:s:TLim
(10)
in which, tr is a user-defined constant parameter.
and: Vþ steady ¼
Electric motor output power
Q þ R:Iþ C
(8)
The mechanical power is calculated as:
Technical information of Ultracapacitor equipment is shown in Table 3 [38].
PMec ¼ Tm :u
Electric motor
where Tm and u are respectively the torque [Nm] and rotary velocity [rad/s]. The lost power is computed either from:
The electric motor which is used in this study is a motor/ generator with its converter. Static operating conditions in the linear domain of the motor are considered. This model can be used for dynamic simulations if the establishing time of the current is fast enough compared to the dynamic of the system. More information about the motor-generator unit is in Table 4.
Plost ¼ ð1 hÞ:Pmec
(11)
(12)
The motor/generator efficiency corresponds to: Motor mode:
h ¼ 2 Pelec :Pmec
(13)
Generator mode: Table 4 e Motor/Generator description. Specifications Time constant to determine the torque Maximum positive power Minimum positive torque Maximum rotary velocity for positive torque Mean efficiency
Value
Unit
0.1
s
115 335 8000
kW N.m Rev/min
0.85
e
h ¼ Pelec :Pmec
(14)
Driving cycle To analyze the performance of an urban car, using a driving cycle is inevitable. In this regard, three driving cycle are used. SFTP-SC03 (well known in Japan), NEDC and FTP-75 driving cycles are shown in Fig. 4, Fig. 5 and Fig. 6.
Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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Sampling time: ECU state updates each sampling time. A low value will slow down the simulation and high value will lead to poor results.
100 90
Velocity (Km/h)
80 70 60
Sketches and equations
50
If the power requested by the electric motor is lower than the “inferior power limit for the fuel cell”, the power is supplied by the battery or by the Ultracapacitor that depends on their state of charges and control strategies. In this case, the fuel cell is in idle condition. When the battery SOC is lower than the “lower SOC limit”, the Fuel Cell supplies power for the electric motor and the battery charge, at its maximum efficiency operating point (if this power is large enough for the electric motor) and also for Ultracapacitor the same as battery, fuel cell supplies power requested. If the electric motor needs more power, the battery charging and/or Ultracapacitor charging will stop. The battery charge occurs until the battery SOC is greater than the “upper SOC limit” and as well as for Ultracapacitor. For better understanding Fig. 7 has been shown. It should be noticed that Ultracapacitor recharging plan is as the same as the battery pack. This ECU uses a simple control strategy to manage battery, Ultracapacitor, and fuel cell. It is presented in Fig. 8. Previously a variable Pmot0 is computed which is the power needed by the motor.
40 30 20 10 0
0
200
400
600
800
1000
1200
Time (S)
Fig. 5 e FTP-75 driving cycle. 100 90
Velocity (Km/h)
80 70 60 50 40 30 20 10 0
0
100
200
300
400
500
600
Time (S)
Fig. 6 e SFTP-SC03 driving cycle.
Strategy control This is a sub-model of the control unit for Fuel Cell. It computes the power between battery, Ultracapacitor, Fuel cell and electric motor. Parameters for the strategy: Inferior power limit for the fuel cell: if the power requested by the electric motor is under this limit, the power is supplied by the battery or Ultracapacitor that depends on control strategy and state of charge battery and Ultracapacitor. In this case, the fuel cell is in idle conditions. Lower and upper SOC limits: to control the charge of the battery and Ultracapacitor. Current and power for maximum fuel cell efficiency: for battery charging or Ultracapacitor charging, the fuel cell runs at its maximum efficiency. If the power requested by the electric motor is greater than this power, the battery charging and Ultracapacitor charging is stopped. Maximum current for fuel cell: this value limits current asked to the fuel cell if current needed by the motor is greater than this value, the battery and/or Ultracapacitor will help the fuel cell. This value should be equal to the value in the fuel cell.
Pmot ¼ jIM :VM j
(16)
where IM is motor current [A] and VM is motor voltage [V]. For different control modes, the performance of each node is mentioned in Table 5. It should be noted that in each step. Once the required current is greater than the maximum fuel cell current, the battery will in operation. Battery current is calculated as follows: IB ¼
IM ,VM IFC ,VFC VB
(17)
where IM is the motor current, VM is motor voltage, IFC is fuel cell current, VFC is fuel cell voltage and VB is the battery voltage.
Vehicle control unit strategy Vehicle Control Unit (VCU) receives information from the driver, battery and electric motor. It analyses them in order to minimize the battery consumption. . The electric motor could be used as a generator to charge the battery during braking period. The control unit sends commands to the electric motor and to the vehicle.
Parameters for time management: Voltage time constant: motor voltage is computed using a first lag order.
Fig. 7 e Control algorithm of charge and discharge of the battery.
Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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Fig. 8 e Control strategy of the motor.
Drivercommand is the torque computed using the acceleration and the braking commands from the driver, as shown in Fig. 9. Where MaxtorqME is maximum torque that electric motor can provide for traction, MintorqME is minimum torque that electric motor can provide for traction situation which is related to electric motor torque curve and Maxtorqveh is maximum braking torque for the vehicle that is demanded by the driver.
Results and discussions
As it clear, the amount of fuel consumption in all three driving cycles is closely related to the distance that the vehicle is traveled. In this regard, the amount of fuel consumption in SFTP-SC03 driving cycle is higher than others. It is necessary to mention that the traveled distance in each driving cycle is equal to 57.84 km in SFTP-SC03, 55.08 km in NEDC and 45.80 km for FTP-75 driving cycle. In Table 6 the amount of fuel consumption in each driving cycle has been shown. For a better analysis of vehicle’s performance in these driving cycles, battery state of charge and Ultracapacitor state of charge has been shown in Figs. 10 and 11, consequently.
Fuel consumption Battery state of charge (SOC) As mentioned previously, specific control strategies for better vehicle performance have been considered. The better performance of the vehicle can be shown by releasing fuel consumption. In this regard, the results of total fuel consumption in different driving cycles are shown in Fig. 10.
In addition to the fuel consumption in hybrid cars, the state of charge of the battery pack plays a major role in analyzing hybrid-electric vehicles fuel cells in each driving cycle. So, the battery state of charge in each driving cycle has been presented in Fig. 11. In all driving cycles, although battery state of
Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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Fig. 8 e (continued).
charge in SFTP-SC03 driving cycle is bigger than others, the average battery state of charge is almost constant. It means that the importance of control strategy in controlling the battery state of charge and prevention of battery pack full discharge.
innovative electronic controller has been used. As Fig. 12 shows, Ultracapacitor during the driving cycle has been used regularly. It can be deduced that in all driving cycles, the Ultracapacitor has been used where a controller will regulate its performance.
Ultracapacitor state of charge (SOC)
Power outputs
Employment of Ultracapacitors in fuel cell hybrid-electric vehicles is under consideration nowadays. Nonetheless, in this paper, an Ultracapacitor is used in order to improve the vehicle performance in each driving cycle. In this regard, an
In order to consider the impact of using the battery pack and Ultracapacitor as additional power sources, the output power curves could be a good response to prove the claim of being effective in a FCHEVs. In the following, the received power
Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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Fig. 8 e (continued).
from Ultracapacitor, battery pack and fuel cell have been illustrated respectively. As it can be seen in Fig. 13, Ultracapacitor as a backup source of energy for FCHEV, plays its role suitable an efficiently. In other words, the role of Ultracapacitor in a FCHEV is to provide a huge amount of energy when it is necessary. Afterward, depends on strategy control a driving cycle pattern, Ultracapacitor has done its role well. Also in Fig. 14, the battery output power has been illustrated clearly. The role of
the battery pack as a second energy source in FCHEV, can be so critical to provide a suitable amount of energy when necessary. It should be mentioned that a negative amount of output power that can be seen in these figures, has related to the recharging mode of battery pack and Ultracapacitor. In each period of recharging, the controller depends on battery pack SOC, Ultracapacitor SOC and the amount of power demand make a suitable decision to recharge each component. A fuel cell as the main power source in this vehicle produces
Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
international journal of hydrogen energy xxx (xxxx) xxx
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Fig. 8 e (continued).
enough amount of power to satisfy power demand. The produced amount of energy by a fuel cell has been illustrated in Fig. 15.
Regenerative braking control strategy As mentioned before, battery SOC in a hybrid electric vehicles could play a major role in better performance of the vehicle and more mileage. Especially in some cases that hybrid cars are used in an urban driving cycle and driver power demand is mostly provides by an electric motor, battery SOC is a major factor of fuel more mileage. In this regard, there are 4 regenerative braking strategies that may have effect on hydrogen consumption and battery SOC. These strategies are the following: Strategy 1 (series): The motor is used to brake the vehicle and regenerate the battery. If the motor is not able to provide the requested torque the VCU uses vehicle brakes to complete braking Strategy 2 (Fixed repartition): The motor and the vehicle brakes are used together with a fixed repartition but only if the motor can provide requested torque. Strategy 3 (Full parallel): The motor and the vehicle brakes are used together. The motor provides maximum torque when driver brake torque is maximum. Strategy 4 (Parallel): The motor and the vehicle brakes are used together. The motor provides maximum torque when driver brake torque reaches a specified value. In the following, these 4 control strategies have been shown in Fig. 16. It should be noted that in this study, the series control strategy has been used. There is some limitations that limits the usage of regenerative braking include high and low battery
SOC threshold, upper electric motor angular velocity and lower electric motor angular velocity. In this study, the high and low battery SOC thresholds are 90 and 20% respectively and upper and lower electric motor angular velocity 4500 and 200 rev/min respectively. The output power of regenerative braking system has been illustrated in Fig. 17. As it is clear in Fig. 17, in the braking phase of the driving cycle, the regenerative braking system acts as a power source for the battery pack. In these periods of time, based on the braking system control strategy, braking torque demand divided into regenerative braking torque and pedal braking torque. Braking torque demand depends on the driving cycle and its deceleration modes. In Fig. 18 the regenerative braking output power versus battery state of charge has been illustrated.
Overall effects of using ultracapacitor In this study, an Ultracapacitor has been utilized in order to improve the overall performance of the hybrid vehicle. Therefore, an Ultracapacitor pack, has been employed. The overall effects of using Ultracapacitor have been shown in Fig. 19. As it is shown in Fig. 19, the application of Ultracapacitor in the output power of the battery pack in an electric motor is significant. The maximum power output of the battery pack while Ultracapacitor is being used, is less than the situation where the ultracapacitor is not being used. The maximum amount of power that has been provided by the battery pack in the presence of Ultracapacitor is 13.95 Kilo Watt in discharging mode and 30.4 Kilo Watt in charging mode. However, in the absence of the Ultracapacitor, these amounts are equal to 17.52 Kilo Watt and 57.07 Kilo Watt respectively. The difference between using and not using an Ultracapacitor in this figure in period of [0,200] seconds, can be considered
Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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Condition
Mode 0
idle situation
Mode 1 Mode 2
ULa
Battery
b
Fuel cell efficiency situation
Battery charging
UL charging
Fuel cell at maximum efficiency
idle situation idle situation
Battery not charging Battery charging
UL charging UL not charging
Fuel cell at maximum efficiency Fuel cell at maximum efficiency
Mode 3 Mode 4
idle situation Power needed by the motor
Battery not charging Battery charging
UL not charging UL charging
Fuel cell at idle situation Fuel cell at maximum efficiency
Mode 5
Power needed by the motor
Battery not charging
UL charging
Fuel cell at maximum efficiency
Mode 6
Power needed by the motor
Battery charging
UL not charging
Fuel cell at maximum efficiency
Mode 7 Mode 8
Power needed by the motor Power needed by the motor
Battery not charging Battery charging
UL not charging UL charging
Fuel cell at maximum efficiency Fuel cell at maximum efficiency
Mode 9
Power needed by the motor
Battery charging
UL charging
Fuel cell at maximum efficiency
Mode 10
Power needed by the motor
Battery charging
UL charging
Mode 11
Power needed by the motor
Battery not charging
UL charging
Fuel cell supplies the power with maximum power Fuel cell supplies the power
Mode 12
Power needed by the motor
Battery not charging
UL charging
Fuel cell supplies the power
Mode 13
Power needed by the motor
Battery not charging
UL charging
Mode 14
Power needed by the motor
Battery charging
UL not charging
Mode 15
Power needed by the motor
Battery charging
UL not charging
Mode 16
Power needed by motor
Battery charging
UL not charging
Mode 17
Power needed by motor
Battery not charging
UL not charging
Mode 18
Power needed by motor
Battery not charging
UL not charging
Mode 19
Power needed by motor
Battery not charging
UL not charging
Mode Mode Mode Mode
Electric Electric Electric Electric
Battery Battery Battery Battery
UL is UL is UL is UL is
Fuel cell supplies the power maximum efficiency Fuel cell supplies the power maximum efficiency Fuel cell supplies the power maximum efficiency Fuel cell supplies the power maximum efficiency Fuel cell supplies the power maximum efficiency Fuel cell supplies the power maximum efficiency Fuel cell supplies the power maximum efficiency Fuel cell at idle situation Fuel cell at idle situation Fuel cell at idle situation Fuel cell at idle situation
20 21 22 23
motor motor motor motor
provides provides provides provides
current current current current
Note: In this table the purpose of UL means Ultracapacitor. a Ultra Capacitor. b No power needed or supplied by electric motor.
is is is is
being recharged not being recharged being recharged not being recharged
being recharged being recharged not being recharged not being recharged
Operational situations Fuel cell recharges the battery and UL Fuel cell recharges UL Fuel cell drives motor and recharge battery The battery drives the motor Fuel cell recharges battery and UL and drives motor Fuel cell recharges UL and drives motor Fuel cell recharges the battery and drives motor Fuel cell drives motor Fuel cell recharges battery and UL and drives motor Fuel cell recharges battery and UL and drives motor Fuel cell drives motor
at
Fuel cell recharges the UL and drives motor Fuel cell recharges the UL and drives motor Fuel cell recharges the UL and drives motor Fuel cell recharges the battery and drives motor Fuel cell recharges the battery and drives motor Fuel cell recharges the battery and drives motor Fuel cell drives the motor
at
Fuel cell drives the motor
at
Fuel cell drives the motor
at at at at
Power supplied by electric motor Power supplied by electric motor Power supplied by electric motor No power is being used
international journal of hydrogen energy xxx (xxxx) xxx
Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
Table 5 e Different control modes in fuel cell ECU. Modes
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Fig. 9 e Computational pattern for braking and accelerating modes.
Fig. 10 e Total fuel consumption in driving cycles.
13
Fig. 12 e Ultracapacitor state of charge percent in driving cycles.
Fig. 13 e Output power of Ultracapacitor in NEDC driving cycle.
Table 6 e Fuel consumption in driving cycles. Driving cycle NEDC FTP-75 SFTP-SC03
Total fuel consumption per cycle (g)
Fuel consumption (kgH2/km)
90.7382 102.6634 52.7894
0.1205 0.1081 0.1172
Fig. 14 e Output power of battery pack in NEDC driving cycle.
Fig. 11 e Battery state of charge (SOC) percent in driving cycles.
Fig. 15 e Received output power of fuel cell in NEDC driving cycle. Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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Fig. 16 e Regenerative braking strategies. Fig. 18 e Regenerative Output power versus Battery state of charge percent (SOC). clearly. It should be noted that in this period of time, in the presence of Ultracapacitor, fuel cell works at its maximum power efficiency that provides power demand and charges the battery pack. On the contrary in the absence of Ultracapacitor, fuel cell will work in its maximum efficiency point that provides less power than its maximum power efficiency point. Fig. 20 has been shown the output power of fuel cell in both mentioned scenarios in the proposed period of time. Furthermore, the overall effect of using an Ultracapacitor in total fuel consumption has been shown in Fig. 21. As it can be seen from Fig. 21, the total amount of fuel which has been used during simulation, decreases through using Ultracapacitor. It should be mentioned that the controller that has been used in this simulation has the major role in the vehicle power management which means that in each time step, the amount of power that has been produced or has been provided by each element should be managed in order to achieve the best performance of the vehicle. The maximum amount of total fuel consumption in NEDC driving cycle in the presence of the Ultracapacitor is 551 g and in the absence of Ultracapacitor is about 572 g. However, NEDC driving cycle had been used and the related results have been achieved after 6000 s.
Performance improvement of the vehicle based on battery SOC limit After analyzing the vehicle’s performance in all three driving cycles, NEDC driving cycle has been chosen to improve vehicle
performance by decreasing total fuel consumption. Due to the role of upper and lower band of battery state of charge in vehicle performance and total fuel consumption, these fixed limiting bands are considered in this study which has direct effect on the hydrogen fuel consumption. Based on Fig. 22, by increasing the upper limit of battery pack SOC, the total fuel consumption in the vehicle in NEDC driving cycle changes. The pattern of fuel consumption isn’t unique in all points. Based on Fig. 22, it can be concluded that in point 24, the total amount of fuel consumption has the lowest amount among all points similar to points 1, 3, 5 and 10. Additionally, the effect of upper limit of battery pack SOC on the difference between final battery SOC and initial battery SOC should be considered. For better consideration Fig. 23 has been shown. Based on Fig. 23 the initial state of charge of the battery pack in all solitary simulation is 55%. The upper band for the battery SOC has been analysed and the difference between the final amount of battery pack SOC and the initial amount of battery pack SOC (that is equal to 55) has been shown. In iteration 20, where the amount of initial battery pack SOC is 55 and the upper band for battery pack SOC is 74%, the maximum difference between the final amount of battery pack SOC (that is equal to 75.18) and initial battery pack SOC (that is equal to 55) is occurred. It should be noticed that for a better performance of the vehicle in a
Fig. 17 e Regenerative Output power versus vehicle velocity. Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
international journal of hydrogen energy xxx (xxxx) xxx
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Fig. 19 e Effect of using Ultracapacitor (UL) in output power of battery pack in NEDC driving cycle.
Fig. 20 e Fuel cell output power in [0,200] period of time in NEDC driving cycle.
Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
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Fig. 21 e Effect of using Ultracapacitor (UL) in the total fuel consumption in NEDC driving cycle.
Fig. 22 e Pattern of increasing battery initial SOC on fuel consumption.
Fig. 23 e Effect of Battery Initial SOC on Final battery SOC difference.
single driving cycle, the difference between initial battery SOC and final battery SOC should be minimum. In this regard, the upper band for battery SOC, 78% have been chosen.
Conclusion In the present study, an integrated model for a fuel cell hybridelectric vehicle in Simecnter Amesim software has been
modeled. Furthermore, an Ultracapacitor has been considered as a backup power source in order to improve the overall performance in driving cycles. The battery pack has been validated and designed for the best performance in the simulation. Other major elements such as electric motor, electronic control unit (ECU), environmental conditions and fuel cell have been modeled to enhance the reliability of the simulation. ECU plays a major role in controlling and management of whole system power. An innovative control unit for this purpose has been designed and considered which covers all the situations of the automotive in each driving cycle including charging and discharging of the battery pack and Ultracapacitor in each point of all driving cycles. Furthermore, in order to enhance vehicle performance, the simulation has been done in three different driving cycles. In each driving cycle, the performance ofthe vehicle with Ultracapacitor has been considered and its effects on the NEDC driving cycle have been shown. Based on the effect of charging and discharging of Ultracapacitor on its lifespan, in this control strategy in each driving cycle only one time Ultracapacitor discharges and provides a huge amount of power for the vehicle to satisfy driver power demand. Ultracapacitor power in a critical situations of driving cycle like fast acceleration has caused that the output power of fuel cell limits to 100 Amper respectively. Results show that for maximum battery pack output power, 20.2% decrease in output power has been reached that could be a considerable amount for this battery pack. Also, to reduce fuel consumption, by means of Ultracapacitor, 3.3% decrease in the amount of fuel consumption has been obtained. Considering more than 4 driving cycles (6000 s), the decrease in fuel consumption would be significant. The performance of the vehicle for each driving cycle can be different, depending on strategy controls and vehicle velocity. The application of Ultracapacitor has a positive effect on performance improvement. Also, the upper band limit for battery pack state of charge has a role in decreasing or increasing the amount of fuel consumption. The results for NEDC driving cycle show that 78% for the upper band of battery state of charge gives the best performance in reducing the amount of fuel consumption. Although battery aging will be affected by this limit, in this study, this optimized amount has been reached.
Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015
international journal of hydrogen energy xxx (xxxx) xxx
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Please cite this article as: Changizian S et al., Performance optimization of hybrid hydrogen fuel cell-electric vehicles in real driving cycles, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2020.01.015