1 Preface

Due to environmental pollution, global warming and energy shortage, governments and auto companies over the world encourages development of new technology in automobile industry, and proposed more strict emission regulations. Automobile policies such as environmental protection and energy efficiency have engendered great focus in automobile industry. Various large-scale auto companies have devoted themselves actively to developing clean fuel vehicles, hybrid electric vehicles, battery electric vehicles and other new energy vehicles. Therefore, development of new energy vehicles has become a pressing task. Owing to the advantages of energy-saving and “zero emission”, electric vehicles have been known and accepted popularly among the folks; therefore, it is inevitable to promote and develop electrical vehicles in the near future (Honglan 2017).

The fuel cell, as the most ideal power supply of the new energy vehicles, is a highly efficient, environment-friendly power generation device. Fuel cell technology has made great progress in recent years, particularly in the areas of transportation, logistics and ships, especially that of buses, heavy-duty trucks, trains and ships (Tommasi et al. 2014; Tsai et al. 2017; Wang et al. 2014; Moulzolf et al. 2014). At present, though both battery electric vehicles and fuel cell vehicles could satisfy “zero emission”, further development of battery electric vehicles and fuel cell vehicles was restricted. Rechargeable batteries suffer from large dimension, low energy density and long charging time, while fuel cells have the disadvantage of high cost, poor start performance with low temperature and insufficient reserve capacity. It is widely believed that the best solution so far for pure fuel cell vehicles mentioned above is hybrid electric vehicle, which combines fuel cells and other power source. Structurally, ultra capacitor or rechargeable battery (such as Ni-MH, plumbic acid and lithium-ion battery, etc.) is usually adopted as the auxiliary power source in parallel with fuel cells. For hybrid electric vehicles composed of auxiliary power source and fuel cells, “peak-load shifting” of the auxiliary power source can guarantee that the fuel cells work within the optimal efficiency intervals, which could improve the efficiency and dynamics of the whole vehicle (Tribioli et al. 2016). In National 863 program (Hua et al. 2014), development of fuel cell vehicles was ranked clearly first regarding the development of Chinese electric vehicles. The Twelfth Five-year Plan also stressed it was necessary to promote industrial application of new energy vehicles, including fuel cell hybrid technology (Sabaté et al. 2014; Yu et al. 2017; Huo et al. 2014; Popel’ et al. 2018; An et al. 2017).

So, hybrid electric vehicle, with fuel cells as the major power source and storage batteries or super capacitors as the auxiliary power source, will inevitably make the major contribution to the development of future green, environment-friendly cars (Xiao et al. 2019; Wei et al. 2008; Cao 2014; Horrein et al. 2016; Pin and Xiaofeng 2010; Zhuo 2000; Appleby and Twidell 1996). However, how to manage the two independent power system is one of the key problems for hybrid vehicles. The hybrid power system of fuel cell electric vehicle is a complex system with multiple components. On one hand, the parameter optimization of the hybrid system is a complex optimization problem, including non-differentiability, discontinuity, multi-dimensionality, conditional constraint, and strong non-linearity. On the other hand, the genetic algorithm (GA), is independent of gradient information for multi-peak, non-continuous, non-differentiable multi-objective optimization problems, which do not meet Lipschitz conditions. Besides, the genetic algorithm is able to achieve the global optimization (Xiangzhe et al. 2011; Bozhang 2010; Shengmin 2017; Zhiwei 2008). Therefore, it has been proved as a suitable method for parameter optimization of hybrid power systems.

In China, bus routes are relatively fixed. For there are few hydrogen charging stations in China, fuel cell buses is quite promising. Therefore, this paper is targeted at fuel cell bus and establishes a corresponding prototype. In addition, the paper also involves calculation for the parameters of automobile power system and energy management strategy, that is, power-follow control strategy to design cell fuel vehicles. Then, simulation model is establish with incorporation of ADVISOR with a verification. At the last, genetic algorithm was adopted to optimize the parameters of control algorithm.

2 Parameter calculation power system of fuel cell vehicles

The system structure of fuel cell electric vehicle designed in this essay is illustrated as Fig. 1. The prototype vehicle is Golden Dragon Coasters electric motor coach, specific parameters of which are given below: full mass m = 7500 kg, coefficient of rolling resistance is 0.012, aerodynamic drag factor CD = 0.6, frontal area A = 5.4 m (Tommasi et al. 2014), driving wheel radius R = 0.303 m, efficiency of drive system is 0.85, wheel base is 3.935 m.

Fig. 1
figure 1

Structure of “FC + B” hybrid power system of fuel cell vehicles

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2.1 Selection of motor data

The motor and the controller of drive system are the key parts of fuel cell electric vehicle, which is composed of electric motor, power converter, controller, various detecting sensors and power source (rechargeable battery). Its task is to convert the electric power of the battery into kinetic energy of the wheels as well as to recover electric energy from the rotating wheels under the control of the driver. At present, motor drive systems that could be adopted by fuel cell vehicles include DC motor, brushless DC motor, asynchronous motor, permanent magnet synchronous motor and switched reluctance motor. There are several requirements for motors in electric vehicle. Firstly, the motor running characteristics shall meet the requirements of various runding conditions, including the start, climbing and high-speed driving on plat road. Secondly, motors should have powerful overload capacity, good loading starting performance, long service life, good reliability and low price. In addition, it is also required that the motor shall be dust-proof and water-proof.

Main parameters of the drive motor include motor power, motor torque and motor rotational speed. And the designing basis of motor rotational speed is determined by the specific accelerating ability, the climbing performance and the maximum speed of the coach.

  1. (1)

    Selection of drive motor power

Rated power and peak power are the most important parameters of drive motor. To satisfy performance requirement of drive motor, the motor power shall be determined by the maximum speed and climbing gradient of the electric vehicle.

  1. ①

    The motor power is determined by the maximum speed.

When the electric vehicle runs at constant high velocity on flat road condition with the slope resistance and the accelerating resistance being zero, motor power could be obtained according to power balance equation:

$$ P_{max1} = \frac{1}{{\eta_{T} }}\left( {\frac{mgf}{3600}v_{max} + \frac{{C_{D} A}}{76140}v_{max}^{3} } \right) $$
(1)

Substitute the parameters from the selected bus and the following could be obtained: \( P_{max1} \) = 80 kw.

  1. ②

    The motor power is determined by the climbing gradient.

When the electric vehicle is climbing a road with certain slope gradient at the speed of \( v_{a} \) = 20 km/h, with the accelerating resistance of zero, the consumed power is:

$$ P_{max2} = \frac{1}{{\eta_{T} }}\left( {\frac{mgf\cos \alpha }{3600}v_{a} + \frac{{C_{D} A}}{76140}v_{a}^{3} + \frac{mgf\sin \alpha }{3600}v_{a} } \right) $$
(2)

Substitute parameters from the selected bus and the following could be obtained: \( P_{max2} = 114{\text{kw}} \)

The motor power of the electric vehicle shall meet the requirements of maximum running speed and the climbing gradient, so the peak power of its motor is:

$$ P_{m} = max\left\{ {P_{max1} ,P_{max2} } \right\} $$
(3)

where, \( P_{m} \) is the peak power of the motor 90 kw.

So, the peak power of the motor \( P_{m} \) = 120 kw.

The rated power of the motor is:

$$ P_{e} = \frac{{P_{m} }}{\lambda } = 60\,{\text{kw}} $$
(4)

where, \( P_{e} \) is the rated power of the motor (kw), \( {{\uplambda }} \) is the overload factor of the motor, which generally is \( 2\sim 3 \).

  1. (2)

    Determination of the torque of drive motor

As the sole main source of power, drive motor transmits directly the torque on the wheels, the size of which affects directly the acceleration performance and the climbing ability of the fuel cell vehicle. However, to guarantee the vehicle has sufficient acceleration power and climbing torque, it is necessary to select torque parameters of the motor mainly on the basis of climb ability. Considering that the influences of accelerating resistance and air resistance are small in climbing condition, the form equation of the vehicle could be simplified as:

$$ \frac{{T_{tq} i_{g} i_{0} \eta_{T} }}{r} = Gf + Gi $$
(5)

Transformed from the above formula, the starting climbing calculation formula of the coach in this study is as:

$$ T_{m} = \frac{{mgf{ \cos }\left( {\arctan \left( {\frac{\alpha }{100}} \right)} \right)}}{{i_{g} i_{0} \eta_{T} }} + \frac{{mg{ \sin }\left( {\arctan \left( {\frac{\alpha }{100}} \right)} \right)r}}{{i_{g} i_{0} \eta_{T} }} $$
(6)

Since deceleration of the vehicle is achieved with main reducing gear for the fuel cell vehicle in this study, and the speed changer is omitted, so, \( i_{g} \) is 1. After looking up relevant documents and data (Bozhang 2010), speed reducing ratio \( i_{0} \) is selected as 4.5, which is substituted into the above equation. And the maximum torque could be obtained as 706 Nm.

The maximum torque of the drive motor is determined as 710 Nm, and suppose the rated torque of the motor is half of its maximum torque, then the rated torque of the motor is 355 Nm.

  1. (3)

    Selection of drive motor speed

Rotational speed of the drive motor could affect directly the running performance of the vehicle. When the rated power is constant, the rotational speed is inversely proportional to the torque, that is, the lower the rated speed, the larger the corresponding rated torque of the motor. So, to ensure starting acceleration and stable running of the vehicle, it is necessary to put a limit on the rated speed of the motor. Relationship between the running speed and the transmission ratio of the vehicle is as shown in the formula:

$$ v = 0.337\frac{nr}{{i_{g} i_{0} }} $$
(7)

Provided the designed maximum running speed of the vehicle is 100 km/h, to achieve the maximum running speed, the maximum rotational speed of the motor is:

$$ n_{max} = \frac{{v_{max} i_{0} i_{g} }}{0.337r} = 3933 $$
(8)

By substituting the related parameters the maximum rotational speed of the motor is obtained as 4000 r/min.

Then, combining the following formula:

$$ P_{e} = \frac{{T_{tq} n_{e} }}{9550} $$
(9)

the final calculation result is 1614 r/min, so the speed of the motor is 1600 r/min.

In summary, the selected parameters of the AC motor of the fuel cell vehicle are as follows: the rated power is 60 kw, the maximum power is 120 kw, the rated torque is 355 Nm, the maximum torque is 710 Nm, the maximum speed is 4000 r/min, and the rated speed is 1600 r/min.

2.2 Selection of fuel cells

  1. (1)

    Structure and principle of vehicle fuel cell system

As the major power source, fuel cell could provide the power required by the vehicles. Therefore, selection of fuel cells is of vital importance for the performance of the complete fuel cell vehicle. PEM fuel cells is composed of fuel feeding and circulation system, oxidant feed system, power generating system, water/thermal management system, power system, security system and a controlling system that could coordinate the above systems. The schematic diagram of all the systems of vehicle fuel cells is shown in Fig. 2 (in which the dotted line shows the recycled gas) (Sabaté et al. 2014).

Fig. 2
figure 2

Schematic diagram of the systems of vehicle fuel cell

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  1. (2)

    Type selection of the fuel cells adopted by the coach in this study

Through development in decades of years, fuel cells has been extensively applied, from military to aviation, to vehicles and even to household appliances of various sizes. It could be seen from related researches that the proton exchange membrane fuel cell is featured with high efficiency, compact structure, light weight, large specific power, non-corrosiveness, insensitivity to carbon dioxide and wide applicability to fuel sources. Its greatest advantage it could also operate normally at room temperature outside its optimal temperature range of 80 °C–90 °C; therefore, it is especially suitable to be used as the mobile power of traffic vehicles. Moreover, it needs no compression or decompression; it takes macromolecule proton exchange membrane as its conduction medium; it has no chemical liquid and it generates purely water and heat through electricity generation.

The coach in this study selected PEM fuel cell as the main power. In the following, a comparison would be made between PEM fuel cell and traditional internal combustion engine: Fuel cell is a static energy conversion device without moving parts, and theoretically the energy efficiency of the PEMFC stack is nearly 83% and practically its energy efficiency could reach 50%–60%. Energy consumption distribution of NECAR4 tested on roler bench under NEDC working condition is shown in Fig. 3.

Fig. 3
figure 3

Energy consumption of NECAR4

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In the diagram, (1) heat loss of the fuel cell; (2) energy consumed by the accessories (including accessories of the fuel cell and the vehicle accessories); (3) the energy loss due to efficiencies of the drive system components such as the motor and the controller and the drive system; (4) System efficiency, i.e., percentage of the net drive energy obtained by the drive wheels in the fuel energy consumed by the fuel cell. Fuel efficiency of traditional vehicles is poor because its internal combustion engine usually operates under lower-load working condition. In constrast, the system efficiency of fuel cells is higher under lower load. So, in this study PEM fuel cells are adopted to be used in the vehicle. The efficiency curves (Bozhang 2010) for both traditional internal combustion engine and the fuel cell is as shown in Fig. 4.

Fig. 4
figure 4

Efficiency and load rate curves

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  1. (3)

    Parameter design of fuel cell system

Peak value is used only to provide peak power within short time and it has a limited energy. So, when the vehicle runs at constant high speed for long distance, it must be guaranteed that the fuel cell could supply sufficient electric power, and that the vehicle speed could overcome appropriate slope with absence of no peak power. The power \( p_{f} \) of the fuel cell system is:

$$ p_{f} = \frac{{P_{m} }}{{\eta_{inv} \eta_{dc} }} = 61kw $$
(10)

where, \( P_{m} \) ~ is the power of the drive motor; \( \eta_{inv} \) ~ the efficiency of the inverter is 0.9; \( \eta_{dc} \) ~ the efficiency of the DC/DC converter is 0.9. Besides, because of the efficiency of the fuel cell system and the necessity to retain certain margin to maintain normal rotation, the maximum output power of the fuel cell is considered comprehensively as 70 kw.

2.3 Selection of power battery system and its parameter design

Power battery is incorporated as the auxiliary power source of the coach adopted in this study so that the driving mileage of the vehicle could be enhanced. Under the working conditions of starting, climbing and accelerating of the vehicle, in case of insufficient output power of single fuel cell system, a battery power could contribute to driving the vehicle, and operation of the fuel cell system retain could be stablized (Huo et al. 2014; Popel’ et al. 2018). Meanwhile, rechargeable battery could save the redundant energy of the power system, when the drive power is less than the power of the fuel cell or when the vehicle is decelerating. By means of recycling of the braking energy, the efficiency of the whole power system is promoted.

  1. (1)

    Selection of rechargeable battery

Battery power could be divided mainly into two types, energy battery and power battery. The former refers to the battery with high energy density, mainly used for high-energy output. It is suitable for battery electric vehicle. The latter refers to the battery with high power density, mainly used for transient high-power output and input. It is suitable for hybrid electric vehicle. Parameters representing performances of the batteries shall include electrical property, mechanical property, storage property, sealing property and geometric shape, the electric property includes mainly charge–discharge voltage, internal resistance, capacity, specific characteristic and service life of the cells, while storage property includes mainly storage life and self discharge. Table 1 compares the performances of several power batteries which are commonly used in electric vehicles.

Table 1 Comparison of cell performances (An et al. 2017)
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At present, most of power batteries used in electric vehicles are Ni-MH battery or Li-ion battery. The ionization cells is the battery with the highest energy density, which is approximately 1.5–3 times of that for Ni-MH. Li-ion battery adopts highly electronegative electrode that contains the metallic element lithium, and the average voltage of 3.2 V for single Li-ion battery could reduce effectively the numbers of battery assembly. Moreover, the chargeable efficiency of Li-ion battery is higher than that of Ni-MH battery and its self-discharge is as low, as 5%–10%. Furthermore, it is featured with high storage stability with seldom chemical reaction during idle time. Meanwhile, Li-ion battery is easy to install on board, and it is compatible with application technique of the hybrid drive vehicle. Therefore, Li-ion battery is adopted as the auxiliary power source of the hybrid fuel cell vehicle in this study.

  1. (2)

    Determination of the battery parameters

According to the above analysis, the fuel cell and the rechargeable battery could provide a combined maximum power required for the fuel cell vehicle, so the maximum output power of the rechargeable battery shall be larger than or equal to the difference between the maximum power of the drive motor and the maximum power of the fuel cell:

$$ P_{max} \ge P_{m} - P_{f} = 50kw $$
(11)

where, \( P_{max} \) is the maximum power of the rechargeable battery.

The Li-ion battery, with the capacity of 100 Ah, the voltage for single battery of 3.2 V and the maximum output power for single battery of 0.8 kw is selected as the power battery, is selected as the power battery.

The total output power of the rechargeable battery must be larger or equal to the maximum power consumption of the electric vehicle to guarantee the running requirements of the electric vehicle. As a result, the required number of the battery packs is:

$$ n_{1} = \frac{{P_{bmax} }}{{P_{bmax} \eta_{e} \eta_{ec} }} =78$$
(12)

where, \( P_{bmax} \) is the maximum output power of single cell, \( \eta_{\text{e}} \) is the working efficiency of the motor, and \( \eta_{ec} \) is the working efficiency of the motor controller.

2.4 Determination of the parameters of fuel cell vehicle power system

The parameter mating results could be obtained based on the above calculation:

AC motor Peak power capacity: 20 kw, rated power: 60 kw, peak torque: 710 Nm, rated torque: 355 Nm, maximum RPM: 4000 r/min, rated RPM: 1600 r/min, voltage: 380 V, weight: 380 kg;

Fuel cell Power: 70kw, output voltage: 380 V;

Li-ion battery Single voltage: 3.2 V, capacity: 100AH, amount: 78, specific energy: 103 wh/kg.

3 Simulation results and analysis

3.1 Matching parameter verification of fuel cell/rechargeable battery hybrid

Performances of fuel cell/rechargeable cell dual-energy hybrid electric vehicle include dynamic parameters and economic parameters. Dynamic parameters, same as traditional fuel vehicle, include maximum speed, acceleration time and climbing capacity, while economic parameter refers to conversion from power consumption and hydrogen consumption into fuel consumption per hundred kilometers.

As mentioned above, basic parameters of the fuel cell hybrid electric coach, the type selection and parameter design of the power system components have been completed. The modeling and simulation of the rechargeable battery and fuel cell hybrid power coach selected in this study is carried out based on ADVISOR and Matlab/Simulink software. Parameters are varied in the simulation on ADVISOR, and dynamic and economic simulation test of the established full vehicle model are conducted under selected circulation working condition. The working condition for current performance evaluation mainly include: Highway passenger vehicle fuel economy test cycle (HWFET), the Environmental Protection Agency certification city road driving cycles vehicle emissions test program (UDDS), economic commission for Europe’s working condition of vehicle emission test modal (NEDC), China’s urban road fuel economy test conditions (CONSTANT_60). According to international standards, the car’s performance index is mainly composed of maximum speed, acceleration time and climbing ability for evaluation. So analysis of automobile dynamic performance assumes under the UDDS condition in present paper. The output simulation results obtained are shown in Fig. 5.

Fig. 5
figure 5

Interface of simulation results

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It could be seen from Fig. 5 that the travel speed under simulated working condition of UDDS test is basically the same as the actual speed (Both curves almost coincide), suggesting the power performance meet completely the requirements of the power performances under urban road condition. It could be seen from the results of the Acceleration Test that the maximum gradeability is 19.1% at 20 km/h when the coach drives at the maximum speed of 127.4 km/h, the dynamic requirement of the coach in this study is satisfied.

Figure 5 shows the fuel consumption of the simulation model under UDDS condition is 93.3 (L/100 km), equivalent to fuel consumption of about 6.3 (L/100 km). It indicates an excellent fuel efficiency.

From the other two diagrams of the left interface, it could be seen that the emission of the fuel cell hybrid vehicle is zero. The initial value of SOC(State of Charge)is set as 0.7. Variation of the SOC of the rechargeable battery before and after the circulation is 0. From the changing curve of the state of charge of the rechargeable battery, it could be seen that the SOC of the rechargeable battery drops at the start of the hybrid vehicle to provide power for the start of the fuel cell system, so the problem of slow start of pure fuel cell electric vehicle is solved. Later, hydrogen fuel cell could provide surplus power to the electric generator to recharge the battery. At 1200 s, the maximum electric quantity of the rechargeable battery is 0.8, suggesting the optimal target upper limit of SOC is 0.8 in power following control strategy. The value of SOC always fluctuates between the initial value and the optimal upper limit.

3.2 Parameter optimization of dynamical system control strategy based on genetic algorithm

The hybrid power system of fuel cell electric vehicle is a complex system constituted by multiple components, and it can provide a solution for many deficiencies in the single power system of fuel cell, consequently, both power performance and fuel economy are improved. However, because of the complexity of the structure and control system of the hybrid power system, optimizing its various power components and control parameters is the key to the exploitation of the hybrid power system (Zhiwei 2008). As the parameter optimization of the hybrid system is a non-differentiable, discontinuous, multi-dimensional, conditionally constrained and highly nonlinear optimization problem, there are mainly gradient algorithm and non-gradient algorithm available for such problems (Guangqiang and Huiyong 2009). Although the efficiency and precision of traditional gradient algorithm such as SQP(sequential quadratic programming) is relatively prominent, the optimization function is derivative, and it is difficult to achieve a global optimization. Non-gradient algorithm, such as genetic algorithm (GA), is independent on gradient information for multi-peak, non-continuous, non-differentiable multi-objective optimization problems that do not meet Lipschitz conditions in addition, such algorithm can achieve a global optimization. Therefore, it has been proved suitable for parameter optimization of hybrid power systems (Jianwu 2005; Wang et al. 2007; Huang et al. 2007).

Therefore, in this paper, the genetic algorithm will be used as the optimization tool to establish and optimize the nonlinear planning model, with the economic and dynamic performance of the vehicle as the multi-objective function.

3.2.1 Optimization design process of genetic algorithm

As the fuel cell hybrid vehicle in the study is a complex multi-variable nonlinear system, relevant parameters in the control strategy of the hybrid drive system were optimized through a large range of optimization characteristics of genetic algorithm. The detailed optimization process is as follows:

  1. 1.

    Selection of optimization variables

Some basic parameters in the optimization model need to be constantly corrected and adjusted in the optimization design process. These parameters are design variables, which are persistently adjusted.

In this paper, the fuel cell power, the number of lithium batteries as well as motor power were taken as the design variables.

$$ X = \left[ {P_{f} ,n,P_{m} } \right]^{T} $$
(13)
  1. 2.

    Establishment of objective function

Because the electric buses still exhibits insufficient power performance, driving distance is short. Thus in this paper, power performance and economical efficiency of electric buses will be the optimization target of multi-objective function.

  1. (1)

    Optimization objective of power performance

The power performance of vehicles is mainly indicated by acceleration performance, climbing performance as well as maximum speed. The maximum speed of vehicles was taken as the optimization objective of power performance in this paper.

$$ v_{max} = 0.377\frac{{n_{max} r}}{{i_{g} i_{0} }} $$
(14)
  1. (2)

    Optimization objective of economical efficiency

One of the important indicators for the overall performance of vehicles is the economic efficiency of pure electric vehicles. Therefore, the energy required by travelling distance of 300 km is selected as the optimization objective of fuel economy in this paper.

$$ w = \frac{{L\left( {21.15Gf + C_{D} Av_{a}^{2} } \right)}}{76140\eta } $$
(15)

The multi-objective function was transformed into single-objective function

$$ f\left( X \right) = \lambda_{1} \frac{1}{{v_{max} }} + \lambda_{2} \frac{1}{W} $$
(16)

In the above formula, \( \lambda_{1} \) is the power weighting factor; \( \lambda_{2} \) is the weighting factor of fuel economy. For the comprehensive dynamic performance and fuel economy factors, the preliminary selection in this paper is \( \lambda_{1} = \lambda_{2} \) = 0.5 according to the empirical method.

4 Establishment of constraints

In this paper, the maximum gradeability and maximum speed were selected as the constraints, and the specific expression is as follows.

The maximum gradeability was not less than the maximum gradeability proposed in original design:

$$ g_{1} \text{(}X\text{)} = - i_{max} + 20\% \le 0 $$
(17)
$$ {\text{In the above formula}}\;i_{max} = tan\left( {arcsin\frac{{F_{t} - F_{f} - F_{w} }}{G}} \right) $$
(18)

The maximum speed was not less than the maximum speed of original design:

$$ g_{2} (X) = - v_{max} + 100 \le 0 $$
(19)

The maximum transmission ratio should meet the adhesive condition, and \( {{\upvarphi }} \) was 0.5–0.6:

$$ g_{3} X = \frac{{T_{tqmax} i_{g} i_{0} \eta_{T} }}{r} - F_{z} \varphi \le 0 $$
(20)

The energy of fuel cell and lithium battery should meet the requirement of the motor’s maximum power:

$$ g_{4} \text{(}X\text{)} = P_{m} - \left( {nP_{bmax} \eta_{e} \eta_{ec} + P_{f} } \right) \le 0 $$
(21)

4.1 Optimization results and analysis

Based on the MATLAB software platform, the method selected in this paper was adopted to solve the power system optimization problem of fuel cell electric vehicle, and the optimization results of the parameter optimization problem of constrained hybrid fuel cell vehicle power system are shown in Table 2.

Table 2 Parameter optimization results
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The parameter optimization results based on the optimization of genetic algorithm were input into ADVISOR for simulation. The running results were compared with the parameters before optimization, and the results are shown in Table 3. The optimized design scheme met the design index, and the deviation of the maximum speed exhibited a negligibly small value of 0.07%. The range increased slightly from 283.4 km to 309.1 km. While the climbing ability increased obviously by 16.2%. Overall, fuel cell vehicles showed a good power performance and fuel economy, and the power system parameters were well matched.

Table 3 Results of parameter comparison before and after optimization
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Through comparing and analyzing the results of the two schemes, power system parameter optimization based on genetic algorithm got an excellent parameter matching, while consistent with the requirements of the design index. In addition, there is great improvement in the power performance and fuel economy of hybrid fuel cell vehicles. Therefore, it is feasible and reliable to apply this scheme to the parameter optimization of power system in a hybrid fuel cell automobile.

5 Conclusion

In view of the development status and tendency of clean fuel or new-energy vehicles at home and abroad in recent years, the matching design and simulated optimization of the power system of fuel cell and battery hybrid vehicles were carried out. Taking the basic parameters of a coach as the prototype, the design, selection and matching of the system’s various components were conducted. Then, based on ADVISOR software, power system were modeled, and the simulation test of the hybrid bus under the urban road conditions was selected to obtain the results of power performance and fuel economy. The major conclusions are listed as follows:

  1. 1.

    It mainly introduced the selection and matching of the components: part pem fuel cell was chosen for the fuel cell, and the efficiency and load rate compared with internal combustion engine were analyzed. The parameter design for the power of fuel cell system was conducted, and then the maximum output power of fuel cells was determined to be 70 kw. The ac induction motor was selected as the driving motor, and the parameters such as power, torque and speed of the selected motor were designed. The rated power was 60 kw; the maximum power was 120 kw; the rated torque was 355 Nm; the maximum torque was 710 Nm; the maximum speed was 4000 r/min; and the rated speed was 1600 r/min. The principle of DC/DC inverter was analyzed and designed, and full-bridge inverter was selected. The power accumulator battery was determined as Li ion battery with a bus voltage of 250 V. The power parameter of the battery was designed to be 50 kw according to the output power of the motor. So far, the design and selection of key components of fuel cell power system have been completed.

  2. 2.

    For the modeling and simulation of fuel cell hybrid electric vehicles in ADVISOR, the modified power system parameters and control strategies were employed, and the operation simulation was conducted under the working condition of UDDS, so the dynamic performance results of the designed automobiles were obtained. It could be seen from the dynamic performance figure under UDDS criterion that the speed was basically the same as the actual speed, and the max speed of vehicles was 127.5 km/h. The maximum gradeability was 19.1% when the speed was 20 km/h. And the fuel consumption per 100 km with a travelling distance of 11.8 km under UDDS condition was 93.3 (L/100 km), which was equivalent to a fuel consumption of about 6.3 (L/100 km). Therefore, it basically met the requirements for dynamic performance and fuel economy of passenger cars.

  3. 3.

    The genetic algorithm was adopted to optimize control algorithm parameters. The power of fuel cell, the number of lithium batteries as well as the motor power were taken as the design variables. Meanwhile, the power performance and fuel economy were taken as the optimization objectives, and the maximum gradeability and maximum speed were used as the constraints. Relevant parameters in the power system control strategy of the whole vehicle were obtained through genetic algorithm. After being applied to ADVISOR, the optimized design scheme met the design requirement. The deviation of maximum speed was 0.07%. The range increased from 283.4 to 309.1 km; and the gradeability increased by 16.2%. The optimization results provides some guidance for the actual design of the hybrid automobiles.