Fuel automobile power system optimization method, device and equipment and storage medium

Abstract

The invention discloses a fuel automobile power system optimization method, device and equipment and a storage medium, and relates to the technical field of hydrogen fuel automobiles, the fuel automobile power system optimization method comprises the following steps: optimizing a driving motor parameter based on a vehicle power optimization strategy and a driving motor target function to obtain a target motor parameter; optimizing the fuel cell power range and the power cell capacity based on the vehicle power optimization strategy and the vehicle cost target function to obtain target fuel cell power and target power cell capacity; and optimizing a power system of a to-be-optimized vehicle according to the target motor parameter, the target fuel cell power and the target power cell capacity. Parameters are comprehensively optimized, and all relations such as a fuel cell and a power cell are fully considered; a design area is reasonably planned, and different objective functions are set for different parameters, so that the control parameter influence is scientifically evaluated, and the system efficiency and the whole vehicle performance are improved.

Description

Technical Field

[0001] The present application relates to the technical field of hydrogen fuel cell vehicles, and in particular to methods, devices, equipment and storage media for optimizing the power system of fuel cell vehicles. Background Art

[0002] Under the background of the "dual carbon" strategy, the electrification transformation of the automobile industry has accelerated. Fuel vehicles, as the ultimate environmentally friendly vehicles, have attracted much attention. The matching of their power system and control system parameters is crucial. However, the existing technology has many shortcomings. First, the optimization parameters of the power system are not comprehensive. The traditional selection relies on the critical value of the theoretical formula. It is difficult to ensure the optimality of the parameters in complex real-life scenarios, and it is impossible to give full play to the performance advantages of the power system, which limits the improvement space of the economy and power of fuel vehicles. Secondly, the design of component characteristic parameters is isolated, focusing only on a single component, and not considering the reasonable power distribution and complex coupling relationship of the overall power system. The collaborative work effect of various components is not good, the energy transfer and conversion efficiency are impaired, and it is easy to cause problems such as unstable power output and increased component wear, which reduces the reliability and durability of the system, increases maintenance costs and failure risks, and hinders the efficient and stable operation of fuel vehicles. Furthermore, the simulation analysis lacks a reasonable design area, the design process is complicated and easy to fall into the blind spot of local optimality. Because the effective design range is not accurately defined, it is necessary to make repeated trial and error adjustments, which consumes a lot of time and computing resources, delays the product development cycle, and it is difficult to efficiently obtain accurate and globally optimal design solutions, which is not conducive to the rapid iteration and market promotion of fuel vehicles. In summary, the existing hydrogen fuel vehicle power system technology has limitations in parameter optimization, component coordination, simulation analysis and control parameter processing. Therefore, how to solve the limitations of the existing hydrogen fuel vehicle power system technology in parameter optimization, component coordination, simulation analysis and control parameter processing has become a problem that needs to be solved urgently. Summary of the invention

[0003] The main purpose of this application is to provide a fuel vehicle power system optimization method, device, equipment and storage medium, aiming to solve the technical problems of the existing hydrogen fuel vehicle power system technology in parameter optimization, component coordination, simulation analysis and control parameter processing.

[0004] To achieve the above objectives, the present application proposes a fuel vehicle power system optimization method, the fuel vehicle power system optimization method comprising: Optimize the drive motor parameters based on the vehicle power optimization strategy and the drive motor objective function to obtain the target motor parameters; Based on the vehicle power optimization strategy and the vehicle cost objective function, the fuel cell power range and the power battery capacity are optimized to obtain the target fuel cell power and the target power battery capacity; The power system of the vehicle to be optimized is optimized according to the target motor parameters, the target fuel cell power, and the target power battery capacity.

[0005] In one embodiment, the step of optimizing the drive motor parameters based on the vehicle power optimization strategy and the drive motor objective function to obtain the target motor parameters includes: Determine the drive motor speed, drive motor torque, vehicle transmission ratio and drive motor power according to the drive motor parameters; Optimizing the drive motor speed, the drive motor torque and the vehicle transmission ratio based on the vehicle power optimization strategy and the drive motor objective function to obtain a target motor speed, a target motor torque and a target transmission ratio; Optimizing the driving motor power based on the vehicle power optimization strategy to obtain a target motor power; A target motor parameter is determined according to the target motor speed, the target motor torque, the target transmission ratio, and the target motor power.

[0006] In one embodiment, before the step of optimizing the drive motor speed, the drive motor torque and the vehicle transmission ratio based on the vehicle power optimization strategy and the drive motor objective function to obtain the target motor speed, the target motor torque and the target transmission ratio, the step further includes: When the current motor power requirement is not less than the motor power requirement threshold, determining the first motor power requirement according to the drive motor power requirement under a typical vehicle operating condition; When the current motor required power is less than the motor required power threshold, determining the second motor required power according to a preset ratio of the current motor required power; A drive motor objective function is constructed according to the first motor required power and the second motor required power.

[0007] In one embodiment, the step of optimizing the drive motor speed, the drive motor torque and the vehicle transmission ratio based on the vehicle power optimization strategy and the drive motor objective function to obtain a target motor speed, a target motor torque and a target transmission ratio includes: Determine an initialization population according to the driving motor speed, the driving motor torque and the vehicle transmission ratio; Calculating the fitness of the initialized population according to the drive motor objective function to obtain the current fitness; Optimizing the initialized population based on the vehicle power optimization strategy and calculating the fitness to obtain the optimized fitness; Compare the current fitness with the optimized fitness, and perform iterative optimization according to the comparison result to obtain the current number of iterations; When the current number of iterations is not less than the iteration number threshold, a target motor speed, a target motor torque and a target transmission ratio are obtained.

[0008] In one embodiment, before the step of optimizing the fuel cell power range and the power battery capacity based on the vehicle power optimization strategy and the vehicle cost objective function to obtain the target fuel cell power and the target power battery capacity, the step further includes: Determine a first fuel cell power according to a target motor power, and obtain a second fuel cell power by performing power calculation based on a fuel cell calculation strategy; Obtaining a fuel cell power range according to the first fuel cell power and the second fuel cell power; The capacity is calculated according to the vehicle's cruising range strategy to obtain the power battery capacity.

[0009] In one embodiment, the step of optimizing the fuel cell power range and the power battery capacity based on the vehicle power optimization strategy and the vehicle cost objective function to obtain the target fuel cell power and the target power battery capacity includes: Establish a target battery vehicle model based on the fuel cell power range and power battery capacity; Determining power fitness according to the target battery vehicle model, a preset dynamic programming strategy, and a vehicle cost objective function; Iterative optimization is performed according to the power fitness and the vehicle power optimization strategy to obtain the target fuel cell power and the target power battery capacity.

[0010] In one embodiment, the step of determining the power fitness according to the target battery vehicle model, the preset dynamic programming strategy and the vehicle cost objective function includes: Perform vehicle simulation according to the target battery vehicle model and the preset dynamic programming strategy to obtain hydrogen consumption data, power battery data, and fuel cell data; The fitness of the hydrogen consumption data, the power battery data and the fuel cell data is calculated based on the vehicle cost objective function to obtain the power fitness.

[0011] In addition, to achieve the above-mentioned purpose, the present application also proposes a fuel vehicle power system optimization device, the fuel vehicle power system optimization device comprising: A processing module, used for optimizing the drive motor parameters based on the vehicle power optimization strategy and the drive motor objective function to obtain target motor parameters; The processing module is further used to optimize the fuel cell power range and the power battery capacity based on the vehicle power optimization strategy and the vehicle cost objective function to obtain the target fuel cell power and the target power battery capacity; The optimization module is used to optimize the power system of the vehicle to be optimized according to the target motor parameters, the target fuel cell power and the target power battery capacity.

[0012] In addition, to achieve the above-mentioned purpose, the present application also proposes a fuel vehicle power system optimization device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the fuel vehicle power system optimization method as described above.

[0013] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the fuel vehicle power system optimization method as described above are implemented.

[0014] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the fuel vehicle power system optimization method as described above.

[0015] This application optimizes the drive motor parameters based on the vehicle power optimization strategy and the drive motor objective function to obtain the target motor parameters; optimizes the fuel cell power range and power battery capacity based on the vehicle power optimization strategy and the vehicle cost objective function to obtain the target fuel cell power and target power battery capacity; optimizes the power system of the vehicle to be optimized according to the target motor parameters, the target fuel cell power and the target power battery capacity. By comprehensively optimizing the parameters and fully considering various relationships, such as fuel cells and power batteries; rationally planning the design area, setting different objective functions for different parameters, and scientifically evaluating the influence of control parameters, improving system efficiency and vehicle performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0018] Figure 1 A schematic diagram of a process flow provided for the first embodiment of the method for optimizing the power system of a fuel vehicle of the present application; Figure 2 A schematic diagram of a fuel cell vehicle power system optimization framework provided in Example 1 of the fuel cell vehicle power system optimization method of the present application; Figure 3 A schematic diagram of a fuel cell vehicle topology structure provided in Example 1 of the fuel cell vehicle power system optimization method of the present application; Figure 4 A schematic diagram of a fuel cell and power battery parameter optimization process provided in Example 1 of the fuel vehicle power system optimization method of the present application; Figure 5 A schematic diagram of a flow chart provided for the second embodiment of the method for optimizing the power system of a fuel vehicle of the present application; Figure 6 A schematic diagram of the optimization process of the maximum torque, maximum speed and transmission ratio of the driving motor provided in the second embodiment of the fuel vehicle power system optimization method of the present application; Figure 7 This is a schematic diagram of the module structure of the fuel vehicle power system optimization device according to an embodiment of the present application; Figure 8 This is a schematic diagram of the device structure of the hardware operating environment involved in the fuel vehicle power system optimization method in the embodiment of the present application.

[0019] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0020] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0021] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0022] The main solutions of the embodiments of the present application are: optimizing the drive motor parameters based on the vehicle power optimization strategy and the drive motor objective function to obtain target motor parameters; optimizing the fuel cell power range and the power battery capacity based on the vehicle power optimization strategy and the vehicle cost objective function to obtain the target fuel cell power and the target power battery capacity; optimizing the power system of the vehicle to be optimized according to the target motor parameters, the target fuel cell power and the target power battery capacity.

[0023] Under the background of the "dual carbon" strategy, the electrification transformation of the automobile industry has accelerated. Fuel vehicles, as the ultimate environmentally friendly vehicles, have attracted much attention. The matching of their power system and control system parameters is crucial. However, the existing technology has many shortcomings. First, the optimization parameters of the power system are not comprehensive. The traditional selection relies on the critical value of the theoretical formula. It is difficult to ensure the optimality of the parameters in complex real-life scenarios, and it is impossible to give full play to the performance advantages of the power system, which limits the improvement space of the economy and power of fuel vehicles. Secondly, the design of component characteristic parameters is isolated, focusing only on a single component, and not considering the reasonable power distribution and complex coupling relationship of the overall power system. The collaborative work effect of various components is not good, the energy transfer and conversion efficiency are impaired, and it is easy to cause problems such as unstable power output and increased component wear, which reduces the reliability and durability of the system, increases maintenance costs and failure risks, and hinders the efficient and stable operation of fuel vehicles. Furthermore, the simulation analysis lacks a reasonable design area, the design process is complicated and easy to fall into the blind spot of local optimality. Because the effective design range is not accurately defined, it is necessary to make repeated trial and error adjustments, which consumes a lot of time and computing resources, delays the product development cycle, and it is difficult to efficiently obtain accurate and globally optimal design solutions, which is not conducive to the rapid iteration and market promotion of fuel vehicles. In summary, the existing hydrogen fuel vehicle power system technology has limitations in parameter optimization, component coordination, simulation analysis and control parameter processing. Therefore, how to solve the limitations of the existing hydrogen fuel vehicle power system technology in parameter optimization, component coordination, simulation analysis and control parameter processing has become a problem that needs to be solved urgently.

[0024] This application optimizes the drive motor parameters based on the vehicle power optimization strategy and the drive motor objective function to obtain the target motor parameters; optimizes the fuel cell power range and power battery capacity based on the vehicle power optimization strategy and the vehicle cost objective function to obtain the target fuel cell power and target power battery capacity; optimizes the power system of the vehicle to be optimized according to the target motor parameters, the target fuel cell power and the target power battery capacity. By comprehensively optimizing the parameters and fully considering various relationships, such as fuel cells and power batteries; rationally planning the design area, setting different objective functions for different parameters, and scientifically evaluating the influence of control parameters, improving system efficiency and vehicle performance.

[0025] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or a fuel vehicle power system optimization device that can achieve the above functions. The following takes the fuel vehicle power system optimization device as an example of the execution subject to illustrate this embodiment and the following embodiments.

[0026] Based on this, the present application embodiment provides a fuel vehicle power system optimization method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the fuel vehicle power system optimization method of the present application.

[0027] In this embodiment, the fuel vehicle power system optimization method includes steps S10 to S30: Step S10, optimizing the drive motor parameters based on the vehicle power optimization strategy and the drive motor objective function to obtain target motor parameters; It should be noted that this embodiment determines the driving form of the hydrogen fuel cell vehicle by comprehensively comparing the fuel cell vehicle power system, adopts the Crayfish Optimization Algorithm (COA) and the parameter scanning method, and takes the average required power of the motor as the fitness value to determine the optimal transmission ratio, the maximum speed of the motor and the maximum torque of the motor; secondly, combined with the operating point of the typical operating condition library that the maximum power of the motor must meet, a motor cost function is constructed, and COA is used to search for the best, and finally the optimal maximum torque, maximum speed and transmission ratio of the motor are determined, and the optimal maximum power of the motor is selected; then, for the maximum power of the fuel cell and the power battery capacity, the cost function is also established, and COA is used to search for the best result, thereby completing the optimization of the entire power system, thereby improving the fuel economy and vehicle performance of the fuel vehicle.

[0028] It is important to understand that the optimization framework of the fuel cell vehicle power system is as follows: Figure 2 As shown in the figure, its structure mainly consists of two parts, namely: fuel cell vehicle drive form determination module, drive motor and drive axle parameter optimization module, fuel cell and power battery parameter optimization module. The fuel cell vehicle drive form determination module determines the appropriate drive form by comparing the existing research drive forms. The drive motor and drive axle parameter optimization can be further divided into the determination of the drive axle transmission ratio and the maximum speed and maximum torque of the drive motor. On the basis of this optimization result, the objective function is further established to obtain the maximum power of the drive motor. The fuel cell and power battery parameter optimization module determines the feasible domain through theoretical formulas, thereby obtaining the optimal parameters of the optimal fuel cell and power battery.

[0029] It can be understood that the vehicle power optimization strategy refers to the strategy of using the crayfish optimization algorithm (COA) to optimize the power system of a fuel cell hybrid commercial vehicle, and the drive motor objective function refers to the objective function used to optimize the drive motor parameters. The target motor parameters include but are not limited to the optimal drive motor maximum speed, drive motor maximum torque, drive motor maximum power and main reducer transmission ratio.

[0030] In the specific implementation, the crayfish optimization algorithm (COA) is used to optimize the strategy of the fuel cell hybrid commercial vehicle power system, and combined with the objective function for optimizing the drive motor parameters, the maximum speed of the drive motor, the maximum torque of the drive motor, the maximum power of the drive motor and the main reducer transmission ratio are optimized to obtain the optimal maximum speed of the drive motor, the maximum torque of the drive motor, the maximum power of the drive motor and the main reducer transmission ratio, that is, the target motor parameters.

[0031] It should be noted that fuel cell vehicles are a type of new energy vehicle. Unlike traditional vehicles, they can have multiple power sources. Various combinations of power sources are suitable for various vehicle models. In order to meet the power and economy of the vehicle, it is particularly important to choose a suitable structure.

[0032] At present, fuel cell vehicles mainly have four driving forms: pure fuel drive, fuel cell and power battery hybrid drive system (a driving form in fuel cell vehicles) (FC+B), fuel cell and supercapacitor hybrid drive system (FC+C), fuel cell, power battery and supercapacitor hybrid drive system (FC+B+C).

[0033] A pure fuel cell power system is an ideal power system, but its dynamic response capability is poor, and when faced with a sharp change in load power, it will have a significant negative impact on the output performance of the fuel cell. FC+B can not only effectively meet the power requirements of the car under conditions such as acceleration and climbing, but also recover energy during deceleration braking or coasting braking, thereby significantly extending the car's cruising range; in addition, the system successfully solves the problem of cold start difficulties in pure fuel cell systems and makes up for its inherent defects. In FC+C, although supercapacitors have unique advantages, due to their low energy density, it is difficult to maintain high power output for a long time, and the voltage fluctuates significantly during discharge. The measures taken in series with impedance elements to reduce fluctuations will increase the cost of the vehicle, which limits its application to a certain extent. FC+B+C can give full play to the performance advantages of the three power sources, is suitable for a variety of operating conditions, and can make the power and economy of the vehicle reach the optimal state. However, the control strategy of the system is complex, and it occupies a large space in the vehicle, resulting in an increase in the weight of the vehicle and an increase in manufacturing costs, which is not conducive to large-scale promotion and application.

[0034] Taking into account the advantages and disadvantages of different structures and the complexity of control strategies, this embodiment selects FC+B (the power source is a hybrid drive mode of fuel cells and power batteries). The topology of the fuel cell vehicle is as follows: Figure 3 This choice aims to balance power performance, economy and feasibility of practical application, and provide a more optimized driving solution for the development of fuel cell vehicles.

[0035] Step S20, optimizing the fuel cell power range and the power battery capacity based on the vehicle power optimization strategy and the vehicle cost objective function to obtain a target fuel cell power and a target power battery capacity; It can be understood that the vehicle cost objective function refers to the cost objective function used to optimize the maximum power of the fuel cell and the power battery capacity, the fuel cell power range refers to the value range of the maximum power of the fuel cell, the power battery capacity refers to the amount of electrical energy that the battery can store, the target fuel cell power refers to the optimal maximum power of the fuel cell, and the target power battery capacity refers to the optimal power battery capacity.

[0036] In the specific implementation, the crayfish optimization algorithm is used to optimize the strategy of the fuel cell hybrid commercial vehicle power system, and combined with the cost objective function used to optimize the maximum power of the fuel cell and the power battery capacity, the maximum power of the fuel cell and the power battery capacity are optimized to obtain the optimal maximum power of the fuel cell and the power battery capacity.

[0037] In a feasible implementation manner, before step S20, steps A11 to A13 may also be included: Step A11, determining the first fuel cell power according to the target motor power, and performing power calculation based on the fuel cell calculation strategy to obtain the second fuel cell power; It should be noted that the first fuel cell power refers to the upper limit value of the maximum power of the fuel cell, and the second fuel cell power refers to the lower limit value of the maximum power of the fuel cell.

[0038] In specific implementation, the parameters of the fuel cell are mainly the maximum output power. The selection of the maximum output power of the fuel cell is generally aimed at meeting the maximum speed requirement of the vehicle. The lower limit of the maximum power of the fuel cell (the second fuel cell power) can be determined by formula calculation. At the same time, the use of a high-power fuel cell system can improve the lightweight and integration convenience of the entire vehicle. However, if the fuel cell system is set too large, it will increase the cost, so the maximum power of the drive motor is set to the upper limit of the maximum power of the fuel cell (the first fuel cell power).

[0039] Step A12, obtaining a fuel cell power range according to the first fuel cell power and the second fuel cell power; In a specific implementation, the upper limit value and the lower limit value of the maximum power of the fuel cell are aggregated and processed to obtain the value range of the maximum power of the fuel cell.

[0040] Step A13, performing capacity calculation according to the vehicle range strategy to obtain the power battery capacity.

[0041] It can be understood that the vehicle range strategy refers to the strategy of calculating the power battery capacity based on the pure electric range formula.

[0042] In specific implementation, the number of power batteries connected in series can be determined based on the voltage platform used by the selected vehicle and other auxiliary consumption factors, and the power battery capacity can be calculated through the pure electric cruising range formula to determine the lower limit of the power battery capacity that meets the performance requirements. The larger the power battery capacity, the better the vehicle's power, endurance, and energy recovery efficiency. However, different capacities will cause changes in the mass of the vehicle, thereby increasing costs.

[0043] In a feasible implementation, step S20 may include steps B11 to B13: Step B11, establishing a target battery vehicle model based on the fuel cell power range and the power battery capacity; It can be understood that the target battery vehicle model refers to a mathematical model used to simulate the working conditions of a hydrogen fuel cell vehicle.

[0044] In the specific implementation, this embodiment establishes a simplified mathematical model of a hydrogen fuel cell vehicle in MATLAB. This model can calculate vehicle performance indicators such as energy consumption, acceleration performance, etc. based on the given fuel cell power output, power battery capacity and other parameters. And this model will serve as the basis for evaluating fitness.

[0045] Step B12, determining the power fitness according to the target battery vehicle model, the preset dynamic programming strategy and the vehicle cost objective function; It can be understood that the preset dynamic programming strategy refers to a pre-set energy management strategy using a dynamic programming method, and the power fitness refers to the fitness when optimizing the maximum power of the fuel cell and the power battery capacity.

[0046] In the specific implementation, the fitness is calculated by calling the fuel cell vehicle model, that is, for each individual in each generation, the previously established hydrogen fuel cell vehicle model is called in combination with the pre-set dynamic programming method to formulate the energy management strategy and the vehicle cost objective function to calculate its fitness value. The fitness value reflects the overall performance of the vehicle under this set of parameters.

[0047] In a feasible implementation, step B12 may include steps C11-C12: Step C11, performing vehicle simulation according to the target battery vehicle model and a preset dynamic programming strategy to obtain hydrogen consumption data, power battery data, and fuel cell data; It can be understood that the hydrogen consumption data refers to the hydrogen consumption cost data, the power battery data refers to the power battery cost data, and the fuel cell data refers to the fuel cell cost data.

[0048] In the specific implementation, based on the mathematical model used to simulate the working conditions of hydrogen fuel cell vehicles and the pre-set dynamic programming method to formulate an energy management strategy, simulation calculations are performed in combination with the maximum power of the fuel cell and the power battery capacity to obtain hydrogen consumption cost data, power battery cost data and fuel cell cost data.

[0049] Step C12, performing fitness calculation on the hydrogen consumption data, the power battery data and the fuel cell data based on the vehicle cost objective function to obtain power fitness.

[0050] It can be understood that the fitness is calculated based on the cost objective function used to optimize the maximum power of the fuel cell and the power battery capacity, and the fitness is calculated by combining the hydrogen consumption cost data, the power battery cost data and the fuel cell cost data to obtain the fitness when optimizing the maximum power of the fuel cell and the power battery capacity.

[0051] Step B13, performing iterative optimization according to the power fitness and the vehicle power optimization strategy to obtain a target fuel cell power and a target power battery capacity.

[0052] It can be understood that based on the adaptability when optimizing the maximum power of the fuel cell and the power battery capacity, and by adopting the crayfish optimization algorithm to optimize the strategy of the fuel cell hybrid commercial vehicle power system, the maximum power of the fuel cell and the power battery capacity are optimized, and then the optimal maximum power of the fuel cell and the power battery capacity are obtained.

[0053] It should be noted that, in this embodiment, for the fuel cell and power battery parameter optimization model, the fuel cell parameter is mainly the maximum output power. The selection of the maximum output power of the fuel cell is generally aimed at meeting the maximum speed requirement of the vehicle. The lower limit of the maximum power of the fuel cell can be determined by formula (1). At the same time, the selection of a high-power fuel cell system is beneficial to improving the lightweight and integration convenience of the whole vehicle. However, if the fuel cell system is set too large, it will increase the cost, so the maximum power of the drive motor is set to the upper limit of the maximum power of the fuel cell. Finally, according to the above conditions, the maximum power range of the fuel cell is determined. Similarly, according to the voltage platform used by the selected vehicle and other auxiliary consumption factors, the number of power batteries in series can be determined, and the lower limit of the power battery capacity that meets the performance requirements can be determined by the pure electric cruising range formula (2-4). The larger the power battery capacity, the better the vehicle's power, cruising range, and energy recovery efficiency. However, different capacities will cause changes in the weight of the whole vehicle, thereby increasing costs. The power battery capacity can be determined by the pure electric cruising range formula (2-4):

[0054]

[0055]

[0056]

[0057] Where: They are transmission system efficiency, DC / DC efficiency, and motor efficiency. is the battery pack power, is the battery efficiency, is the motor efficiency, is the cruising speed, For pure electric driving range, is the discharge depth of the power battery, Power battery voltage.

[0058] Considering that the increase of fuel cell maximum power and power battery capacity will lead to increased cost, in order to determine the optimal fuel cell maximum power and power battery capacity, this paper establishes the objective function, see formula (5).

[0059]

[0060] Where: is the total cost, is the hydrogen consumption cost, is the power battery cost, The cost of fuel cells.

[0061] Finally, this embodiment establishes a hydrogen fuel cell vehicle model in MATLAB, establishes a dynamic programming algorithm (DP) as the energy management strategy for fuel cell vehicles, and uses the crayfish optimization algorithm to optimize the maximum power of the fuel cell and the capacity of the power battery to obtain the optimal result, that is, the optimization variables fuel cell maximum power and power battery maximum capacity are set in the COA optimization algorithm, the maximum number of iterations is set to 100, and the initial population is 30. The fitness value can be calculated by calling the fuel cell vehicle model. DP is a numerical method for solving multi-stage decision problems. The algorithm discretizes the multi-stage optimization problem, obtains the state function of each decision process, and then reversely obtains the global optimal decision according to the set cost function, which is a global optimal management strategy. Its optimization flow chart is as follows Figure 4 In summary, the optimal maximum power of the fuel cell and the power battery capacity can be obtained, thereby completing the optimization of the fuel cell vehicle power system.

[0062] Step S30 , optimizing the power system of the vehicle to be optimized according to the target motor parameters, the target fuel cell power, and the target power battery capacity.

[0063] In the specific implementation, the vehicle to be optimized refers to the hydrogen fuel cell vehicle whose power system is to be optimized. Based on the optimal maximum power of the drive motor, the maximum speed of the drive motor, the maximum torque of the drive motor and the transmission ratio of the main reducer, combined with the optimal maximum power of the fuel cell and the power battery capacity, the power optimization of the hydrogen fuel cell vehicle whose power system is to be optimized is performed to complete the optimization of the power system of the hydrogen fuel cell vehicle whose power system is to be optimized.

[0064] This embodiment optimizes the drive motor parameters based on the vehicle power optimization strategy and the drive motor objective function to obtain the target motor parameters; optimizes the fuel cell power range and the power battery capacity based on the vehicle power optimization strategy and the vehicle cost objective function to obtain the target fuel cell power and the target power battery capacity; optimizes the power system of the vehicle to be optimized according to the target motor parameters, the target fuel cell power and the target power battery capacity. By comprehensively optimizing the parameters and fully considering various relationships, such as the fuel cell and the power battery; rationally planning the design area, setting different objective functions for different parameters, and scientifically evaluating the influence of the control parameters, the system efficiency and vehicle performance are improved.

[0065] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 5 , step S10 in the fuel vehicle power system optimization method also includes steps S11 to S14: Step S11, determining the driving motor speed, driving motor torque, vehicle transmission ratio and driving motor power according to the driving motor parameters; It can be understood that the maximum speed of the drive motor, the maximum torque of the drive motor, the main reducer transmission ratio and the maximum power of the drive motor to be optimized are determined according to the associated parameters of the drive motor.

[0066] Step S12, optimizing the drive motor speed, the drive motor torque and the vehicle transmission ratio based on the vehicle power optimization strategy and the drive motor objective function to obtain a target motor speed, a target motor torque and a target transmission ratio; It can be understood that by adopting the crayfish optimization algorithm to optimize the strategy of the fuel cell hybrid commercial vehicle power system, and combining it with the objective function for optimizing the drive motor parameters, the maximum speed of the drive motor, the maximum torque of the drive motor and the main reducer transmission ratio are optimized, and then the optimal maximum speed of the drive motor, the maximum torque of the drive motor and the main reducer transmission ratio are obtained.

[0067] In a feasible implementation manner, before step S12, steps D11 to D13 may also be included: Step D11, when the current motor power requirement is not less than the motor power requirement threshold, determining the first motor power requirement according to the drive motor power requirement under typical vehicle operating conditions; It can be understood that the current motor demand power refers to the current demand power of the driving motor, and the motor demand power threshold refers to the power critical value used to determine the average demand power of the driving motor. In this embodiment, the motor demand power threshold is taken as 0 for example. Typical vehicle operating conditions include but are not limited to CLTC-C (urban driving conditions), CLTC-A (suburban driving conditions) and CLTC-H (high-speed driving conditions), etc. The first motor demand power refers to the average demand power when the driving motor demand power is not less than 0.

[0068] In a specific implementation, when the current demand power of the drive motor is not less than the power critical value used to determine the average demand power of the drive motor, it indicates that the average demand power of the drive motor of the fuel cell vehicle under typical operating conditions can be set as the target value, thereby determining the average demand power when the demand power of the drive motor is greater than 0, that is, the first motor demand power.

[0069] Step D12, when the current motor required power is less than the motor required power threshold, determining the second motor required power according to a preset ratio of the current motor required power; It can be understood that the preset ratio refers to a pre-set ratio value used to determine the average required power of the drive motor, for example, 40%. This embodiment does not limit the preset ratio, and the size of the preset ratio can be determined based on actual conditions. The second motor required power refers to the average required power when the drive motor required power is less than 0.

[0070] In a specific implementation, when the current demand power of the drive motor is less than the power critical value used to determine the average demand power of the drive motor, it indicates that the present embodiment sets the braking recovery energy to 0.4 times the braking energy, that is, when the motor power is a negative value, only 40% of its power value is considered as a measure of the average power of the motor, and then the average demand power when the demand power of the drive motor is less than 0 is determined based on the product of the preset ratio and the current demand power of the drive motor, that is, the demand power of the second motor.

[0071] Step D13: constructing a drive motor objective function according to the first motor required power and the second motor required power.

[0072] It can be understood that, based on the average required power when the drive motor required power is greater than 0 and the average required power when the drive motor required power is less than 0, an objective function for optimizing the drive motor parameters is constructed, that is, the drive motor objective function is obtained.

[0073] In a feasible implementation, step S12 may include steps E11 to E15: Step E11, determining an initialization population according to the driving motor speed, the driving motor torque and the vehicle transmission ratio; It is understood that the initialization population refers to the set of optimization variables created at the beginning of the algorithm.

[0074] In the specific implementation, in order to obtain the optimal maximum speed of the drive motor, the maximum torque of the drive motor and the transmission ratio of the main reducer, this embodiment adopts the crayfish optimization algorithm to find the optimal value, and then sets the maximum speed of the drive motor, the maximum torque of the drive motor and the transmission ratio of the main reducer as the optimization variables, that is, obtains the initialized population.

[0075] Step E12, calculating the fitness of the initialized population according to the drive motor objective function to obtain the current fitness; It can be understood that the current fitness refers to the fitness corresponding to the initialized population.

[0076] In a specific implementation, the fitness of the initialized population is calculated by using an objective function for optimizing the drive motor parameters, and then the fitness corresponding to the initialized population, ie, the current fitness, is obtained.

[0077] Step E13, optimizing the initialized population based on the vehicle power optimization strategy and calculating the fitness to obtain the optimized fitness; It can be understood that the optimization fitness refers to the fitness corresponding to the optimized optimization variable.

[0078] In the specific implementation, the crayfish optimization algorithm is used to optimize the strategy of the fuel cell hybrid commercial vehicle power system, and the maximum speed of the drive motor, the maximum torque of the drive motor and the transmission ratio of the main reducer are optimized, and then the optimized maximum speed of the drive motor, the maximum torque of the drive motor and the transmission ratio of the main reducer are obtained, and then the fitness corresponding to the optimized maximum speed of the drive motor, the maximum torque of the drive motor and the transmission ratio of the main reducer is calculated, and the optimized fitness is obtained.

[0079] Step E14, comparing the current fitness with the optimized fitness, and performing iterative optimization according to the comparison result to obtain the current number of iterations; It can be understood that the current number of iterations refers to the number of current iteration optimizations.

[0080] In the specific implementation, the fitness corresponding to the initialized population is compared with the fitness corresponding to the optimized optimization variable, and the optimization variable with better fitness is selected for iterative optimization to obtain the optimal variable, and the number of iterations is recorded to obtain the current number of iterative optimizations.

[0081] Step E15, when the current number of iterations is not less than the iteration number threshold, obtain the target motor speed, target motor torque and target transmission ratio.

[0082] It can be understood that the iteration number threshold refers to the critical value of the iteration number used to determine whether the optimal variable is obtained.

[0083] In a specific implementation, when the current number of iterative optimizations is not less than the critical value of the number of iterations used to determine whether the optimal variable is obtained, it indicates that the optimal number of iterations has been reached, that is, the optimal maximum speed of the drive motor, the maximum torque of the drive motor and the main reducer transmission ratio are obtained. For example, the maximum number of iterations is set to 100, and when the current number of iterations reaches 100, the optimal optimization variable is obtained.

[0084] Step S13, optimizing the driving motor power based on the vehicle power optimization strategy to obtain a target motor power; It can be understood that by adopting the crayfish optimization algorithm to optimize the strategy of the fuel cell hybrid commercial vehicle power system, and combining it with the objective function for optimizing the maximum power of the drive motor, the maximum power of the drive motor is optimized, and then the optimal maximum power of the drive motor is obtained.

[0085] Step S14, determining target motor parameters according to the target motor speed, the target motor torque, the target transmission ratio and the target motor power.

[0086] In a specific implementation, the optimal maximum speed of the drive motor, the maximum torque of the drive motor, the transmission ratio of the main reducer and the maximum power of the drive motor are summarized and processed to obtain the target motor parameters.

[0087] It should be noted that this embodiment takes into account the reasonable power distribution and complex coupling relationship of the entire power system, and this embodiment optimizes the drive motor related parameters and drive axle parameters at the same time. First, determine the transmission ratio and the maximum speed and maximum torque of the drive motor. In order to obtain a reasonable design area, the research structure is divided into two layers, the first layer is the transmission ratio selection layer, and the second layer is the drive maximum speed and maximum torque selection layer. For the transmission ratio of the first layer, each corresponds to a set of large speed and maximum torque parameter sets, which contains discrete subsets of the allowable maximum motor speed and maximum motor torque. In order to make the two parameters meet the display requirements, the lower limits of the two parameters can be obtained by equations (6) and (7), and the upper limit is set to a function related to the lower limit, by comparing the heavy-duty commercial vehicle motor parameters provided by various motor manufacturers. In this embodiment, the upper limits of the motor speed and torque are set to and , that is, by comparing the commercial vehicle motor parameters provided by various motor manufacturers, this embodiment sets the upper limits of the motor speed and torque to and ; In the motor and transmission system model layer, each model corresponds to a set of motor parameters. Through the above structure, the maximum torque and maximum speed with the best average power of the drive motor under each transmission ratio can be obtained.

[0088]

[0089] In the formula, For car quality, is the acceleration due to gravity, is the rolling resistance coefficient, For transmission efficiency, is the maximum speed of the vehicle, is the drag coefficient, is the windward area, is the slope angle, is the vehicle speed, Rotational mass coefficient, is the acceleration time, is the tire radius, is the main reducer transmission ratio, , , are the maximum speed and maximum torque of the motor respectively.

[0090] To determine the objective function, the average required power of the drive motor of a fuel cell vehicle under typical operating conditions can be Set as the target value, see equation (8). , 40% of the power value is used as the average required power as a measure (the working process of the drive motor involves the problem of braking recovery. In this embodiment, the braking recovery energy is set to 0.4 times the braking energy, that is, when the motor power is a negative value, only 40% of its power value is considered as a measure of the average motor power).

[0091]

[0092] Regarding the selection of optimization methods, COA has a faster convergence speed than genetic algorithms, particle swarm algorithms, etc. (the COA optimization algorithm parameters set the initial population to 30 and the maximum number of iterations to 100). Therefore, this embodiment uses the crayfish optimization algorithm (COA) to optimize the transmission ratio, the maximum speed of the drive motor, and the maximum torque of the drive motor to obtain the optimal maximum torque and maximum speed of the drive motor and the transmission ratio under the optimal average required power. The process is as follows: Figure 6 , the specific steps are: (1) Initialize the population:

[0093] In the formula, For individuals The optimization variables (maximum speed of the drive motor, maximum torque, transmission ratio) parameters, To denote the lower bound of the optimization variable, To optimize the upper bound of the variable, is a random value.

[0094] (2) Define temperature and intake: COA by temperature Change to control the algorithm to enter the exploration or development phase. ,and When the summer vacation begins, When, or and The development stage is the period when crayfish have strong competitiveness and good foraging behavior. Their foraging intake is express.

[0095]

[0096]

[0097] In the formula, , As a constant, control the intake of crayfish at different temperatures. This is the most suitable temperature for crayfish.

[0098] (3) Summer vacation: when When the temperature rises, crayfish will enter the cave to avoid the heat, which can be expressed by the following formula:

[0099]

[0100] In the formula, For the cave location, represents the global optimal position obtained with iteration, represents the global optimal position obtained with iteration. There will be no competition for caves. Indicates the number of current iterations. is a descending curve, where , The maximum number of iterations is 100.

[0101] (4) Competition: when and This means that there will be competition among crayfish, which can be expressed as follows:

[0102] In the formula, represents a random crayfish individual, where .

[0103] 5) Foraging: when When the crayfish starts to look for food, it will move to the food location. The formula for judging the size of the food is as follows:

[0104]

[0105] In the formula, For food size, is the food factor, indicating the maximum food Indicates The fitness value of a crayfish, Represents the fitness value (and average power requirement) of the food.

[0106] (6) Output result judgment: Last Updated , , and determine whether the maximum number of iterations 100 is reached. If the maximum number of iterations is not reached, proceed to (2), otherwise output the optimal parameters. It should be noted that, at present, the maximum power of the drive motor (theoretical formula solution method, that is, the vehicle performance indicators (maximum speed, climbing grade, acceleration time) are used to determine the maximum power) is usually determined according to formula (9) - formula (12):

[0107]

[0108]

[0109]

[0110] Where: , , The motor power is determined according to the maximum vehicle speed, climbing grade, and acceleration time, respectively. The maximum vehicle speed and climbing grade can be determined according to the designed vehicle performance indicators.

[0111] The maximum power of the drive motor should meet the data of the typical operating condition library, and the percentage of non-satisfaction of the general vehicle driving condition point should be less than 1%. In order to obtain a better maximum power of the fuel cell, the drive motor price V can be obtained based on the commercial vehicle motors provided by various motor manufacturers. Optimization is performed, and formula (11) is used as the objective function.

[0112]

[0113] Where: ] is the weight factor, It is the percentage of operating point not meeting the requirement, in %.

[0114] The COA optimization algorithm parameters set the initial population to 30, the maximum number of iterations to 100, set the optimization variable for the maximum power of the drive motor, and the fitness function to be , the optimal maximum power of the drive motor can be determined through iterative cycles. Finally, the drive motor parameters are determined based on the determined optimal maximum drive power, optimal maximum torque, optimal maximum speed and transmission ratio, and combined with the market. Finally, the optimal motor maximum torque, maximum speed, and transmission ratio are determined and the optimal maximum power of the drive motor is selected.

[0115] This embodiment determines the drive motor speed, drive motor torque, vehicle transmission ratio and drive motor power according to the drive motor parameters; optimizes the drive motor speed, drive motor torque and vehicle transmission ratio based on the vehicle power optimization strategy and the drive motor objective function to obtain the target motor speed, target motor torque and target transmission ratio; optimizes the drive motor power based on the vehicle power optimization strategy to obtain the target motor power; and determines the target motor parameters according to the target motor speed, target motor torque, target transmission ratio and target motor power. In the above manner, the maximum power of the drive motor, the maximum speed of the drive motor, the maximum torque of the drive motor and the transmission ratio of the main reducer are optimized to obtain the optimal maximum speed of the drive motor, the maximum torque of the drive motor and the transmission ratio of the main reducer, and then the optimal drive motor parameters are obtained, which solves the problem that the optimization parameters of the existing optimization power system method are not comprehensive.

[0116] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the fuel vehicle power system optimization method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0117] This application also provides a fuel vehicle power system optimization device, please refer to Figure 7 , the fuel vehicle power system optimization device comprises: The processing module 10 is used to optimize the drive motor parameters based on the vehicle power optimization strategy and the drive motor objective function to obtain target motor parameters; The processing module 10 is further used to optimize the fuel cell power range and the power battery capacity based on the vehicle power optimization strategy and the vehicle cost objective function to obtain the target fuel cell power and the target power battery capacity; The optimization module 20 is used to optimize the power system of the vehicle to be optimized according to the target motor parameters, the target fuel cell power and the target power battery capacity.

[0118] Optionally, the processing module 10 is further used for: Determine the drive motor speed, drive motor torque, vehicle transmission ratio and drive motor power according to the drive motor parameters; Optimizing the drive motor speed, the drive motor torque and the vehicle transmission ratio based on the vehicle power optimization strategy and the drive motor objective function to obtain a target motor speed, a target motor torque and a target transmission ratio; Optimizing the driving motor power based on the vehicle power optimization strategy to obtain a target motor power; A target motor parameter is determined according to the target motor speed, the target motor torque, the target transmission ratio, and the target motor power.

[0119] Optionally, the processing module 10 is further used for: When the current motor power requirement is not less than the motor power requirement threshold, determining the first motor power requirement according to the drive motor power requirement under a typical vehicle operating condition; When the current motor required power is less than the motor required power threshold, determining the second motor required power according to a preset ratio of the current motor required power; A drive motor objective function is constructed according to the first motor required power and the second motor required power.

[0120] Optionally, the processing module 10 is further used for: Determine an initialization population according to the driving motor speed, the driving motor torque and the vehicle transmission ratio; Calculating the fitness of the initialized population according to the drive motor objective function to obtain the current fitness; Optimizing the initialized population based on the vehicle power optimization strategy and calculating the fitness to obtain the optimized fitness; Compare the current fitness with the optimized fitness, and perform iterative optimization according to the comparison result to obtain the current number of iterations; When the current number of iterations is not less than the iteration number threshold, a target motor speed, a target motor torque and a target transmission ratio are obtained.

[0121] Optionally, the processing module 10 is further used for: Determine a first fuel cell power according to a target motor power, and obtain a second fuel cell power by performing power calculation based on a fuel cell calculation strategy; Obtaining a fuel cell power range according to the first fuel cell power and the second fuel cell power; The capacity is calculated according to the vehicle's cruising range strategy to obtain the power battery capacity.

[0122] Optionally, the processing module 10 is further used for: Establish a target battery vehicle model based on the fuel cell power range and power battery capacity; Determining power fitness according to the target battery vehicle model, a preset dynamic programming strategy, and a vehicle cost objective function; Iterative optimization is performed according to the power fitness and the vehicle power optimization strategy to obtain the target fuel cell power and the target power battery capacity.

[0123] Optionally, the processing module 10 is further used for: Perform vehicle simulation according to the target battery vehicle model and the preset dynamic programming strategy to obtain hydrogen consumption data, power battery data, and fuel cell data; The fitness of the hydrogen consumption data, the power battery data and the fuel cell data is calculated based on the vehicle cost objective function to obtain the power fitness.

[0124] The fuel vehicle power system optimization device provided by the present application adopts the fuel vehicle power system optimization method in the above embodiment, which can solve the technical problems that the existing hydrogen fuel vehicle power system technology has limitations in parameter optimization, component coordination, simulation analysis and control parameter processing. Compared with the prior art, the beneficial effects of the fuel vehicle power system optimization device provided by the present application are the same as the beneficial effects of the fuel vehicle power system optimization method provided by the above embodiment, and the other technical features in the fuel vehicle power system optimization device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0125] The present application provides a fuel vehicle power system optimization device, which includes: at least one processor; and a memory that is communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the fuel vehicle power system optimization method in the above-mentioned embodiment one.

[0126] Reference below Figure 8, which shows a schematic diagram of the structure of a fuel vehicle power system optimization device suitable for implementing the embodiment of the present application. The fuel vehicle power system optimization device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 8 The fuel vehicle power system optimization device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0127] like Figure 8 As shown, the fuel vehicle power system optimization device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the fuel vehicle power system optimization device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the fuel vehicle power system optimization device to communicate wirelessly or wired with other devices to exchange data. Although the fuel vehicle power system optimization device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0128] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0129] The fuel vehicle power system optimization device provided by the present application adopts the fuel vehicle power system optimization method in the above embodiment, which can solve the technical problems that the existing hydrogen fuel vehicle power system technology has limitations in parameter optimization, component coordination, simulation analysis and control parameter processing. Compared with the prior art, the beneficial effects of the fuel vehicle power system optimization device provided by the present application are the same as the beneficial effects of the fuel vehicle power system optimization method provided by the above embodiment, and the other technical features in the fuel vehicle power system optimization device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0130] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0131] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0132] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the fuel vehicle power system optimization method in the above-mentioned embodiment.

[0133] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0134] The above-mentioned computer-readable storage medium may be included in the fuel vehicle power system optimization device; or it may exist independently without being assembled into the fuel vehicle power system optimization device.

[0135] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the fuel vehicle power system optimization device, the fuel vehicle power system optimization device: optimizes the drive motor parameters based on the vehicle power optimization strategy and the drive motor objective function to obtain target motor parameters; optimizes the fuel cell power range and the power battery capacity based on the vehicle power optimization strategy and the vehicle cost objective function to obtain the target fuel cell power and the target power battery capacity; optimizes the power system of the vehicle to be optimized according to the target motor parameters, the target fuel cell power and the target power battery capacity.

[0136] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0137] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0138] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0139] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned fuel vehicle power system optimization method, and can solve the technical problems that the existing hydrogen fuel vehicle power system technology has limitations in parameter optimization, component coordination, simulation analysis, and control parameter processing. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the fuel vehicle power system optimization method provided in the above-mentioned embodiment, and will not be repeated here.

[0140] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned fuel vehicle power system optimization method when executed by a processor.

[0141] The computer program product provided by this application can solve the technical problems that the existing hydrogen fuel vehicle power system technology has limitations in parameter optimization, component coordination, simulation analysis and control parameter processing. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the fuel vehicle power system optimization method provided by the above embodiment, and will not be repeated here.

[0142] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for optimizing a fuel vehicle power system, characterized in that: The fuel vehicle power system optimization method comprises: Optimize the drive motor parameters based on the vehicle power optimization strategy and the drive motor objective function to obtain the target motor parameters; Based on the vehicle power optimization strategy and the vehicle cost objective function, the fuel cell power range and the power battery capacity are optimized to obtain the target fuel cell power and the target power battery capacity; The power system of the vehicle to be optimized is optimized according to the target motor parameters, the target fuel cell power, and the target power battery capacity.

2. The method according to claim 1, characterized in that The step of optimizing the drive motor parameters based on the vehicle power optimization strategy and the drive motor objective function to obtain the target motor parameters includes: Determine the drive motor speed, drive motor torque, vehicle transmission ratio and drive motor power according to the drive motor parameters; Optimizing the drive motor speed, the drive motor torque and the vehicle transmission ratio based on the vehicle power optimization strategy and the drive motor objective function to obtain a target motor speed, a target motor torque and a target transmission ratio; Optimizing the driving motor power based on the vehicle power optimization strategy to obtain a target motor power; A target motor parameter is determined according to the target motor speed, the target motor torque, the target transmission ratio, and the target motor power.

3. The method according to claim 2, characterized in that Before the step of optimizing the drive motor speed, the drive motor torque and the vehicle transmission ratio based on the vehicle power optimization strategy and the drive motor objective function to obtain the target motor speed, the target motor torque and the target transmission ratio, the step further includes: When the current motor power requirement is not less than the motor power requirement threshold, determining the first motor power requirement according to the drive motor power requirement under a typical vehicle operating condition; When the current motor required power is less than the motor required power threshold, determining the second motor required power according to a preset ratio of the current motor required power; A drive motor objective function is constructed according to the first motor required power and the second motor required power.

4. The method according to claim 2, characterized in that The step of optimizing the drive motor speed, the drive motor torque and the vehicle transmission ratio based on the vehicle power optimization strategy and the drive motor objective function to obtain a target motor speed, a target motor torque and a target transmission ratio includes: Determine an initialization population according to the driving motor speed, the driving motor torque and the vehicle transmission ratio; Calculating the fitness of the initialized population according to the drive motor objective function to obtain the current fitness; Optimizing the initialized population based on the vehicle power optimization strategy and calculating the fitness to obtain the optimized fitness; Compare the current fitness with the optimized fitness, and perform iterative optimization according to the comparison result to obtain the current number of iterations; When the current number of iterations is not less than the iteration number threshold, a target motor speed, a target motor torque and a target transmission ratio are obtained.

5. The method according to claim 1, characterized in that Before the step of optimizing the fuel cell power range and the power battery capacity based on the vehicle power optimization strategy and the vehicle cost objective function to obtain the target fuel cell power and the target power battery capacity, the step further includes: Determine a first fuel cell power according to a target motor power, and obtain a second fuel cell power by performing power calculation based on a fuel cell calculation strategy; Obtaining a fuel cell power range according to the first fuel cell power and the second fuel cell power; The capacity is calculated according to the vehicle's cruising range strategy to obtain the power battery capacity.

6. The method according to claim 1, characterized in that The step of optimizing the fuel cell power range and the power battery capacity based on the vehicle power optimization strategy and the vehicle cost objective function to obtain the target fuel cell power and the target power battery capacity includes: Establish a target battery vehicle model based on the fuel cell power range and power battery capacity; Determining power fitness according to the target battery vehicle model, a preset dynamic programming strategy, and a vehicle cost objective function; Iterative optimization is performed according to the power fitness and the vehicle power optimization strategy to obtain the target fuel cell power and the target power battery capacity.

7. The method according to claim 6, characterized in that The step of determining the power fitness according to the target battery vehicle model, the preset dynamic programming strategy and the vehicle cost objective function comprises: Perform vehicle simulation according to the target battery vehicle model and the preset dynamic programming strategy to obtain hydrogen consumption data, power battery data, and fuel cell data; The fitness of the hydrogen consumption data, the power battery data and the fuel cell data is calculated based on the vehicle cost objective function to obtain the power fitness.

8. A fuel vehicle power system optimization device, characterized in that: The device comprises: A processing module, used for optimizing the drive motor parameters based on the vehicle power optimization strategy and the drive motor objective function to obtain target motor parameters; The processing module is further used to optimize the fuel cell power range and the power battery capacity based on the vehicle power optimization strategy and the vehicle cost objective function to obtain the target fuel cell power and the target power battery capacity; The optimization module is used to optimize the power system of the vehicle to be optimized according to the target motor parameters, the target fuel cell power and the target power battery capacity.

9. A fuel vehicle power system optimization device, characterized in that: The device comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the fuel vehicle power system optimization method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the fuel vehicle power system optimization method as described in any one of claims 1 to 7 are implemented.

Images