Fuel cell vehicle power system optimization method, device, equipment and storage medium

Abstract

This application discloses a method, apparatus, device, and storage medium for optimizing the powertrain system of a fuel cell vehicle, relating to the field of hydrogen fuel cell vehicle technology. The optimization method includes: optimizing drive motor parameters based on a vehicle power optimization strategy and a drive motor objective function to obtain target motor parameters; optimizing the fuel cell power range and power battery capacity based on a vehicle power optimization strategy and a vehicle cost objective function to obtain target fuel cell power and target power battery capacity; and optimizing the powertrain 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 parameters and fully considering various relationships, such as between the fuel cell and the power battery; rationally planning the design area; and setting different objective functions for different parameters, the influence of control parameters can be scientifically evaluated, thereby improving system efficiency and overall vehicle performance.

Description

Technical Field

[0001] This application relates to the field of hydrogen fuel cell vehicle technology, and in particular to methods, apparatus, equipment and storage media for optimizing the power system of fuel cell vehicles. Background Technology

[0002] Currently, parameter matching for the powertrain and control systems of fuel cell vehicles is crucial. However, existing technologies have several shortcomings. First, the optimization parameters for the powertrain are incomplete. Traditional selection relies on theoretical formulas and critical values, making it difficult to ensure the optimality of parameters under complex real-world scenarios. This fails to fully leverage the performance advantages of the powertrain, limiting the potential for improving the economy and power performance of fuel cell vehicles. Second, component characteristic parameter design is isolated, focusing only on individual components without considering the overall power distribution and complex coupling relationships of the powertrain. Poor coordination among components leads to impaired energy transfer and conversion efficiency, easily causing unstable power output, accelerated component wear, and other problems. This reduces system reliability and durability, increases maintenance costs and failure risks, and hinders the efficient and stable operation of fuel cell vehicles. Third, simulation analysis lacks a reasonable design area, making the design process complex and prone to getting stuck in local optima. The lack of a precise definition of the effective design range necessitates repeated trial and error adjustments, consuming significant time and computational resources, delaying product development cycles, and making it difficult to efficiently obtain accurate, globally optimal design solutions. This hinders the rapid iteration and market promotion of fuel cell vehicles. In summary, existing hydrogen fuel cell vehicle power system technologies have limitations in parameter optimization, component coordination, simulation analysis, and control parameter processing. Therefore, how to address these limitations in these areas has become an urgent problem to be solved. Summary of the Invention

[0003] The main objective of this application is to provide a method, apparatus, device, and storage medium for optimizing the power system of a fuel cell vehicle, aiming to solve the technical problems of limitations in parameter optimization, component coordination, simulation analysis, and control parameter processing in existing hydrogen fuel cell vehicle power system technologies.

[0004] To achieve the above objectives, this application proposes a method for optimizing the powertrain system of a fuel cell vehicle, the method comprising:

[0005] The drive motor parameters are optimized based on the vehicle power optimization strategy and the drive motor objective function to obtain the target motor parameters;

[0006] The fuel cell power range and power battery capacity are optimized 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.

[0007] The powertrain system of the vehicle to be optimized is optimized based on the target motor parameters, the target fuel cell power, and the target power battery capacity.

[0008] 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:

[0009] Determine the drive motor speed, drive motor torque, vehicle transmission ratio, and drive motor power based on the drive motor parameters;

[0010] Based on the vehicle power optimization strategy and the objective function of the drive motor, the speed of the drive motor, the torque of the drive motor and the vehicle transmission ratio are optimized to obtain the target motor speed, the target motor torque and the target transmission ratio.

[0011] The power of the drive motor is optimized based on the vehicle power optimization strategy to obtain the target motor power;

[0012] The target motor parameters are determined based on the target motor speed, the target motor torque, the target transmission ratio, and the target motor power.

[0013] 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, target motor torque, and target transmission ratio, the method further includes:

[0014] When the current motor power requirement is not less than the motor power requirement threshold, the first motor power requirement is determined based on the drive motor power requirement under typical vehicle operating conditions.

[0015] When the current motor power demand is less than the motor power demand threshold, the second motor power demand is determined according to the current motor power demand in a preset ratio.

[0016] Construct the objective function for the drive motor based on the power requirements of the first motor and the power requirements of the second motor.

[0017] 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 the target motor speed, target motor torque, and target transmission ratio includes:

[0018] The initial population is determined based on the drive motor speed, the drive motor torque, and the vehicle transmission ratio.

[0019] The fitness of the initial population is calculated based on the objective function of the drive motor to obtain the current fitness.

[0020] The initial population is optimized based on the vehicle dynamics optimization strategy, and the fitness is calculated to obtain the optimized fitness.

[0021] The current fitness is compared with the optimized fitness, and iterative optimization is performed based on the comparison result to obtain the current iteration number;

[0022] When the current iteration number is not less than the iteration number threshold, the target motor speed, target motor torque, and target transmission ratio are obtained.

[0023] In one embodiment, before the step of optimizing 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, the method further includes:

[0024] The first fuel cell power is determined based on the target motor power, and the second fuel cell power is obtained by power calculation based on the fuel cell calculation strategy.

[0025] The fuel cell power range is obtained based on the first fuel cell power and the second fuel cell power.

[0026] The capacity of the power battery is calculated based on the vehicle's range strategy.

[0027] In one embodiment, the step of optimizing 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 includes:

[0028] Establish a target battery vehicle model based on the fuel cell power range and power battery capacity;

[0029] The power adaptability is determined based on the target battery vehicle model, the preset dynamic programming strategy, and the vehicle cost objective function.

[0030] Based on the aforementioned power adaptability and vehicle power optimization strategy, iterative optimization is performed to obtain the target fuel cell power and target power battery capacity.

[0031] In one embodiment, the step of determining the power adaptability based on the target battery vehicle model, a preset dynamic programming strategy, and a vehicle cost objective function includes:

[0032] Vehicle simulation is performed based on the target battery vehicle model and the preset dynamic programming strategy to obtain hydrogen consumption data, power battery data and fuel cell data.

[0033] 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.

[0034] Furthermore, to achieve the above objectives, this application also proposes a fuel cell vehicle powertrain optimization device, which includes:

[0035] The processing module is used to optimize the drive motor parameters based on the vehicle power optimization strategy and the drive motor objective function to obtain the target motor parameters;

[0036] The processing module is also used to optimize 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 the target power battery capacity.

[0037] The optimization module is used to optimize the power system of the vehicle to be optimized based on the target motor parameters, the target fuel cell power, and the target power battery capacity.

[0038] In addition, to achieve the above objectives, this application also proposes a fuel cell vehicle power system optimization device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the fuel cell vehicle power system optimization method as described above.

[0039] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the fuel cell vehicle power system optimization method described above.

[0040] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the fuel cell vehicle power system optimization method described above.

[0041] This application optimizes the drive motor parameters based on a vehicle power optimization strategy and a drive motor objective function to obtain target motor parameters; it also optimizes the fuel cell power range and power battery capacity based on the vehicle power optimization strategy and a vehicle cost objective function to obtain target fuel cell power and target power battery capacity; and optimizes the power system of the vehicle to be optimized based on the target motor parameters, the target fuel cell power, and the target power battery capacity. By comprehensively optimizing parameters and fully considering various relationships, such as between the fuel cell and the power battery; rationally planning the design area; and setting different objective functions for different parameters, the application scientifically evaluates the impact of control parameters, thereby improving system efficiency and overall vehicle performance. Attached Figure Description

[0042] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart illustrating an embodiment of the fuel cell vehicle powertrain optimization method of this application.

[0045] Figure 2 This is a schematic diagram of the fuel cell vehicle power system optimization framework provided in Embodiment 1 of the fuel cell vehicle power system optimization method of this application;

[0046] Figure 3 This is a schematic diagram of the fuel cell vehicle topology provided in Embodiment 1 of the fuel cell vehicle power system optimization method of this application;

[0047] Figure 4 A schematic diagram of the fuel cell and power battery parameter optimization process provided in Embodiment 1 of the fuel cell vehicle power system optimization method of this application;

[0048] Figure 5 This is a flowchart illustrating Embodiment 2 of the fuel cell vehicle powertrain optimization method of this application.

[0049] Figure 6 This is a schematic diagram illustrating the optimization process of the drive motor's maximum torque, maximum speed, and transmission ratio, provided in Embodiment 2 of the fuel cell vehicle power system optimization method of this application.

[0050] Figure 7This is a schematic diagram of the module structure of the fuel cell vehicle power system optimization device according to an embodiment of this application;

[0051] Figure 8 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the fuel cell vehicle power system optimization method in this application embodiment.

[0052] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0053] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0054] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0055] The main solution of this application embodiment is: to optimize the drive motor parameters based on the vehicle power optimization strategy and the drive motor objective function to obtain the target motor parameters; to optimize 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 the target power battery capacity; and 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.

[0056] Currently, parameter matching for the powertrain and control systems of fuel cell vehicles is crucial. However, existing technologies have several shortcomings. First, the optimization parameters for the powertrain are incomplete. Traditional selection relies on theoretical formulas and critical values, making it difficult to ensure the optimality of parameters under complex real-world scenarios. This fails to fully leverage the performance advantages of the powertrain, limiting the potential for improving the economy and power performance of fuel cell vehicles. Second, component characteristic parameter design is isolated, focusing only on individual components without considering the overall power distribution and complex coupling relationships of the powertrain. Poor coordination among components leads to impaired energy transfer and conversion efficiency, easily causing unstable power output, accelerated component wear, and other problems. This reduces system reliability and durability, increases maintenance costs and failure risks, and hinders the efficient and stable operation of fuel cell vehicles. Third, simulation analysis lacks a reasonable design area, making the design process complex and prone to getting stuck in local optima. The lack of a precise definition of the effective design range necessitates repeated trial and error adjustments, consuming significant time and computational resources, delaying product development cycles, and making it difficult to efficiently obtain accurate, globally optimal design solutions. This hinders the rapid iteration and market promotion of fuel cell vehicles. In summary, existing hydrogen fuel cell vehicle power system technologies have limitations in parameter optimization, component coordination, simulation analysis, and control parameter processing. Therefore, how to address these limitations in these areas has become an urgent problem to be solved.

[0057] This application optimizes the drive motor parameters based on a vehicle power optimization strategy and a drive motor objective function to obtain target motor parameters; it also optimizes the fuel cell power range and power battery capacity based on the vehicle power optimization strategy and a vehicle cost objective function to obtain target fuel cell power and target power battery capacity; and optimizes the power system of the vehicle to be optimized based on the target motor parameters, the target fuel cell power, and the target power battery capacity. By comprehensively optimizing parameters and fully considering various relationships, such as between the fuel cell and the power battery; rationally planning the design area; and setting different objective functions for different parameters, the application scientifically evaluates the impact of control parameters, thereby improving system efficiency and overall vehicle performance.

[0058] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or a fuel cell vehicle power system optimization device capable of performing the above functions. The following description uses a fuel cell vehicle power system optimization device as the executing entity to illustrate this embodiment and the subsequent embodiments.

[0059] Based on this, embodiments of this application provide a method for optimizing the powertrain system of a fuel cell vehicle, referring to... Figure 1 , Figure 1This is a flowchart illustrating the first embodiment of the fuel cell vehicle power system optimization method of this application.

[0060] In this embodiment, the fuel cell vehicle powertrain optimization method includes steps S10 to S30:

[0061] Step S10: Optimize the drive motor parameters based on the vehicle power optimization strategy and the drive motor objective function to obtain the target motor parameters;

[0062] It should be noted that this embodiment determines the drive form of a hydrogen fuel cell vehicle by comprehensively comparing the power systems of fuel cell vehicles. It employs the Crayfish Optimization Algorithm (COA) and parameter scanning method, using the average power demand of the motor as the fitness value to determine the optimal transmission ratio, maximum motor speed, and maximum motor torque. Secondly, combining the operating points of the typical operating condition library that the maximum motor power must meet, a motor cost function is constructed. COA is then used for optimization to ultimately determine the optimal maximum motor torque, maximum speed, and transmission ratio, and the optimal maximum motor power is selected. Next, for the maximum power of the fuel cell and the capacity of the power battery, a cost function is also established, and COA is used for optimization to obtain the optimal result, thereby completing the optimization of the entire power system and improving the fuel economy and overall vehicle performance of the fuel cell vehicle.

[0063] It is important to understand that the optimization framework diagram for fuel cell vehicle powertrain systems is as follows: Figure 2 As shown, its structure mainly consists of two parts: a fuel cell vehicle drive type determination module, a drive motor and drive axle parameter optimization module, and a fuel cell and power battery parameter optimization module. The fuel cell vehicle drive type determination module determines a suitable drive type by comparing it with existing research on drive types. The drive motor and drive axle parameter optimization can be further subdivided into determining the drive axle transmission ratio and the maximum speed and maximum torque of the drive motor. Based on these optimization results, an 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 region through theoretical formulas, thereby obtaining the optimal parameters for the fuel cell and power battery.

[0064] It is understandable that the vehicle power optimization strategy refers to the strategy of using the Crayfish Optimization Algorithm (COA) to optimize the power system of fuel cell hybrid commercial vehicles. The drive motor objective function refers to the objective function used to optimize the drive motor parameters. The objective motor parameters include, but are not limited to, the optimal maximum speed of the drive motor, the maximum torque of the drive motor, the maximum power of the drive motor, and the transmission ratio of the main reducer.

[0065] In practice, the strategy of optimizing the power system of fuel cell hybrid commercial vehicles by adopting the Crayfish Optimization Algorithm (COA) and combining it with the objective function for optimizing the drive motor parameters is to find the optimal maximum speed, maximum torque, maximum power and main reducer transmission ratio of the drive motor, thereby obtaining the optimal maximum speed, maximum torque, maximum power and main reducer transmission ratio of the drive motor, i.e. the target motor parameters.

[0066] It should be noted that fuel cell vehicles are a type of new energy vehicle, and unlike traditional vehicles, they can have multiple power sources. Various combinations of power sources are suitable for different vehicle models, and choosing a suitable structure is particularly important in order to meet the needs of overall vehicle power and improve overall vehicle economy.

[0067] Currently, fuel cell vehicles mainly have four types of drive systems: pure fuel drive, fuel cell and power battery hybrid drive system (a type of drive system in fuel cell vehicles) (FC+B), fuel cell and supercapacitor hybrid drive system (FC+C), and fuel cell, power battery and supercapacitor hybrid drive system (FC+B+C).

[0068] Pure fuel cell power systems represent an ideal power system, but their dynamic response is poor, and their output performance is significantly negatively impacted by rapid changes in load power. FC+B not only effectively meets the power demands of vehicles during acceleration and hill climbing but also recovers energy during deceleration or coasting, significantly extending the vehicle's range. Furthermore, this system successfully solves the cold-start problem of pure fuel cell systems, overcoming their inherent limitations. In FC+C, while supercapacitors have unique advantages, their low energy density makes it difficult to maintain high power output for extended periods, and significant voltage fluctuations during discharge necessitate series connection with impedance components to reduce these fluctuations, increasing overall vehicle cost and limiting their application. FC+B+C fully leverages the performance advantages of three power sources, is suitable for various operating conditions, and optimizes the vehicle's power and economy. However, this system's complex control strategy, large interior space requirement, and increased vehicle weight and manufacturing costs hinder large-scale deployment.

[0069] Taking into account the advantages and disadvantages of different structures and the complexity of control strategies, this embodiment selects FC+B (a hybrid drive mode with a fuel cell and a power battery as the power source), and its fuel cell vehicle topology is as follows. Figure 3 As shown. This choice aims to balance power performance, economy, and practical feasibility, providing a more optimized drive solution for the development of fuel cell vehicles.

[0070] Step S20: Optimize the fuel cell power range and power battery capacity based on the vehicle power optimization strategy and vehicle cost objective function to obtain the target fuel cell power and target power battery capacity.

[0071] It is understandable that the vehicle cost objective function refers to the cost objective function used to optimize the maximum power of the fuel cell and the capacity of the power battery. The fuel cell power range refers to the range of values ​​for 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.

[0072] In practice, the strategy of optimizing the power system of fuel cell hybrid commercial vehicles by adopting the crayfish optimization algorithm, combined with the cost objective function for optimizing the maximum power of fuel cells and the capacity of power batteries, is used to find the optimal maximum power of fuel cells and the capacity of power batteries, thereby obtaining the optimal maximum power of fuel cells and the capacity of power batteries.

[0073] In one feasible implementation, steps A11 to A13 may be included before step S20:

[0074] Step A11: Determine the first fuel cell power based on the target motor power, and calculate the second fuel cell power based on the fuel cell calculation strategy;

[0075] It should be noted that the first fuel cell power refers to the upper limit of the maximum power of the fuel cell, while the second fuel cell power refers to the lower limit of the maximum power of the fuel cell.

[0076] In practical implementation, the main parameter of the fuel cell is the maximum output power. The selection of the maximum output power of the fuel cell is generally aimed at meeting the vehicle's maximum speed requirements. 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, using a high-power fuel cell system can improve the vehicle's lightweight and integration convenience. However, if the fuel cell system is set too large, it will increase the cost. Therefore, the maximum power of the drive motor is set as the upper limit of the maximum power of the fuel cell (the first fuel cell power).

[0077] Step A12: Obtain the fuel cell power range based on the power of the first fuel cell and the power of the second fuel cell;

[0078] In practice, the upper and lower limits of the maximum power of the fuel cell are combined to obtain the range of values ​​for the maximum power of the fuel cell.

[0079] Step A13: Calculate the capacity based on the vehicle's range strategy to obtain the power battery capacity.

[0080] It is understandable that the vehicle range strategy refers to the strategy of calculating the power battery capacity based on the pure electric range formula.

[0081] In practice, the number of series connections of the power battery can be determined based on the voltage platform used by the selected vehicle and other auxiliary consumption factors. The lower limit of the power battery capacity that meets the performance requirements can be determined by calculating the pure electric range formula. The larger the power battery capacity, the better the vehicle's power, range, and energy recovery efficiency. However, different capacities will cause changes in the overall vehicle weight, thereby increasing costs.

[0082] In one feasible implementation, step S20 may include steps B11-B13:

[0083] Step B11: Establish a target battery vehicle model based on the fuel cell power range and power battery capacity;

[0084] Understandably, the target battery vehicle model refers to a mathematical model used to simulate the operation of hydrogen fuel cell vehicles.

[0085] In this implementation, a simplified mathematical model of a hydrogen fuel cell vehicle is established in MATLAB. This model can calculate vehicle performance indicators, such as energy consumption and acceleration performance, based on given fuel cell power output, battery capacity, and other parameters. Furthermore, this model will serve as the basis for evaluating the vehicle's fitness.

[0086] Step B12: Determine the power adaptability based on the target battery vehicle model, the preset dynamic programming strategy, and the vehicle cost objective function;

[0087] Understandably, the preset dynamic programming strategy refers to a pre-defined strategy for energy management using dynamic programming methods, while the power adaptability refers to the adaptability when optimizing the maximum power of the fuel cell and the capacity of the power battery.

[0088] In practice, the fitness is calculated by calling the fuel cell vehicle model. Specifically, for each individual in each generation, the fitness value is calculated by calling the previously established hydrogen fuel cell vehicle model, combining it with a pre-defined energy management strategy using dynamic programming, and a vehicle cost objective function. The fitness value reflects the overall performance of the vehicle under that set of parameters.

[0089] In one feasible implementation, step B12 may include steps C11-C12:

[0090] Step C11: Perform vehicle simulation based on the target battery vehicle model and the preset dynamic programming strategy to obtain hydrogen consumption data, power battery data and fuel cell data.

[0091] It is understandable that hydrogen consumption data refers to hydrogen consumption cost data, power battery data refers to power battery cost data, and fuel cell data refers to fuel cell cost data.

[0092] In practice, based on the mathematical model used to simulate the working conditions of hydrogen fuel cell vehicles and the pre-set energy management strategy using dynamic programming, the maximum power of the fuel cell and the capacity of the power battery are combined to perform simulation calculations to obtain hydrogen consumption cost data, power battery cost data and fuel cell cost data.

[0093] Step C12: Based on the vehicle cost objective function, perform fitness calculation on the hydrogen consumption data, the power battery data, and the fuel cell data to obtain the power fitness.

[0094] It is understandable that the fitness is calculated based on the cost objective function used to optimize the maximum power of the fuel cell and the capacity of the power battery. The fitness is calculated by combining hydrogen consumption cost data, power battery cost data and fuel cell cost data, and the fitness is obtained when optimizing the maximum power of the fuel cell and the capacity of the power battery.

[0095] Step B13: Iterative optimization is performed based on the power adaptability and vehicle power optimization strategy to obtain the target fuel cell power and target power battery capacity.

[0096] Understandably, based on the fitness of optimizing the maximum power of the fuel cell and the capacity of the power battery, and by using the crayfish optimization algorithm to optimize the power system of the fuel cell hybrid commercial vehicle, the maximum power of the fuel cell and the capacity of the power battery are optimized, thereby obtaining the optimal maximum power of the fuel cell and the capacity of the power battery.

[0097] It should be noted that in this embodiment, for the parameter optimization model of fuel cell and power battery, the main parameter of fuel cell is maximum output power. The selection of maximum output power of fuel cell is generally aimed at meeting the maximum speed driving requirements of the vehicle. The lower limit of maximum power of fuel cell can be determined by formula (1). At the same time, the selection of high-power fuel cell system can improve the lightweighting and integration convenience of the whole vehicle. However, if the fuel cell system is set too large, it will cause an increase in cost. Therefore, the maximum power of drive motor is set as the upper limit of maximum power of fuel cell. Finally, based on the above conditions, the range of maximum power of fuel cell is determined. Similarly, the number of series connection of power battery can be determined according to the voltage platform used by the selected vehicle and other auxiliary consumption factors. As for the capacity of power battery, the lower limit of power battery capacity that meets the performance requirements can be determined by pure electric range formula (2-4). The larger the capacity of power battery, the better the vehicle's power performance, range and energy recovery efficiency. However, different capacities will cause changes in the overall vehicle weight, thereby increasing costs. The capacity of power battery can be determined by pure electric range formula (2-4):

[0098]

[0099]

[0100]

[0101]

[0102] In the formula: These are the transmission system efficiency, DC / DC efficiency, and motor efficiency, respectively. For battery pack power, For battery efficiency, For motor efficiency, For cruising speed, For pure electric driving range, This refers to the depth of discharge of the power battery. Power battery voltage.

[0103] Considering that increasing the maximum power of the fuel cell and the capacity of the power battery will lead to an increase in cost, this paper establishes an objective function to determine the optimal maximum power of the fuel cell and the capacity of the power battery, as shown in formula (5).

[0104]

[0105] In the formula: For total cost, For the cost of hydrogen consumption, For the cost of power batteries, Cost of fuel cells.

[0106] This embodiment concludes by establishing a hydrogen fuel cell vehicle model in MATLAB and implementing a dynamic programming (DP) algorithm as the energy management strategy for the fuel cell vehicle. The Co-Operation Optimization (COA) algorithm is used to optimize the maximum power of the fuel cell and the capacity of the power battery, yielding the optimal result. Specifically, the COA algorithm sets the maximum power of the fuel cell and the maximum capacity of the power battery as the optimization variables, sets the maximum number of iterations to 100, and the initial population to 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. This algorithm discretizes the multi-stage optimization problem, calculates the state function of each decision process, and then reversely calculates the globally optimal decision based on the set cost function, thus achieving a globally optimal management strategy. Its optimization flowchart is shown below. Figure 4 As shown in the figure. In summary, the optimal maximum power of the fuel cell and the power battery capacity can be obtained, thus completing the optimization of the fuel cell vehicle power system.

[0107] Step S30: Optimize the power system of the vehicle to be optimized based on the target motor parameters, the target fuel cell power, and the target power battery capacity.

[0108] In practice, the vehicle to be optimized refers to the hydrogen fuel cell vehicle with the power system 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 capacity of the power battery, the power system of the hydrogen fuel cell vehicle with the power system to be optimized is optimized to complete the optimization of the power system of the hydrogen fuel cell vehicle with the power system to be optimized.

[0109] This embodiment optimizes the drive motor parameters based on a vehicle power optimization strategy and a drive motor objective function to obtain target motor parameters; it also optimizes the fuel cell power range and battery capacity based on the vehicle power optimization strategy and a vehicle cost objective function to obtain target fuel cell power and target battery capacity; and optimizes the power system of the vehicle to be optimized according to the target motor parameters, the target fuel cell power, and the target battery capacity. By comprehensively optimizing parameters and fully considering various relationships, such as between the fuel cell and the battery, and by rationally planning the design area and setting different objective functions for different parameters, the influence of control parameters can be scientifically evaluated, thereby improving system efficiency and overall vehicle performance.

[0110] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 5 The fuel cell vehicle power system optimization method further includes steps S11 to S14 in step S10:

[0111] Step S11: Determine the drive motor speed, drive motor torque, vehicle transmission ratio, and drive motor power based on the drive motor parameters.

[0112] Understandably, the maximum speed, maximum torque, main reducer gear ratio, and maximum power of the drive motor to be optimized are determined based on the associated parameters of the drive motor.

[0113] Step S12: Based on the vehicle power optimization strategy and the objective function of the drive motor, optimize the speed of the drive motor, the torque of the drive motor and the transmission ratio of the vehicle to obtain the target motor speed, the target motor torque and the target transmission ratio;

[0114] It is understandable that by adopting the crayfish optimization algorithm to optimize the power system of fuel cell hybrid commercial vehicles, and combining it with the objective function used to optimize the parameters 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, thereby obtaining the optimal maximum speed of the drive motor, the maximum torque of the drive motor, and the transmission ratio of the main reducer.

[0115] In one feasible implementation, steps D11 to D13 may be included before step S12:

[0116] Step D11: When the current motor power demand is not less than the motor power demand threshold, determine the first motor power demand based on the drive motor power demand under typical vehicle operating conditions.

[0117] It is understood that the current motor power demand refers to the current power demand of the drive motor, and the motor power demand threshold refers to the power threshold used to determine the average power demand of the drive motor. In this embodiment, the motor power demand threshold is taken as 0 for example. Typical vehicle operating conditions include, but are not limited to, CLTC-C (urban driving condition), CLTC-A (suburban driving condition), and CLTC-H (highway driving condition). The first motor power demand refers to the average power demand when the drive motor power demand is not less than 0.

[0118] In specific implementation, when the current drive motor's required power is not less than the power threshold used to determine the average required power of the drive motor, it indicates that the average required 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 required power when the drive motor's required power is greater than 0, i.e., the first motor's required power.

[0119] Step D12: When the current motor power demand is less than the motor power demand threshold, determine the second motor power demand based on the current motor power demand in a preset ratio.

[0120] It is understood that the preset ratio refers to a pre-set ratio value used to determine the average power demand of the drive motor, such as 40%. This embodiment does not limit the preset ratio, and the size of the preset ratio can be determined according to the actual situation. The second motor demand power refers to the average demand power when the drive motor demand power is less than 0.

[0121] In specific implementation, when the current required power of the drive motor is less than the power threshold used to determine the average required power of the drive motor, it indicates that this embodiment sets the regenerative braking energy to 0.4 times the braking energy. That is, when the motor power is negative, only 40% of its power value is considered as the measure of the average power of the motor. Then, based on the product of the preset ratio and the current required power of the drive motor, the average required power when the required power of the drive motor is less than 0 is determined, which is the second motor required power.

[0122] Step D13: Construct the objective function for the drive motor based on the power requirements of the first motor and the power requirements of the second motor.

[0123] It is understandable that an objective function for optimizing the drive motor parameters is constructed based on the average demand power when the drive motor demand power is greater than 0 and the average demand power when the drive motor demand power is less than 0, thus obtaining the drive motor objective function.

[0124] In one feasible implementation, step S12 may include steps E11 to E15:

[0125] Step E11: Determine the initial population based on the drive motor speed, the drive motor torque, and the vehicle transmission ratio;

[0126] Understandably, the initial population refers to the set of optimization variables created at the start of the algorithm.

[0127] In 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 uses the crayfish optimization algorithm for optimization, 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 optimization variables, thus obtaining the initial population.

[0128] Step E12: Calculate the fitness of the initial population based on the objective function of the drive motor to obtain the current fitness;

[0129] It is understandable that the current fitness refers to the fitness of the initial population.

[0130] In practice, the fitness of the initial population is calculated using the objective function used to optimize the parameters of the drive motor, thereby obtaining the fitness of the initial population, i.e., the current fitness.

[0131] Step E13: Optimize the initial population based on the vehicle dynamics optimization strategy and calculate the fitness to obtain the optimized fitness;

[0132] It is understandable that optimization fitness refers to the fitness of the optimized variable after optimization.

[0133] In specific implementation, the strategy of optimizing the power system of fuel cell hybrid commercial vehicles by adopting the crayfish optimization algorithm is to optimize the maximum speed of the drive motor, the maximum torque of the drive motor, and the transmission ratio of the main reducer, thereby obtaining the optimized maximum speed of the drive motor, the maximum torque of the drive motor, and the transmission ratio of the main reducer. 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, that is, the optimization fitness is obtained.

[0134] Step E14: Compare the current fitness with the optimized fitness, and perform iterative optimization based on the comparison result to obtain the current iteration number;

[0135] It is understandable that the current iteration number refers to the number of times the optimization has been performed in the current iteration.

[0136] In practice, the fitness of the initial population is compared with the fitness of the optimized variable, and the optimized variable with better fitness is selected for iterative optimization to obtain the optimal variable. The number of iterations is recorded to obtain the current number of iterations.

[0137] Step E15: When the current iteration number is not less than the iteration number threshold, the target motor speed, target motor torque, and target transmission ratio are obtained.

[0138] It is understandable that the iteration threshold refers to the critical number of iterations used to determine whether the optimal variable has been obtained.

[0139] In practice, when the number of iterations is not less than the critical value of the number of iterations used to determine whether the optimal variable has been 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 transmission ratio of the main reducer have been obtained. For example, the maximum number of iterations is set to 100, and when the number of iterations reaches 100, the optimal optimization variable has been obtained.

[0140] Step S13: Optimize the power of the drive motor based on the vehicle power optimization strategy to obtain the target motor power;

[0141] It is understandable that by adopting the crayfish optimization algorithm to optimize the power system of fuel cell hybrid commercial vehicles, and combining it with the objective function used to optimize the maximum power of the drive motor, the maximum power of the drive motor is optimized, thereby obtaining the optimal maximum power of the drive motor.

[0142] Step S14: Determine the target motor parameters based on the target motor speed, the target motor torque, the target transmission ratio, and the target motor power.

[0143] In practice, 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.

[0144] It should be noted that, considering the overall power distribution and complex coupling relationship of the power system, this embodiment optimizes the relevant parameters of the drive motor and the drive axle parameters simultaneously. First, the transmission ratio and the maximum speed and maximum torque of the drive motor are determined. 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, there is a set of maximum speed and maximum torque parameters. This set contains a discrete subset of the allowable maximum speed and maximum torque of the motor. In order to make the two parameters meet the display requirements, the lower limit of the two parameters can be obtained by formula (6) and formula (7), while the upper limit is set as a function related to the lower limit. By comparing the heavy commercial vehicle motor parameters provided by various motor manufacturers, this embodiment sets the upper limit of the motor speed and torque to be respectively set as and In other words, by comparing the commercial vehicle motor parameters provided by various motor manufacturers, this embodiment sets the upper limits of motor speed and torque to be respectively... and In the motor and transmission system model layer, each model corresponds to a set of motor parameters. Using this structure, the optimal maximum torque and maximum speed for each transmission ratio can be obtained, maximizing the average power of the drive motor.

[0145]

[0146]

[0147] In the formula, For the quality of the car, It is the acceleration due to gravity. For rolling resistance coefficient, For transmission efficiency, For the vehicle's maximum speed, For drag coefficient, For windward area, The slope angle, For vehicle speed, Rotational mass coefficient, To speed up the process, For tire radius, Main reducer transmission ratio, , and represent the maximum speed and maximum torque of the motor, respectively.

[0148] To determine the objective function, the average power demand of the drive motor of a fuel cell vehicle under typical operating conditions can be considered. Let it be the target value, as shown in equation (8). Where, when When the power value is negative, 40% of the power value is used as the average power demand as a measure (in the operation of the drive motor, the braking recovery problem is involved. In this embodiment, the braking recovery energy is set to 0.4 times the braking energy, that is, when the motor power is negative, only 40% of its power value is considered as the measure of the motor's average power).

[0149]

[0150] Regarding the selection of optimization methods, COA has a faster convergence speed compared to genetic algorithms and particle swarm optimization (COA optimization algorithm parameters are set with an initial population of 30 and a maximum number of iterations of 100). Therefore, this embodiment uses the Crayfish Optimization Algorithm (COA) to optimize the transmission ratio, maximum speed of the drive motor, and maximum torque of the drive motor, obtaining the optimal maximum torque, maximum speed, and transmission ratio of the drive motor under the optimal average power demand. The process is as follows: Figure 6 The specific steps are as follows:

[0151] (1) Initialize the population:

[0152]

[0153] In the formula, For individuals The optimization variables (maximum speed of the drive motor, maximum torque, transmission ratio) parameters, To represent the lower bound of the optimization variable, To optimize the upper bound of the variable, It is a random value.

[0154] (2) Define temperature and intake:

[0155] COA via temperature Changes are used to control whether the algorithm enters the exploration or development phase. ,and At that time, we entered the summer resort exploration phase. Sometimes, or and During the development stage, crayfish exhibit strong competitiveness and good foraging behavior, with their food intake increasing from [previous period]. express.

[0156]

[0157]

[0158] In the formula, , The intake of crayfish is kept constant to control the amount consumed at different temperatures. The ideal temperature for crayfish.

[0159] (3) Escaping the summer heat:

[0160] when At this time, crayfish will enter burrows to escape the heat, which can be represented by the following formula:

[0161]

[0162]

[0163] In the formula, Location of the cave. This represents the globally optimal position obtained through iteration. Represents the globally optimal position obtained with iteration, when At that time, there will be no competition for caves. This indicates the current iteration number. The curve is a descending curve, where , The maximum number of iterations is 100.

[0164] (4) Competition:

[0165] when and This means that crayfish will face competition, which can be expressed by the following formula:

[0166]

[0167] In the formula, This represents a random individual crayfish, among which .

[0168] 5) Foraging:

[0169] when At this time, the crayfish begin to forage. The crayfish will move to the food location, and the formula for determining the size of the food is as follows:

[0170]

[0171]

[0172] In the formula, For food size, The food factor represents the maximum food quantity. Indicates the first The fitness value of each crayfish. This indicates the fitness value (and average power requirement) of the food.

[0173] (6) Output result judgment:

[0174] Last Update , And determine whether the maximum number of iterations of 100 has been reached. If the maximum number of iterations has not been reached, proceed to (2); otherwise, output the optimal parameters.

[0175] It should be noted that currently, the maximum power of the drive motor (determined by theoretical formula, i.e., by vehicle performance indicators (maximum speed, gradeability, acceleration time)) is usually determined according to formula (9) - formula (12):

[0176]

[0177]

[0178]

[0179]

[0180] In the formula: , , The motor power is determined by the maximum vehicle speed, gradeability, and acceleration time, respectively. The maximum vehicle speed and gradeability can be determined based on the designed vehicle performance indicators.

[0181] The maximum power of the drive motor should meet the data in the typical operating condition database, and the percentage of driving conditions where this is not met should be less than 1%. To obtain a more optimized maximum power for the fuel cell, the price V of the drive motor can be obtained based on the commercial vehicle motors provided by various motor manufacturers. Therefore, the optimization algorithm can be used to further optimize the... We perform optimization, using equation (11) as the objective function.

[0182]

[0183] In the formula: [ [ ] represents the weighting factor. The percentage of operating points that do not meet the requirements is expressed in percent.

[0184] The COA optimization algorithm parameters are set as follows: initial population is 30, maximum number of iterations is 100, maximum power of the drive motor is optimized as the variable, and fitness function is... The optimal maximum power of the drive motor can be determined through iterative iteration. Finally, based on the determined optimal maximum drive power, maximum torque, maximum speed, and transmission ratio, and in conjunction with market research, the drive motor parameters are determined. Finally, by combining the determined optimal maximum motor torque, maximum speed, and transmission ratio, the optimal maximum power of the drive motor is selected.

[0185] This embodiment determines the drive motor speed, drive motor torque, vehicle transmission ratio, and drive motor power based on drive motor parameters; optimizes the drive motor speed, drive motor torque, and vehicle transmission ratio based on a vehicle power optimization strategy and a drive motor objective function to obtain target motor speed, target motor torque, and target transmission ratio; optimizes the drive motor power based on the vehicle power optimization strategy to obtain target motor power; and determines target motor parameters based on the target motor speed, target motor torque, target transmission ratio, and target motor power. Through this method, the maximum power, maximum speed, maximum torque, and final drive ratio of the drive motor are optimized to obtain the optimal maximum speed, maximum torque, and final drive ratio of the drive motor, thereby obtaining the optimal drive motor parameters. This solves the problem of incomplete parameter optimization in existing powertrain optimization methods.

[0186] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the fuel cell vehicle power system optimization method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0187] This application also provides a fuel cell vehicle powertrain optimization device; please refer to [reference needed]. Figure 7 The fuel cell vehicle powertrain optimization device includes:

[0188] 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 the target motor parameters;

[0189] The processing module 10 is also used to optimize 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 the target power battery capacity.

[0190] The optimization module 20 is used to optimize the power system of the vehicle to be optimized based on the target motor parameters, the target fuel cell power, and the target power battery capacity.

[0191] Optionally, the processing module 10 is further configured to:

[0192] Determine the drive motor speed, drive motor torque, vehicle transmission ratio, and drive motor power based on the drive motor parameters;

[0193] Based on the vehicle power optimization strategy and the objective function of the drive motor, the speed of the drive motor, the torque of the drive motor and the vehicle transmission ratio are optimized to obtain the target motor speed, the target motor torque and the target transmission ratio.

[0194] The power of the drive motor is optimized based on the vehicle power optimization strategy to obtain the target motor power;

[0195] The target motor parameters are determined based on the target motor speed, the target motor torque, the target transmission ratio, and the target motor power.

[0196] Optionally, the processing module 10 is further configured to:

[0197] When the current motor power requirement is not less than the motor power requirement threshold, the first motor power requirement is determined based on the drive motor power requirement under typical vehicle operating conditions.

[0198] When the current motor power demand is less than the motor power demand threshold, the second motor power demand is determined according to the current motor power demand in a preset ratio.

[0199] Construct the objective function for the drive motor based on the power requirements of the first motor and the power requirements of the second motor.

[0200] Optionally, the processing module 10 is further configured to:

[0201] The initial population is determined based on the drive motor speed, the drive motor torque, and the vehicle transmission ratio.

[0202] The fitness of the initial population is calculated based on the objective function of the drive motor to obtain the current fitness.

[0203] The initial population is optimized based on the vehicle dynamics optimization strategy, and the fitness is calculated to obtain the optimized fitness.

[0204] The current fitness is compared with the optimized fitness, and iterative optimization is performed based on the comparison result to obtain the current iteration number;

[0205] When the current iteration number is not less than the iteration number threshold, the target motor speed, target motor torque, and target transmission ratio are obtained.

[0206] Optionally, the processing module 10 is further configured to:

[0207] The first fuel cell power is determined based on the target motor power, and the second fuel cell power is obtained by power calculation based on the fuel cell calculation strategy.

[0208] The fuel cell power range is obtained based on the first fuel cell power and the second fuel cell power.

[0209] The capacity of the power battery is calculated based on the vehicle's range strategy.

[0210] Optionally, the processing module 10 is further configured to:

[0211] Establish a target battery vehicle model based on the fuel cell power range and power battery capacity;

[0212] The power adaptability is determined based on the target battery vehicle model, the preset dynamic programming strategy, and the vehicle cost objective function.

[0213] Based on the aforementioned power adaptability and vehicle power optimization strategy, iterative optimization is performed to obtain the target fuel cell power and target power battery capacity.

[0214] Optionally, the processing module 10 is further configured to:

[0215] Vehicle simulation is performed based on the target battery vehicle model and the preset dynamic programming strategy to obtain hydrogen consumption data, power battery data and fuel cell data.

[0216] 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.

[0217] The fuel cell vehicle power system optimization device provided in this application, employing the fuel cell vehicle power system optimization method in the above embodiments, can solve the technical problems of limitations in parameter optimization, component coordination, simulation analysis, and control parameter processing in existing hydrogen fuel cell vehicle power system technologies. Compared with the prior art, the beneficial effects of the fuel cell vehicle power system optimization device provided in this application are the same as those of the fuel cell vehicle power system optimization method provided in the above embodiments, and other technical features in the fuel cell vehicle power system optimization device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0218] This application provides a fuel cell vehicle power system optimization device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the fuel cell vehicle power system optimization method in Embodiment 1 above.

[0219] The following is for reference. Figure 8 The diagram illustrates a structural schematic suitable for implementing the fuel cell vehicle powertrain optimization device in the embodiments of this application. The fuel cell vehicle powertrain optimization device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 8 The fuel cell vehicle powertrain optimization device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0220] like Figure 8 As shown, the fuel cell vehicle powertrain optimization device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the fuel cell vehicle powertrain optimization device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 allows the fuel cell vehicle powertrain optimization equipment to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a fuel cell vehicle powertrain optimization equipment with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0221] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0222] The fuel cell vehicle power system optimization equipment provided in this application, employing the fuel cell vehicle power system optimization method described in the above embodiments, can solve the technical problems of limitations in parameter optimization, component coordination, simulation analysis, and control parameter processing in existing hydrogen fuel cell vehicle power system technologies. Compared with the prior art, the beneficial effects of the fuel cell vehicle power system optimization equipment provided in this application are the same as those of the fuel cell vehicle power system optimization method provided in the above embodiments, and other technical features in this fuel cell vehicle power system optimization equipment are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0223] It should be understood that the various parts disclosed in this application can be implemented using 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 suitable manner in one or more embodiments or examples.

[0224] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0225] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the fuel cell vehicle power system optimization method in the above embodiments.

[0226] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0227] The aforementioned computer-readable storage medium may be included in the fuel cell vehicle powertrain optimization device; or it may exist independently and not be installed in the fuel cell vehicle powertrain optimization device.

[0228] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the fuel cell vehicle power system optimization device, the fuel cell vehicle power system optimization device performs the following actions: 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 power battery capacity based on the vehicle power optimization strategy and the vehicle cost objective function to obtain target fuel cell power and target power battery capacity; and 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.

[0229] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0230] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0231] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0232] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described fuel cell vehicle power system optimization method. This addresses the technical limitations of existing hydrogen fuel cell vehicle power system technologies in parameter optimization, component coordination, simulation analysis, and control parameter processing. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the fuel cell vehicle power system optimization method provided in the above embodiments, and will not be elaborated upon here.

[0233] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the fuel cell vehicle powertrain optimization method described above.

[0234] The computer program product provided in this application can solve the technical problems of limitations in parameter optimization, component coordination, simulation analysis, and control parameter processing in existing hydrogen fuel cell vehicle power system technologies. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the fuel cell vehicle power system optimization method provided in the above embodiments, and will not be repeated here.

[0235] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for optimizing the powertrain system of a fuel cell vehicle, characterized in that, The method for optimizing the powertrain system of fuel cell vehicles includes: Using the crayfish optimization algorithm, with the average power demand of the motor as the fitness value, the optimal transmission ratio, maximum motor speed, and maximum motor torque are determined. Then, combined with the operating points of the typical operating condition library that the maximum power of the motor must meet, a motor cost function is constructed. The crayfish optimization algorithm is used to find the optimal value, and finally the optimal maximum motor torque, maximum speed, and transmission ratio are determined, and the optimal maximum motor power is selected. The crayfish optimization algorithm is adopted and combined with the cost objective function for optimizing the maximum power of fuel cell and the capacity of power battery to find the optimal maximum power of fuel cell and the capacity of power battery. The powertrain system of the vehicle to be optimized is then optimized based on the optimized motor parameters, fuel cell power, and power battery capacity.

2. The method as described in claim 1, characterized in that, The step of employing the crayfish optimization algorithm, using the average power demand of the motor as the fitness value, to determine the optimal transmission ratio, maximum motor speed, and maximum motor torque; secondly, combining the operating points of the typical operating condition library that the maximum motor power must meet, constructing the motor cost function, and using the crayfish optimization algorithm for optimization, finally determining the optimal maximum motor torque, maximum speed, and transmission ratio, and selecting the optimal maximum motor power, also includes: When the current motor demand power is not less than the motor demand power threshold, the first motor demand power is determined according to the drive motor demand power under typical vehicle operating conditions. The first motor demand power refers to the average demand power when the drive motor demand power is not less than 0. When the current motor power demand is less than the motor power demand threshold, the second motor power demand is determined according to the current motor power demand in a preset ratio. Construct the objective function for the drive motor based on the power requirements of the first motor and the power requirements of the second motor.

3. The method as described in claim 1, characterized in that, The steps of using the crayfish optimization algorithm to determine the optimal transmission ratio, maximum motor speed, and maximum motor torque, with the average power demand of the motor as the fitness value, include: Determine the drive motor speed, drive motor torque, vehicle transmission ratio, and drive motor power based on the drive motor parameters; The initial population is determined based on the drive motor speed, the drive motor torque, and the vehicle transmission ratio. The fitness of the initial population is calculated based on the objective function of the drive motor to obtain the current fitness. The initial population was optimized based on the crayfish optimization algorithm, and the fitness was calculated to obtain the optimized fitness. The current fitness is compared with the optimized fitness, and iterative optimization is performed based on the comparison result to obtain the current iteration number; When the current iteration number is not less than the iteration number threshold, the target motor speed, target motor torque, and target transmission ratio are obtained.

4. The method as described in claim 1, characterized in that, Before the step of using the crayfish optimization algorithm and combining it with a cost objective function to optimize the maximum power of the fuel cell and the capacity of the power battery, and thus obtaining the optimal maximum power of the fuel cell and the capacity of the power battery, the following steps are also included: The first fuel cell power is determined based on the target motor power, and the second fuel cell power is obtained by power calculation based on the fuel cell calculation strategy. The first fuel cell power refers to the upper limit of the maximum power of the fuel cell, and the second fuel cell power refers to the lower limit of the maximum power of the fuel cell. The fuel cell calculation strategy refers to calculating the lower limit of the maximum power of the fuel cell with the goal of meeting the maximum vehicle speed requirement. The fuel cell power range is obtained based on the first fuel cell power and the second fuel cell power. The capacity of the power battery is calculated based on the vehicle's range strategy.

5. The method as described in claim 1, characterized in that, The steps of employing the crayfish optimization algorithm and combining it with a cost objective function for optimizing the maximum power of the fuel cell and the capacity of the power battery to find the optimal maximum power of the fuel cell and the capacity of the power battery, and thus obtaining the optimal maximum power of the fuel cell and the capacity of the power battery, include: Establish a target battery vehicle model based on the fuel cell power range and power battery capacity; The power adaptability is determined based on the target battery vehicle model, the preset dynamic programming strategy, and the vehicle cost objective function. Based on the aforementioned power adaptability, iterative optimization is performed in the crayfish optimization algorithm to obtain the target fuel cell power and the target power battery capacity.

6. The method as described in claim 5, characterized in that, The step of determining the power adaptability based on the target battery vehicle model, the preset dynamic programming strategy, and the vehicle cost objective function includes: Vehicle simulation is performed based on 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.

7. A fuel cell vehicle power system optimization device, characterized in that, The device includes: The processing module uses the crayfish optimization algorithm to determine the optimal transmission ratio, maximum motor speed, and maximum motor torque using the average motor power demand as the fitness value. Then, it constructs a motor cost function by combining the operating points of the typical operating condition library that the maximum motor power must meet, and uses the crayfish optimization algorithm to find the optimal value. Finally, it determines the optimal maximum motor torque, maximum speed, and transmission ratio, and selects the optimal maximum motor power. The processing module is also used to use the crayfish optimization algorithm and combine it with the cost objective function for optimizing the maximum power of the fuel cell and the capacity of the power battery to optimize the maximum power of the fuel cell and the capacity of the power battery, thereby obtaining the optimal maximum power of the fuel cell and the capacity of the power battery. The optimization module is used to optimize the power system of the vehicle to be optimized based on the optimized motor parameters, fuel cell power, and power battery capacity.

8. A fuel cell vehicle power system optimization device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the fuel cell vehicle powertrain optimization method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the fuel cell vehicle power system optimization method as described in any one of claims 1 to 6.

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