A method for collaborative optimization of fuel cell tractor mass and powertrain system

Abstract

A method for co-optimizing the mass and power system of a fuel cell tractor, relating to the field of tractor power system optimization design, links the design of tractor counterweights, power components, and energy storage components. By setting and optimizing the counterweight, the method ensures the tractor's operational stability and traction performance. Based on the power performance, it co-optimizes the spatial position and mass of the hydrogen storage tank, power battery, fuel cell, and motor. Using a genetic algorithm, it co-optimizes the spatial position and mass of the hydrogen storage tank, power battery, fuel cell, and motor with the counterweight. This improves traction performance while ensuring the tractor's operational stability, optimizes the overall machine's performance, reduces manufacturing costs, and improves energy efficiency. This method achieves co-optimization of the mass and spatial position of each component of the fuel cell tractor with the power system parameters. It can quickly obtain the mass and position information of each component of the fuel cell tractor and calculate reasonable power system parameters based on limited development data.

Description

Technical Field

[0001] This invention relates to the field of tractor power system optimization design, specifically a method for the coordinated optimization of the mass and power system of a fuel cell tractor. Background Technology

[0002] With rapid economic development, my country's mechanization level is increasing, and energy consumption is also growing rapidly. Traditional tractors suffer from high energy consumption and severe exhaust pollution. To achieve green agriculture, it is essential to find new power sources. Compared to batteries, fuel cells have higher energy density, making them suitable for tractors operating under heavy loads and large-area field conditions. Furthermore, fuel cells can achieve zero emissions, overcoming the problems of low efficiency, high noise, and high energy consumption associated with traditional internal combustion engines. Therefore, researching fuel cell tractors suitable for agricultural operating environments is of great significance.

[0003] Many scholars have conducted research on fuel cell tractors, mainly focusing on energy management and integrated application analysis. However, research on the mass and spatial position of fuel cell tractors is relatively limited. As a novel type of power tractor, the mass and spatial position of its components are unknown at the initial design stage. Different component masses and spatial positions lead to different power system parameters and tractor operating quality, directly affecting the tractor's traction performance, operational stability, and energy economy. Therefore, conducting synergistic optimization of tractor mass and power system is essential for improving the efficiency of tractor parameter optimization design. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method for the coordinated optimization of the mass and power system of a fuel cell tractor to improve the efficiency of tractor parameter optimization design.

[0005] The technical solution adopted by this invention to solve the above-mentioned technical problems is: a method for synergistic optimization of the mass and power system of a fuel cell tractor, wherein the fuel cell tractor adopts dual-motor drive, with the fuel cell and power battery coordinating power supply, and the tractor's operational stability and traction performance are ensured by setting and optimizing the counterweight, and the spatial position and mass of the hydrogen storage tank, power battery, fuel cell and motor are synergistically optimized based on power performance, including the following steps:

[0006] Step 1: Generate a population containing the spatial locations of hydrogen storage tanks, power batteries, fuel cells, and motors, as well as the masses of hydrogen storage tanks and power batteries, using a genetic algorithm;

[0007] Step 2: Add front counterweights and rear counterweights to the front counterweight box and the rear drive wheel of the tractor, respectively. Based on the spatial position and mass of the hydrogen storage tank, power battery, fuel cell and motor, optimize the mass of the front counterweight to meet the design index of the front axle load distribution coefficient, and optimize the mass of the rear counterweight to meet the design index of the tractor slip efficiency.

[0008] Step 3: Input the tractor's predetermined operating condition information. Based on the optimized front and rear counterweight masses in Step 2, as well as the spatial positions of the hydrogen storage tank, power battery, fuel cell, and motor generated in Step 1, and the mass population of the hydrogen storage tank and power battery, obtain the power of the dual motors and fuel cell according to the operating condition information under the constraints of the power performance index. Then, allocate the power of the dual motors. Under this dual motor power allocation ratio, obtain the mass of the dual motors and fuel cell based on the power and power density of the dual motors and fuel cell.

[0009] Step 4: Use an instantaneous optimization algorithm to obtain the optimal dual-motor torque, then allocate the power of the fuel cell and the power battery according to the power requirements of the dual motors under the predetermined working conditions, and solve the energy consumption of the fuel cell and the power battery as well as the energy consumption of the tractor. Calculate the mass of the power battery and the hydrogen storage tank based on the energy consumption of the power battery and the fuel cell.

[0010] Step 5: Determine whether the power distribution of the two motors is complete. If it is complete, proceed to Step 6. If it is not complete, return to Step 3 and perform the power distribution of the two motors again.

[0011] Step Six: Based on the tractor energy consumption under different dual-motor power allocation ratios, select the optimal dual-motor power allocation ratio and the corresponding mass of the dual motors, fuel cell, power battery, and hydrogen storage tank with the goal of minimizing energy consumption; compare the difference between the population of power battery and hydrogen storage tank mass generated by the genetic algorithm and the mass of power battery and hydrogen storage tank obtained under the predetermined working conditions, and determine whether the absolute value of the difference is less than the set threshold. If it is satisfied, proceed to Step Seven; if it is not satisfied, return to Step One and repeat the optimization process after updating the population through the genetic algorithm.

[0012] Step 7: Output the mass and position of the front counterweight, rear counterweight, hydrogen storage tank, power battery, fuel cell and motor, as well as the corresponding power system optimization parameters.

[0013] In step two, the front axle load of the tractor is calculated based on the spatial position and mass of the hydrogen storage tank, power battery, fuel cell and motor. The front axle design load is calculated based on the tractor's operating mass and the designed front axle load distribution coefficient. The front counterweight is then optimized and adjusted based on the difference between the front axle load and the front axle design load. The front axle load is then recalculated based on the adjusted front counterweight until the front counterweight meets the design requirements of the front axle load distribution coefficient.

[0014] The formulas for calculating the rear axle wheelbase and front axle load of the tractor are as follows:

[0015]

[0016]

[0017] in, The rear axle wheelbase of the nth generation tractor. For the nth generation front axle load, For the quality of the nth generation tractor, For the quality of the power battery, For the quality of the motor, For the quality of fuel cells, For the quality of the hydrogen storage tank, For the overall structural quality, Let the mass of the front counterweight of the nth generation tractor be [missing information]. For the weight of the tractor's rear counterweight, For the horizontal coordinates of the power battery, The horizontal coordinate of the motor For the horizontal coordinates of the fuel cell, The horizontal coordinates of the hydrogen storage tank are: The horizontal coordinates of the entire machine structure The horizontal coordinate of the rear counterweight; It is the acceleration due to gravity. For tractor wheelbase, The rolling resistance torque of the tractor's front wheels, The rolling resistance torque of the tractor's rear wheels, For tractor traction resistance, The height of the traction point. The horizontal distance from the traction point of the agricultural implement to the rear axle. The angle between the traction resistance and the horizontal plane;

[0018] The mass of the nth generation tractor is: ;

[0019] Calculate the front axle design load based on the designed front axle load distribution coefficient, and adjust the front counterweight accordingly. The formula is as follows:

[0020]

[0021]

[0022] in, Design load for the front axle of the nth generation. The front axle load distribution factor is the design factor. Let be the front counterweight mass of the (n+1)th generation tractor.

[0023] In step two, the rear axle load of the tractor is calculated based on the spatial position and mass of the hydrogen storage tank, power battery, fuel cell and motor. The slip efficiency of the tractor is then calculated based on the rear axle load. If the slip efficiency does not meet the design requirements, the design load of the rear axle is calculated based on the tractor's operating mass. The counterweight is then optimized and adjusted based on the difference between the rear axle load and the design load until the design requirements for the tractor's slip rate are met.

[0024] The formulas for calculating the front axle wheelbase and rear axle load of the tractor are as follows:

[0025]

[0026]

[0027] in, The front axle wheelbase of the nth generation tractor. For the front counterweight mass of the tractor, The mass of the rear counterweight of the nth generation tractor. This refers to the rear axle load of the nth generation.

[0028] The slip efficiency of the tractor is:

[0029]

[0030] in, Let be the slip efficiency of the tractor in the nth generation. Characteristic slip ratio, The maximum load utilization factor for the drive wheels. For tractor driving force;

[0031] The formulas for updating the tractor rear axle design load and rear counterweight are as follows:

[0032]

[0033]

[0034] in, Design load for the rear axle of the nth generation. The rolling resistance coefficient, Let be the mass of the rear counterweight of the (n+1)th generation tractor.

[0035] In step three, the rated power of the dual motors and fuel cell to meet the tractor's power requirements is determined based on the plowing conditions, using the following formula:

[0036]

[0037]

[0038]

[0039]

[0040] in, The power of motor A, The power of motor B, The speed of the tractor. For traction efficiency, For motor efficiency, Tractor transmission efficiency. Tractor rolling efficiency, For slip efficiency, The rolling resistance coefficient, For the quality of tractor use, It is the acceleration due to gravity. For tractor traction resistance, The angle between the traction resistance and the horizontal plane;

[0041] The rated power of the two motors is allocated as follows:

[0042]

[0043]

[0044] in, The power allocation ratio for the two motors is set to a range of (0,1);

[0045] The mass of the dual motor and fuel cell, obtained from their power and power density, is expressed as:

[0046]

[0047]

[0048]

[0049]

[0050] in, For the power of motor A in the nth generation, For the power of motor B in the nth generation, For the mass of motor A in the nth generation, For the mass of motor B in the nth generation, For the quality of the nth generation dual motor, For the power density of the motor, This represents the discharge power of the nth generation fuel cell. For the mass of the nth generation fuel cell, This represents the power density of the fuel cell.

[0051] In step four, based on the operating condition information, an instantaneous optimization algorithm is used to obtain the optimal torque of the dual motors and the output power of the energy source composed of the fuel cell and the power battery. When the fuel cell tractor is in a steady-state operating condition and the fluctuation of the energy source output power does not exceed the liter power of the fuel cell, the fuel cell provides power. When the output power fluctuation exceeds the liter power of the fuel cell, the power battery makes up the difference in power. The energy consumption of the fuel cell and the power battery is calculated based on the power distribution of the fuel cell and the power battery, and then the mass of the fuel cell and the power battery is calculated based on the energy density of the fuel cell and the power battery.

[0052] The output power of the energy source consisting of the fuel cell and the power battery is obtained based on the speed, torque, and efficiency of the dual motors, and is expressed as:

[0053]

[0054] in, Let be the power of the energy source at time t. Let be the torque of motor A at time t. Let B be the torque of motor at time t. Let t be the speed of motor A. Let t be the speed of motor B. Let be the efficiency of motor A at time t. Let be the efficiency of motor B at time t;

[0055] When the power output fluctuation of the energy source exceeds the power output per liter of the fuel cell, the output power of the fuel cell and the power battery can be expressed as:

[0056]

[0057] in, Let be the fuel cell power at time t. Let be the power of the energy source at time t-1. Let t be the power of the battery. This represents the maximum power output of the fuel cell.

[0058] When the power output fluctuation of the energy source does not exceed the power output per liter of the fuel cell, the output power of the fuel cell and the power battery can be expressed as:

[0059]

[0060] Using the energy consumption of fuel cell tractors as the objective function:

[0061]

[0062] in, For fuel cell tractors, For power battery energy consumption, For fuel cell energy consumption, This refers to the discharge power of the power battery. For fuel cell discharge power, For power battery efficiency, For fuel cell efficiency;

[0063] The mass of the power battery and hydrogen storage tank is:

[0064]

[0065]

[0066] in, For battery energy density, This refers to the energy density of the hydrogen storage tank.

[0067] In step five, within the given dual-motor power allocation ratio range, the dual-motor power allocation is considered complete when all predetermined allocation methods have been completed.

[0068] In step six, the difference between the mass of the hydrogen storage tank and battery generated by the genetic algorithm and the actual required mass can be expressed as:

[0069]

[0070] in, This represents the difference between the assumed mass of the hydrogen storage tank and the power battery and the actual required mass. For optimized power battery quality, For the optimized quality of hydrogen storage tanks.

[0071] The beneficial effects of this invention are: it links the design of tractor counterweight, power components, and energy storage components; it uses a genetic algorithm to synergistically optimize the spatial position and mass of the hydrogen storage tank, power battery, fuel cell, and motor with the counterweight; it improves traction performance while ensuring the stability of tractor operation; it optimizes the overall machine's performance; it reduces the overall machine manufacturing cost and improves energy efficiency; it achieves synergistic optimization of the mass and spatial position of each component of the fuel cell tractor with the power system parameters; and it can quickly obtain the mass and position information of each component of the fuel cell tractor and calculate reasonable power system parameters based on limited development data. Attached Figure Description

[0072] Figure 1 This is a schematic diagram of a fuel cell tractor.

[0073] Figure 2 This is a flowchart of the method for synergistic optimization of the mass and power system of a fuel cell tractor according to the present invention.

[0074] The markings in the diagram are: 1. Motor A, 2. Motor B, 3. Coupling device, 4. PTO reducer, 5. Power output shaft, 6. Main reducer, 7. Central transmission device. Detailed Implementation

[0075] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings and specific embodiments. The specific contents listed in the following embodiments are not limited to the technical features necessary to solve the technical problem of the present invention. Furthermore, the listed embodiments are merely a part of the present invention, and not all embodiments.

[0076] like Figure 1 As shown, the fuel cell tractor includes a hydrogen storage tank, fuel cell, DC / DC converter, power battery, dual motors (motor A1, motor B2), coupling device 3, PTO reducer 4, power output shaft 5, main reducer 6, central transmission device 7, wheels, etc. The fuel cell tractor is driven by dual motors, with the fuel cell and power battery working together to provide power. At the initial design stage, the mass and spatial position of each component of the fuel cell tractor are unknown, making it impossible to accurately obtain the tractor's performance indicators. This invention proposes a tractor mass and position co-optimization method based on genetic algorithms, integrating tractor counterweight design methods, energy storage component design methods, and power component design methods.

[0077] First, a genetic algorithm is used to generate a population containing the positions of the hydrogen storage tank, power battery, fuel cell, and motor, as well as the assumed masses of the hydrogen storage tank and power battery. Second, a counterweight design method is used to add counterweights to the front counterweight box and the rear drive wheel at determined locations to ensure the tractor's handling performance while improving its traction performance. Then, the power component design method and the energy storage component design method are used to optimize the design of the power component and the energy storage component. Finally, the difference between the masses of the hydrogen storage tank and power battery generated by the genetic algorithm and the masses of the hydrogen storage tank and power battery designed by the energy storage component is determined, and the optimization parameters with the minimum energy consumption are output. This completes the coordinated optimization of the mass and spatial position of each component of the fuel cell tractor with the parameters of the power system.

[0078] The specific optimization design process is as follows: Figure 2 As shown.

[0079] Step 1: Given the effective mass and spatial location of the main structure of the fuel cell tractor, generate a population containing the spatial locations of the hydrogen storage tank, power battery, fuel cell, and motor, as well as the masses of the hydrogen storage tank and power battery, using a genetic algorithm.

[0080] Step 2: Using the counterweight design method, front counterweights and rear counterweights are added to the front counterweight box and the rear drive wheel side of the tractor, respectively. Based on the spatial position and mass of the hydrogen storage tank, power battery, fuel cell and motor, the load on the front and rear axles during the operation of the tractor is calculated to determine whether the tractor is within the designed drive wheel slip rate range and maintains steering performance. The front counterweight mass is optimized with the goal of meeting the front axle load distribution coefficient design index, and the rear counterweight mass is optimized with the goal of meeting the tractor slip efficiency design index.

[0081] Step 3: Input the tractor's predetermined operating condition information, input the population and front and rear axle counterweights into the power component design method. Given the positions of the tractor's front and rear counterweights, hydrogen storage tank, battery, fuel cell, and motor, the masses of the front and rear counterweights, hydrogen storage tank, and battery, as well as the tractor's structural mass and spatial position, obtain the power of the dual motors and fuel cell based on the operating condition information under the constraints of the power performance indicators. Then, allocate the power of the dual motors, and finally obtain the mass of the dual motors and fuel cell based on their power and power density.

[0082] Step 4: In the design method of energy storage components, the instantaneous optimization algorithm is used to obtain the optimal dual-motor torque. Then, the power of the fuel cell and battery is allocated according to the power requirements of the dual motors, and the energy consumption of the fuel cell, power battery and tractor is solved. The mass of power battery and hydrogen storage tank is calculated based on the energy consumption of power battery and fuel cell.

[0083] Step 5: Determine whether the power distribution of the two motors is complete. If it is complete, proceed to Step 6. If it is not complete, return to Step 3 and perform the power distribution of the two motors again.

[0084] Step 6: Based on the tractor energy consumption under different dual-motor power allocation ratios, select the optimal dual-motor power allocation ratio and the corresponding mass of the dual motors, fuel cell, power battery, and hydrogen storage tank with the goal of minimizing energy consumption; compare the difference between the population of power battery and hydrogen storage tank mass generated by the genetic algorithm and the power battery and hydrogen storage tank mass obtained under the predetermined working conditions, and determine whether the absolute value of the difference is less than the set threshold C (minimum value). If it is satisfied, proceed to Step 7; if it is not satisfied, return to Step 1 and repeat the optimization process after updating the population through the genetic algorithm.

[0085] Step 7: Output the mass and position of the front counterweight, rear counterweight, hydrogen storage tank, power battery, fuel cell and motor, as well as the corresponding power system optimization parameters.

[0086] The specific methods for each step are as follows:

[0087] 1. Tractor counterweight design method

[0088] The counterweight mass of a tractor is unknown during operation, requiring continuous adjustment of the front and rear counterweights based on the implement's working conditions to achieve an optimal load distribution between the front and rear axles. However, repeated installation and removal of counterweights is time-consuming, labor-intensive, and reduces work efficiency. Furthermore, manually adjusting the front and rear counterweights by observing the tractor's operating status does not guarantee optimal performance. To address this issue, a counterweight design method incorporating front and rear counterweight adjustment is proposed to obtain a tractor front and rear axle counterweight that meets the requirements of actual working conditions.

[0089] (1) Tractor front counterweight adjustment design

[0090] When the rear-suspension implements are lifted by the tractor's three-point suspension, the weight is transferred from the front wheels to the rear wheels. Based on the spatial location and mass of the hydrogen storage tank, power battery, fuel cell, and motor, the tractor's front axle load is calculated. The tractor's rear axle wheelbase and front axle load can be expressed as:

[0091] (1)

[0092] (2)

[0093] In the formula, The rear axle wheelbase of the nth generation tractor. For the nth generation front axle load, For the quality of the nth generation tractor, For the quality of the power battery, For the quality of the motor, For the quality of fuel cells, For the quality of the hydrogen storage tank, For the overall structural quality, Let the mass of the front counterweight of the nth generation tractor be [missing information]. For the weight of the tractor's rear counterweight, For the horizontal coordinates of the power battery, The horizontal coordinate of the motor For the horizontal coordinates of the fuel cell, The horizontal coordinates of the hydrogen storage tank are: The horizontal coordinates of the entire machine structure The horizontal coordinate of the rear counterweight; It is the acceleration due to gravity. For tractor wheelbase, The rolling resistance torque of the tractor's front wheels, The rolling resistance torque of the tractor's rear wheels, For tractor traction resistance, The height of the traction point. The horizontal distance from the traction point of the agricultural implement to the rear axle. The angle between the traction resistance and the horizontal plane.

[0094] The mass of the nth generation tractor is:

[0095] (3)

[0096] To stabilize the tractor's attitude and steering control, and ensure stability during operation, the tractor's front axle load distribution coefficient must be greater than or equal to 20%. The front axle design load is calculated based on the tractor's operating weight and the designed front axle load distribution coefficient. The front counterweight is then optimized and adjusted based on the difference between the actual front axle load and the design load, expressed as:

[0097] (4)

[0098] (5)

[0099] in, Design load for the front axle of the nth generation. The front axle load distribution factor is the design factor. Let be the front counterweight mass of the (n+1)th generation tractor.

[0100] Update the tractor's operating weight, rear axle wheelbase, and front axle load based on the matched front counterweight:

[0101] (6)

[0102] (7)

[0103] (8)

[0104] in, For the (n+1)th generation tractor's operating mass, This refers to the rear axle wheelbase of the (n+1)th generation tractor. This is the front axle load of the (n+1)th generation.

[0105] Therefore, through continuous iteration and updates, it is eventually possible to achieve the design target for the front axle load distribution coefficient of the tractor when a suitable front counterweight is matched, thus ensuring the operational stability of the tractor.

[0106] (2) Tractor rear counterweight adjustment design:

[0107] During heavy-duty operations such as plowing, improper axle load distribution or use of low-quality tractors can easily lead to low drive axle load, resulting in a high slip rate of the tractor's drive wheels and affecting the tractor's traction performance. Therefore, it is necessary to adjust the rear counterweight according to the slip rate during operation to obtain optimal traction performance. Based on the spatial location and mass of the hydrogen storage tank, power battery, fuel cell, and motor, the tractor's rear axle load can be calculated. The tractor's front axle wheelbase and rear axle load can be expressed as:

[0108] (9)

[0109] (10)

[0110] in, The front axle wheelbase of the nth generation tractor. For the front counterweight mass of the tractor, The mass of the rear counterweight of the nth generation tractor. This is the rear axle load of the nth generation.

[0111] Calculate the tractor slip efficiency based on the rear axle load:

[0112] (11)

[0113] in, Let be the slip efficiency of the tractor in the nth generation. Characteristic slip ratio, The maximum load utilization factor for the drive wheels. It provides the driving force for the tractor.

[0114] To improve the tractor's traction performance, the rear counterweight needs to be adjusted based on the slip ratio during operation. If the slip ratio does not meet the design requirements, the rear axle design load is calculated based on the tractor's operating weight, and the rear counterweight is optimized and adjusted based on the difference between the rear axle load and the rear axle design load, expressed as:

[0115] (12)

[0116] (13)

[0117] in, Design load for the rear axle of the nth generation. The rolling resistance coefficient, Let be the mass of the rear counterweight of the (n+1)th generation tractor.

[0118] Update the tractor's operating weight, front axle wheelbase, and rear axle load based on the matched rear counterweight:

[0119] (14)

[0120] (15)

[0121] (16)

[0122] in, For the (n+1)th generation tractor's operating mass, This refers to the front axle wheelbase of the (n+1)th generation tractor. This is the rear axle load of the (n+1)th generation.

[0123] Therefore, through continuous iteration and updates, it is eventually possible to ensure that the slip ratio of the tractor's drive wheel reaches the design target under the condition of matching the appropriate tractor rear counterweight, thereby improving the tractor's traction performance.

[0124] It should be noted that in the first iteration of the genetic algorithm, the initial population does not generate a population of motor and fuel cell mass, and the mass of motor and fuel cell has not yet been updated using the power component design method. Therefore, in the first iteration, the front and rear counterweights can be matched as if the mass of motor and fuel cell is 0. The power component and energy storage component design can be carried out after the front and rear counterweights are updated.

[0125] This tractor counterweight design method effectively solves the problem of changes in performance caused by repeated counterweight adjustments during tractor design, improving tractor traction performance while ensuring operational stability.

[0126] 2. Power Component Design Method

[0127] The operating mass of a fuel cell tractor mainly consists of six parts: structural mass, motor mass, fuel cell mass, power battery mass, hydrogen storage tank mass, and counterweight mass. The optimization design of the front and rear counterweights is completed in the tractor counterweight design method, while the optimization of the hydrogen storage tank and power battery is completed in the energy storage component design method. Here, the power component design method is used to optimize the design of the fuel cell and motor. Given the positions of the tractor's front and rear counterweights, hydrogen storage tank, battery, fuel cell, and motor, as well as the masses, structural masses, and spatial positions of the front and rear counterweights, hydrogen storage tank, and battery, the power component design method can obtain reasonable motor and fuel cell masses based on performance constraints and operating condition information. The operating condition information refers to the operating parameters of a reference tractor with the same horsepower selected according to the tractor's design horsepower. For example, if the designed tractor is a 50-horsepower fuel cell tractor, then the reference tractor is also 50 horsepower, and the specific operating condition information input is the full-load plowing parameters of the reference 50-horsepower tractor. The power of the dual motors and fuel cell is matched based on the plowing parameters, and then the mass parameters of the power components are calculated. The optimal energy storage component parameters are obtained by using an instantaneous optimization algorithm based on the plowing parameters.

[0128] (1) Calculate the power of the motor and the fuel cell:

[0129] The power of the dual motors and fuel cell in a fuel cell tractor must meet the tractor's power requirements. Since plowing is the heaviest-load operation in tractor traction, the rated power design of the dual motors and fuel cell should meet the following requirements based on plowing conditions:

[0130] (17)

[0131] (18)

[0132] (19)

[0133] (20)

[0134] in, The power of motor A, The power of motor B, The speed of the tractor. For traction efficiency, For motor efficiency, Tractor transmission efficiency. Tractor rolling efficiency, For slip efficiency, The rolling resistance coefficient, For the quality of tractor use, It is the acceleration due to gravity. For tractor traction resistance, The angle between the traction resistance and the horizontal plane.

[0135] (2) Power distribution of dual motors in fuel cell tractors:

[0136] Since the power distribution of the two motors affects the overall operating efficiency of the machine, it is necessary to optimize the rated power distribution of the motors. Therefore, the power distribution of the two motors can be expressed as follows:

[0137] (twenty one)

[0138] (twenty two)

[0139] In the formula, The power allocation ratio for the two motors is set to a range of (0,1).

[0140] (3) The masses of the motor and the fuel cell are:

[0141] During the design process, we assume that the power density of the motor and fuel cell is constant during optimization. Therefore, the mass can be calculated based on the power of the motor and fuel cell.

[0142] (twenty three)

[0143] (twenty four)

[0144] (25)

[0145] (26)

[0146] in, For the power of motor A in the nth generation, For the power of motor B in the nth generation, For the mass of motor A in the nth generation, For the mass of motor B in the nth generation, For the quality of the nth generation dual motor, For the power density of the motor, This represents the discharge power of the nth generation fuel cell. For the mass of the nth generation fuel cell, This represents the power density of the fuel cell.

[0147] 3. Energy storage component design methods

[0148] To reduce tractor manufacturing costs, instantaneous optimization algorithms are employed in the energy storage component design to improve the operating efficiency of the power system. Tractors often operate under low-speed, high-torque conditions; therefore, torque coupling is commonly used in dual-motor coupling devices. The speeds of the dual motors are calculated based on the vehicle speed, transmission ratio, and coupling ratio under the given operating conditions. After determining the speed, the required torque is calculated based on the plowing resistance and transmission ratio. Then, the torques of the dual motors (the torque range achievable by each motor at known speeds) are iterated and searched to find the optimal torque distribution and minimum energy consumption for each motor under different torque conditions. Finally, the optimal torque distribution and corresponding speed for each motor under different required torque conditions are output. Based on the known power distribution ratio of the dual motors in the fuel cell tractor and the optimized torque distribution, the energy consumption of the fuel cell and battery is calculated. Finally, hydrogen storage tanks and batteries of appropriate capacity and mass are matched according to the energy consumption.

[0149] (1) Output power of energy source

[0150] During the optimization process, the output power of the energy source composed of fuel cells and batteries can be expressed by the speed, torque, and efficiency of the dual motors as follows:

[0151] (27)

[0152] in, Let be the power of the energy source at time t. Let be the torque of motor A at time t. Let B be the torque of motor at time t. Let t be the speed of motor A. Let t be the speed of motor B. Let be the efficiency of motor A at time t. Let be the efficiency of motor B at time t.

[0153] (2) Output power of fuel cells and batteries

[0154] Considering the poor dynamic performance of fuel cells in fuel cell tractors, when the operating power fluctuates significantly, the power battery assists the fuel cell in operation to maintain energy source output efficiency and extend the fuel cell's lifespan. When the motor output power fluctuates beyond the fuel cell's power output, the power battery compensates for the difference. The output power of the fuel cell and the battery can be expressed as:

[0155] (28)

[0156] in, Let be the fuel cell power at time t. Let be the power of the energy source at time t-1. Let t be the power of the battery. This represents the maximum power output per liter of the fuel cell.

[0157] If the fuel cell tractor is in steady-state operation and the power fluctuation of the motor does not exceed the power output of the fuel cell, the output power of the fuel cell and the power battery can be expressed as:

[0158] (29)

[0159] (3) Tractor energy consumption

[0160] Solve for the energy consumption of fuel cells, power batteries, and tractors. The energy consumption of a fuel cell tractor is expressed as:

[0161] (30)

[0162] In the formula, For fuel cell tractors, For power battery energy consumption, For fuel cell energy consumption, This refers to the discharge power of the power battery. For fuel cell discharge power, For power battery efficiency, For fuel cell efficiency.

[0163] (4) The mass of the power battery and hydrogen storage tank required to meet the overall machine requirements under specific operating conditions, calculated using the operating condition method, is as follows:

[0164] (31)

[0165] (32)

[0166] In the formula, For the energy density of power batteries, This refers to the energy density of the hydrogen storage tank.

[0167] 4. Optimize the objective function

[0168] The power distribution range of the two motors is set to (0,1). Within this range, the power is distributed proportionally in a predetermined manner. After each proportional distribution, the power component design method and the energy storage component design method are used for optimization.

[0169] To improve the operating efficiency of the fuel cell tractor power system, after all proportional allocations are completed, the minimum fuel cell tractor energy consumption is taken as the objective function:

[0170] (33)

[0171] By comparing the energy consumption of tractors under different dual-motor power distribution ratios, the dual-motor power distribution ratio with the lowest tractor energy consumption and the corresponding mass of the dual motors, fuel cell, power battery and hydrogen storage tank were selected as the parameters for this round of optimization.

[0172] Compare the differences between the population of power battery and hydrogen storage tank masses generated by the genetic algorithm and the optimized power battery and hydrogen storage tank masses under predetermined operating conditions:

[0173] (34)

[0174] in, This represents the difference between the assumed mass of the hydrogen storage tank and the power battery and the actual required mass. For optimized power battery quality, For the optimized quality of hydrogen storage tanks.

[0175] Determine if the absolute value of the value is less than the set threshold. If not, return to the first step of updating and generating the population through the genetic algorithm and repeat the optimization process. If it is satisfied, output the mass and position of the currently optimized front counterweight, rear counterweight, hydrogen storage tank, power battery, fuel cell and motor, as well as the corresponding power system optimization parameters.

[0176] As can be seen from the above optimization, the method of co-optimization of the mass and power system of fuel cell tractors can co-optimize and match suitable power components and energy storage devices, improve traction performance while ensuring the stability of tractor operation, achieve the optimal quality of the whole machine, reduce the manufacturing cost of the whole machine and improve energy consumption economy, and realize the co-optimization of the mass and spatial position of each component of fuel cell tractors with the parameters of the power system.

[0177] The above description of specific embodiments is only for the purpose of helping to understand the technical concept and core idea of ​​the present invention. Although specific preferred embodiments have been used to describe and illustrate the technical solutions, they should not be construed as limiting the present invention itself. Those skilled in the art can make various changes in form and detail without departing from the technical concept of the present invention. These easily conceived changes or substitutions should all be covered within the protection scope of the present invention.

Claims

1. A method for collaborative optimization of fuel cell tractor mass and power system, the fuel cell tractor adopts double motor drive, fuel cell and power battery collaborative power supply, through setting and optimizing counterweight to ensure tractor operation stability and traction performance, and based on power performance collaborative optimization of spatial position and mass of hydrogen storage tank, power battery, fuel cell and motor, characterized in that: Includes the following steps: Step 1: Given the effective mass and spatial location of the main structure of the fuel cell tractor, generate a population containing the spatial locations of the hydrogen storage tank, power battery, fuel cell, and motor, as well as the masses of the hydrogen storage tank and power battery, using a genetic algorithm. Step 2: Add front and rear counterweights to the front counterweight box and the rear drive wheel of the tractor, respectively. Based on the spatial position and mass of the hydrogen storage tank, power battery, fuel cell, and motor, optimize the front counterweight mass to meet the design index of front axle load distribution coefficient, and optimize the rear counterweight mass to meet the design index of tractor slip efficiency. In the first iteration of the genetic algorithm, the initial population does not generate a population of motor and fuel cell mass, so the optimization of front and rear counterweights is performed as if the mass of motor and fuel cell is 0. Step 3: Input the tractor's predetermined operating condition information. Based on the optimized front and rear counterweight masses in Step 2, as well as the spatial positions of the hydrogen storage tank, power battery, fuel cell, and motor generated in Step 1, and the mass population of the hydrogen storage tank and power battery, obtain the power of the dual motors and fuel cell according to the operating condition information under the constraints of the power performance index. Then, allocate the power of the dual motors. Under this dual motor power allocation ratio, obtain the mass of the dual motors and fuel cell based on the power and power density of the dual motors and fuel cell. Step 4: Use an instantaneous optimization algorithm to obtain the optimal dual-motor torque, then allocate the power of the fuel cell and the power battery according to the power requirements of the dual motors under the predetermined working conditions, and solve the energy consumption of the fuel cell and the power battery as well as the energy consumption of the tractor. Calculate the mass of the power battery and the hydrogen storage tank based on the energy consumption of the power battery and the fuel cell. Step 5: Determine whether the power distribution of the two motors is complete. If it is complete, proceed to Step 6. If it is not complete, return to Step 3 and perform the power distribution of the two motors again. Step Six: Based on the tractor energy consumption under different dual-motor power allocation ratios, select the optimal dual-motor power allocation ratio and the corresponding mass of the dual motors, fuel cell, power battery, and hydrogen storage tank with the goal of minimizing energy consumption; compare the difference between the population of power battery and hydrogen storage tank mass generated by the genetic algorithm and the mass of power battery and hydrogen storage tank obtained under the predetermined working conditions, and determine whether the absolute value of the difference is less than the set threshold. If it is satisfied, proceed to Step Seven; if it is not satisfied, return to Step One and repeat the optimization process after updating the population through the genetic algorithm. Step 7: Output the mass and position of the front counterweight, rear counterweight, hydrogen storage tank, power battery, fuel cell and motor, as well as the corresponding power system optimization parameters.

2. A method of co-optimizing the mass and power systems of a fuel cell tractor as recited in claim 1, wherein: In step two, the front axle load of the tractor is calculated based on the spatial position and mass of the hydrogen storage tank, power battery, fuel cell and motor. The front axle design load is calculated based on the tractor's operating mass and the designed front axle load distribution coefficient. The front counterweight is then optimized and adjusted based on the difference between the front axle load and the front axle design load. The front axle load is then recalculated based on the adjusted front counterweight until the front counterweight meets the design requirements of the front axle load distribution coefficient.

3. A method of co-optimizing a fuel cell tractor mass and power system according to claim 2, wherein: The formulas for calculating the rear axle wheelbase and front axle load of the tractor are as follows: in, L 2 n The rear axle wheelbase of the nth generation tractor. F z1 n For the nth generation front axle load, m s n For the quality of the nth generation tractor, m bat For the quality of the power battery, m me For the quality of the motor, m fc For the quality of fuel cells, m H For the quality of the hydrogen storage tank, m e For the overall structural quality, m p1 n Let the mass of the front counterweight of the nth generation tractor be [value]. m p2 For the weight of the tractor's rear counterweight, x bat For the horizontal coordinates of the power battery, x me The horizontal coordinate of the motor x fc For the horizontal coordinates of the fuel cell, x H The horizontal coordinates of the hydrogen storage tank are: x e The horizontal coordinates of the entire machine structure x p2 The horizontal coordinate of the rear counterweight; It is the acceleration due to gravity. For tractor wheelbase, T 1 represents the rolling resistance torque of the tractor's front wheel. T 2 represents the rolling resistance torque of the tractor's rear wheels. F D For tractor traction resistance, h D The height of the traction point. The horizontal distance from the traction point of the agricultural implement to the rear axle. θ The angle between the traction resistance and the horizontal plane; The quality of the nth generation tractor is: ; Calculate the front axle design load based on the designed front axle load distribution coefficient, and adjust the front counterweight accordingly. The formula is as follows: wherein, F s1 n is the n-th generation front axle design load, k s is the design front axle load distribution factor, m p1 n+1 is the n+1-th generation tractor front weight.

4. A method of co-optimizing a fuel cell tractor mass and power system according to claim 3, characterized by: In step two, the rear axle load of the tractor is calculated based on the spatial position and mass of the hydrogen storage tank, power battery, fuel cell and motor. The slip efficiency of the tractor is then calculated based on the rear axle load. If the slip efficiency does not meet the design requirements, the design load of the rear axle is calculated based on the tractor's operating mass. The counterweight is then optimized and adjusted based on the difference between the rear axle load and the design load until the design requirements for the tractor's slip rate are met.

5. A method of co-optimizing a fuel cell tractor mass and power system according to claim 4, wherein: The formulas for calculating the front axle wheelbase and rear axle load of the tractor are as follows: wherein, L 1 n is the n th generation front axle track of the tractor, m p1 is the front counterweight mass of the tractor, m p2 n is the n th generation rear counterweight mass of the tractor, F z2 n is the n th generation rear axle load; The slip efficiency of the tractor is: in, η δ n Let n be the slip efficiency of the tractor in the nth generation. δ * Characteristic slip ratio, φ max This is the maximum load utilization factor for the drive wheels. F q For tractor driving force; The formulas for updating the tractor rear axle design load and rear counterweight are as follows: in, F s2 n Design load for the rear axle of the nth generation. The rolling resistance coefficient, m p2 n+1 Let be the mass of the rear counterweight of the (n+1)th generation tractor.

6. The method for synergistic optimization of the mass and power system of a fuel cell tractor as described in claim 1, characterized in that: In step three, the rated power of the dual motors and fuel cell to meet the tractor's power requirements is determined based on the plowing conditions, using the following formula: in, P me1 The power of motor A, P me2 The power of motor B, The speed of the tractor. η T For traction efficiency, η me For motor efficiency, η c Tractor transmission efficiency. η f Tractor rolling efficiency For slip efficiency, The rolling resistance coefficient, m s For the quality of tractor use, It is the acceleration due to gravity. F D For tractor traction resistance, θ The angle between the traction resistance and the horizontal plane; The rated power of the two motors is allocated as follows: in, k m The power allocation ratio for the two motors is set to a range of (0,1); The mass of the dual motor and fuel cell, obtained from their power and power density, is expressed as: in, P me1 n For the power of motor A in the nth generation, P me2 n For the power of motor B in the nth generation, m me1 n For the mass of motor A in the nth generation, m me2 n For the mass of motor B in the nth generation, m me n For the quality of the nth generation dual motor, ρ m For the power density of the motor, P fc n This represents the discharge power of the nth generation fuel cell. m fc n For the mass of the nth generation fuel cell, ρ fc This represents the power density of the fuel cell.

7. The method for synergistic optimization of the mass and power system of a fuel cell tractor as described in claim 1, characterized in that: In step four, based on the operating condition information, an instantaneous optimization algorithm is used to obtain the optimal torque of the dual motors and the output power of the energy source composed of the fuel cell and the power battery. When the fuel cell tractor is in a steady-state operating condition and the fluctuation of the energy source output power does not exceed the liter power of the fuel cell, the fuel cell provides power. When the output power fluctuation exceeds the liter power of the fuel cell, the power battery makes up the difference in power. The energy consumption of the fuel cell and the power battery is calculated based on the power distribution of the fuel cell and the power battery, and then the mass of the fuel cell and the power battery is calculated based on the energy density of the fuel cell and the power battery.

8. The method for synergistic optimization of the mass and power system of a fuel cell tractor as described in claim 7, characterized in that: The output power of the energy source consisting of the fuel cell and the power battery is obtained based on the speed, torque, and efficiency of the dual motors, and is expressed as: in, P mc ( t Let t be the power of the energy source at time t. T 1( t Let t be the torque of motor A at time t. T 2( t Let t be the torque of motor B at time t. N 1( t Let t be the rotational speed of motor A at time t. N 2( t Let t be the rotational speed of motor B at time t. η 1( t Let be the efficiency of motor A at time t. η 2( t Let t be the efficiency of motor B at time t; When the power output fluctuation of the energy source exceeds the power output per liter of the fuel cell, the output power of the fuel cell and the power battery are expressed as follows: in, P fc ( t Let t be the fuel cell power at time t. P mc ( t -1) represents the power of the energy source at time t-1. P bat ( t ( ) represents the power of the battery at time t. P b This represents the maximum power output of the fuel cell. When the power output fluctuation of the energy source does not exceed the power output per liter of the fuel cell, the output power of the fuel cell and the power battery is expressed as: Using the energy consumption of fuel cell tractors as the objective function: in, For fuel cell tractors, Q bat For the energy consumption of the power battery, Q fc For fuel cell energy consumption, P bat This refers to the discharge power of the power battery. P fc For fuel cell discharge power, η bat For power battery efficiency, η fc For fuel cell efficiency; The mass of the power battery and hydrogen storage tank is: in, ρ bat For battery energy density, ρ H This refers to the energy density of the hydrogen storage tank.

9. The method for synergistic optimization of the mass and power system of a fuel cell tractor as described in claim 1, characterized in that: In step five, within the given dual-motor power allocation ratio range, the dual-motor power allocation is considered complete when all predetermined allocation methods have been completed.

10. The method for synergistic optimization of the mass and power system of a fuel cell tractor as described in claim 1, characterized in that: In step six, the difference between the mass of the hydrogen storage tank and battery generated by the genetic algorithm and the actual required mass is expressed as: in, This represents the difference between the assumed mass of the hydrogen storage tank and the power battery and the actual required mass. m ba and m H The masses of the power battery and hydrogen storage tank generated by the genetic algorithm are respectively. m ba ’ For optimized power battery quality, m H ’ For the optimized quality of hydrogen storage tanks.

Images