Hybrid electric vehicle power system parameter matching method based on mass house model

Abstract

The invention is suitable for the technical field of extended-range automobile power, and provides a hybrid automobile power system parameter matching method based on a mass house model, and the method comprises the steps: calculating key parameters of a drive motor, a battery, an APU system and a transmission system; market demand weights are quantified, a quality house model of the R-EEV model is constructed, and technical index priorities are determined. And designing an orthogonal table, calculating parameter range sensitivity and comprehensive performance indexes, and revealing a parameter coupling rule. CPI is used as a target function, optimization variables are determined by combining an orthogonal test result, and a global optimal parameter combination is iteratively solved through a PSO algorithm. After optimization, the comprehensive performance index is improved by 24.24%, the driving range is increased by 39.18%, the cost is reduced by 11.21%, and the dynamic property, the economical efficiency and the practicability are effectively balanced. According to the method, a systematic solution is provided for parameter matching of the hybrid electric vehicle power system through a multi-objective optimization framework driven by market demands, and the method has remarkable engineering application value.

Description

Technical Field

[0001] The present invention belongs to the technical field of extended-range vehicle power technology, and in particular relates to a hybrid vehicle power system parameter matching method based on a house of quality model. Background Art

[0002] With the global energy crisis and the improvement of environmental protection requirements, the extended-range electric vehicle (R-EEV) is a high-efficiency and low-emission new energy vehicle. The rationality of the power system parameter matching directly affects the performance of the entire vehicle.

[0003] Traditional power system parameter matching has problems such as difficulty in multi-objective optimization and high consumption of computing resources. Existing technologies use single-objective optimization or local optimization methods, which are difficult to balance economy, power and cost. Summary of the invention

[0004] The purpose of the present invention is to provide a hybrid vehicle power system parameter matching method based on a house of quality model, aiming to solve the technical problems existing in the prior art identified in the background technology.

[0005] The present invention is implemented as follows: a hybrid vehicle power system parameter matching method based on a quality house model, the method comprising: Step 1: E-REV power system modeling: Based on a hybrid vehicle, determine the basic parameters and performance indicators such as vehicle mass, drag coefficient, maximum speed, and pure electric driving range. Cruise The whole vehicle simulation model is established in the system, including the driver, motor, battery and engine models. Each model calculates the output according to the corresponding formula. For example, the driver model gives the pedal signal according to the speed deviation, and the motor model obtains the output torque by looking up the table.

[0006] Step 2, E-REV power system parameter matching: Calculate the peak power of the drive motor according to the vehicle's dynamic performance indicators, determine the rated power in combination with the motor overload coefficient, determine the rated speed according to the distribution of the motor's operating points under typical working conditions, calculate the maximum speed through parameters such as the maximum vehicle speed, and finally determine the drive motor parameters; match from three aspects: voltage, power, and energy. Determine the battery voltage according to the drive motor parameters, determine the battery power according to the motor and accessory power requirements, determine the battery energy according to the pure electric driving range requirements, and select the power battery parameters based on comprehensive considerations; the engine power must meet the drive motor and accessory power requirements, taking into account efficiency, and is determined through constant speed cruising power calculations and typical working condition power statistical analysis; the generator rated power matches the engine's high efficiency zone. Select appropriate test data to improve the model, and the selected engine and generator power is slightly greater than the calculated value; the simulation results show that the vehicle's maximum climbing grade, acceleration time, and maximum vehicle speed meet the performance indicators, verifying that the drive motor parameter matching is reasonable.

[0007] Step 3. Control strategy design: CD-CS type optimal curve power following strategy: This strategy controls the operation of the range-extending system based on the power battery SOC and the vehicle's required power. When the SOC is high, the vehicle is powered by the power battery; when the SOC is lower than the lower limit or the required power is higher than the battery's rated discharge power, the range-extending system is turned on and the engine operates on the optimal efficiency curve.

[0008] Control strategy simulation analysis: Set the SOC initial value, upper and lower limits, and power follow-up range, and simulate the WLTC working condition. The results show that this strategy can make the battery SOC change slowly, and the charging and discharging currents are in line with the battery characteristics. Although the fuel economy is slightly inferior to the fixed-point control strategy, the average charging current is reduced, the number of engine starts and stops is reduced, and it is more suitable for the current vehicle design parameters.

[0009] Step 4: E-REV power system parameter optimization: Main reduction ratio optimization: Take the power consumption of the drive motor in pure electric mode as the evaluation index, and determine the main reduction ratio range based on the maximum motor speed, maximum vehicle speed, maximum climbing grade and maximum motor torque. The simulation shows that the power consumption is the lowest when the main reduction ratio is 6.2.

[0010] The beneficial effects of the present invention are: 1. Based on Matlab-Simulink and AVL-Cruise The software builds a joint simulation model of the power system. The advantage of Matlab-Simulink lies in its powerful control system modeling and algorithm development capabilities, while AVL-Cruise has an advantage in high-precision modeling of vehicle power systems. The joint use can combine the strengths of both and improve the accuracy and efficiency of simulation.

[0011] 2. Use Quality Function Deployment (QFD) QFD ), based on market demand, establish the quality house model of R-EEV, which can structure demand mapping to avoid missing key requirements; realize cross-domain collaboration and technology coupling, thereby achieving more comprehensive optimization; and Matlab-Simulink and AVL-Cruise Combined with simulation, the key parameter range is output through the quality house to guide the subsequent optimization algorithm design.

[0012] 3. Combine orthogonal experiments with particle swarm optimization to obtain more reliable optimization results more efficiently. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a diagram of the calculation and analysis process of the weight values ​​of automobile performance indicators; Figure 2 This is the model diagram of the quality house; Figure 3 To optimize the flow chart. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0015] A hybrid vehicle power system parameter matching method based on a house of quality model, the method comprising: Step 1: Select and match the vehicle drive motor, power battery, APU system and transmission system, and calculate the parameters of the vehicle components; The drive machine selection preferably selects the internal permanent magnet synchronous motor, and matches its parameters using the following formula: ; In the formula, The power required for the vehicle, is the rolling friction coefficient, is the fully loaded mass of the vehicle, is the acceleration due to gravity, is the road slope angle, is the drag coefficient, is the windward area, is the moment of inertia coefficient. Get the rated power.

[0016] Calculate vehicle dynamics index (maximum speed , Minimum acceleration time from 0 to 100 km / h and the maximum climbing grade at a steady speed of 20 km / h ) and the change in vehicle power demand.

[0017] When the vehicle is running at the maximum speed, the corresponding peak speed of the motor and its relationship with the rated speed are as follows: ; ; In the formula, is the wheel radius, is the base speed of the drive motor, is the total transmission ratio of the transmission system, is the maximum motor speed, is the motor speed ratio, The value range is generally [2, 4]. Get the rated speed.

[0018] The drive motor torque needs to meet the requirements of hill climbing, starting and acceleration. The maximum required torque calculation formula is: ; In the formula, is the maximum required torque, is the system mechanical efficiency. Get the motor torque.

[0019] Power battery selection and parameter matching: Lithium-ion batteries are the ideal choice for current power batteries.

[0020] The maximum power should meet the maximum power requirements of the drive motor and other electrical equipment: ; In the formula, is the maximum output power of the power battery pack; is the average power of vehicle accessories (5kW); is the motor efficiency (value is 0.98); is the power battery discharge efficiency (value is 0.95); get the maximum power.

[0021] The battery capacity is calculated as follows: ; ; In the formula, is the battery capacity / Ah, is the power battery discharge depth (value 0.7), is the rated voltage of the power battery, which is limited by the voltage level of the motor and controller and is 336V. For pure electric driving mileage, is the battery pack energy / kwh.

[0022] APU system parameters: Engine rated power: APU starts to participate in power generation to ensure the normal operation of the vehicle. The maximum power of APU can cover the high-speed driving power demand of the whole vehicle. Considering other auxiliary consumption of the whole vehicle, the rated power range of the APU system selected in the study is [20kW, 60kW].

[0023] Transmission system parameter matching: Maximum gear ratio The inequality satisfied is: ; In the formula, is the wheelbase of the car, is the distance from the center of gravity to the rear axle, and we get scope; Minimum gear ratio The inequality that should be satisfied is: ; Gear The number satisfies the formula: .

[0024] Step 2: Establish a vehicle powertrain simulation model, establish a quality house model based on market demand, and calculate the weight coefficient of each technical performance indicator; like Figure 2 As shown in the figure, a vehicle powertrain simulation model is established based on the software environment to optimize the design and functional verification of various performance characteristics of the vehicle. AVL / Curise , Matlab / Simulink and Insight Completed by software.

[0025] Using the quality function deployment method ( Quality Function Deployment, QFD ), based on market demand, establish the quality house model of R-EEV ( House of Quality Mode, HOQM ), based on the analytic hierarchy process ( Analytic Hierarchy Process, AHP ), Entropy method ( The Entropy method, TEM ) and grey correlation method ( Grey relational analysis, GRA ) Calculate the weights of each technical performance index of R-EEV based on performance requirements, and then determine the weight coefficients of each sub-objective function in the corresponding multi-objective optimization problem. Use market demand instead of experience to quantify the weight value of automobile performance indicators. The calculation and analysis process is as follows Figure 1 As shown in a.

[0026] The study selected dynamic requirements ( Driving Need, N d ), economic needs ( Economic Need, N e ), practical needs ( Practical Need, N p )、Security requirements( Secaring Need, N s ) is the vehicle performance requirement. The eight performance indicators selected are: maximum speed ( ), minimum acceleration time per 100 kilometers ( )、Maximum climbing grade( ), purchase cost ( ), hybrid mode power consumption per 100 kilometers ( ), fuel consumption per 100 kilometers in hybrid mode ( ), maximum driving range in pure electric mode ( ), maximum driving range after the fuel tank is exhausted ( M max / km ).

[0027] Select five models C1-C5 on the market as competitive models and establish the HOQM of R-EEV models. Figure 1As shown in b, first get the basic weight vector of functional demand ξ c , using the entropy method and the competitive vehicle model data to calculate the weight coefficient correction vector c i , and finally obtain the performance requirement weight vector f i。

[0028] Considering the fuzziness and uncertainty of performance requirement information, the hierarchical analysis method is used to determine the weight of vehicle performance requirements oriented by market demand. There are n performance requirements ( N d , N e , N p , N s ), define a market-oriented vehicle performance demand evaluation matrix C= [ c ij ] nn , c ij =C i / C j , c ij × c ji = 1( i,j =1, 2, 3, 4). The quantitative values ​​and meanings of the comparative impact are shown in the following table: ; Find the maximum eigenvalue and eigenvector of matrix C λ max , ξ max , according to the consistency test formula: ; Get consistency test results , the test passed. The feature vector is homogenized to obtain the basic weight vector of functional requirement demand degree ξ i , that is, the basic weight value of the four performance demand indicators is the dynamic demand degree ξ d , Economic needs ξ e , Practical needs ξ p , security requirements ξ s , sorted from large to small. Get the basic weight vectorξ c After that, the technical competitiveness of similar models was compared and the entropy method was used to ξ c Make corrections.

[0029] definition k The evaluation matrix of the four performance requirements of the competing models (C1-C5) is: , For the Competitive Model No. The competitiveness evaluation index of the requirement.

[0030] Potential entropy value reflecting the competitive advantage of vehicle models The calculation is as follows: ; ; Corrected weight coefficient vector Calculated as: ; Final weight coefficient vector for vehicle performance requirements calculate: ; get , , we can see the market demand for various performance indicators.

[0031] Based on the grey relational analysis (GRA), the relationship between each performance requirement and each performance indicator is analyzed, and a coupling model between the two is established to determine the weight of the performance indicator. k Competing products t Item performance indicator information, establish performance indicator competitive evaluation matrix , as shown in the following table: ; Pair Matrix and matrix After dimensionless processing, we get and . Middle The requirement is a reference series , To construct a grey system for comparison series, the technical characteristics and the first Correlation coefficient of item requirements : ; ; The performance requirement-performance indicator correlation matrix is ​​calculated as: ; Get the matrix As shown in the following table.

[0032] ; The performance indicator weight is calculated according to the formula: ; After calculation and analysis, the R-EEV model was finally obtained according to market demand. HOQM。

[0033] The final weight coefficient of vehicle performance index is: , the weight coefficients are sorted from large to small, and the demand weight can be obtained to match the product performance.

[0034] Step 3: Perform orthogonal test, calculate the extreme sensitivity of technical parameters and vehicle comprehensive performance index, and perform variance analysis; like Figure 3 As shown, the experimental exploration is carried out based on the orthogonal experimental method, aiming to conduct sensitivity analysis between design parameters and performance indicators, explore the coupling rules between design parameters, and provide an entry point and optimization basis for parameter optimization based on intelligent algorithms.

[0035] Orthogonal experimental design uses an "orthogonal array" to arrange the experiment. express, is the code for the orthogonal array, is the number of trials, is the number of levels, is the number of factors, where .

[0036] The design parameters selected for the study are: the base speed of the drive motor , Peak Power , speed ratio , Overload factor 、Number of power battery cells , Battery capacity , Engine power Total transmission ratio , control strategy Upper and lower limits , construct an orthogonal experimental table .

[0037] Selecting 10 characteristic parameters can be expressed as: , define the extreme sensitivity of technical parameters , calculated as follows: ; In the formula, Indicates Performance indicators No. Characteristic parameters Very bad.

[0038] According to the performance requirements, a reasonable weight coefficient is selected, and the vehicle comprehensive performance index (ComprehensivePerformanceIndex, CPI ) is calculated as follows: ; In the formula, Representative The weight coefficient of each performance indicator is For the The correlation coefficient between the performance index value and the optimal value.

[0039] The variance analysis was performed based on the orthogonal test results and the calculation was performed according to the following formula: ; ; ; ; ; ; In the formula, is the number of trials, and the variation of the experimental observations is the factor , for The number of replicates of the factor levels, is the correction number, for The sum of squares of the variances of the factors, is the sum of squared errors, for The degrees of freedom of the factor, is the total degrees of freedom, is the error degrees of freedom.

[0040] Step 4: Use the particle swarm algorithm to perform multi-objective optimization on the technical parameters, determine the optimization variable range based on the parameter sensitivity analysis results of the orthogonal test, use the comprehensive performance index as the objective function, set constraints, and conduct joint simulation tests and optimization result analysis.

[0041] During the optimization process, the PSO algorithm initializes a group of particles in the feasible domain. Each particle has three characteristics: position, velocity, and fitness function value. The particle information characteristics are updated by considering its own factors, individual optimality, and global optimality.

[0042] Speed ​​update formula: ; Position update formula: ; In the formula, and It is a particle In the Daidi The velocity and position of the dimensional component, is the inertia weight [0.5,0.9], is the individual learning factor (1.55), is the social learning factor (1.49), represents a random number between 0 and 1, For particles The individual optimal position of For particles The global optimal position of the population is set to 200, and the number of iterations is , termination accuracy .

[0043] The orthogonal experiment results in the influence and sensitivity of each design parameter on CPI and a certain performance index, but the conclusion cannot directly lead to the optimal solution. Combined with the orthogonal experiment results, the optimization design is carried out by comprehensively considering the technical parameters with greater sensitivity. , drive motor speed ratio , overload factor , upper and lower limits of power battery discharge range . Finally select the drive motor power , Battery capacity , Number of battery cells , APU system power and powertrain ratio Five parameters were optimized.

[0044] Define the optimization variable vector as: ; Under multi-dimensional constraints To describe the optimization problem as the goal, it can be expressed as follows: ; In the formula, Represents the minimum comprehensive evaluation index of the control system and defines the objective function , with minimum To optimize the direction.

[0045] In order to lock the search range of the objective function, the optimization constraints are set based on the design objectives, the relevant technical characteristic indicators are used as constraints, and the value range of the optimization variables is set.

[0046] Joint simulation test and optimization results analysis: The results show that the design parameters finally converge to a stable value / range after about 150 iterations, indicating that the optimization algorithm developed in the study works well. Gradually increase, after 160 iterations, Converged to 0.82. After optimization, the comprehensive index increased by 24.24%, and the optimization effect was significant.

[0047] In order to compare the better performance of the design parameter results after optimization based on the PSO algorithm, a simulation test was conducted based on 40 sets of orthogonal test data, and the design parameter group obtained based on the range analysis and the design parameter group obtained based on the PSO algorithm. First, the change rate of vehicle performance indicators before and after optimization was defined. The calculation is as follows: ; In the formula, For the The experimental results of the group ( ), To optimize the results based on the PSO algorithm, Based on the range analysis results.

[0048] The optimization results are summarized in the following table.

[0049] ; It can be seen that most of the vehicle technical performance indicators corresponding to the design parameter group optimized by the PSO algorithm have been improved to a certain extent. The three dynamic indicators have been improved to a certain extent; the purchase cost A significant decrease of 11.21%; power consumption per 100 kilometers decreased, A decrease of about 2.67%; The increase was about 3.75%, mainly due to the increase in APU participation and the increase in comprehensive driving energy consumption; the mileage increased significantly, as shown by The increase was about 39.18%, and the increase in MEV was about 53.64%. The driving range was significantly improved, the cost was significantly reduced, the power performance was also improved to a certain extent, and the comprehensive driving energy consumption increased slightly due to the increased participation of APU.

[0050] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0051] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0052] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0053] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A hybrid vehicle power system parameter matching method based on a quality house model, characterized in that: The method comprises: Select and match the vehicle's drive motor, power battery, APU system and transmission system, and calculate the parameters of vehicle components; Establish a vehicle powertrain simulation model, establish a quality house model based on market demand, and calculate the weight coefficient of each technical performance indicator; Perform orthogonal tests, calculate the extreme sensitivity of technical parameters and vehicle comprehensive performance index, and conduct variance analysis; The particle swarm algorithm is used to perform multi-objective optimization of technical parameters. The optimization variable range is determined based on the parameter sensitivity analysis results of the orthogonal experiment. The comprehensive performance index is used as the objective function, constraints are set, and joint simulation tests and optimization results analysis are carried out.

2. The method according to claim 1, characterized in that: The calculating of the parameters of the vehicle components specifically includes: Calculate the rated power, rated speed, maximum speed and torque of the drive motor according to the required power and dynamic performance indicators of the vehicle; Calculate the maximum power and capacity of the power battery based on the maximum power demand of the drive motor and other electrical equipment and the pure electric driving mileage requirements; Select the configuration scheme of direct injection gasoline engine and permanent magnet motor with motor controller, and determine the rated power range of the engine; Determine the maximum transmission ratio, minimum transmission ratio and number of gears of the transmission system according to vehicle performance requirements; Among them, the maximum transmission ratio Satisfies the inequality: ； In the formula, is the rolling friction coefficient, is the fully loaded mass of the vehicle, is the acceleration due to gravity, is the road slope angle, is the drag coefficient, is the windward area, is the wheelbase of the car, is the distance from the center of gravity to the rear axle, is the maximum required torque, is the system mechanical efficiency, is the wheel radius; Minimum gear ratio Satisfies the inequality: ； in, is the maximum motor speed, is the maximum vehicle speed; Gear The number satisfies the formula: ； in, is the base speed of the drive motor.

3. The method according to claim 1, characterized in that The establishment of a vehicle powertrain simulation model, the establishment of a quality house model based on market demand, and the calculation of the weight coefficients of various technical performance indicators specifically include: Establish a vehicle powertrain simulation model based on simulation software; Adopting the quality function deployment method and based on market demand, the quality house model of R-EEV is established; The weights of various technical performance indicators of R-EEV based on performance requirements are calculated based on the analytic hierarchy process, entropy method and grey correlation method.

4. The method according to claim 3, characterized in that The process of establishing the quality house model includes: Select power requirements, economic requirements, practical requirements, and safety requirements as vehicle performance requirements; The performance indicators include maximum vehicle speed, minimum acceleration time per 100 kilometers, maximum climbing grade, purchase cost, power consumption per 100 kilometers in hybrid mode, fuel consumption per 100 kilometers in hybrid mode, maximum mileage in pure electric mode, and maximum driving range after the fuel tank is exhausted. Select competitive models, establish the quality house model of the R-EEV model, calculate the basic weight vector of functional demand, and use the entropy method to correct it to obtain the performance demand weight vector ; ； ； ； ； in, represents the entropy value reflecting the competitive advantage of the vehicle model, Indicates Competitive Model No. Competitiveness evaluation index of item demand Competitiveness evaluation index, is the modified weight coefficient vector, Indicates The consistency ratio of the requirements, Indicates Competitive Model No. Normalized result of competitiveness evaluation index of each requirement; Analyze the relationship between various performance requirements and performance indicators based on the grey correlation method, establish a coupling model between performance requirements and performance indicators, and determine the weights of performance indicators : ； ； ； ； in, Indicates technical characteristics and The correlation coefficient of the item requirements, represents the performance requirement-performance indicator association matrix, The final weight coefficient vector representing the vehicle performance requirements, is the performance indicator number, is the number of competing products, For the serial number of competing products, Indicates the degree of association, Indicates the comparison sequence elements, The reference sequence elements.

5. The method according to claim 1, characterized in that The calculation of the extreme sensitivity of technical parameters and the comprehensive performance index of the vehicle and the variance analysis specifically include: Select the design parameters of the drive motor and construct an orthogonal experimental table; Calculate the extreme sensitivity of technical parameters and calculate the comprehensive performance index of the vehicle : ； In the formula, Representative The weight coefficient of each performance indicator is For the The correlation coefficient between the performance index value and the optimal value; The orthogonal test results were used for variance analysis.

6. The method according to claim 5, characterized in that The design parameters of the orthogonal test include the base speed, peak power, speed ratio, overload factor, number of power battery cells, battery capacity, engine power, total transmission ratio of the transmission system, and upper and lower limits of the control strategy SoC.

7. The method according to claim 1, characterized in that The optimization of technical parameters by using particle swarm algorithm specifically includes: Initialize a set of particles in the feasible domain, each particle has three characteristics: position, velocity and fitness function value; Update particle information according to the speed update formula and position update formula; Combined with the orthogonal experimental results, the technical parameters with sensitivity higher than the preset threshold are comprehensively considered for optimization, and the drive motor power, battery capacity, number of battery cells, APU system power and power system transmission ratio are selected as optimization parameters; Use relevant technical characteristic indicators as constraints and set the value range of optimization variables; The optimization effect is verified through joint simulation experiments, and the change rate of vehicle performance indicators before and after optimization is analyzed.

8. The method according to claim 7, characterized in that During the optimization process of the particle swarm algorithm, the speed update formula and the position update formula are: Speed ​​update formula: ； Position update formula: ； In the formula, and It is a particle In the Daidi The velocity and position of the dimensional component, is the inertia weight, is the individual learning factor, is the social learning factor, represents a random number between 0 and 1, For particles The individual optimal position of For particles The global optimal position of .

9. The method according to claim 8, characterized in that In verifying the optimization effect through the joint simulation test, the change rate of vehicle performance indicators before and after optimization is defined as: ； In the formula, It represents the change rate of vehicle performance index before and after optimization, For the The experimental results of the group Indicates that based on the range analysis results, This is the optimization result based on PSO algorithm.

Images