Method for obtaining optimal transmission ratio of speed reducer of electric vehicle through combination of Matlab and AMEsim

Abstract

According to the method for obtaining the optimal transmission ratio of the speed reducer of the electric vehicle through the combination of the Matlab and the AMEsim, a vehicle simulation model is established in AMEsim software, vehicle parameters can be conveniently input, and the simulation model is corrected in combination with a test result. The method comprises the following steps: firstly, calculating a value range of transmission ratios according to vehicle parameters, sequentially inputting the transmission ratios in the value range into an AMEsim simulation model through a fixed step size search method to perform vehicle dynamic property simulation, judging acceleration time of a simulation result, screening out a transmission ratio data set which meets dynamic property requirements, and then taking the data set as an initial population to perform dynamic property simulation. According to the method, the grey wolf algorithm is adopted, the lowest energy consumption of the working condition serves as a fitness function, the speed reducer transmission ratio which meets the power performance requirement and has the lowest energy consumption is searched, namely the optimal transmission ratio under the vehicle parameters, complex manual work is omitted, the parameter replaceability is high, when other vehicle parameters are used, only parameters of corresponding modules are changed in AMEsim software, and the method is easy to implement. And a set of standardized transmission ratio optimization process can be formed.

Description

Technical Field

[0001] The invention relates to the technical field of electric vehicles, and in particular to a method for obtaining an optimal transmission ratio of an electric vehicle reducer by combining Matlab with AMEsim. Background Art

[0002] Electric vehicles use fixed gear ratio reducers with a fixed transmission ratio. If the transmission ratio is too large, the wheel output torque is large, but it is difficult to reach the target maximum speed; if the transmission ratio is too small, the wheel output torque is insufficient. In addition, the size of the transmission ratio is related to the working efficiency of the motor, which affects the energy consumption of the whole vehicle. Therefore, it is necessary to find a transmission ratio that can meet the requirements of the dynamic index and minimize the energy consumption of the whole vehicle. In addition, in the early stage of project development, the dynamic and economic indicators are often not met, which requires coordination with relevant departments to optimize parameters such as curb weight or wind resistance coefficient, or even replace the electric drive assembly, so the transmission ratio also needs to be continuously optimized.

[0003] 1. The development of electric vehicle power and economy requires the design of the main parameters of the vehicle, such as curb weight, drag coefficient, reducer transmission ratio, battery power, motor power and torque, so that the vehicle's power meets the project requirements, such as 0-50, 0-100, 50-100 acceleration time. These vehicle parameters affect the entire system. Once a parameter changes, it needs to be recalculated, which is a heavy task and delays the progress of project development. It is difficult to quickly form competitiveness in the context of the rapid iteration of electric vehicle battery and motor performance.

[0004] 2. Electric vehicles must meet not only the dynamic index but also the economic index, that is, the power consumption per 100 kilometers of the vehicle cycle is less than the target value, which is generally the CLTC cycle. The economic index is difficult to meet in the early stage, and it is necessary to pull the relevant departments to optimize the energy consumption of the whole vehicle, such as reducing the energy consumption of the thermal management system and the low-voltage system, so as to achieve the energy consumption index of the whole vehicle. Therefore, when selecting the transmission ratio, it is only necessary to consider the optimal energy consumption. Even if the optimal transmission ratio still cannot meet the energy consumption index, it can be further optimized by pulling the relevant departments.

[0005] 3. In the current development of power and economy, the transmission ratio is selected mostly by experience selection, use of related vehicle series, theoretical calculation and other methods. The experience selection method relies on the experience of developers and has uncertainty. When using the related vehicle series method, the parameters such as the curb weight and rolling resistance coefficient of different vehicle series are inconsistent. Selecting the same transmission ratio may not necessarily make the motor operating point in the high-efficiency zone. The theoretical calculation method has cumbersome formulas and poor interchangeability. Due to the uncertainty of the vehicle transmission system and mechanical losses in the power transmission process, there is a gap between theory and practice. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a method for obtaining the optimal transmission ratio of an electric vehicle reducer by combining Matlab with AMEsim, which omits complex calculations, has strong model replaceability and scalability, and is accurate in optimizing.

[0007] To achieve the above technical purpose, the adopted technical solution is: a method for obtaining the optimal transmission ratio of an electric vehicle reducer by combining Matlab with AMEsim, the method comprising: Step 1: Input vehicle parameters into AMEsim software to establish vehicle simulation model; Step 2: Calculate the range of reducer transmission ratios required to meet the maximum vehicle speed and maximum slope starting according to the power system parameters, and then use Matlab software to output the reducer transmission ratios from the range to the vehicle simulation model in sequence; Step 3: When the vehicle simulation model receives the transmission ratio of the reducer, a control signal that the accelerator pedal opening is 1 and the brake pedal opening is 0 is input into the vehicle simulation model, and the simulation is started. The acceleration time of each speed interval is output by the simulation, and compared with the target time. If the acceleration time of each speed interval is less than the target acceleration time, the current transmission ratio value is stored in the data set A, and the transmission ratio value is increased by the fixed step search method to find the next transmission ratio that meets the requirements until all the transmission ratio values ​​within the transmission ratio value range of the reducer are selected; Step 4: In the Matlab software, the data set A is used as the initialization population of the gray wolf algorithm, the minimum vehicle energy consumption is used as the fitness function, the transmission ratio is output to the vehicle simulation model, the vehicle simulation model is driven to perform batch simulation of the CLTC working condition, and the vehicle energy consumption corresponding to each transmission ratio in the data set A is returned to the gray wolf algorithm of Matlab, the fitness of each transmission ratio is generated, and the fitness of each transmission ratio is sorted from large to small, and the transmission ratio with large fitness is selected for iterative optimization until the maximum number of iterations is reached, the optimization operation is terminated, and the transmission ratio with the largest fitness is output.

[0008] Furthermore, the specific implementation of step 1 is as follows: Step 1.1, parameter confirmation: determine the vehicle parameters of the initial prototype, and all parameters are consistent with the initial prototype; Step 2.2, model construction: establish a vehicle simulation model connected to the corresponding simulation interface in AMEsim, and input the determined vehicle parameters into each module in the vehicle simulation model; Step 2.3, simulation settings: perform dynamic and economic simulations. The simulation parameters should be consistent with the test conditions of the prototype vehicle. During the prototype vehicle test, the thermal management system should be turned on and off for comparison, and the energy consumption of the thermal management system should be measured. At the same time, the low-voltage system should be kept stable. The low-voltage power consumption during stability should be measured by a power meter, and the thermal management system energy consumption and low-voltage energy consumption should be added to the vehicle simulation model. Step 2.4, model correction: Based on the simulation results, compare with the prototype vehicle test results, calculate the error between the simulation and the test. If the error requirements are met, the simulation model is proven to be accurate and the vehicle simulation model is output. If the error requirements are not met, based on the test results, focus on determining the motor torque and power loss other than the reducer, correct the vehicle simulation model, re-simulate until the error requirements are met, and output the vehicle simulation model.

[0009] Furthermore, vehicle parameters for establishing a vehicle simulation model include curb weight, tire size, road resistance coefficient, motor characteristic diagram, motor efficiency diagram, battery capacity, and battery internal resistance curve.

[0010] Furthermore, the specific implementation of step 4 is as follows: Step 4.1, population initialization: Use the above-obtained data set A as the initial population of the gray wolf algorithm, initialize it, set the population size to n, and the maximum number of iterations to t max ; Step 4.2, individual fitness calculation: the fitness of all individuals in the population is obtained by running the vehicle simulation model; Step 5.3, wolf pack screening: the three wolves with the highest fitness and the remaining individuals are distinguished as ω wolves, and they are stratified from large to small according to fitness; Step 5.4, search for prey: After the hierarchical stratification is completed, the positions of the three wolves with the highest fitness and the ω wolf are initialized and analyzed, the positions of the three wolves with the highest fitness relative to the prey are defined, and then the prey is tracked and its own position is updated; Step 5.5, surround the prey: In the process of the three wolves with the highest fitness surrounding the prey, the relationship model between the three wolves with the highest fitness and the prey is established through the distance between the wolf pack composed of the three wolves with the highest fitness and the wolf pack composed of the rest of the wolves, and the distance between the three wolves with the highest fitness and the prey. The specific formula is X(t+1)=X p (t)-AD p A=2ar-a Where A represents the encirclement distance. When |A|≥1, it means that the gray wolf is searching globally. When |A|<1, it means that the gray wolf is searching nearby. X(t+1) represents the positions of the three wolves with the highest fitness in the next iteration. D p is the distance between the three wolves with the highest fitness and their prey, X p (t) is the prey position at this time, r is any random number between 0 and 1; Step 5.5, attack prey: During the final hunting activity, the leader α wolf discovers the location of the prey, deploys hunting strategies, and leads the entire gray wolf population to hunt and obtain the final optimal solution.

[0011] The beneficial effects of the present invention are: 1. AMEsim physical simulation software has become one of the main choices for vehicle dynamics and economy simulation due to its high accuracy, openness, modularity and large number of physical simulation types. Using Matlab and AMEsim to match the optimal transmission ratio, relying on the powerful simulation capabilities of AMEsim software, a large number of complex calculations are omitted, and the dynamics and economy indicators and related parameters of electric vehicles with the current transmission ratio can be obtained. It is also highly open and comes with a Matlab joint simulation interface, and the optimization target can be optimized using Matlab's algorithm.

[0012] 2. The model is highly replaceable and extensible. If you need to change the vehicle parameters or improve the model structure, you only need to change it in the corresponding module and add the corresponding module. There is no need to modify it one by one in the complicated formulas. The software has a custom post-processing function that supports customization, which is convenient for processing the simulation results and obtaining the required vehicle performance parameters.

[0013] 3. Based on the improved Grey Wolf optimization algorithm, the control parameters of the algorithm are improved to achieve global optimization and rapid convergence. The transmission ratio data set that meets the dynamic requirements is optimized with the lowest energy consumption. While ensuring the accuracy of the optimization, the algorithm is simple and the calculation is fast, which reduces the threshold for use.

[0014] 4. First, select the transmission ratio data set that meets the dynamic requirements, and then select the optimal energy consumption transmission ratio from it, so as to obtain the results of each process and form a standardized process method for analysis and optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of the present invention; Figure 2 This is a vehicle simulation model diagram of the present invention; Figure 3 Establishing a flow chart for the vehicle fax model of the present invention; Figure 4 is a screening graph of the data set A of the present invention; Figure 5 This is a comparison chart of the changes of the improved control parameters of the present invention and the existing control parameters; Figure 6 This is a comparison chart of the prediction optimization results of the present invention; Figure 7 This is a flow chart of the Grey Wolf algorithm for finding the optimal transmission ratio. DETAILED DESCRIPTION

[0016] The preferred embodiments of the invention are given below in conjunction with the accompanying drawings to explain the technical solution of the present invention in detail. Here, the corresponding drawings are given to explain the present invention in detail. It should be particularly noted that the preferred embodiments described here are only used to illustrate and explain the present invention, and are not used to limit or restrict the present invention.

[0017] A method for obtaining the optimal transmission ratio of an electric vehicle reducer by combining Matlab with AMEsim. Matlab and AMEsim are used to match the optimal transmission ratio. AMEsim itself has a supported simulation interface, and the accuracy of the simulation can be guaranteed. In addition, if the initial battery and motor parameters do not meet the power or economy requirements, they can also be quickly changed to perform the following matching process without redundant calculations, and the modules are highly replaceable. The specific implementation steps are: Step 1: Build a vehicle simulation model Enter the vehicle parameters in the AMEsim software, including curb weight, tire size, road resistance coefficient (a, b, c), motor characteristic diagram (motor power torque curve with torque), motor efficiency diagram, battery capacity, capacitor internal resistance curve, etc., to establish a vehicle simulation model, which includes a driver module, battery module, VCU module, motor module and vehicle module, etc. The specific implementation method is as follows: An accurate simulation model is the basis for finding the optimal transmission ratio. Therefore, the first step is to modify the simulation model in combination with the test results of the prototype vehicle, focusing on determining the motor torque and power loss other than the reducer, simulating the mechanical loss of the whole vehicle, and making the results of the simulation model as accurate as possible to meet the error requirements, such as Figure 3 As shown, the specific process is as follows: 1) Parameter confirmation. Determine the vehicle parameters of the initial prototype, including curb weight, tire size, road resistance coefficient (a, b, c), motor power torque characteristic diagram and efficiency diagram, battery capacity and battery internal resistance curve, etc. It is necessary to ensure that all parameters are consistent with the initial prototype.

[0018] 2) Model building. Build a vehicle simulation model in AMEsim and connect the corresponding electrical, mechanical and other simulation interfaces. The model includes driver module, battery module, VCU module, motor module and vehicle module. Each module inputs the determined vehicle parameters as follows Figure 2 shown.

[0019] The energy released by the battery is provided to the low voltage, thermal management system and electric drive system of the whole vehicle. The electric drive system requires a lot of power during the acceleration process. When the power demand of the whole vehicle exceeds the current maximum discharge power of the battery, the BMS system will limit the power output to protect the battery safety, thereby limiting the power of the electric drive system and affecting the acceleration capability. Therefore, the low-voltage side power and thermal management system power modules are added. The power of both is measured by the initial prototype through experiments. The BMS system inputs the maximum charge / discharge power curve of the battery, receives the current battery voltage, current, soc and temperature signals, and queries the maximum charge / discharge power allowed by the current battery through the current battery temperature and soc. After subtracting the low-voltage power and the thermal management system power, it is the power that the electric drive system can obtain, and then divided by the battery voltage to obtain the maximum charge / discharge current allowed by the current battery. Compare it with the current battery current, if the current current exceeds the maximum charge / discharge current, the battery is limited to discharge at the maximum discharge current, thereby limiting the power output of the electric drive system, and truly restores the power limitation during the acceleration process of the real vehicle, so that the simulated acceleration performance is more accurate and more meaningful for reference. At the same time, a thermal model is established for the battery body to simulate the battery discharge process of the vehicle's acceleration. The vehicle's thermal management system dissipates heat to make the battery temperature of the simulation model more accurate for management by the BMS system.

[0020] 3) Simulation settings. Carry out dynamic and economic simulations. The simulation parameters, especially the parameters of the whole vehicle and the ambient temperature, must be consistent with the test conditions of the prototype vehicle. Dynamic simulation involves the thermal management system and low-voltage power, while economic simulation does not. The prototype vehicle needs to conduct comparative tests of turning on and off the thermal management system to determine the energy consumption of the thermal management system. At the same time, the low-voltage system must be kept stable. The low-voltage energy consumption is measured by a power meter, and the thermal management system energy consumption and low-voltage energy consumption are added to the simulation model to reduce the error between the simulation and the actual prototype vehicle test.

[0021] 4) Model correction. According to the simulation results, compare with the test results of the prototype vehicle, calculate the error between the simulation and the test, if the error requirements are met, it proves that the vehicle simulation model is accurate and output; if the error requirements are not met, then according to the test results, focus on determining the motor torque and power loss other than the reducer, correct the vehicle simulation model, re-simulate until the error requirements are met, and output the simulation model.

[0022] Step 2: Preliminarily determine the transmission ratio range According to the power system parameters, the range of reducer transmission ratios required to meet the maximum vehicle speed and maximum slope starting is calculated through theoretical calculation, and then the Matlab software outputs the reducer transmission ratios from the range to the vehicle simulation model in sequence.

[0023] Step 3: Store the transmission ratio data that meets the dynamic requirements Carry out vehicle dynamics simulation in the vehicle simulation model, input control signals of accelerator pedal opening 1 and brake pedal opening 0 into the vehicle simulation model, start simulation, and output acceleration time of each speed interval of 0-50, 50-80, 60-100, 80-120, 0-100, and compare with the target time. If the acceleration time of each speed interval is less than the target acceleration time, store the current transmission ratio value in data set A and return it, increase the transmission ratio value by fixed step search method, and find the next transmission ratio that meets the requirements.

[0024] From the above, we can obtain the transmission ratio data set A that meets the vehicle dynamics requirements. Next, we use the Grey Wolf algorithm in data set A to find the transmission ratio with the lowest energy consumption under the CLTC condition.

[0025] Generally, it is not necessary to find the optimal transmission ratio for dynamics, but only to meet the conditions. Therefore, this part uses the fixed step search method to select the transmission ratio values ​​that meet the dynamic conditions within the determined transmission ratio range, and output them to data set A for storage. In this way, data set A contains transmission ratio values ​​that meet the dynamic conditions. Figure 4 As shown, the specific process is as follows: 1) Determine the parameters. Determine the vehicle parameters, including curb weight, tire size, road resistance coefficient (a, b, c), motor characteristic diagram and efficiency diagram, battery capacity and internal resistance curve, etc.

[0026] 2) Determine the transmission ratio range [i min ,i max If the transmission ratio is too small, the wheel end torque will be insufficient and the vehicle will not be able to start. If the transmission ratio is too large, it will be difficult to reach the designed maximum speed. Therefore, the transmission ratio needs to meet the following conditions: Where m is the vehicle's curb mass, g is the acceleration of gravity, f is the rolling resistance coefficient, and i is max is the maximum climbing grade designed for the vehicle, r is the rolling radius, T max is the peak torque of the motor, η T is the transmission efficiency, n max is the peak speed of the motor, v max Designed for maximum vehicle speed.

[0027] 3) Fixed-step search. Write a fixed-step search program in Matlab software, using the i determined above min is the initial transmission ratio, which is output to the vehicle simulation model in AMEsim software for dynamic simulation.

[0028] 4) Simulation result judgment. Compare the simulated acceleration time with the judgment condition. If the judgment condition is met, output the current transmission ratio value and return to the fixed step search method to execute the i=i+0.1 operation. If not, return directly and execute the i=i+0.1 operation. The fixed step used is 0.1.

[0029] 5) Output the result. Until the transmission ratio is greater than i max When , Matlab outputs the command to end the simulation. The obtained data set A is the set of all transmission ratios that meet the dynamic conditions.

[0030] Step 4: Grey Wolf Algorithm Optimization In Matlab software, data set A is used as the initialization population of the Grey Wolf Algorithm, the minimum vehicle energy consumption is used as the fitness function, the transmission ratio is output to the vehicle simulation model, the vehicle simulation model is driven to perform batch simulation of CLTC working conditions, and the vehicle energy consumption corresponding to each transmission ratio is returned to the Grey Wolf Algorithm of Matlab to generate the fitness of each transmission ratio for optimization.

[0031] According to the fitness of each transmission ratio returned, the fitness is sorted from large to small, and the transmission ratio with large fitness is selected for iterative optimization until the maximum number of iterations is reached. The optimization operation is terminated and the transmission ratio with the largest fitness is output, which is the transmission ratio with the best economy while meeting the dynamic requirements.

[0032] Based on the Gray Wolf Algorithm, the transmission ratio of electric vehicles is optimized with a single objective to obtain a transmission ratio that meets the dynamic conditions and has the lowest energy consumption. The Gray Wolf Algorithm is inspired by observing some of the life behaviors of wolves, and then imitates their tracking, encircling, hunting, and attacking behaviors, making optimization possible. The main features of the Gray Wolf Algorithm are as follows: ① Simple principle; ② Few parameters to adjust; ③ Easy to implement; ④ Strong global search capability. Figure 7 As shown, the specific process is as follows: 1) Population initialization. The above-obtained data set A is used as the initial population of the gray wolf algorithm for initialization. The population size is set to n and the maximum number of iterations is set to t. max .

[0033] 2) Calculation of individual fitness. The fitness of individuals in the population is obtained by running the vehicle simulation model. The fitness function of each individual is expressed by the following formula: min f(i)=W loss(i) -W reg(i) (1) Where W loss(i) is the total energy consumption of individual motors, W reg(i) The energy recovery of individual motors can be directly obtained from the power sensor in AMEsim.

[0034] 3) Wolf pack screening. The three individuals with the highest fitness are defined as a wolf, β wolf and δ wolf, and the remaining individuals are ω wolves. A wolf is one level, β wolf is one level, δ wolf is one level, and ω wolf is one level, that is, four levels.

[0035] 4) Searching for prey. After the hierarchical stratification is completed, the positions of wolf a, wolf β, wolf δ, and wolf ω are initialized and analyzed, and the positions of wolf a, wolf β, and wolf δ relative to the prey are defined. Then wolf a, wolf β, and wolf δ track the prey and update their own positions at the same time. Where t is the number of iterations, X(t) represents the positions of wolf a, wolf β, and wolf δ at the tth iteration, and X p (t) is the prey position at this time, D p is the distance between wolf a, wolf β, wolf δ and their prey, r 1 Any random number between 0 and 1.

[0036] 5) Surrounding the prey. In the process of the gray wolf surrounding the prey, the relationship model between the wolf a, wolf β, wolf δ and the prey is established through the distance between the wolf pack composed of wolf a, wolf β, wolf δ and the wolf pack composed of the rest of the ω wolves, and the distance between wolf a, wolf β, wolf δ and the prey. X(t+1)=X p (t)-AD p (3) A=2ar 2 -a (4) Among them, A represents the encirclement distance, X(t+1) represents the positions of the three wolves with the highest fitness in the next iteration, and r 2 is any random number between 0 and 1. The control parameter a decreases linearly with the increase of the number of algorithm iterations. When |A|≥1, it means that the wolf is searching globally, and when |A|<1, it means that the wolf is searching nearby. The random initialization of A and C ensures that the wolf can easily reach the global optimal position during the search process.

[0037] It can be seen that the encirclement distance A is greatly affected by the control parameter a. The optimization process of the gray wolf algorithm is essentially the process in which a gradually decreases from 2 to 0 as the number of iterations increases, corresponding to the process of the gray wolf preying on prey. As can be seen from formula (5), a decreases linearly with the increase in the number of iterations. When solving practical problems, the complexity and diversity of the problem are ignored, especially when solving multi-extreme optimization problems, premature phenomena occur, especially when finding the minimum energy consumption transmission ratio, which is easy to fall into the local optimum. When the value of a is large, |A|≥1, the algorithm has a strong global search ability; when the value of a is small, |A|<1, the algorithm has a strong local search ability.

[0038] Therefore, the nonlinear characteristics of the exponential function are used to improve the value function of the control parameter a, as shown in the following equation (6), so that a takes a larger value when the number of iterations is small, so as to facilitate global optimization, and a takes a smaller value when the number of iterations is large, so as to converge quickly. Figure 5 As shown, it can be seen that when the number of iterations is small, the improved a value is large, and when the number of iterations is large, the a value decreases rapidly, corresponding to Figure 6 As shown in the figure, the conventional Grey Wolf Algorithm searches for the optimal solution quickly in the early stage of iteration, but fails to find the optimal solution in the later stage and falls into local convergence. The improved Grey Wolf Algorithm searches for the optimal solution globally in the early stage and quickly approaches the optimal solution in the later stage, with the lowest energy consumption. 6) Attack the prey. During the final hunting activity, the leader α wolf finds the location of the prey, deploys the hunting strategy, and leads the entire gray wolf population to hunt. The movement direction and step length of other wolves approaching the target are updated as shown in the following formula: Among them, D a , D β , D δ represents the distance between wolf α, wolf β and wolf δ and the bottom wolf ω in the t-th iteration process, X is the current position of wolf ω, and X 1 , X 2 , X 3 A represents the direction and distance that the ω wolf moves toward the α, β, and δ wolves, respectively. 1 , A 2 , A 3 Represents three random numbers, C 1 , C 2 , C 3 Take any random number between 0 and 1. ω (t+1) is the position of ω wolf in the next iteration after it is led by α wolf, β wolf and δ wolf.

[0039] The gray wolf has strong global capabilities through strategies such as speed variation, search radius change, position update, and random changes in parameters A and C, which enables the gray wolf to search for the optimal solution or suboptimal solution in the global scope. In the solution process, the algorithm continuously repeats the above three steps of searching, encircling and attacking until the maximum number of iterations is reached and the final optimal solution is obtained.

[0040] The above are only preferred embodiments of the present invention and are not intended to limit or restrict the present invention. For researchers or technicians in this field, the present invention may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection declared by the present invention.

Claims

1. A method for obtaining the optimal transmission ratio of an electric vehicle reducer by combining Matlab with AMEsim, characterized in that: The method comprises: Step 1: Input vehicle parameters into AMEsim software to establish vehicle simulation model; Step 2: Calculate the range of reducer transmission ratios required to meet the maximum vehicle speed and maximum slope starting according to the power system parameters, and then use Matlab software to output the reducer transmission ratios from the range to the vehicle simulation model in sequence; Step 3: When the vehicle simulation model receives the transmission ratio of the reducer, a control signal that the accelerator pedal opening is 1 and the brake pedal opening is 0 is input into the vehicle simulation model to start simulation. The simulation outputs the acceleration time of each speed interval and compares it with the target time. If the acceleration time of each speed interval is less than the target acceleration time, the current transmission ratio value is stored in data set A, and the transmission ratio value is increased by a fixed step search method to find the next transmission ratio that meets the requirements until all transmission ratio values ​​within the transmission ratio value range of the reducer are selected; Step 4. In Matlab software, use data set A as the initialization population of the Grey Wolf Algorithm, use the minimum vehicle energy consumption as the fitness function, output the transmission ratio to the vehicle simulation model, drive the vehicle simulation model to perform batch simulation of the CLTC working condition, return the vehicle energy consumption corresponding to each transmission ratio in data set A to the Grey Wolf Algorithm of Matlab, generate the fitness of each transmission ratio, sort the fitness of each transmission ratio from large to small, select the transmission ratio with large fitness for iterative optimization, until the maximum number of iterations is reached, end the optimization operation, and output the transmission ratio with the largest fitness.

2. The method of obtaining the optimal transmission ratio of an electric vehicle reducer by using Matlab and AMEsim as claimed in claim 1, characterized in that: The specific implementation of step 1 is: Step 1.1, parameter confirmation: determine the vehicle parameters of the initial prototype, and all parameters are consistent with the initial prototype; Step 2.2, model building: establish a vehicle simulation model connected to the corresponding simulation interface in AMEsim, and input the determined vehicle parameters into each module in the vehicle simulation model; Step 2.3, simulation settings: perform dynamic and economic simulations. The simulation parameters should be consistent with the test conditions of the prototype vehicle. During the prototype vehicle test, the thermal management system should be turned on and off for comparison, and the energy consumption of the thermal management system should be measured. At the same time, the low-voltage system should be kept stable. The low-voltage power consumption during stability should be measured by a power meter, and the thermal management system energy consumption and low-voltage energy consumption should be added to the vehicle simulation model. Step 2.4, model correction: According to the simulation results, compare with the test results of the prototype vehicle, calculate the error between the simulation and the test, if the error requirements are met, it proves that the simulation model is accurate, and output the vehicle simulation model; If the error requirement is not met, the motor torque and power loss other than the reducer are determined based on the test results, the vehicle simulation model is corrected, and the simulation is performed again until the error requirement is met, and the vehicle simulation model is output.

3. A method for obtaining the optimal transmission ratio of an electric vehicle reducer by using Matlab and AMEsim as claimed in claim 1 or 2, characterized in that: The vehicle parameters for establishing the vehicle simulation model include curb weight, tire size, road resistance coefficient, motor characteristic diagram, motor efficiency diagram, battery capacity and battery internal resistance curve.

4. The method for obtaining the optimal transmission ratio of an electric vehicle reducer by using Matlab and AMEsim as claimed in claim 1, characterized in that: The specific implementation of step 4 is as follows: Step 4.1, population initialization: Use the above-obtained data set A as the initial population of the gray wolf algorithm, initialize it, set the population size to n, and the maximum number of iterations to t max ; Step 4.2, individual fitness calculation: the fitness of all individuals in the population is obtained by running the vehicle simulation model; Step 5.3, wolf pack screening: distinguish the three wolves with the highest fitness and the remaining individuals as ω wolves, and stratify them according to fitness from large to small; Step 5.4, search for prey: After the hierarchical stratification is completed, the positions of the three wolves with the highest fitness and the ω wolf are initialized and analyzed, the positions of the three wolves with the highest fitness relative to the prey are defined, and then the prey is tracked and its own position is updated; Step 5.5, surround the prey: In the process of the three wolves with the highest fitness surrounding the prey, the relationship model between the three wolves with the highest fitness and the prey is established through the distance between the wolf pack composed of the three wolves with the highest fitness and the wolf pack composed of the rest of the wolves, and the distance between the three wolves with the highest fitness and the prey. The specific formula is X(t+1)=X p (t)-AD p A=2ar-a Where A represents the encirclement distance, X(t+1) represents the position of the three wolves with the highest fitness in the next iteration, when |A|≥1, it means that the gray wolf is searching globally, and when |A|<1, it means that the gray wolf is searching nearby, D p is the distance between the three wolves with the highest fitness and their prey, X p (t) is the prey position at this time; Step 5.5, attack prey: During the final hunting activity, the leader α wolf discovers the location of the prey, deploys hunting strategies, and leads the entire gray wolf population to hunt and obtain the final optimal solution.

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