Vehicle motor parameter identification method based on improved wolf pack algorithm and related device

Abstract

The invention provides a vehicle motor parameter identification method based on an improved wolf pack algorithm and a related device, and the improved wolf pack algorithm provided by the invention introduces a novel population initialization method, so that generated wolf packs can be distributed in a solution space as widely as possible; besides, by applying an improved search position updating mechanism, the wolf pack can dynamically adjust the step length according to the environment quality, so that the global exploration range is expanded at the initial stage of identification, local fine search is accelerated when parameters are close to real values, the contradiction between the convergence speed and the precision can be effectively balanced, and the algorithm robustness is improved.

Description

Technical Field

[0001] This invention belongs to the field of automotive motor parameter identification, and relates to a method and related device for automotive motor parameter identification based on an improved wolf pack algorithm. Background Technology

[0002] The operating environment of automotive motors is complex and variable. For example, in electric vehicles, motors may experience frequent start-stop, acceleration, deceleration, and sudden load changes, leading to dynamic changes in motor parameters. Traditional offline parameter testing methods cannot adapt to this time-varying characteristic, while online parameter identification technology can track parameter changes in real time, making the control system more robust. Furthermore, during mass production, due to manufacturing tolerances and material variations, the parameters of different motors may vary. Using uniform nominal parameters may result in poor control performance for some motors. Parameter identification allows for personalized calibration of each automotive motor, improving product consistency and reliability.

[0003] In the field of motor parameter identification, traditional methods such as least squares or gradient descent have some applications, but they still have some significant limitations. These methods typically rely on specific mathematical models, such as equivalent circuit models or state-space equations, while the actual operating conditions of motors are often complex and variable, limiting the accuracy and applicability of traditional identification methods. For example, motor parameters may change with factors such as temperature, magnetic saturation, and aging, but traditional methods often assume that the parameters are constant, leading to inaccurate identification results under dynamic conditions. Furthermore, the nonlinear characteristics of motors, such as hysteresis and eddy current losses, are difficult to describe accurately with simple linear models, and traditional identification methods perform poorly when dealing with these nonlinear problems.

[0004] Compared to traditional identification methods, the wolf pack algorithm, as a novel swarm intelligence optimization algorithm, demonstrates significant advantages in parameter identification of permanent magnet synchronous motors due to its unique group cooperation mechanism. This algorithm simulates the social hierarchy and cooperative hunting behavior of a wolf pack, achieving efficient searching of complex parameter spaces through the division of labor among three roles: alpha wolf leading, aggressive wolves attacking, and lurking wolves. In the nonlinear optimization problem of multi-parameter motor identification, the wolf pack algorithm exhibits stronger global optimization capabilities than traditional gradient-based algorithms. Especially when dealing with strong coupling relationships between motor parameters, the algorithm effectively avoids premature convergence and finds parameter combinations closer to the true values ​​through information sharing among the wolves. This distributed search characteristic of swarm intelligence greatly reduces the algorithm's dependence on initial parameter settings, providing convenience for practical engineering applications.

[0005] While conventional wolf pack algorithms possess the advantages of swarm intelligence, they are prone to premature convergence or insufficient search efficiency when dealing with highly dynamic and strongly nonlinear parameter identification problems such as automotive motors due to fixed step size and a single leader mechanism. Summary of the Invention

[0006] In view of the defects and shortcomings of the existing technology, the purpose of this invention is to propose a method and related device for identifying automotive motor parameters based on an improved wolf pack algorithm, so as to solve the problems of premature convergence or insufficient search efficiency in the motor parameter identification process of conventional wolf pack algorithms.

[0007] This invention is achieved through the following technical solution: In a first aspect, the present invention provides a method for identifying automotive motor parameters based on an improved wolf pack algorithm, comprising: S1, collect current, voltage, and speed data of the vehicle motor; initialize the wolf pack based on the parameters to be identified of the vehicle motor, and the wolf pack... n individual The d Dimensional elements Given by the following formula:

[0008] in, Indicates will and Perform an XOR operation; the range of values ​​for the positive integer X must satisfy the following condition: , Indicates the lower limit and upper limit Take a random number between them; This is a binary bitwise XOR operator; This is the bitwise XOR operator for binary data. Indicates the first n The first individual wolf in the pack's response to the vehicle's motor d The initial estimated value of each parameter to be identified; N is the number of individuals in the wolf pack; D represents the number of vehicle motor parameters to be identified stored by each individual in the wolf pack. S2. Based on the estimated values ​​of the parameters to be identified by individual wolves for the vehicle motor and the collected current, voltage and speed data, the evaluation index of the vehicle motor is obtained, and the fitness of all individuals in the wolf pack is obtained based on the evaluation index. S3, update the search position of each individual based on the fitness of all individuals in the wolf pack; S4, dynamically adjusts the optimization area based on the updated search position of each individual; S5, determine whether the maximum number of optimization steps has been reached. If so, obtain the individual with the smallest fitness, and use the values ​​corresponding to each dimension of this individual as the identification values ​​of the vehicle motor parameters. Otherwise, return to S2.

[0009] Preferably, in S1, the parameters to be identified for the vehicle motor are one or more of the following: stator resistance, d-axis inductance, q-axis inductance, and flux linkage.

[0010] Preferably, in S2, the evaluation metrics include one or more of tracking error, overshoot percentage, delay time, rise time, peak time, and settling time.

[0011] Furthermore, in S2, the fitness is calculated using the following formula:

[0012] in, , , , , and These are the tracking error, overshoot percentage, delay time, rise time, peak time, and settling time of the vehicle motor, obtained through motor simulation experiments based on the estimated values ​​of the parameters to be identified by individual wolves and the collected current, voltage, and speed data. , , , , , They are respectively , , , , and The corresponding weighting coefficient; t is the test time for the vehicle motor.

[0013] Preferably, in S3, the search position is updated using the following method:

[0014]

[0015] in, and Individuals The velocity and position vectors at step i; and Individuals The new velocity and position vectors at step i+1; and Individuals up to time i The historical advantages of wolves and the historical advantages of wolf packs; and The inertial weight represents the contribution of the individual's position and velocity at previous moments to the next optimization. Let be a random variable with a standard normal distribution; Let be a random variable with a uniform distribution on the interval [-1, 1]. This is the bitwise XOR operator for binary data. For the i-th individual The corresponding fitness level.

[0016] Preferably, in S4, the optimization region is adjusted using the following method:

[0017]

[0018]

[0019] in, To perform individual operations at step i+1 Gradient estimation; This is a binary bitwise XOR operator; and For individuals at steps i and i+1 respectively The correction vector is used to optimize the optimization region; Indicates the intermediate vector Transpose; Let be a random variable that has a uniform distribution on the interval [0,1].

[0020] Secondly, the present invention provides a vehicle motor parameter identification system based on an improved wolf pack algorithm, comprising: The data acquisition module is used to collect current, voltage, and speed data of the vehicle motor; The initialization module is used to initialize the wolf pack based on the parameters to be identified from the vehicle motor. The wolf pack consists of the first... n individual The d Dimensional elements Given by the following formula:

[0021] in, Indicates will and Perform an XOR operation; the range of values ​​for the positive integer X must satisfy the following condition: , Indicates the lower limit and upper limit Take a random number between them; This is a binary bitwise XOR operator; This is the bitwise XOR operator for binary data. Indicates the first n The first individual wolf in the pack's response to the vehicle's motor d The initial estimated value of each parameter to be identified; N is the number of individuals in the wolf pack; D represents the number of vehicle motor parameters to be identified stored by each individual in the wolf pack. The optimization module is used to obtain the evaluation index of the vehicle motor based on the estimated values ​​of the parameters to be identified by individual wolves and the collected current, voltage and speed data. Based on the evaluation index, the fitness of all individuals in the wolf pack is obtained. The search position of each individual is updated based on the fitness of all individuals in the wolf pack. The optimization area is dynamically adjusted based on the updated search position of each individual. The judgment module is used to determine whether the maximum number of optimization steps has been reached. If so, the individual with the smallest fitness is obtained, and the values ​​corresponding to each dimension of this individual are used as the identification values ​​of the vehicle motor parameters. Otherwise, the algorithm returns to the optimization module.

[0022] Preferably, updating the search position of each individual based on the fitness of all individuals in the wolf pack specifically involves:

[0023]

[0024] in, and Individuals The velocity and position vectors at step i; and Individuals The new velocity and position vectors at step i+1; and Individuals up to time i The historical advantages of wolves and the historical advantages of wolf packs; and The inertial weight represents the contribution of the individual's position and velocity at previous moments to the next optimization. Let be a random variable with a standard normal distribution; Let be a random variable with a uniform distribution on the interval [-1, 1]. This is the bitwise XOR operator for binary data. For the i-th individual The corresponding fitness level.

[0025] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the vehicle motor parameter identification method based on the improved wolf pack algorithm as described above.

[0026] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the vehicle motor parameter identification method based on the improved wolf pack algorithm as described above.

[0027] Compared with the prior art, the present invention has the following beneficial effects: The improved wolf pack algorithm proposed in this invention introduces a novel population initialization method, enabling the generated wolf pack to be distributed as widely as possible across the solution space. Furthermore, by applying an improved search position update mechanism, the wolf pack can dynamically adjust its step size based on environmental conditions, thereby expanding the global exploration range in the early stages of identification and accelerating local fine-tuning searches as parameters approach their true values. This effectively balances the contradiction between convergence speed and accuracy, improving the algorithm's robustness. Automotive motor systems suffer from strong electromagnetic interference and measurement noise. Conventional wolf pack algorithms may suffer from overall identification bias due to misguidance by individual wolf packs. However, the improved algorithm of this invention significantly reduces noise interference and improves identification efficiency through sharing the overall historical optimal information of the population and collaborative searching among individuals. Moreover, conventional wolf pack algorithms require a uniform search across the entire parameter space, while the improved algorithm of this invention can construct a directional search space based on prior knowledge of the automotive motor, reducing invalid iterations and shortening parameter identification time. Compared to conventional wolf pack algorithms, the improved wolf pack algorithm proposed in this invention exhibits stronger adaptability and identification performance in automotive motor parameter identification. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a structural diagram of the vehicle motor control system of the present invention; Figure 2 This is a flowchart illustrating the parameter identification process based on the improved wolf pack algorithm of this invention. Figure 3 This is the identification curve of the stator resistor in an embodiment of the present invention; Figure 4This is the identification curve of the d-axis inductance in an embodiment of the present invention; Figure 5 This is the identification curve of the q-axis inductor in an embodiment of the present invention; Figure 6 This is the identification curve of the magnetic flux in an embodiment of the present invention; Figure 7 This is a comparison between simulation experiments and actual experiments using the identification parameters obtained from the embodiments of the present invention. Detailed Implementation

[0030] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention.

[0031] It should be noted that the process equipment or apparatus not specifically mentioned in the following embodiments are all conventional equipment or apparatus in the art.

[0032] It should be noted that the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or apparatuses. Furthermore, unless otherwise stated, the numbering of each method step is merely a convenient tool for identifying each method step, and not intended to limit the order of the method steps or define the scope of the invention. Changes or adjustments to their relative relationships, without substantially altering the technical content, should also be considered within the scope of the invention.

[0033] See Figure 1 This invention constructs a control system model for an automotive motor, which includes a space vector pulse width modulation control module, a current loop regulation module, and a speed loop regulation module. The motor speed is set to 5000 r / min, and the output torque is 200 N·m. The experiment uses... and The control strategy is implemented. After the motor runs stably, the current, voltage, and speed data during the motor operation are collected. After filtering the collected current, voltage, and speed data, the improved wolf pack algorithm of this invention is used to identify the four parameters: stator resistance, d-axis inductance, q-axis inductance, and flux linkage.

[0034] See Figure 2 The flowchart below shows a method for identifying automotive motor parameters based on an improved wolf pack algorithm, including the following steps: Step 1: In this embodiment, the parameters to be identified for the automotive motor include four: stator resistance, d-axis inductance, q-axis inductance, and flux linkage. An initial wolf pack is defined based on these parameters. In this embodiment, the dimension of the initial wolf pack is... In this embodiment, N=100 represents the total number of individuals in the wolf pack, and D=4 represents the number of vehicle motor parameters to be identified stored by each individual. The maximum number of optimization steps allowed is set to 100 steps.

[0035] In the wolf pack sequence W initialized in this invention, the first... individual The Dimensional elements Given by the following formula:

[0036] in, Indicates will and Perform an XOR operation; the range of values ​​for the positive integer X must satisfy the following condition: , Indicates the lower limit and upper limit Take a random number between them; This is a binary bitwise XOR operator; This is the bitwise XOR operator for binary data. This represents the initial estimated value of the nth wolf individual for the dth parameter to be identified in the vehicle motor.

[0037] The wolf pack initialization method described above enables the generated wolf pack to be distributed as widely as possible in the solution space. Through this method, the initialization of the wolf pack can be more advantageous in terms of convergence speed and adaptability. Even when some individuals have poor initial positions, the algorithm can still find a high-quality solution through group cooperation.

[0038] Step 2: Based on the estimated values ​​of the parameters to be identified by individual wolves for the vehicle motor and the collected current, voltage, and speed data, obtain the evaluation index of the vehicle motor, and obtain the fitness J of individual wolves based on the evaluation index.

[0039] In this embodiment, the evaluation indicators of the acquired vehicle motor performance specifically include: delay time, rise time, peak time, settling time, tracking error, and overshoot percentage. Therefore, the fitness J of an individual wolf pack is calculated using the following formula:

[0040] in, , , , , and These are six performance evaluation indicators for automotive motors obtained through motor simulation experiments based on the estimated values ​​of the parameters to be identified by individual wolves and the collected current, voltage, and speed data: delay time, rise time, peak time, settling time, tracking error, and overshoot percentage. , , , , , These are the weighting coefficients corresponding to the six evaluation indicators; t is the test time; when the optimization of the vehicle motor parameters begins, the initial values ​​of the weighting coefficients are preset: , , , , , To make it meet the conditions: , , , , , Then for each individual wolf in the pack Calculate the corresponding J value, which is its fitness.

[0041] Step 3: To improve the algorithm's global optimization capability and maintain the diversity of the offspring population, the search positions of individual wolves are updated using the following improved method: First, all individuals in the entire wolf pack W are sorted in ascending order according to their corresponding J values. Then, in the (i+1)th iteration, the search positions of the nth individual are updated. The steps for updating the search location are as follows:

[0042]

[0043] in, and Individuals The velocity and position vectors at step i; and Individuals The new velocity and position vectors at step i+1; and Individuals up to time i The historical advantages of wolves and the historical advantages of wolf packs; and The inertial weight represents the contribution of the individual's position and velocity at previous moments to the next optimization. Let be a random variable with a standard normal distribution; Let be a random variable with a uniform distribution on the interval [-1, 1]. This is the bitwise XOR operator for binary data. For the i-th individual The corresponding fitness; through the above calculations, the wolf pack can dynamically adjust its step size according to the quality of the environment, thereby balancing the ability to explore globally and exploit locally, and improving the robustness of the algorithm.

[0044] Step 4: The nth individual The optimization region in step i+1 is set using the following improved method. By adopting the following adaptive correction strategy, its optimization region can be dynamically adjusted, thereby enabling the wolf pack W to approach the optimal solution more quickly:

[0045]

[0046]

[0047] in, To perform individual operations at step i+1 Gradient estimation; This is a binary bitwise XOR operator; and For individuals at steps i and i+1 respectively The correction vector is used to optimize the optimization region; Indicates the intermediate vector Transpose; Let be a random variable with a uniform distribution in the interval [0,1]. Through the above operations, the algorithm can maintain good adaptability when dynamically searching the target area. When the objective function or constraints change, the wolf pack can quickly adjust its strategy and reorganize the search. Step 5: After completing the search in step j, determine whether j has reached the maximum number of optimization steps. If not, return to step 2 to continue the optimization; otherwise, stop the optimization and select the wolf with the lowest fitness in the current wolf pack. Values ​​corresponding to each dimension The identification values ​​are used as the final identification values ​​for the automotive motor parameters obtained.

[0048] The new method was tested and verified using data obtained in the Matlab / Simulink simulation environment.

[0049] Example The improved wolf pack algorithm of this invention is used to identify four parameters of automotive motor (stator resistance, d-axis inductance, q-axis inductance, and flux linkage), while using the conventional wolf pack algorithm as a control.

[0050] The stator resistance identification curves obtained using two wolf pack algorithms are as follows: Figure 3 As shown, the d-axis inductance identification curve is as follows: Figure 4 As shown, the q-axis inductance identification curve is as follows: Figure 5 As shown, the magnetic flux identification curve is as follows: Figure 6 As shown. From Figures 3-6 As can be seen, in the process of identifying automotive motor parameters using the improved wolf pack algorithm of this invention, the identification curves of stator resistance, d-axis inductance, q-axis inductance, and flux linkage all exhibit fast initial convergence speeds and can enter a stable state relatively early in the later stages of iteration. Compared to the conventional wolf pack algorithm, the improved wolf pack algorithm of this invention, in the initial stage of the search, uses a larger step size for extensive exploration, which can quickly locate potential solution space regions; as the search progresses, the step size is automatically adjusted and reduced, so that the search gradually focuses on the region near the optimal solution. This dynamic adjustment strategy can avoid the problem of low search efficiency caused by a fixed step size, ensuring both rapid convergence in the early stage and accurate convergence in the later stage.

[0051] In this embodiment, the improved wolf pack algorithm of this invention yields four identification parameters for the vehicle motor: {0.6917.65 12.37 0.365}. These parameters are used to conduct a motor speed control simulation experiment, and the results are compared with those from an actual motor speed control experiment. The results are as follows: Figure 7 As shown, the simulation data obtained by using the improved wolf pack algorithm of this invention has a high degree of agreement with the measured values, and the error is kept within a small range. Its identification accuracy can meet the needs of automotive motor modeling and simulation.

[0052] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not omitted in the apparatus embodiments, please refer to the embodiments of the method of the present invention.

[0053] In another embodiment of the present invention, a vehicle motor parameter identification system based on an improved wolf pack algorithm is provided, comprising: The data acquisition module is used to collect current, voltage, and speed data of the vehicle motor; The initialization module is used to initialize the wolf pack based on the parameters to be identified from the vehicle motor. The wolf pack consists of the first... n individual The d Dimensional elements Given by the following formula:

[0054] in, Indicates will and Perform an XOR operation; the range of values ​​for the positive integer X must satisfy the following condition: , Indicates the lower limit and upper limit Take a random number between them; This is a binary bitwise XOR operator; This is the bitwise XOR operator for binary data. Indicates the first n The first individual wolf in the pack's response to the vehicle's motor d The initial estimated value of each parameter to be identified; N is the number of individuals in the wolf pack; D represents the number of vehicle motor parameters to be identified stored by each individual in the wolf pack. The optimization module is used to obtain the evaluation index of the vehicle motor based on the estimated values ​​of the parameters to be identified by individual wolves and the collected current, voltage and speed data. Based on the evaluation index, the fitness of all individuals in the wolf pack is obtained. The search position of each individual is updated based on the fitness of all individuals in the wolf pack. The optimization area is dynamically adjusted based on the updated search position of each individual. The judgment module is used to determine whether the maximum number of optimization steps has been reached. If so, the individual with the smallest fitness is obtained, and the values ​​corresponding to each dimension of this individual are used as the identification values ​​of the vehicle motor parameters. Otherwise, the algorithm returns to the optimization module.

[0055] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used for the operation of a vehicle motor parameter identification method based on an improved wolf pack algorithm.

[0056] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be Random Access Memory (RAM) or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the vehicle motor parameter identification method based on the improved wolf pack algorithm in the above embodiments.

[0057] This embodiment also provides a computer program product, which includes a computer program that, when executed by a processor, implements the corresponding steps of the vehicle motor parameter identification based on the improved wolf pack algorithm in the above embodiment.

[0058] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, etc.) containing computer-usable program code.

[0059] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, systems (apparatus), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0060] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0061] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for identifying automotive motor parameters based on an improved wolf pack algorithm, characterized in that, include: S1 collects current, voltage, and speed data of the vehicle motor; The wolf pack is initialized based on the parameters to be identified from the vehicle motor. The wolf pack is then... n individual The d Dimensional elements Given by the following formula: in, Indicates will and Perform an XOR operation; the range of values ​​for the positive integer X must satisfy the following condition: , Indicates the lower limit and upper limit Take a random number between them; This is a binary bitwise XOR operator; This is the bitwise XOR operator for binary data. Indicates the first n The first individual wolf in the pack's response to the vehicle's motor d The initial estimated value of each parameter to be identified; N is the number of individuals in the wolf pack; D represents the number of vehicle motor parameters to be identified stored by each individual in the wolf pack. S2. Based on the estimated values ​​of the parameters to be identified by individual wolves for the vehicle motor and the collected current, voltage and speed data, the evaluation index of the vehicle motor is obtained, and the fitness of all individuals in the wolf pack is obtained based on the evaluation index. S3, update the search position of each individual based on the fitness of all individuals in the wolf pack; S4, dynamically adjusts the optimization area based on the updated search position of each individual; S5, determine whether the maximum number of optimization steps has been reached. If so, obtain the individual with the smallest fitness, and use the values ​​corresponding to each dimension of this individual as the identification values ​​of the vehicle motor parameters. Otherwise, return to S2.

2. The method for identifying automotive motor parameters based on the improved wolf pack algorithm according to claim 1, characterized in that, In S1, the parameters to be identified for the vehicle motor are one or more of the following: stator resistance, d-axis inductance, q-axis inductance, and flux linkage.

3. The method for identifying automotive motor parameters based on the improved wolf pack algorithm according to claim 1, characterized in that, In S2, the evaluation metrics include one or more of the following: tracking error, overshoot percentage, delay time, rise time, peak time, and settling time.

4. The method for identifying automotive motor parameters based on the improved wolf pack algorithm according to claim 2, characterized in that, In S2, the fitness is calculated using the following formula: in, , , , , and These are the tracking error, overshoot percentage, delay time, rise time, peak time, and settling time of the vehicle motor, obtained through motor simulation experiments based on the estimated values ​​of the parameters to be identified by individual wolves and the collected current, voltage, and speed data. , , , , , They are respectively , , , , and The corresponding weighting coefficient; t is the test time for the vehicle motor.

5. The method for identifying automotive motor parameters based on the improved wolf pack algorithm according to claim 1, characterized in that, In S3, the search position is updated using the following method: in, and Individuals The velocity and position vectors at step i; and Individuals The new velocity and position vectors at step i+1; and Individuals up to time i The historical advantages of wolves and the historical advantages of wolf packs; and The inertial weight represents the contribution of the individual's position and velocity at previous moments to the next optimization. Let be a random variable with a standard normal distribution; Let be a random variable with a uniform distribution on the interval [-1, 1]. This is the bitwise XOR operator for binary data. For the i-th individual The corresponding fitness level.

6. The method for identifying automotive motor parameters based on the improved wolf pack algorithm according to claim 1, characterized in that, In S4, the optimization region is adjusted using the following method: in, To perform individual operations at step i+1 Gradient estimation; This is a binary bitwise XOR operator; and For individuals at steps i and i+1 respectively The correction vector is used to optimize the optimization region; Indicates the intermediate vector Transpose; Let be a random variable that has a uniform distribution on the interval [0,1].

7. A vehicle motor parameter identification system based on an improved wolf pack algorithm, characterized in that, include: The data acquisition module is used to collect current, voltage, and speed data of the vehicle motor; The initialization module is used to initialize the wolf pack based on the parameters to be identified from the vehicle motor. The wolf pack consists of the first... n individual The d Dimensional elements Given by the following formula: in, Indicates will and Perform an XOR operation; the range of values ​​for the positive integer X must satisfy the following condition: , Indicates the lower limit and upper limit Take a random number between them; This is a binary bitwise XOR operator; This is the bitwise XOR operator for binary data. Indicates the first n The first individual wolf in the pack's response to the vehicle's motor d The initial estimated value of each parameter to be identified; N is the number of individuals in the wolf pack; D represents the number of vehicle motor parameters to be identified stored by each individual in the wolf pack. The optimization module is used to obtain the evaluation index of the vehicle motor based on the estimated values ​​of the parameters to be identified by individual wolves and the collected current, voltage and speed data. Based on the evaluation index, the fitness of all individuals in the wolf pack is obtained. The search position of each individual is updated based on the fitness of all individuals in the wolf pack. The optimization area is dynamically adjusted based on the updated search position of each individual. The judgment module is used to determine whether the maximum number of optimization steps has been reached. If so, the individual with the smallest fitness is obtained, and the values ​​corresponding to each dimension of this individual are used as the identification values ​​of the vehicle motor parameters. Otherwise, the algorithm returns to the optimization module.

8. The vehicle motor parameter identification system based on the improved wolf pack algorithm according to claim 7, characterized in that, The process of updating the search position of each individual based on the fitness of all individuals in the wolf pack is as follows: in, and Individuals The velocity and position vectors at step i; and Individuals The new velocity and position vectors at step i+1; and Individuals up to time i The historical advantages of wolves and the historical advantages of wolf packs; and The inertial weight represents the contribution of the individual's position and velocity at previous moments to the next optimization. Let be a random variable with a standard normal distribution; Let be a random variable with a uniform distribution on the interval [-1, 1]. This is the bitwise XOR operator for binary data. For the i-th individual The corresponding fitness level.

9. A computer device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the vehicle motor parameter identification method based on the improved wolf pack algorithm as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the vehicle motor parameter identification method based on the improved wolf pack algorithm as described in any one of claims 1 to 6.

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