Improved milu deer swarm algorithm-based optimization method for permanent magnet synchronous motor of new energy vehicle

Abstract

The application discloses a new energy automobile permanent magnet synchronous motor optimization method based on an improved eland group algorithm, and comprises the following steps: a two-dimensional simulation model of the permanent magnet synchronous motor is established; an improved eland group algorithm is constructed; the improved eland group algorithm is based on the eland group algorithm, and opposite learning is introduced to initialize a population, a time-decreasing dynamic inertia weight is adopted, a global search step is generated by adopting a Levy flight mechanism, and an elite reservation mechanism is adopted; position parameters and size parameters of a stator auxiliary slot are used as optimization variables of the improved eland group algorithm, the improved eland group algorithm is used for iterative optimization, and when a preset convergence condition is reached, the iteration is stopped, and an optimization result is output. The application can solve the problems that the global search capability and the convergence speed of the prior art are poor, and the prior art is prone to falling into a local optimum, and the optimization efficiency and the precision of the motor are improved.

Description

Technical Field

[0001] This invention relates to the field of new energy vehicle technology, and specifically to an optimization method for permanent magnet synchronous motors in new energy vehicles based on an improved elk herd algorithm. Background Technology

[0002] With the rapid development of new energy vehicles, passengers are demanding higher levels of comfort. Researching the vibration and noise of motors in new energy vehicles can optimize mechanical structures, reduce mechanical wear caused by vibration, and extend product lifespan. Built-in V-type permanent magnet synchronous motors are widely used in new energy vehicles due to their advantages such as high efficiency and high power density. During vehicle operation, the performance requirements for motors are extremely stringent, demanding not only good power output but also stable and low-noise operation.

[0003] In existing technologies, the optimization algorithms built into traditional ANSYS simulation software, such as particle swarm optimization and genetic algorithm, are mainly used for motor optimization. However, these traditional algorithms have poor global search capabilities and convergence speed, and are prone to getting trapped in local optima, which affects the optimization efficiency and accuracy of motors. Summary of the Invention

[0004] In view of this, the present invention provides an optimization method for permanent magnet synchronous motors of new energy vehicles based on an improved elk herd algorithm, in order to solve the problems of poor global search capability and convergence speed of existing technologies, and the tendency to get trapped in local optima, thereby improving the optimization efficiency and accuracy of the motor.

[0005] An optimization method for permanent magnet synchronous motors in new energy vehicles based on an improved elk herding algorithm includes:

[0006] Step S1: Collect basic data and operating data of the permanent magnet synchronous motor, and establish a two-dimensional simulation model of the permanent magnet synchronous motor using electromagnetic field simulation software;

[0007] Step S2: Construct an improved elk herd algorithm. The improved elk herd algorithm is based on the elk herd algorithm, introducing adversarial learning to initialize the population, using time-decreasing dynamic inertial weights, using the Levy flight mechanism to generate the global search step size, and using an elite retention mechanism.

[0008] Step S3: The position parameters and size parameters of the stator auxiliary slots are used as optimization variables of the improved elk herd algorithm. The improved elk herd algorithm is used for iterative optimization. The objective function values ​​corresponding to the position parameters and size parameters of different stator auxiliary slots are recorded. The objective function values ​​include the cogging torque peak value, average efficiency, average output torque, and output torque pulsation value.

[0009] Step S4: When the preset convergence condition is met, stop the iteration and output the position parameters and size parameters of the stator auxiliary slot corresponding to the recorded optimal objective function value as the optimization result.

[0010] The optimization method for permanent magnet synchronous motors in new energy vehicles based on the improved elk herd algorithm provided by the present invention has the following beneficial effects:

[0011] (1) In the improved elk herd algorithm, the opposition learning initialization population is introduced. First, the population matrix is ​​randomly generated, and then the opposition population matrix is ​​generated through the opposition learning strategy. At the same time, the objective function value of each individual in the population is calculated. Individuals whose objective function values ​​meet the preset conditions are selected as the initial population. This can effectively increase the diversity of the initial population, thereby expanding the exploration space in the early stage, avoiding the search from getting stuck in local optima, and enhancing the global search capability and convergence speed.

[0012] (2) In the improved elk herd algorithm, a time-decreasing dynamic inertial weight is adopted, which can realize adaptive adjustment to enhance global exploration in the early stage and strengthen local development in the later stage.

[0013] (3) In the improved elk herd algorithm, the Levy flight mechanism is used to generate the global search step size, which can realize global jump and local fine-tuning, thereby enhancing the search capability.

[0014] (4) In the improved elk herd algorithm, the elite retention mechanism is adopted, which can enhance the ability to escape in complex multi-peak optimization space.

[0015] (5) The present invention uses an improved elk herd algorithm to optimize the position parameters and size of the stator auxiliary slots, taking into account both global jump and local search, improving the global search capability and convergence speed, avoiding getting trapped in local optima, and can reduce the cogging torque value and rated torque ripple value of the motor under no-load conditions and ensure motor efficiency while meeting the requirements of the peak value of the motor's no-load back EMF and the rated operating torque value. This improves the optimization efficiency and accuracy of the motor. Attached Figure Description

[0016] Figure 1 A flowchart illustrating the optimization method for permanent magnet synchronous motors in new energy vehicles based on an improved elk herd algorithm provided in an embodiment of the present invention.

[0017] Figure 2 This is a schematic diagram of an exemplary permanent magnet synchronous motor;

[0018] Figure 3 A comparison chart showing the cogging torque of the motors under no-load conditions before and after optimization;

[0019] Figure 4 A comparison chart showing the cogging torque of the motor before and after load optimization;

[0020] Figure 5 A comparison chart showing the efficiency of the motor before and after optimization. Detailed Implementation

[0021] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain embodiments of the present invention, and should not be construed as limiting the present invention.

[0022] Please see Figure 1 The present invention provides an optimization method for permanent magnet synchronous motors in new energy vehicles based on an improved elk herd algorithm, comprising steps S1 to S4:

[0023] Step S1: Collect basic data and operating data of the permanent magnet synchronous motor, and establish a two-dimensional simulation model of the permanent magnet synchronous motor using electromagnetic field simulation software.

[0024] In this embodiment, please refer to Figure 2 The permanent magnet synchronous motor to be optimized includes a stator 4 and a rotor 7. The stator 4 includes a stator winding 2, a stator auxiliary slot 3 and a stator yoke 1. The rotor 7 is provided with a permanent magnet 6, a first rivet hole 8, a second rivet hole 9 and a magnetic isolation bridge 5.

[0025] Then, the various dimensional parameters of the motor are determined. In this embodiment, the outer diameter of the stator core is 248mm, the inner diameter of the stator core is 168mm, the outer diameter of the rotor core is 166.4mm, and the inner diameter of the rotor core is 63mm. The motor has 48 auxiliary slots in the stator and 8 poles in the rotor. The diameter of the first rivet hole 8 is 1mm, and the diameter of the second rivet hole 9 is 3mm.

[0026] Then, finite element modeling of the motor is performed to determine its material properties, operating data, design variables, and optimization objectives. In this embodiment, ANSYS Maxwell is used to build a two-dimensional simulation model of the permanent magnet synchronous motor. The stator and rotor materials are M19_29G, the permanent magnet material is NdFe35N, and the stator windings are made of copper wire. The material density of silicon steel sheet M19_29G is 7650 kg / m³. 3 The density of the permanent magnet is 7400 kg / m³. 3 The density of copper wire is 8933 kg / m 3 After assigning material properties to each component, further simulations were conducted to obtain various electromagnetic performance characteristics of the permanent magnet synchronous motor. The design variables were the size and position of the stator auxiliary slots, specifically the width, height, and location of the stator auxiliary slots. The optimization objective was to reduce the no-load cogging torque and rated torque ripple of the motor while meeting the requirements for motor efficiency and rated torque.

[0027] Step S2: Construct an improved elk herd algorithm. The improved elk herd algorithm is based on the elk herd algorithm, introduces adversarial learning to initialize the population, adopts time-decreasing dynamic inertial weights, uses the Lévy flight mechanism to generate the global search step size, and adopts an elite retention mechanism.

[0028] Specifically, the initialization of the population through oppositional learning includes:

[0029] Establish upper and lower bounds for the position and size parameters of the stator auxiliary slots, and randomly generate a population matrix. Generate and through opposition learning strategies Opposing population matrix To increase the diversity of the initial population, calculations were performed separately. and The corresponding objective function value, in and Individuals whose objective function values ​​satisfy preset conditions are selected as the initial population. and They respectively satisfy the following formulas:

[0030]

[0031]

[0032] in, Indicates random generation The matrix is ​​a set of elements, each of which takes a random value between [0,1), and N is the population size. For population size, To design the dimension of variables, To design the upper bound vector of the variable, To design the lower bound vector of the variable, This indicates the range of variables for each dimension. To and A matrix of opposing individuals.

[0033] By comparing the values ​​of the objective function and We select better individuals to improve the quality and diversity of the initial population, thereby expanding the exploration space in the early stages, avoiding the search from getting stuck in local optima, and enhancing global search capabilities and convergence speed.

[0034] Time-decreasing dynamic inertia weights satisfy the following equation:

[0035]

[0036] in, For the first The inertia weight of the next iteration, This represents the maximum weight. The minimum weight value, This represents the maximum number of iterations.

[0037] By designing a time-decreasing dynamic inertia weight, it is possible to achieve adaptive adjustment that enhances global exploration in the early stage and strengthens local development in the later stage.

[0038] The formula for calculating the step size in the process of generating the global search step size using the Lévy flight mechanism is as follows:

[0039] ;

[0040] in, Indicates the step size; This indicates that a custom function was called to generate the function. The Levy step size matrix is ​​used for global random jumps; The exponential parameter of the step size distribution controls the distribution characteristics of the step size. A smaller value enhances global jumps, while a larger value strengthens local searches, thus achieving a balance between the search range and convergence accuracy. This represents the iterative decay coefficient, with large step sizes in the initial stage and fine convergence in the later stage. As a scaling factor, the step size gradually decreases but is not zero. It is a step scaling matrix that applies different step scaling to different variables, controlling the search step size of each variable.

[0041] Using the Levy flight mechanism to generate the global search step size enables global jumps and local fine-tuning, enhancing search capabilities.

[0042] In the elite retention mechanism, the best individual in the current global order is retained in each iteration. Elite individuals with fitness values ​​greater than the threshold do not participate in updates and directly enter the next iteration, which can improve their ability to escape from complex multi-peak optimization spaces.

[0043] In this embodiment, the behavior update formula of the improved elk herd algorithm is:

[0044]

[0045] in, Indicates the first The th iteration in the Individual, Indicates the first The th iteration in the Individual, express Random numbers within a range This represents the currently optimal individual globally.

[0046] Step S3: The position parameters and size parameters of the stator auxiliary slots are used as optimization variables of the improved elk herd algorithm. The improved elk herd algorithm is used for iterative optimization. The objective function values ​​corresponding to the position parameters and size parameters of different stator auxiliary slots are recorded. The objective function values ​​include the peak value of cogging torque, average efficiency, average output torque, and output torque ripple value.

[0047] In this embodiment, the improved elk algorithm is written into MATLAB. Ansys Maxwell is controlled via MATLAB using VBS scripts. Motor simulations are performed for each parameter combination, and the peak cogging torque, average efficiency, average output torque, and output torque ripple value are derived. Data interaction between MATLAB and Ansys Maxwell uses a system call command-line interface, which automatically completes model simulation execution and data result acquisition and comparison, constructing a closed-loop optimization system.

[0048] Step S4: When the preset convergence condition is met, stop the iteration and output the position parameters and size parameters of the stator auxiliary slot corresponding to the recorded optimal objective function value as the optimization result.

[0049] In this study, an improved stag optimization algorithm was used in MATLAB to optimize the size and position of the stator auxiliary slots of the motor. The objective function value was evaluated, and for each candidate solution, an external VBS script was called to transmit the stator auxiliary slot size and position parameters to the Ansys Maxwell simulation software via command line. The simulation was run automatically, and the MATLAB program read the simulation results, determined the convergence condition, and recorded the global optimum and population information at each iteration until the iteration was complete. If not, the parameters of the improved stag algorithm were initialized repeatedly to obtain the optimal stator auxiliary slot position and size parameters, as well as the corresponding peak cogging torque, average efficiency, average output torque, and output torque ripple value.

[0050] In this embodiment, the population size of the improved elk optimization algorithm is 16, that is, 16 random solutions are initialized, the complementary solutions are calculated, the top 16 of the 32 better solutions are retained, the number of iterations is 30, and the maximum number of seconds to wait for the ANSYS Maxwell export file is 300 seconds.

[0051] In this embodiment, the final optimization result not only outputs the optimal design variable values, but also generates a Pareto front plot and multi-dimensional performance visualization to assist in design decisions.

[0052] Figure 3 , Figure 4 , Figure 5 Table 1 shows the comparison results between the optimized motor of the present invention and the original motor.

[0053] from Figure 3 It can be seen that the optimized motor cogging torque under no-load conditions is 3.69 N·m, while the original motor's cogging torque under no-load conditions is 6.10 N·m. The peak value of the optimized motor's cogging torque under no-load conditions is reduced by 40%. Figure 4 As can be seen from Table 1, the effective value of the output torque under rated operating conditions remains basically unchanged, while the ripple value of the output torque under rated operating conditions decreases from 0.55 N·m to 0.47 N·m. Figure 5 It can be seen that the average efficiency of the motor increased from 97.08% to 97.33%. Since cogging torque and torque ripple are the main sources of electromagnetic noise, reducing torque ripple will reduce motor noise and make operation smoother. At the same time, under load, while ensuring output power, the peak value of load cogging torque is reduced, making the motor run more smoothly and avoiding the "creeping" phenomenon. Therefore, this invention achieves the goal of reducing the cogging torque value and rated torque ripple value of the motor under no-load conditions while meeting the requirements of motor efficiency and rated torque value.

[0054] Table 1

[0055]

[0056] In summary, the optimization method for permanent magnet synchronous motors in new energy vehicles based on the improved elk herd algorithm according to the above embodiments has the following beneficial effects:

[0057] (1) In the improved elk herd algorithm, the opposition learning initialization population is introduced. First, the population matrix is ​​randomly generated, and then the opposition population matrix is ​​generated through the opposition learning strategy. At the same time, the objective function value of each individual in the population is calculated. Individuals whose objective function values ​​meet the preset conditions are selected as the initial population. This can effectively increase the diversity of the initial population, thereby expanding the exploration space in the early stage, avoiding the search from getting stuck in local optima, and enhancing the global search capability and convergence speed.

[0058] (2) In the improved elk herd algorithm, a time-decreasing dynamic inertial weight is adopted, which can realize adaptive adjustment to enhance global exploration in the early stage and strengthen local development in the later stage.

[0059] (3) In the improved elk herd algorithm, the Levy flight mechanism is used to generate the global search step size, which can realize global jump and local fine-tuning, thereby enhancing the search capability.

[0060] (4) In the improved elk herd algorithm, the elite retention mechanism is adopted, which can enhance the ability to escape in complex multi-peak optimization space.

[0061] (5) The present invention uses an improved elk herd algorithm to optimize the position parameters and size of the stator auxiliary slots, taking into account both global jump and local search, improving the global search capability and convergence speed, avoiding getting trapped in local optima, and can reduce the cogging torque value and rated torque ripple value of the motor under no-load conditions and ensure motor efficiency while meeting the requirements of the peak value of the motor's no-load back EMF and the rated operating torque value. This improves the optimization efficiency and accuracy of the motor.

[0062] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. An optimization method for permanent magnet synchronous motors in new energy vehicles based on an improved elk herding algorithm, characterized in that, include: Step S1: Collect basic data and operating data of the permanent magnet synchronous motor, and establish a two-dimensional simulation model of the permanent magnet synchronous motor using electromagnetic field simulation software; Step S2: Construct an improved elk herd algorithm. The improved elk herd algorithm is based on the elk herd algorithm, introducing adversarial learning to initialize the population, using time-decreasing dynamic inertial weights, using the Levy flight mechanism to generate the global search step size, and using an elite retention mechanism. Step S3: The position parameters and size parameters of the stator auxiliary slots are used as optimization variables of the improved elk herd algorithm. The improved elk herd algorithm is used for iterative optimization. The objective function values ​​corresponding to the position parameters and size parameters of different stator auxiliary slots are recorded. The objective function values ​​include the cogging torque peak value, average efficiency, average output torque, and output torque pulsation value. Step S4: When the preset convergence condition is met, stop the iteration and output the position parameters and size parameters of the stator auxiliary slot corresponding to the recorded optimal objective function value as the optimization result. The time-decreasing dynamic inertia weight satisfies the following equation: in, For the first The inertia weight of the next iteration, This represents the maximum weight. The minimum weight value, The maximum number of iterations; The formula for calculating the step size in the process of generating the global search step size using the Lévy flight mechanism is as follows: ； in, Indicates the step size. This indicates that a custom function was called to generate the function. The Levy step size matrix, For the exponential parameter of the step size distribution, It is a step scaling matrix. To design the upper bound vector of the variable, The lower bound vector for the design variables; The improved behavior update formula for the elk herd algorithm is: in, Indicates the first The th iteration in the Individual, Indicates the first The th iteration in the Individual, express Random numbers within a range This represents the currently optimal individual globally.

2. The optimization method for permanent magnet synchronous motors in new energy vehicles based on the improved elk herd algorithm according to claim 1, characterized in that, The specific components of the oppositional learning initialization population include: Establish upper and lower bounds for the position and size parameters of the stator auxiliary slots, and randomly generate a population matrix. Generate and through opposition learning strategies Opposing population matrix Calculate separately and The corresponding objective function value, in and Individuals whose objective function values ​​satisfy preset conditions are selected as the initial population. and They respectively satisfy the following formulas: in, Indicates random generation The matrix, For population size, To design the dimension of variables, To and A matrix of opposing individuals.

3. The optimization method for permanent magnet synchronous motors in new energy vehicles based on the improved elk herd algorithm according to claim 1, characterized in that, In the elite retention mechanism, the best individual in the current global order is retained in each iteration. Elite individuals with fitness values ​​greater than the threshold do not participate in the update and directly enter the next iteration.

4. The optimization method for permanent magnet synchronous motors in new energy vehicles based on the improved elk herd algorithm according to claim 1, characterized in that, In step S1, a two-dimensional simulation model of the permanent magnet synchronous motor is established using ANSYS Maxwell.

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