Energy storage motor optimization design method and device based on dynamic microphone optimization algorithm

Abstract

The invention discloses an energy storage motor optimization design method and device based on a motor-microphone optimization algorithm, and belongs to the field of energy storage motor optimization design. The method comprises the steps that a notch width decision variable, a notch opening width decision variable and motor structure constraint data of an energy storage motor are acquired; an optimization mathematical model with the goal of minimizing cogging torque assignment is established; according to the mathematical model, iteratively updating the population to reach a preset number of iterations by adopting a moving microphone optimization algorithm, and outputting an optimal individual in the population; and finally, determining a motor structure size scheme according to the optimal individual, and performing optimization design on the energy storage motor according to the scheme. Therefore, by implementing the method, the problem that the convergence speed and the global search capability of motor optimization are difficult to improve while the optimization precision is ensured in the prior art can be solved.

Description

Technical Field

[0001] This invention relates to the field of energy storage motor optimization design, and in particular to an energy storage motor optimization design method and apparatus based on the dynamic optimization algorithm. Background Technology

[0002] In the optimization design of energy storage motors, the performance of the energy storage motor, as the core energy conversion component of the energy storage system, directly affects the system efficiency, power density, and operational stability. These motors typically need to simultaneously meet multiple design requirements such as high efficiency, high power density, excellent thermal management performance, and low cost. The design problem is essentially a typical multi-objective, multi-constraint engineering optimization problem.

[0003] In existing technologies, traditional optimization methods such as gradient descent and sequential quadratic programming face significant limitations when solving such complex problems. These methods heavily rely on the gradient information of the objective function and are extremely sensitive to the choice of initial points, easily getting trapped in local optima in non-convex, discontinuous search spaces. Furthermore, traditional methods lack effective mechanisms for handling discrete variables when dealing with mixed-integer programming problems, making it difficult to obtain the global optimum in motor structural parameter optimization.

[0004] With the development of computational intelligence technology, swarm intelligence optimization algorithms such as the Active Optimization (AOO) algorithm have attracted widespread attention due to their lack of gradient information requirement and inherent parallel search capabilities. The AOO algorithm simulates the dynamic interaction behavior of biological populations, conducting global exploration in the solution space, and theoretically can effectively handle complex optimization problems that are difficult to solve using traditional methods. However, in practical applications for energy storage motor optimization design, the original AOO algorithm still has significant shortcomings: First, its search strategy is prone to getting trapped in local extrema in the later stages of iteration, lacking an effective escape mechanism; second, the population diversity maintenance mechanism is insufficient, leading to premature convergence in complex multimodal functions; third, the parameter control strategy is relatively fixed, making it difficult to achieve a dynamic balance between exploration and exploitation, affecting the convergence speed and solution accuracy; finally, when dealing with motor optimization problems with strong constraints, the constraint handling mechanism is not perfect, reducing the quality of feasible solutions. These shortcomings cause the original AOO algorithm to often exhibit poor convergence stability, large fluctuations in optimization results, and low computational efficiency when solving practical motor optimization problems, failing to meet the stringent requirements of optimization accuracy and reliability in engineering practice. Therefore, there is an urgent need to make targeted improvements to the existing AOO algorithm and develop a new optimization method that can adapt to the characteristics of motor optimization, so as to enhance its practical value in complex engineering optimization. Summary of the Invention

[0005] This invention provides a method and apparatus for optimizing the design of energy storage motors based on the dynamic optimization algorithm, which can solve the problem in the prior art that it is difficult to improve the convergence speed and global search capability of motor optimization while ensuring optimization accuracy.

[0006] In a first aspect, embodiments of the present invention provide an energy storage motor optimization design method based on the dynamic optimization algorithm, comprising: Obtain the slot width decision variable, slot opening width decision variable, and motor structure constraint data of the energy storage motor; A mathematical model for motor optimization is established based on the slot width decision variable, the slot opening width decision variable, and the motor structure constraint data, with the minimization of the cogging torque assignment as the optimization objective. The cogging torque assignment is obtained based on the slot width decision variable and the slot opening width decision variable. The constraints of the mathematical model include back EMF constraints, slot width constraints, slot opening width constraints, and slot area constraints. Based on the aforementioned mathematical model for motor optimization, the population is iteratively updated using the dynamic optimization algorithm until the current iteration number reaches the preset iteration number, and the optimal individual in the population during the current iteration is output. The optimal individual is used to determine the motor structure size scheme of the energy storage motor, and the energy storage motor is optimized based on the motor structure size scheme.

[0007] This application, through acquiring decision variables for slot width, slot opening width, and motor structural constraints, lays a solid foundation for subsequent optimization that aligns with engineering realities, reducing invalid searches and infeasible solutions. Based on this data, a mathematical model is established with the objective of minimizing cogging torque, incorporating constraints for back EMF, slot width, slot opening width, and slot area. This model directly addresses the core issue of motor torque fluctuations, which affects operational stability and energy conversion efficiency. Simultaneously, multi-dimensional constraints prevent a single objective from being optimal while other key performance aspects become unbalanced, ensuring comprehensive and compliant optimization. The use of the Dynamic Optimization Algorithm to iteratively update the population to a preset number of iterations and output the optimal individual breaks through the dependence on gradient information in traditional gradient-based methods. It enables global search in complex solution spaces, avoiding local optima problems, and the preset number of iterations balances optimization accuracy and computational efficiency, continuously filtering for better parameter combinations. The process of determining the motor structural dimensions based on the optimal individual and conducting optimization design connects the algorithm results with engineering applications, ensuring the motor structure matches the core optimization objective. Therefore, this application solves the problem in existing technologies of improving the convergence speed and global search capability of motor optimization while maintaining optimization accuracy.

[0008] As a preferred example of the first aspect, the step of iteratively updating the population using the dynamic optimization algorithm based on the motor optimization mathematical model until the current iteration number reaches the preset iteration number, and outputting the population corresponding to each iteration number, specifically: Based on the slot width constraint and slot opening width constraint in the mathematical model, an initial population is generated using an initialization formula. Based on the mathematical model, the set of biological parameters for the current iteration, and the initial population, the dynamic optimization algorithm is used to iteratively update the population until the current iteration count reaches the preset number of iterations, and the optimal individual in the current iteration population is output. The set of biological parameters for the current iteration is obtained by calculating biological parameters using the formula based on the current iteration count, the total number of iterations, the problem dimension, and the population size. In each iteration, the population for the current iteration is explored and developed sequentially according to the Log-Tent distribution mechanism to obtain the population for the next iteration.

[0009] In this preferred example, the scientific rigor and effectiveness of the optimization process are further enhanced by refining the iterative process of the AOO algorithm. Generating the initial population based on slot width and slot opening width constraints avoids initial solutions deviating from the feasible region, reduces invalid iterations, and lays a high-quality foundation for subsequent optimization. Simultaneously, the biological parameter set is dynamically calculated based on the number of iterations and problem dimensions, breaking the limitation of fixed parameters in the original algorithm and allowing parameters to adapt to the needs of different optimization stages. Furthermore, the combination of exploration and development under the Log-Tent distribution mechanism expands the early search range to avoid missing optimal solutions and strengthens later local refinement to improve solution accuracy. This effectively solves the problems of premature convergence and search imbalance in the original AOO algorithm, ensuring efficient and stable iterative processes, and ultimately outputting an optimal individual that better meets the actual design requirements of the motor.

[0010] As a preferred example of the first aspect, the method of exploring and developing the population of the current iteration sequentially according to the Log-Tent distribution mechanism to obtain the population of the next iteration is as follows: According to the exploratory update formula in the Log-Tent distribution mechanism, the positions of each individual in the current iteration of the population are updated to obtain the first intermediate population. According to the developmental update formula in the Log-Tent distribution mechanism, the positions of each individual in the first intermediate population are updated to obtain the population for the next iteration.

[0011] In this preferred example, a phased population update strategy precisely balances the algorithm's global exploration and local exploitation capabilities. First, an exploratory update formula is used to update the population position, effectively expanding the solution space coverage and preventing the algorithm from being limited to local area searches. This solves the problem of narrow search range and easy miss of the global optimum in traditional optimization methods. Then, an exploitative update formula is used to optimize the first intermediate population, allowing for deeper mining of potential optimal regions based on the initial exploration, improving the accuracy and reliability of the solution. This two-step update process forms an iterative logic of "broad search first, then fine screening," gradually improving the quality of the population in each iteration. This not only reduces the total number of iterations and improves computational efficiency but also ensures that the final output population is suitable for the multi-constraint, high-complexity optimization scenario of the energy storage motor, guaranteeing the feasibility and high performance of the motor structure and size scheme.

[0012] As a preferred example of the first aspect, the set of biological parameters for the current iteration is obtained by calculating the biological parameters using the formula based on the current iteration number, the total number of iterations, the problem dimension, and the population size, specifically: Based on the problem dimension, the first preset parameter, and the first random number, the seed quality is calculated using the seed quality calculation formula to obtain the seed quality for the current iteration. The main awn length for the current iteration is calculated using the formula for calculating the main awn length based on the problem dimension, the population size, and the second random number. Based on the problem dimension, the second preset parameter, and the third random number, the eccentric rotation coefficient is calculated using the eccentric rotation coefficient calculation formula to obtain the eccentric rotation coefficient for the current iteration. Based on the current iteration number and the total iteration number, the smoothness control parameter is calculated using the smoothness control parameter calculation formula to obtain the smoothness control parameter for the current iteration; Based on the seed quality, primary awn length, eccentric rotation coefficient, and smoothness control parameter of the current iteration, the set of biological parameters for the current iteration is obtained.

[0013] In this preferred example, the dynamic computation of biological parameter sets enhances the adaptability of the Dynamic Mic algorithm to energy storage motor optimization scenarios. Next, seed quality is calculated by combining problem dimensions and random numbers, avoiding homogeneity among individuals in the population, maintaining population diversity during iteration, and preventing premature convergence. The design of the main awn length being correlated with population size allows for adjustment of the search step size according to different optimization needs, adapting to the search intensity at different stages of motor optimization. The eccentric rotation coefficient increases the flexibility of population position updates, helping the algorithm escape local extrema. The smoothness control parameter dynamically changes with the number of iterations, strengthening global exploration capabilities in the early stages and focusing on local development in the later stages, solving the search imbalance problem caused by fixed parameters in the original algorithm. The dynamic parameter set enables the algorithm to accurately match the complex needs of motor optimization, continuously outputting high-quality populations under multiple constraints, improving the stability and engineering value of the optimization results.

[0014] As a preferred example of the first aspect, the step of determining the motor structure size scheme of the energy storage motor based on the optimal individual specifically includes: The slot width value and slot opening width value in the optimal individual are respectively used as the final slot width size and the final slot opening width size; Based on the final slot width dimension, the final slot opening width dimension, and the fixed structural parameters of the energy storage motor, the motor structure dimension scheme of the energy storage motor is obtained.

[0015] In this preferred example, the transformation process from the optimal algorithm result to the engineering design scheme is simplified, improving the practicality and implementation efficiency of the optimized design. The slot width and slot opening width are directly extracted from the optimal individual as the final critical dimensions, eliminating the need for additional complex calculations and avoiding the ambiguity and redundant steps of parameter selection in traditional design, thus shortening the design cycle. Simultaneously, by combining the fixed structural parameters of the energy storage motor to determine the complete dimensional scheme, it is ensured that the optimized critical dimensions are compatible with the original fixed parameters of the motor, preventing parameter conflicts and guaranteeing the feasibility and completeness of the solution.

[0016] Secondly, the present invention provides an energy storage motor optimization design device based on dynamic optimization algorithm, comprising: a data acquisition module, a mathematical model establishment module, a solution module and an optimization design module; The data acquisition module is used to acquire the slot width decision variable, slot opening width decision variable, and motor structure constraint data of the energy storage motor. The mathematical model building module is used to build a motor optimization mathematical model with minimizing the cogging torque assignment as the optimization objective based on the slot width decision variable, the slot opening width decision variable, and the motor structure constraint data; wherein, the cogging torque assignment is obtained based on the slot width decision variable and the slot opening width decision variable, and the constraints of the mathematical model include back EMF constraints, slot width constraints, slot opening width constraints, and slot area constraints; The solution module is used to iteratively update the population using the dynamic optimization algorithm based on the motor optimization mathematical model until the current iteration number reaches the preset iteration number, and output the best individual in the population in the current iteration. The optimization design module is used to determine the motor structure size scheme of the energy storage motor based on the optimal individual, and to optimize the design of the energy storage motor based on the motor structure size scheme.

[0017] As a preferred example of the second aspect, the solving module includes a first solving unit and a second solving unit; The first solving unit is used to generate an initial population using an initialization formula based on the slot width constraint and slot opening width constraint in the mathematical model. The second solving unit is used to iteratively update the population using the Dynamic optimization algorithm based on the mathematical model, the biological parameter set of the current iteration, and the initial population, until the current iteration number reaches the preset iteration number, and output the best individual in the population of the current iteration; wherein, the biological parameter set of the current iteration is obtained by calculating the biological parameter calculation formula based on the current iteration number, the total iteration number, the problem dimension, and the population size. In each iteration, the population of the current iteration is explored and developed in sequence according to the Log-Tent distribution mechanism to obtain the population of the next iteration.

[0018] As a preferred example of the second aspect, the second solving unit includes a first solving subunit and a second solving subunit; The first solution subunit is used to update the position of each individual in the current iteration of the population according to the exploratory update formula in the Log-Tent distribution mechanism, so as to obtain the first intermediate population. The second solution subunit is used to update the position of each individual in the first intermediate population according to the developmental update formula in the Log-Tent distribution mechanism, so as to obtain the population for the next iteration.

[0019] As a preferred example of the second aspect, the second solving unit includes a third solving subunit, a fourth solving subunit, a fifth solving subunit, a sixth solving subunit, and a seventh solving subunit; The third solution subunit is used to calculate the seed quality of the current iteration based on the problem dimension, the first preset parameter and the first random number using the seed quality calculation formula. The fourth solution subunit is used to calculate the main awn length of the current iteration based on the problem dimension, the population size, and the second random number using the main awn length calculation formula. The fifth solution subunit is used to calculate the eccentric rotation coefficient for the current iteration based on the problem dimension, the second preset parameter and the third random number using the eccentric rotation coefficient calculation formula. The sixth solution subunit is used to calculate the smoothness control parameters for the current iteration based on the current iteration number and the total iteration number using the smoothness control parameter calculation formula. The seventh solution subunit is used to obtain the set of biological parameters for the current iteration based on the seed quality, the main awn length, the eccentric rotation coefficient, and the smoothness control parameter.

[0020] As a preferred example of the second aspect, the data acquisition module includes a first acquisition unit and a second acquisition unit; The first acquisition unit is used to take the slot width value and slot opening width value in the optimal individual as the final slot width size and the final slot opening width size, respectively. The second acquisition unit is used to obtain the motor structure size scheme of the energy storage motor based on the final slot width size, the final slot opening width size, and the fixed structure parameters of the energy storage motor.

[0021] In summary, this application's embodiments, by acquiring decision variables for slot width, slot opening width, and motor structural constraints, lay a solid foundation for subsequent optimization that aligns with engineering realities, reducing invalid searches and infeasible solutions. Based on this data, a mathematical model is established with the objective of minimizing cogging torque, incorporating constraints for back EMF, slot width, slot opening width, and slot area. This model directly addresses the core issue of motor torque fluctuations, which affects operational stability and energy conversion efficiency. Simultaneously, multi-dimensional constraints prevent a single objective from being optimal while other key performance aspects become unbalanced, ensuring comprehensive compliance with optimization guidelines. The use of the Dynamic Optimization Algorithm to iteratively update the population to a preset number of iterations and output the optimal individual breaks through the dependence on gradient information in traditional gradient-based methods. It enables global search in complex solution spaces, avoiding local optima problems, and the preset number of iterations balances optimization accuracy and computational efficiency, continuously filtering for better parameter combinations. The process of determining the motor structural dimensions based on the optimal individual and conducting optimization design connects the algorithm results with engineering applications, ensuring the motor structure matches the core optimization objective. Therefore, this application solves the problem in existing technologies of improving the convergence speed and global search capability of motor optimization while maintaining optimization accuracy.

[0022] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the energy storage motor optimization design method based on the dynamic optimization algorithm of the present invention.

[0023] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the energy storage motor optimization design method based on the dynamic optimization algorithm of the present invention. Attached Figure Description

[0024] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1 This is a flowchart illustrating an embodiment of an energy storage motor optimization design method based on the dynamic optimization algorithm provided by the present invention. Figure 2 A schematic diagram of the algorithm flow of an embodiment of the energy storage motor optimization design method based on the dynamic optimization algorithm provided by the present invention; Figure 3A comparison diagram of solution schemes for an embodiment of the energy storage motor optimization design method based on the dynamic optimization algorithm provided by the present invention; Figure 4 An F1 standard test function diagram of an embodiment of an energy storage motor optimization design method based on the dynamic optimization algorithm provided by the present invention; Figure 5 The F2 standard test function diagram is provided as an embodiment of the energy storage motor optimization design method based on the dynamic optimization algorithm provided by the present invention. Figure 6 The F3 standard test function diagram is provided as an embodiment of the energy storage motor optimization design method based on the dynamic optimization algorithm provided by the present invention. Figure 7 The F4 standard test function diagram is provided as an embodiment of the energy storage motor optimization design method based on the dynamic optimization algorithm provided by the present invention. Figure 8 An F5 standard test function diagram of an embodiment of an energy storage motor optimization design method based on the dynamic optimization algorithm provided by the present invention; Figure 9 The F6 standard test function diagram is provided as an embodiment of the energy storage motor optimization design method based on the dynamic optimization algorithm provided by the present invention. Figure 10 An F7 standard test function diagram for an embodiment of an energy storage motor optimization design method based on the dynamic optimization algorithm provided by the present invention; Figure 11 An F8 standard test function diagram for an embodiment of an energy storage motor optimization design method based on the dynamic optimization algorithm provided by the present invention; Figure 12 The F9 standard test function diagram is provided as an embodiment of the energy storage motor optimization design method based on the dynamic optimization algorithm provided by the present invention. Figure 13 The F10 standard test function diagram is provided as an embodiment of the energy storage motor optimization design method based on the dynamic optimization algorithm provided by the present invention. Figure 14 This is a module structure diagram of an embodiment of an energy storage motor optimization design device based on the dynamic optimization algorithm provided by the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0028] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0029] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0030] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0031] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0032] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0033] Example 1 See Figure 1 To address the problem in existing technologies of simultaneously improving the convergence speed and global search capability of motor optimization while maintaining optimization accuracy, an embodiment of this invention provides an energy storage motor optimization design method based on the Dynamics-based optimization algorithm, comprising: S1. Obtain the slot width decision variable, slot opening width decision variable, and motor structure constraint data of the energy storage motor.

[0034] S2. Establish a motor optimization mathematical model with minimizing the cogging torque assignment as the optimization objective based on the slot width decision variable, the slot opening width decision variable, and the motor structure constraint data; wherein, the cogging torque assignment is obtained based on the slot width decision variable and the slot opening width decision variable, and the constraints of the mathematical model include back EMF constraints, slot width constraints, slot opening width constraints, and slot area constraints.

[0035] In some embodiments of this application, the process of establishing the mathematical model for motor optimization is as follows: First, as shown in Table 1, some structural data of the energy storage motor to be optimized are as follows: Table 1. Energy Storage Motor Structure Data Next, select the slot width. and slot opening width To optimize the variables (decision variables), the cogging torque is chosen as the optimization objective, while ensuring that the back electromotive force remains constant. The mathematical model for motor optimization is as follows: in, This indicates the amplitude of the cogging torque fluctuation. This represents the maximum value of the cogging torque. This represents the minimum value of the cogging torque. This represents the slot width decision variable. The slot opening width is the decision variable. The peak value of the back electromotive force given the slot width and slot opening width. For the target back electromotive force, As the allowable error tolerance, and These are the lower and upper limits of the slot width, respectively. and These are the lower and upper limits of the slot opening width, respectively. The area of ​​the groove is determined by the width of the groove opening and the width of the groove inlet. This represents the maximum allowable tank area.

[0036] S3. Based on the motor optimization mathematical model, the population is iteratively updated using the dynamic optimization algorithm until the current iteration number reaches the preset iteration number, and the best individual in the population in the current iteration is output.

[0037] In some embodiments of this application, the step of iteratively updating the population using the dynamic optimization algorithm based on the motor optimization mathematical model until the current iteration number reaches a preset iteration number, and then outputting the population corresponding to each iteration number, specifically involves: Based on the slot width constraint and slot opening width constraint in the mathematical model, an initial population is generated using an initialization formula. Based on the mathematical model, the set of biological parameters for the current iteration, and the initial population, the dynamic optimization algorithm is used to iteratively update the population until the current iteration count reaches the preset number of iterations, and the optimal individual in the current iteration population is output. The set of biological parameters for the current iteration is obtained by calculating biological parameters using the formula based on the current iteration count, the total number of iterations, the problem dimension, and the population size. In each iteration, the population for the current iteration is explored and developed sequentially according to the Log-Tent distribution mechanism to obtain the population for the next iteration.

[0038] It should be noted that, as Figure 2The diagram shows the flowchart of the dynamic optimization algorithm. First, the population positions are randomly initialized, followed by an iterative loop. In each iteration, the algorithm first determines whether the maximum number of iterations has been reached: if the condition is met, the process terminates; otherwise, it continues to perform biological parameter mapping, associating the algorithm parameters with specific biological laws. Next, the algorithm is divided into two key phases: in the exploration phase, a Log-Normal distribution mechanism is introduced to expand the search range and enhance global exploration capabilities; in the development phase, the Log-Normal distribution mechanism is used for local fine-tuning to improve the quality of solutions and convergence accuracy. This process is repeated until the termination condition is met.

[0039] In some preferred embodiments of this application, the initial population can be generated using the following formula: in, Let i be the positional component of the lowest i individuals in the population in the j-th dimension. Let j be the upper bound of the j-th dimension optimization variable. Let r be the lower bound of the j-th dimension of the optimization variable, and r be a random number ranging from 0 to 1. For population size, The number of dimensions of the problem.

[0040] In some embodiments of this application, the step of exploring and developing the population of the current iteration sequentially according to the Log-Tent distribution mechanism to obtain the population of the next iteration specifically involves: According to the exploratory update formula in the Log-Tent distribution mechanism, the positions of each individual in the current iteration of the population are updated to obtain the first intermediate population. According to the developmental update formula in the Log-Tent distribution mechanism, the positions of each individual in the first intermediate population are updated to obtain the population for the next iteration.

[0041] In some preferred embodiments of this application, the exploratory update formula in the Log-Tent distribution mechanism may be as follows: in, The perturbation weight vector, For smoothness control parameters, For dimension-dependent random vectors, Let be the upper bound vector for each dimension. This is the current generation population position matrix. For the next generation population position matrix, The current optimal individual position, This is a random perturbation matrix generated based on the Log-Tent distribution. Population size.

[0042] In some preferred embodiments of this application, the development update formula in the Log-Tent distribution mechanism may be as follows: in, For dynamic boundary control parameters, Let be the upper bound vector for each dimension of the variable. This represents the current iteration number. This represents the total number of iterations. It is a random number, with a value ranging from 0 to 1. For smoothness control parameters, The current optimal individual position, For random disturbance terms, This is the current generation population position matrix. This is the position matrix for the next generation of the population.

[0043] In some embodiments of this application, the set of biological parameters for the current iteration is obtained by calculating the current iteration number, the total number of iterations, the problem dimension, and the population size using a biological parameter calculation formula, specifically: Based on the problem dimension, the first preset parameter, and the first random number, the seed quality is calculated using the seed quality calculation formula to obtain the seed quality for the current iteration. The main awn length for the current iteration is calculated using the formula for calculating the main awn length based on the problem dimension, the population size, and the second random number. Based on the problem dimension, the second preset parameter, and the third random number, the eccentric rotation coefficient is calculated using the eccentric rotation coefficient calculation formula to obtain the eccentric rotation coefficient for the current iteration. Based on the current iteration number and the total iteration number, the smoothness control parameter is calculated using the smoothness control parameter calculation formula to obtain the smoothness control parameter for the current iteration; Based on the seed quality, primary awn length, eccentric rotation coefficient, and smoothness control parameter of the current iteration, the set of biological parameters for the current iteration is obtained.

[0044] In some preferred embodiments of this application, each parameter in the current iteration's set of biological parameters can be calculated using the following formula: in, For seed quality, The length of the main awn. The eccentric rotation coefficient, For smoothness control parameters, It is a random number, with a value ranging from 0 to 1. The number of dimensions of the problem. For population size, This represents the current iteration number. This represents the maximum number of iterations.

[0045] S4. Determine the motor structure size scheme of the energy storage motor based on the optimal individual, and optimize the design of the energy storage motor based on the motor structure size scheme.

[0046] In some embodiments of this application, determining the motor structure size scheme of the energy storage motor based on the optimal individual specifically includes: The slot width value and slot opening width value in the optimal individual are respectively used as the final slot width size and the final slot opening width size; Based on the final slot width dimension, the final slot opening width dimension, and the fixed structural parameters of the energy storage motor, the motor structure dimension scheme of the energy storage motor is obtained.

[0047] It should be noted that, as shown in Table 2 and Figure 3 The figures show the motor structure dimensions obtained by the unoptimized, AOO optimized, and improved LAOO optimized algorithms, respectively. Table 2 Comparison of Solution Schemes Furthermore, to verify the performance of the algorithm proposed in this invention, the CEC2022 standard test function set was used for testing in this embodiment. The test results are as follows: Figures 4 to 13 As shown, by observing the convergence curve, it can be found that the algorithm proposed in this invention is superior to the original AOO algorithm in both convergence accuracy and speed.

[0048] In summary, this application's embodiments, by acquiring decision variables for slot width, slot opening width, and motor structural constraints, lay a solid foundation for subsequent optimization that aligns with engineering realities, reducing invalid searches and infeasible solutions. Based on this data, a mathematical model is established with the objective of minimizing cogging torque, incorporating constraints for back EMF, slot width, slot opening width, and slot area. This model directly addresses the core issue of motor torque fluctuations, which affects operational stability and energy conversion efficiency. Simultaneously, multi-dimensional constraints prevent a single objective from being optimal while other key performance aspects become unbalanced, ensuring comprehensive compliance with optimization guidelines. The use of the Dynamic Optimization Algorithm to iteratively update the population to a preset number of iterations and output the optimal individual breaks through the dependence on gradient information in traditional gradient-based methods. It enables global search in complex solution spaces, avoiding local optima problems, and the preset number of iterations balances optimization accuracy and computational efficiency, continuously filtering for better parameter combinations. The process of determining the motor structural dimensions based on the optimal individual and conducting optimization design connects the algorithm results with engineering applications, ensuring the motor structure matches the core optimization objective. Therefore, this application solves the problem in existing technologies of improving the convergence speed and global search capability of motor optimization while maintaining optimization accuracy.

[0049] Example 2 like Figure 14 As shown, based on the above method embodiments, corresponding device embodiments are provided; An embodiment of the present invention provides an energy storage motor optimization design device based on the dynamic optimization algorithm, including: a data acquisition module 141, a mathematical model establishment module 142, a solution module 143 and an optimization design module 144; Data acquisition module 141 is used to acquire the slot width decision variable, slot opening width decision variable and motor structure constraint data of the energy storage motor; The mathematical model building module 142 is used to build a motor optimization mathematical model with minimizing the cogging torque assignment as the optimization objective based on the slot width decision variable, the slot opening width decision variable, and the motor structure constraint data; wherein, the cogging torque assignment is obtained based on the slot width decision variable and the slot opening width decision variable, and the constraints of the mathematical model include back EMF constraints, slot width constraints, slot opening width constraints, and slot area constraints; The solution module 143 is used to iteratively update the population using the dynamic optimization algorithm based on the motor optimization mathematical model until the current iteration number reaches the preset iteration number, and output the best individual in the population in the current iteration. The optimization design module 144 is used to determine the motor structure size scheme of the energy storage motor based on the optimal individual, and to optimize the design of the energy storage motor based on the motor structure size scheme.

[0050] In some embodiments of this application, the solving module 143 includes a first solving unit and a second solving unit; The first solving unit is used to generate an initial population using an initialization formula based on the slot width constraint and slot opening width constraint in the mathematical model. The second solving unit is used to iteratively update the population using the Dynamic optimization algorithm based on the mathematical model, the biological parameter set of the current iteration, and the initial population, until the current iteration number reaches the preset iteration number, and output the best individual in the population of the current iteration; wherein, the biological parameter set of the current iteration is obtained by calculating the biological parameter calculation formula based on the current iteration number, the total iteration number, the problem dimension, and the population size. In each iteration, the population of the current iteration is explored and developed in sequence according to the Log-Tent distribution mechanism to obtain the population of the next iteration.

[0051] In some embodiments of this application, the second solving unit includes a first solving subunit and a second solving subunit; The first solution subunit is used to update the position of each individual in the current iteration of the population according to the exploratory update formula in the Log-Tent distribution mechanism, so as to obtain the first intermediate population. The second solution subunit is used to update the position of each individual in the first intermediate population according to the developmental update formula in the Log-Tent distribution mechanism, so as to obtain the population for the next iteration.

[0052] In some embodiments of this application, the second solving unit includes a third solving subunit, a fourth solving subunit, a fifth solving subunit, a sixth solving subunit, and a seventh solving subunit; The third solution subunit is used to calculate the seed quality of the current iteration based on the problem dimension, the first preset parameter and the first random number using the seed quality calculation formula. The fourth solution subunit is used to calculate the main awn length of the current iteration based on the problem dimension, the population size, and the second random number using the main awn length calculation formula. The fifth solution subunit is used to calculate the eccentric rotation coefficient for the current iteration based on the problem dimension, the second preset parameter and the third random number using the eccentric rotation coefficient calculation formula. The sixth solution subunit is used to calculate the smoothness control parameters for the current iteration based on the current iteration number and the total iteration number using the smoothness control parameter calculation formula. The seventh solution subunit is used to obtain the set of biological parameters for the current iteration based on the seed quality, the main awn length, the eccentric rotation coefficient, and the smoothness control parameter.

[0053] In some embodiments of this application, the data acquisition module 141 includes a first acquisition unit and a second acquisition unit; The first acquisition unit is used to take the slot width value and slot opening width value in the optimal individual as the final slot width size and the final slot opening width size, respectively. The second acquisition unit is used to obtain the motor structure size scheme of the energy storage motor based on the final slot width size, the final slot opening width size, and the fixed structure parameters of the energy storage motor.

[0054] In summary, this application's embodiments, by acquiring decision variables for slot width, slot opening width, and motor structural constraints, lay a solid foundation for subsequent optimization that aligns with engineering realities, reducing invalid searches and infeasible solutions. Based on this data, a mathematical model is established with the objective of minimizing cogging torque, incorporating constraints for back EMF, slot width, slot opening width, and slot area. This model directly addresses the core issue of motor torque fluctuations, which affects operational stability and energy conversion efficiency. Simultaneously, multi-dimensional constraints prevent a single objective from being optimal while other key performance aspects become unbalanced, ensuring comprehensive compliance with optimization guidelines. The use of the Dynamic Optimization Algorithm to iteratively update the population to a preset number of iterations and output the optimal individual breaks through the dependence on gradient information in traditional gradient-based methods. It enables global search in complex solution spaces, avoiding local optima problems, and the preset number of iterations balances optimization accuracy and computational efficiency, continuously filtering for better parameter combinations. The process of determining the motor structural dimensions based on the optimal individual and conducting optimization design connects the algorithm results with engineering applications, ensuring the motor structure matches the core optimization objective. Therefore, this application solves the problem in existing technologies of improving the convergence speed and global search capability of motor optimization while maintaining optimization accuracy.

[0055] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the energy storage motor optimization design method based on the dynamic optimization algorithm provided by any of the above-described method embodiments of the present invention.

[0056] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0057] Example 3 Based on the above embodiments of the energy storage motor optimization design method based on the dynamic optimization algorithm, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the energy storage motor optimization design method based on the dynamic optimization algorithm of any embodiment of the present invention.

[0058] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0059] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0060] The processor can 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. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0061] Example 4 Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the energy storage motor optimization design method based on the dynamic optimization algorithm described in any of the above-described method embodiments of the present invention.

[0062] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0063] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for optimizing the design of an energy storage motor based on the dynamic optimization algorithm, characterized in that, include: Obtain the slot width decision variables, slot opening width decision variables, and motor structure constraint data of the energy storage motor; A mathematical model for motor optimization is established based on the slot width decision variable, the slot opening width decision variable, and the motor structure constraint data, with the minimization of the cogging torque assignment as the optimization objective. The cogging torque assignment is obtained based on the slot width decision variable and the slot opening width decision variable. The constraints of the mathematical model include back EMF constraints, slot width constraints, slot opening width constraints, and slot area constraints. Based on the aforementioned mathematical model for motor optimization, the population is iteratively updated using the dynamic optimization algorithm until the current iteration number reaches the preset iteration number, and the optimal individual in the population during the current iteration is output. The optimal individual is used to determine the motor structure and size scheme of the energy storage motor, and the energy storage motor is optimized based on the motor structure and size scheme.

2. The energy storage motor optimization design method based on the dynamic optimization algorithm as described in claim 1, characterized in that, The process involves iteratively updating the population using the dynamic optimization algorithm based on the motor optimization mathematical model until the current iteration number reaches a preset iteration number, and then outputting the population corresponding to each iteration number. Specifically: Based on the slot width constraint and slot opening width constraint in the mathematical model, an initial population is generated using an initialization formula. Based on the mathematical model, the set of biological parameters for the current iteration, and the initial population, the dynamic optimization algorithm is used to iteratively update the population until the current iteration count reaches the preset number of iterations, and the optimal individual in the current iteration population is output. The set of biological parameters for the current iteration is obtained by calculating biological parameters using the formula based on the current iteration count, the total number of iterations, the problem dimension, and the population size. In each iteration, the population for the current iteration is explored and developed sequentially according to the Log-Tent distribution mechanism to obtain the population for the next iteration.

3. The energy storage motor optimization design method based on the dynamic optimization algorithm as described in claim 2, characterized in that, The process of exploring and developing the population in the current iteration according to the Log-Tent distribution mechanism to obtain the population for the next iteration is as follows: According to the exploratory update formula in the Log-Tent distribution mechanism, the positions of each individual in the current iteration of the population are updated to obtain the first intermediate population; According to the developmental update formula in the Log-Tent distribution mechanism, the positions of each individual in the first intermediate population are updated to obtain the population for the next iteration.

4. The energy storage motor optimization design method based on the dynamic optimization algorithm as described in claim 2, characterized in that, The set of biological parameters for the current iteration is obtained by calculating the current iteration number, total iteration number, problem dimension, and population size using the biological parameter calculation formula, specifically: Based on the problem dimension, the first preset parameter, and the first random number, the seed quality is calculated using the seed quality calculation formula to obtain the seed quality for the current iteration. The main awn length for the current iteration is calculated using the formula for calculating the main awn length based on the problem dimension, the population size, and the second random number. Based on the problem dimension, the second preset parameter, and the third random number, the eccentric rotation coefficient is calculated using the eccentric rotation coefficient calculation formula to obtain the eccentric rotation coefficient for the current iteration. Based on the current iteration number and the total iteration number, the smoothness control parameter is calculated using the smoothness control parameter calculation formula to obtain the smoothness control parameter for the current iteration; Based on the seed quality, primary awn length, eccentric rotation coefficient, and smoothness control parameter of the current iteration, the set of biological parameters for the current iteration is obtained.

5. The energy storage motor optimization design method based on the dynamic optimization algorithm as described in claim 1, characterized in that, The step of determining the motor structure size scheme of the energy storage motor based on the optimal individual is specifically as follows: The slot width value and slot opening width value in the optimal individual are respectively used as the final slot width size and the final slot opening width size; Based on the final slot width dimension, the final slot opening width dimension, and the fixed structural parameters of the energy storage motor, the motor structure dimension scheme of the energy storage motor is obtained.

6. A device for optimizing the design of an energy storage motor based on a dynamic optimization algorithm, characterized in that, include: The module includes a data acquisition module, a mathematical model building module, a solution module, and an optimization design module. The data acquisition module is used to acquire the slot width decision variable, slot opening width decision variable, and motor structure constraint data of the energy storage motor. The mathematical model building module is used to build a motor optimization mathematical model with minimizing the cogging torque assignment as the optimization objective based on the slot width decision variable, the slot opening width decision variable, and the motor structure constraint data; wherein, the cogging torque assignment is obtained based on the slot width decision variable and the slot opening width decision variable, and the constraints of the mathematical model include back EMF constraints, slot width constraints, slot opening width constraints, and slot area constraints; The solution module is used to iteratively update the population using the dynamic optimization algorithm based on the motor optimization mathematical model until the current iteration number reaches the preset iteration number, and output the best individual in the population in the current iteration. The optimization design module is used to determine the motor structure size scheme of the energy storage motor based on the optimal individual, and to optimize the design of the energy storage motor based on the motor structure size scheme.

7. The energy storage motor optimization design device based on dynamic optimization algorithm as described in claim 6, characterized in that, The solution module includes a first solution unit and a second solution unit; The first solving unit is used to generate an initial population using an initialization formula based on the slot width constraint and slot opening width constraint in the mathematical model. The second solving unit is used to iteratively update the population using the Dynamic optimization algorithm based on the mathematical model, the biological parameter set of the current iteration, and the initial population, until the current iteration number reaches the preset iteration number, and output the best individual in the population of the current iteration; wherein, the biological parameter set of the current iteration is obtained by calculating the biological parameter calculation formula based on the current iteration number, the total iteration number, the problem dimension, and the population size. In each iteration, the population of the current iteration is explored and developed in sequence according to the Log-Tent distribution mechanism to obtain the population of the next iteration.

8. The energy storage motor optimization design device based on dynamic optimization algorithm as described in claim 7, characterized in that, The second solving unit includes a first solving subunit and a second solving subunit; The first solution subunit is used to update the position of each individual in the current iteration of the population according to the exploratory update formula in the Log-Tent distribution mechanism, so as to obtain the first intermediate population. The second solution subunit is used to update the position of each individual in the first intermediate population according to the developmental update formula in the Log-Tent distribution mechanism, so as to obtain the population for the next iteration.

9. The energy storage motor optimization design device based on dynamic optimization algorithm as described in claim 7, characterized in that, The second solving unit includes a third solving subunit, a fourth solving subunit, a fifth solving subunit, a sixth solving subunit, and a seventh solving subunit; The third solution subunit is used to calculate the seed quality of the current iteration based on the problem dimension, the first preset parameter and the first random number using the seed quality calculation formula. The fourth solution subunit is used to calculate the main awn length of the current iteration based on the problem dimension, the population size, and the second random number using the main awn length calculation formula. The fifth solution subunit is used to calculate the eccentric rotation coefficient for the current iteration based on the problem dimension, the second preset parameter and the third random number using the eccentric rotation coefficient calculation formula. The sixth solution subunit is used to calculate the smoothness control parameters for the current iteration based on the current iteration number and the total iteration number using the smoothness control parameter calculation formula. The seventh solution subunit is used to obtain the set of biological parameters for the current iteration based on the seed quality, the main awn length, the eccentric rotation coefficient, and the smoothness control parameter.

10. The energy storage motor optimization design device based on dynamic optimization algorithm as described in claim 6, characterized in that, The data acquisition module includes a first acquisition unit and a second acquisition unit; The first acquisition unit is used to take the slot width value and slot opening width value in the optimal individual as the final slot width size and the final slot opening width size, respectively. The second acquisition unit is used to obtain the motor structure size scheme of the energy storage motor based on the final slot width size, the final slot opening width size, and the fixed structure parameters of the energy storage motor.

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