Flywheel energy storage motor structure optimization method and device based on improved hedera helix algorithm

Abstract

The invention relates to a flywheel energy storage motor structure optimization method and device based on an improved hedera helix algorithm. The method comprises the following steps: acquiring upper and lower limits of slot depth, upper and lower limits of slot width and cogging torque of the flywheel energy storage motor; taking a peak-to-peak value calculation formula of the cogging torque as a target function; each individual is set to comprise two dimensions of groove depth and groove width, the fitness of each individual is a value of an objective function, and an initial population is obtained through cubic chaotic mapping in a solution space; the growth rate and the fitness of each individual are calculated under the current iteration times, and the individual with the minimum fitness is selected as the current optimal individual; calculating the temporary position of the corresponding individual, correcting the temporary position by using the adjustment vector to obtain a corrected position, updating according to the corrected position, and calculating the fitness of each individual after the position is updated; and when iteration is ended, selecting the groove depth and groove width corresponding to the individual with the minimum fitness from the individuals after position updating as optimal solutions of the optimization variables. The method can improve the performance of the flywheel energy storage motor.

Description

Technical Field

[0001] The present application relates to the technical field of flywheel energy storage motor optimization, and in particular to a flywheel energy storage motor structure optimization method, device, computer equipment, computer-readable storage medium and computer program product based on an improved ivy algorithm. Background Art

[0002] With the expansion of renewable energy applications, flywheel energy storage systems are gaining widespread adoption in power peak shaving and frequency regulation due to their high power density, rapid charge and discharge capabilities, and long lifespan. As a key component of flywheel energy storage systems, the performance of the motor directly impacts the overall energy efficiency and reliability of the system.

[0003] Currently, traditional motor structural parameter optimization relies heavily on engineering experience or classical optimization algorithms (such as genetic algorithms and particle swarm optimization) for solution. However, these algorithms often suffer from problems such as being easily trapped in local optimality, slow convergence, and insufficient search diversity in high-dimensional and complex search spaces, making it difficult to obtain a global optimal solution. This results in poor performance of flywheel energy storage motors and, consequently, ineffective improvements in flywheel energy storage system performance. Summary of the Invention

[0004] Based on this, it is necessary to provide a flywheel energy storage motor structure optimization method, device, computer equipment, computer-readable storage medium and computer program product based on an improved ivy algorithm to improve the performance of the flywheel energy storage motor in response to the above technical problems.

[0005] In a first aspect, the present application provides a flywheel energy storage motor structure optimization method based on an improved ivy algorithm, comprising:

[0006] Obtain the upper and lower limits of slot depth, slot width, and cogging torque of the flywheel energy storage motor;

[0007] Constructing an objective function, wherein the objective function is a peak-to-peak value calculation formula of the cogging torque;

[0008] Each ivy individual is set to include two dimensions, namely, groove depth and groove width. The fitness of each ivy individual is the value of the objective function. An ivy population is randomly generated. A solution space is formed using the upper and lower limits of the groove depth and the upper and lower limits of the groove width. A cubic chaotic mapping is performed on the ivy population within the solution space to obtain an initial population.

[0009] Set the maximum number of iterations, calculate the growth rate and fitness of each ivy individual under the current number of iterations, and select the ivy individual with the smallest fitness as the current optimal ivy individual under the current number of iterations;

[0010] Substituting the growth rate of each ivy individual into a position update formula to calculate a temporary position of the corresponding ivy individual, calculating a revised position of each ivy individual using the adjustment vector, the temporary position, and the position of the current optimal ivy individual, updating each ivy individual according to the revised position, and calculating the fitness of each ivy individual after the position update;

[0011] When the number of iterations reaches the maximum number of iterations, the fitness of all ivy individuals after position update in the last iteration is obtained to screen out the minimum fitness, and the ivy individual corresponding to the minimum fitness is used as the target optimal ivy individual, and the groove depth and groove width corresponding to the target optimal ivy individual are used as the optimal solution of the optimization variables.

[0012] In one embodiment, performing cubic chaos mapping on the ivy population in the solution space to obtain an initial population includes: obtaining a mathematical expression of cubic chaos mapping:

[0013]

[0014] Where, I 0,i is the position of the i-th ivy individual in the randomly generated ivy population; is the position of the i-th ivy individual after cubic chaos mapping; the position of each ivy individual in the randomly generated ivy population is substituted into the mathematical expression of the cubic chaos mapping to obtain a cubic chaos mapping population; the cubic chaos mapping population is mapped to the solution space to obtain an initial population.

[0015] In one embodiment, mapping the cubic chaotic map population to the solution space to obtain an initial population includes: obtaining a mapping formula:

[0016]

[0017] Where, I i is the position of the i-th ivy individual, max is the upper boundary vector of the solution space, min is the lower boundary vector of the solution space, I max is the maximum individual vector of population position; I min is the minimum individual vector of population position; ⊙ is Hadamard multiplication; is Hadamard division; the position of each ivy individual in the cubic chaotic mapping population is substituted into the mapping formula to obtain the positions of all ivy individuals in the initial population.

[0018] In one embodiment, the position update formula is a piecewise function; substituting the growth rate of each ivy individual into the position update formula to calculate the temporary position of the corresponding ivy individual includes: calculating a growth correction factor, and calculating the product of the growth correction factor and the fitness of the current optimal ivy individual; randomly selecting an ivy individual as an update object, and comparing the fitness of the update object with the product; if the fitness of the update object is less than the product, substituting the growth rate of the update object into the function expression of the first interval to calculate the temporary position of the update object; if the fitness of the update object is greater than or equal to the product, substituting the growth rate of the update object into the function expression of the second interval to calculate the temporary position of the update object.

[0019] In one embodiment, the process of determining the adjustment vector includes: calculating the adjustment vector using the following formula:

[0020]

[0021] Where weibull(D) is the adjustment vector, D is the dimension of the optimization variable, sign(·) is the sign function, rand represents a random number, and wblrnd() represents the Weibull distribution function.

[0022] In one embodiment, the calculating the corrected position of each ivy individual using the adjustment vector, the temporary position and the position of the current best ivy individual includes: for each ivy individual, calculating the Hadamard product of the adjustment vector and the temporary position; and calculating the sum of the Hadamard product and the position of the current best ivy individual as the corrected position of the corresponding ivy individual.

[0023] In a second aspect, the present application also provides a flywheel energy storage motor structure optimization device based on an improved ivy algorithm, comprising:

[0024] An acquisition module is used to obtain the upper and lower limits of the slot depth, the upper and lower limits of the slot width, and the cogging torque of the flywheel energy storage motor;

[0025] An objective function construction module, used to construct an objective function, wherein the objective function is a peak-to-peak value calculation formula of the cogging torque;

[0026] a population initialization module, configured to set each ivy individual to have two dimensions, namely, groove depth and groove width, and the fitness of each ivy individual to be the value of the objective function; randomly generate an ivy population; form a solution space using the upper and lower limits of the groove depth and the upper and lower limits of the groove width; and perform cubic chaotic mapping on the ivy population within the solution space to obtain an initial population;

[0027] The calculation module is used to set the maximum number of iterations, calculate the growth rate and fitness of each ivy individual under the current number of iterations, and select the ivy individual with the smallest fitness as the current optimal ivy individual under the current number of iterations;

[0028] an updating module, configured to substitute the growth rate of each ivy individual into a position updating formula to calculate a temporary position of the corresponding ivy individual, calculate a revised position of each ivy individual using the adjustment vector, the temporary position, and the position of the current optimal ivy individual, update each ivy individual according to the revised position, and calculate the fitness of each ivy individual after the position update;

[0029] The optimal solution screening module is used to obtain the fitness of all ivy individuals after position update in the last iteration when the number of iterations reaches the maximum number of iterations, so as to screen out the minimum fitness, and use the ivy individual corresponding to the minimum fitness as the target optimal ivy individual, and the groove depth and groove width corresponding to the target optimal ivy individual as the optimal solution of the optimization variables.

[0030] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0031] Obtain the upper and lower limits of slot depth, slot width, and cogging torque of the flywheel energy storage motor;

[0032] Constructing an objective function, wherein the objective function is a peak-to-peak value calculation formula of the cogging torque;

[0033] Each ivy individual is set to include two dimensions, namely, groove depth and groove width. The fitness of each ivy individual is the value of the objective function. An ivy population is randomly generated. A solution space is formed using the upper and lower limits of the groove depth and the upper and lower limits of the groove width. A cubic chaotic mapping is performed on the ivy population within the solution space to obtain an initial population.

[0034] Set the maximum number of iterations, calculate the growth rate and fitness of each ivy individual under the current number of iterations, and select the ivy individual with the smallest fitness as the current optimal ivy individual under the current number of iterations;

[0035] Substituting the growth rate of each ivy individual into a position update formula to calculate a temporary position of the corresponding ivy individual, calculating a revised position of each ivy individual using the adjustment vector, the temporary position, and the position of the current optimal ivy individual, updating each ivy individual according to the revised position, and calculating the fitness of each ivy individual after the position update;

[0036] When the number of iterations reaches the maximum number of iterations, the fitness of all ivy individuals after position update in the last iteration is obtained to screen out the minimum fitness, and the ivy individual corresponding to the minimum fitness is used as the target optimal ivy individual, and the groove depth and groove width corresponding to the target optimal ivy individual are used as the optimal solution of the optimization variables.

[0037] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0038] Obtain the upper and lower limits of slot depth, slot width, and cogging torque of the flywheel energy storage motor;

[0039] Constructing an objective function, wherein the objective function is a peak-to-peak value calculation formula of the cogging torque;

[0040] Each ivy individual is set to include two dimensions, namely, groove depth and groove width. The fitness of each ivy individual is the value of the objective function. An ivy population is randomly generated. A solution space is formed using the upper and lower limits of the groove depth and the upper and lower limits of the groove width. A cubic chaotic mapping is performed on the ivy population within the solution space to obtain an initial population.

[0041] Set the maximum number of iterations, calculate the growth rate and fitness of each ivy individual under the current number of iterations, and select the ivy individual with the smallest fitness as the current optimal ivy individual under the current number of iterations;

[0042] Substituting the growth rate of each ivy individual into a position update formula to calculate a temporary position of the corresponding ivy individual, calculating a revised position of each ivy individual using the adjustment vector, the temporary position, and the position of the current optimal ivy individual, updating each ivy individual according to the revised position, and calculating the fitness of each ivy individual after the position update;

[0043] When the number of iterations reaches the maximum number of iterations, the fitness of all ivy individuals after position update in the last iteration is obtained to screen out the minimum fitness, and the ivy individual corresponding to the minimum fitness is used as the target optimal ivy individual, and the groove depth and groove width corresponding to the target optimal ivy individual are used as the optimal solution of the optimization variables.

[0044] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0045] Obtain the upper and lower limits of slot depth, slot width, and cogging torque of the flywheel energy storage motor;

[0046] Constructing an objective function, wherein the objective function is a peak-to-peak value calculation formula of the cogging torque;

[0047] Each ivy individual is set to include two dimensions, namely, groove depth and groove width. The fitness of each ivy individual is the value of the objective function. An ivy population is randomly generated. A solution space is formed using the upper and lower limits of the groove depth and the upper and lower limits of the groove width. A cubic chaotic mapping is performed on the ivy population within the solution space to obtain an initial population.

[0048] Set the maximum number of iterations, calculate the growth rate and fitness of each ivy individual under the current number of iterations, and select the ivy individual with the smallest fitness as the current optimal ivy individual under the current number of iterations;

[0049] Substituting the growth rate of each ivy individual into a position update formula to calculate a temporary position of the corresponding ivy individual, calculating a revised position of each ivy individual using the adjustment vector, the temporary position, and the position of the current optimal ivy individual, updating each ivy individual according to the revised position, and calculating the fitness of each ivy individual after the position update;

[0050] When the number of iterations reaches the maximum number of iterations, the fitness of all ivy individuals after position update in the last iteration is obtained to screen out the minimum fitness, and the ivy individual corresponding to the minimum fitness is used as the target optimal ivy individual, and the groove depth and groove width corresponding to the target optimal ivy individual are used as the optimal solution of the optimization variables.

[0051] The above-mentioned flywheel energy storage motor structure optimization method, device, computer equipment, computer-readable storage medium and computer program product based on the improved ivy algorithm adopt the peak-to-peak value calculation formula of the tooth slot torque as the objective function, set each ivy individual to include two dimensions of slot depth and slot width, and the fitness of each ivy individual is the value of the objective function. The ivy population is subjected to cubic chaos mapping in the solution space to obtain the initial population; thereby, in the initialization stage, more complex nonlinear perturbations can be generated through cubic chaos mapping, effectively enhancing the global exploration ability of the algorithm and avoiding the algorithm from converging to the local optimal solution too early in the optimization process; in addition, in the iterative stage, the growth rate of each ivy individual is substituted into the position update formula at each iteration to calculate the temporary position of the corresponding ivy individual, and the adjustment vector and temporary position are used to adjust the position of the ivy individual. The position of the current optimal ivy individual is calculated, the corrected position of each ivy individual is calculated, each ivy individual is updated according to the corrected position, and the fitness of each ivy individual after the position update is calculated; therefore, compared with the method of directly using the position update formula to update the individual position in the related art, the present invention comprehensively adjusts the vector, the temporary position and the position of the current optimal ivy individual to obtain the corrected position, and uses the corrected position to update the individual position, which can better improve the global search capability and the global development capability. Therefore, compared with the related art, the present invention can better obtain the global optimal solution of the slot depth and slot width, and use the global optimal solution to optimize the motor structure, which can significantly improve the air gap magnetic flux distribution, thereby reducing the cogging torque, improving the smoothness of the motor operation, and effectively improving the performance of the flywheel energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 1. A schematic flow chart of a flywheel energy storage motor structure optimization method based on an improved Ivy algorithm in one embodiment;

[0054] Figure 2 A partial schematic diagram of an energy storage flywheel motor in one embodiment;

[0055] Figure 3 A schematic flow chart of a flywheel energy storage motor structure optimization method based on an improved ivy algorithm in another embodiment;

[0056] Figure 4 Graph showing the distribution of the initialized population under different algorithms in one embodiment;

[0057] Figure 5 is a graph showing convergence efficiency under different algorithms in one embodiment;

[0058] Figure 6 A histogram of solution time under different algorithms in one embodiment;

[0059] Figure 7 is a graph showing cogging torque under different algorithms in one embodiment;

[0060] Figure 8 1. A structural block diagram of a flywheel energy storage motor structure optimization device based on an improved ivy algorithm in one embodiment;

[0061] Figure 9 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0063] As a key component of the flywheel energy storage system, the performance of the motor directly affects the overall energy efficiency and reliability of the system. During high-speed operation, the cogging torque generated by the motor will cause output fluctuations, which in turn will cause speed pulsation, noise and vibration, affecting the operational stability of the energy storage system. Therefore, reducing the cogging torque has become an important goal in motor design. The magnitude of the cogging torque is affected by multiple structural parameters, among which the stator slot depth (H s2 ) and slot width (B s0 ) is the most direct structural factor. Improper slot parameters can cause periodic variations in the air gap permeability, which in turn increases the cogging torque. Optimizing these two parameters can significantly improve the air gap flux density distribution, thereby reducing cogging torque and improving motor operation smoothness. In flywheel energy storage systems, this improvement helps improve energy storage efficiency, reduce mechanical stress and vibration, and extend system life.

[0064] If the Ivy Intelligent Optimization (IVY) algorithm is directly used to optimize the motor parameters. The IVY algorithm is a natural heuristic algorithm that simulates the spreading behavior of plants for global search, and shows certain search capabilities and balance in solving general optimization problems. However, the algorithm still has obvious deficiencies in convergence accuracy, search diversity and global optimization capabilities for complex problems, and is prone to falling into local optimality, especially when it involves multiple strongly coupled structural parameters (such as slot depth and slot width). The effect is limited. In motor structure design, especially for permanent magnet synchronous motors, parameters such as slot depth and slot width in the stator structure have a significant impact on the cogging torque. Existing technologies usually ignore the deep impact of these parameters on the periodic changes of the cogging torque, or rely only on limited experimental methods and traditional algorithms for optimization. It is difficult to take into account the complex constraints between back electromotive force maintenance and cogging torque suppression, and the optimization results are often unsatisfactory.

[0065] The specific defects of using the IVY algorithm include: (1) Local optimal trap: The IVY algorithm has limited balance between exploration and development. Especially in multi-peak and high-dimensional problems, the algorithm is prone to fall into local optimal solutions, resulting in unsatisfactory overall search results. (2) Insufficient convergence speed and accuracy: The unimproved IVY algorithm shows certain limitations in convergence speed and optimization accuracy. Especially when the search space complexity is high, it is difficult to quickly find the global optimal solution. (3) Lack of search diversity: The unimproved IVY algorithm is relatively simple in introducing perturbations and randomness, and it is difficult to induce complex search behaviors, which limits its adaptability in global and local searches. As the iteration proceeds, the IVY algorithm is prone to lose diversity in the later searches, resulting in a decrease in the algorithm's global exploration ability and affecting the final optimization effect.

[0066] The drawbacks of using traditional chaotic mapping algorithms include: (1) The chaotic behavior of traditional mapping is not as rich as that of complex mapping (such as cubic chaotic mapping) under different parameter values. It can only generate chaotic behavior in a limited range, resulting in insufficient sequence diversity. (2) Traditional mapping (such as Tent mapping) is linear and piecewise, and the chaotic sequences it generates lack the complex structure of cubic or other high-order nonlinear mappings. This may limit the performance of optimization algorithms when searching high-dimensional spaces, especially when more randomness or irregularity is required to cover the search space.

[0067] The defects of using traditional distribution algorithms include: In intelligent optimization algorithms, in order to improve the global search ability of the population, probability distribution functions are often introduced to perturb and update the positions of individuals. However, traditional probability distribution strategies (such as normal distribution, Gaussian distribution or uniform distribution) have the following limitations: (1) Strong local perturbation ability and weak global exploration ability: Traditional distributions often generate perturbation values ​​with the center value as the symmetric center, resulting in the search step being concentrated in a local range, making it difficult to escape the local optimal trap. Especially in high-dimensional and multi-peak function optimization, the global exploration ability is obviously insufficient. (2) Single distribution form and poor adaptability: For example, normal distribution and uniform distribution have fixed shape parameters, which makes it difficult to flexibly adjust the search intensity according to the iterative stage, limiting their role in different stages of the algorithm (exploration and development). (3) Insufficient coverage of the search space: Some traditional distributions are limited by symmetry or boundary characteristics when generating random numbers, which may cause uneven coverage of the search area, thereby affecting the algorithm's comprehensive exploration ability of the solution space.

[0068] To solve the above problems, the present invention proposes a flywheel energy storage motor structure optimization method based on the improved Ivy algorithm. This method adopts the improved IVY algorithm, which can maintain strong search capability and global exploration when optimizing motor structural parameters (especially slot depth and slot width), thereby effectively reducing the cogging torque and improving the performance of the flywheel energy storage system.

[0069] In an exemplary embodiment, Figure 1 As shown, a flywheel energy storage motor structure optimization method based on the improved ivy algorithm is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understandable that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0070] Step 102: Obtain the upper and lower limits of the slot depth, the upper and lower limits of the slot width, and the cogging torque of the flywheel energy storage motor.

[0071] Among them, such as Figure 2 As shown, if the z axis is the axial direction of the stator, the slot width B s0 is the opening width of the slot. The slot depth H s2 is the slot depth, or the distance from the bottom of the slot to the slot opening. The upper and lower limits of the slot depth refer to the maximum and minimum slot depths. The upper and lower limits of the slot width refer to the maximum and minimum slot widths. Cogging torque is the torque generated by the interaction between the permanent magnets and the stator core of a permanent magnet motor when the windings are deenergized.

[0072] Exemplarily, the maximum value of the groove depth, the minimum value of the groove depth, the maximum value of the groove width, and the minimum value of the groove width are obtained; and the cogging torque under different groove depths and groove widths is also obtained.

[0073] Step 104 : constructing an objective function, where the objective function is a peak-to-peak value calculation formula of the cogging torque.

[0074] The objective function is the function to be minimized under given constraints. The peak-to-peak value is calculated by subtracting the minimum value from the maximum value.

[0075] Exemplarily, the peak-to-peak value calculation formula of the cogging torque is the maximum value of the cogging torque minus the minimum value of the cogging torque. The peak-to-peak value calculation formula of the cogging torque is defined as the objective function, and minimizing the peak-to-peak value of the cogging torque is used as the optimization goal.

[0076] Step 106 , setting each ivy individual to include two dimensions, namely, groove depth and groove width. The fitness of each ivy individual is the value of the objective function. An ivy population is randomly generated. The upper and lower limits of the groove depth and the upper and lower limits of the groove width are used to form a solution space. A cubic chaotic mapping is performed on the ivy population within the solution space to obtain an initial population.

[0077] Dimensionality refers to the number of optimization variables. Fitness is a quantitative indicator of the quality of individuals in a population. An ivy population is a collection of ivy individuals. The solution space is a two-dimensional region formed by upper and lower limits on the groove depth and width, which includes all possible candidate solutions. Cubic chaos mapping is a nonlinear processing method that generates complex dynamic behaviors using simple mathematical rules. The initial population position is the population position targeted during the first iteration of the iterative process.

[0078] For example, the optimization variables are set to include groove depth and groove width, so the dimension of each ivy individual is two. Each ivy individual corresponds to a set of groove depths and groove widths, and the position of each ivy individual is a vector composed of the groove depth and groove width. The fitness of each ivy individual is set as the value of the objective function. The population size (i.e., the number of individuals in the population) is determined, and an ivy population is randomly generated. The upper and lower limits of the groove depth and groove width are used to form a solution space. The ivy population is then subjected to cubic chaos mapping within the solution space to obtain the initial population.

[0079] Step 108: Set the maximum number of iterations, calculate the growth rate and fitness of each ivy individual under the current number of iterations, and select the ivy individual with the smallest fitness as the current optimal ivy individual under the current number of iterations.

[0080] The maximum number of iterations refers to the total number of iterations. The growth rate refers to the parameter used by the algorithm to simulate the population expansion rate during the growth of ivy plants.

[0081] Exemplarily, a maximum number of iterations is set, and for each iteration process, the growth rate and fitness of each ivy individual are calculated, and then the ivy individual with the smallest fitness is selected from all ivy individuals as the current optimal ivy individual under the current number of iterations.

[0082] Step 110, substitute the growth rate of each ivy individual into the position update formula to calculate the temporary position of the corresponding ivy individual, calculate the corrected position of each ivy individual using the adjustment vector, the temporary position and the position of the current optimal ivy individual, update each ivy individual according to the corrected position and calculate the fitness of each ivy individual after the position update.

[0083] The position update formula is the formula used to calculate the desired position for each ivy plant. The temporary position is the position calculated using the position update formula. The adjustment vector is the vector used to correct the temporary position. The corrected position is the position after correcting the temporary position.

[0084] Exemplarily, a position update formula is obtained, and the calculated growth rate of each ivy individual is substituted into the position update formula to calculate the temporary position of the corresponding ivy individual; the adjusted vector, the temporary position and the position of the current optimal ivy individual are used to calculate the corrected position of each ivy individual, and after each ivy individual is updated according to the corrected position, the fitness of each ivy individual after the position update is calculated.

[0085] Step 112: When the number of iterations reaches the maximum number of iterations, the fitness of all ivy individuals after position update in the last iteration is obtained to screen out the minimum fitness, and the ivy individual corresponding to the minimum fitness is used as the target optimal ivy individual, and the groove depth and groove width corresponding to the target optimal ivy individual are used as the optimal solution of the optimization variables.

[0086] Among them, the optimal solution of the optimization variables refers to the optimal solution of the groove depth and the optimal solution of the groove width.

[0087] For example, when the number of iterations reaches the maximum number of iterations, the iteration is completed. For the last iteration among all the iterations that have been experienced, the fitness of all ivy individuals after position update in the last iteration is obtained. The minimum fitness is selected from all fitnesses, and the ivy individual corresponding to the minimum fitness is used as the target optimal ivy individual. The groove depth and groove width corresponding to the target optimal ivy individual are the optimal solution of the optimization variables.

[0088] In the above-mentioned flywheel energy storage motor structure optimization method based on the improved ivy algorithm, the peak-to-peak value calculation formula of the tooth slot torque is used as the objective function, and each ivy individual is set to include two dimensions: slot depth and slot width. The fitness of each ivy individual is the value of the objective function, and the ivy population is subjected to cubic chaos mapping in the solution space to obtain the initial population; thereby, in the initialization stage, more complex nonlinear perturbations can be generated through cubic chaos mapping, effectively enhancing the global exploration ability of the algorithm and avoiding the algorithm from converging to the local optimal solution too early during the optimization process; in addition, in the iterative stage, the growth rate of each ivy individual is substituted into the position update formula at each iteration to calculate the temporary position of the corresponding ivy individual, and the position of the adjustment vector, temporary position and current optimal ivy individual is used. The present invention calculates the corrected position of each ivy individual, updates each ivy individual according to the corrected position, and calculates the fitness of each ivy individual after the position update; therefore, compared with the method of directly using the position update formula to update the individual position in the related art, the present invention comprehensively adjusts the vector, temporary position and the position of the current optimal ivy individual to obtain the corrected position, and uses the corrected position to update the individual position, which can better improve the global search capability and global development capability. Therefore, compared with the related art, the present invention can better obtain the global optimal solution of slot depth and slot width, and use the global optimal solution to optimize the motor structure, which can significantly improve the air gap magnetic flux distribution, thereby reducing the cogging torque, improving the smoothness of the motor operation, and effectively improving the performance of the flywheel energy storage system.

[0089] In an exemplary embodiment, performing a cubic chaos map on the ivy population in the solution space to obtain an initial population includes: obtaining a mathematical expression of the cubic chaos map:

[0090]

[0091] Where, I 0,i is the position of the i-th ivy individual in the randomly generated ivy population; is the position of the i-th ivy individual after cubic chaos mapping; substitute the position of each ivy individual in the randomly generated ivy population into the mathematical expression of cubic chaos mapping to obtain the cubic chaos mapping population; map the cubic chaos mapping population to the solution space to obtain the initial population.

[0092] Each individual in the randomly generated ivy population corresponds to a random combination of groove depth and width. A random combination refers to a randomly generated combination of groove depth and width within the solution space. The groove depth and width in each random combination are within the corresponding upper and lower limits. The position of each individual in the cubic chaos map population is the position of the corresponding individual after the cubic chaos map is applied.

[0093] In this embodiment, by performing cubic chaos mapping on the ivy population, the population can be made to have a better uniform distribution in the solution space, thereby avoiding the population concentration problem that may be caused by the traditional random initialization method.

[0094] In an exemplary embodiment, mapping the cubic chaos map population to the solution space to obtain the initial population includes: obtaining a mapping formula:

[0095]

[0096] Where, I i is the position of the i-th ivy individual, max is the upper boundary vector of the solution space, min is the lower boundary vector of the solution space, I max is the maximum individual vector of population position; I min is the minimum individual vector of population position; ⊙ is Hadamard multiplication; is Hadamard division; substitute the position of each ivy individual in the cubic chaos mapping population into the mapping formula to obtain the position of all ivy individuals in the initial population.

[0097] The upper boundary vector of the solution space refers to the vector consisting of the maximum slot depth and the maximum slot width. The lower boundary vector of the solution space refers to the vector consisting of the minimum slot depth and the minimum slot width. The maximum population position individual vector refers to the position of the individual closest to the upper boundary of the solution space. The minimum population position individual vector refers to the position of the individual closest to the lower boundary of the solution space. Hadamard multiplication is an element-by-element multiplication operation on matrices or tensors. Hadamard division is an element-by-element division operation.

[0098] In this embodiment, the cubic chaos map population is mapped through a mapping formula, which can adapt to the variable range of the optimization problem. The generated initial population has better global search capabilities than traditional methods, which is beneficial to the performance of the algorithm in high-dimensional complex optimization problems.

[0099] In an exemplary embodiment, the position update formula is a piecewise function; the growth rate of each ivy individual is substituted into the position update formula to calculate the temporary position of the corresponding ivy individual, including: calculating a growth correction factor, calculating the product of the growth correction factor and the fitness of the current optimal ivy individual; randomly selecting an ivy individual as an update object, and comparing the fitness of the update object with the product; if the fitness of the update object is less than the product, substituting the growth rate of the update object into the function expression of the first interval to calculate the temporary position of the update object; if the fitness of the update object is greater than or equal to the product, substituting the growth rate of the update object into the function expression of the second interval to calculate the temporary position of the update object.

[0100] The piecewise function is a whole composed of functions over multiple domains. The growth correction factor is used to control the magnitude and adaptability of the ivy population's growth rate over time. The first and second intervals represent different domains of the piecewise function.

[0101] In this embodiment, by comparing the fitness of the update object with the product, if the fitness of the update object is less than the product, the growth rate of the update object is substituted into the function expression of the first interval to calculate the temporary position of the update object; if the fitness of the update object is greater than or equal to the product, the growth rate of the update object is substituted into the function expression of the second interval to calculate the temporary position of the update object, which enables each individual to alternately climb and expand based on the relationship between the fitness and the product.

[0102] In an exemplary embodiment, the process of determining the adjustment vector includes: calculating the adjustment vector using the following formula:

[0103]

[0104] Where weibull(D) is the adjustment vector, D is the dimension of the optimization variable, sign(·) is the sign function, rand represents a random number, and wblrnd() represents the Weibull distribution function.

[0105] The Weibull distribution function is also called the Weibull function. The dimension of the optimization variable refers to the number of optimization variables. A sign function is a special function in mathematics with a range of {-1, 0, 1}.

[0106] In this embodiment, by introducing the Weibull distribution strategy, the population distribution can be further adjusted.

[0107] In an exemplary embodiment, the corrected position of each ivy individual is calculated using the adjustment vector, the temporary position, and the position of the current optimal ivy individual, including: for each ivy individual, calculating the Hadamard product of the adjustment vector and the temporary position; calculating the sum of the Hadamard product and the position of the current optimal ivy individual, and using the sum as the corrected position of the corresponding ivy individual.

[0108] Here, the Hadamard product refers to the product obtained by using Hadamard multiplication.

[0109] In this embodiment, by calculating the Hadamard product of the adjustment vector and the temporary position; calculating the sum of the Hadamard product and the position of the current optimal ivy individual and using it as the corrected position of the corresponding ivy individual, the temporary position can be corrected using the Weibull distribution, which can improve the diversity of the population, enhance the global search capability and the global development capability, and ensure the convergence speed in the later stage.

[0110] For example, taking a flywheel energy storage motor with one pair of poles as an example, the flywheel energy storage motor structure optimization method based on the improved ivy algorithm specifically includes:

[0111] Step 202: Obtain the upper and lower limits of the slot depth, the upper and lower limits of the slot width, and the cogging torque of the flywheel energy storage motor.

[0112] In step 202, the structural parameters of the flywheel energy storage motor of this embodiment are set as shown in Table 1.

[0113] Table 1 Structural parameters of flywheel energy storage motor

[0114]

[0115]

[0116] In step 202, the slot width B of the flywheel energy storage motor is defined. s0 (also called slot opening width) and slot depth H s2 (also known as slot opening width) as the motor structure optimization variable. Among them, Figure 2 As shown, the slot width B s0 refers to Figure 2 As shown, the groove depth H s2 It refers to the depth of the groove, that is, the distance from the bottom of the groove to the groove mouth.

[0117] In step 202, if Figure 3 As shown, it is also necessary to determine the upper and lower limits of the motor structure optimization variables. Specifically, it is also necessary to obtain the upper and lower limits of the slot depth of the flywheel energy storage motor, the upper and lower limits of the slot width, and the lower and upper limits of the slot width.

[0118] Step 204 : constructing an objective function, where the objective function is a peak-to-peak value calculation formula of the cogging torque.

[0119] In step 204, the cogging torque assignment is selected as the optimization target, and the mathematical optimization model of the flywheel energy storage motor structure (i.e., the minimization objective function) is:

[0120] min T(H s2 ,B s0 )=CogT max -CogT min ;

[0121] The constraints (also called boundary conditions) set are: H s2,min ≤H s2 ≤H s,max 、B s,min ≤B s0 ≤B s,max .

[0122] Where CogT maxis the maximum value of the cogging torque, CogT min is the minimum value of the cogging torque, H s2,min is the lower limit of the groove depth, H s,max is the upper limit of the groove depth, for example, H s2,min =10mm, H s,max =20mm. B s,min is the lower limit of the slot width, B s,max is the upper limit of the slot width. For example, B s,min =1mm, B s,max =3mm.

[0123] In step 206, each ivy individual is set to include two dimensions, namely, groove depth and groove width. The fitness of each ivy individual is used as the value of the objective function. An ivy population is randomly generated. The upper and lower limits of the groove depth and the upper and lower limits of the groove width are used to form a solution space. A cubic chaotic mapping is performed on the ivy population within the solution space to obtain an initial population.

[0124] Step 206 corresponds to the initialization step of the improved IVY algorithm of the present invention. In this embodiment, the improved IVY algorithm builds upon the traditional IVY intelligent optimization algorithm by introducing a cubic chaos map to initialize the population and adjust individual update strategies. The cubic chaos map, through its cubic nonlinear properties, can generate numerical sequences with high randomness and complex dynamics, thereby expanding the scope of exploration and avoiding local optima during the algorithm's search process.

[0125] like Figure 3 As shown, according to the boundary conditions, the cubic mapping method (i.e. cubic chaos mapping method) is used to initialize the population H s2 (i),B s0 (i), where i ranges from 1 to Nop. Nop is the population size (i.e., the number of populations).

[0126] Specifically: The mathematical expression of cubic chaos map is as follows:

[0127]

[0128] Among them, I0,i is the position of the i-th ivy individual in the randomly generated ivy population, and I0,i is determined by H s2 (i) and B s0 (i) the vector formed; is the position of the i-th ivy individual after cubic chaos mapping;

[0129] The population H is analyzed by the mathematical expression of cubic chaos map. s2 (i),B s0(i), i = 1 to Nop, after cubic chaos mapping processing, a cubic chaos mapping population is obtained. In order to adapt to the variable range of the optimization problem, the cubic chaos mapping population needs to be normalized (this invention refers to mapping to the solution space processing). The normalization processing is completed by the following formula:

[0130]

[0131] Among them, I i is the position of the i-th ivy individual, max is the upper boundary vector of the solution space, min is the lower boundary vector of the solution space, I max is the maximum individual vector of population position; I min is the minimum individual vector of population position; ⊙ is Hadamard multiplication; is Hadamard division; substitute the position of each ivy individual in the cubic chaos mapping population into the mapping formula to obtain the position of all ivy individuals in the initial population.

[0132] In step 206, a cubic chaos map is used to generate an initial population. The nonlinear cubic chaos map ensures that the generated population has a good uniform distribution in the solution space, avoiding the population concentration problem that may be caused by traditional random initialization methods. Specifically, the cubic chaos map formula is used to generate the solution space position of each individual and is mapped to the target range of the problem through a normalization formula. The generated initial solution has a better global search capability than traditional methods, which is beneficial to the performance of the algorithm in high-dimensional complex optimization problems.

[0133] Step 208: Set the maximum number of iterations, calculate the growth rate and fitness of each ivy individual at the current number of iterations, and select the ivy individual with the smallest fitness as the current optimal ivy individual at the current number of iterations.

[0134] Step 210: Substitute the growth rate of each ivy individual into the position update formula to calculate the temporary position of the corresponding ivy individual. Calculate the corrected position of each ivy individual using the adjustment vector, the temporary position, and the position of the current optimal ivy individual. Update each ivy individual according to the corrected position and calculate the fitness of each ivy individual after the position update.

[0135] Step 212: When the number of iterations reaches the maximum number of iterations, the fitness of all ivy individuals after position update in the last iteration is obtained to screen out the minimum fitness, and the ivy individual corresponding to the minimum fitness is used as the target optimal ivy individual, and the groove depth and groove width corresponding to the target optimal ivy individual are used as the optimal solution of the optimization variables.

[0136] Steps 208 and 212 correspond to the exploration process of the improved IVY algorithm of the present invention. Each iteration in the exploration process involves coordinating and orderly population growth, calculating growth correction factors, and obtaining sunlight source growth:

[0137] Coordinated and orderly population growth: Let ΔGv i is the growth rate. In the IVY algorithm, based on data-intensive experiments and simulation processes, the calculation formula for the growth rate is obtained:

[0138]

[0139] Among them, ΔGv i is the growth rate, rand represents a random number, item is the current iteration number, N(1, D) is a random vector of standard normal distribution with dimension D; D is the dimension of the optimization variable, which is 2 in this embodiment.

[0140] Calculate the growth correction factor: Obtain the calculation formula of the growth correction factor, that is, β = (2 + rand) / 2; β is the growth correction factor.

[0141] Access to sunlight: It is crucial for the overall health of ivy in nature to find a surface to attach to (e.g., a wall, rock, or tree) so that it can crawl towards the sunlight. The following equations describe the members How to use Member I Best Climb and move logically along the direction of the light source, and introduce the Weibull distribution strategy at this stage to further adjust the population distribution. The specific process is determined by the following three formulas:

[0142]

[0143] in, is the temporary position of the ith ivy individual (hereinafter referred to as the ith individual), f(I i ) is the fitness of the i-th ivy individual, I Best is the position of the optimal ivy individual, f(I Best ) is the fitness of the optimal ivy individual. ii is the position of the neighbor individual of the i-th ivy individual, that is:

[0144]

[0145] Where, Sequence I s The position of the jth individual, Sequence I s The position of the j-1th body, sequence I s To sort all the fitness of the current population by fitness size to get a sequence, where

[0146]

[0147] Where weibull(D) is the adjustment vector, D is the dimension of the optimization variable, sign(·) is the sign function, rand(·) represents a randomly generated matrix of values, and wblrnd(·) represents the Weibull distribution function. The above formula generates the vector weibull(D) of dimension D using the Weibull distribution function wblrnd and the sign function sign.

[0148]

[0149] in, is the corrected position of the i-th ivy individual, according to Update the individual after getting sunlight. Use the objective function to calculate according to The updated fitness of each ivy individual;

[0150] like Figure 3 As shown in the figure, after setting the maximum number of iterations, determine whether the current number of iterations (for example, the jth iteration) is less than the maximum number of iterations. If so, calculate the population growth rate and growth correction factor for the jth iteration, and update the fitness of the individuals after receiving sunlight and the corresponding individuals. After updating the number of iterations, re-evaluate until the maximum number of iterations is reached, ending the iteration. At the end of the iteration, obtain the fitness of all ivy individuals after the last iteration to screen out the minimum fitness. The ivy individual with the minimum fitness is used as the target optimal ivy individual, and the groove depth and groove width corresponding to the target optimal ivy individual are used as the optimal solution for the optimization variables.

[0151] In the embodiments of the present invention, in order to verify the effect of the improved IVY algorithm of the present invention (the improved IVY of the present invention is an improved algorithm using a hybrid strategy, which can be represented by HIVY), it is mainly compared with related algorithms in terms of the distribution of the initialization population, convergence efficiency, solution time and motor structure size scheme.

[0152] (1) Distribution of the initialized population:

[0153] In the embodiment of the present invention, in the two-dimensional interval [-1,1], cubic chaos mapping, tent chaos mapping (abbreviated as tent mapping), and random method are used to generate the initialization population. Figure 4 It can be seen that the distribution of the population individuals generated by the cubic chaos mapping adopted by the present invention in the feasible space is the most uniform, followed by the Tent mapping, and the random method has the worst distribution.

[0154] (2) Convergence efficiency:

[0155] Figure 5 The relationship between the objective function value and the number of iterations of the three algorithms when solving the F1 function in the CEC standard test function is shown in the figure. The first curves of the unimproved IVY algorithm, the LIVY algorithm improved by cubic chaos map alone, and the HIVY algorithm improved by hybrid strategy are shown in the figure. Figure 5 It can be seen from the figure that in terms of convergence speed, the HIVY algorithm using the hybrid strategy is the fastest, the LIVY algorithm using only cubic chaos map is second, and the unimproved IVY algorithm is the slowest. Therefore, improving the IVY algorithm using the hybrid strategy can improve the convergence efficiency.

[0156] (3) Convergence efficiency:

[0157] For F2 in the CEC standard test function, the improved IVY, LIVY, and HIVY algorithms are used to solve it, and the solving time is statistically analyzed. The results are as follows: Figure 6 As shown, it can be seen that the HIVY algorithm using the hybrid improvement strategy has the shortest solution time.

[0158] (4) Motor structure and size plan

[0159] The motor structure size solutions obtained by unoptimized, IVY optimized, LIVY optimized algorithm, and HIVY algorithm are shown in Table 2.

[0160] Table 2 Motor structure size plan

[0161] <![CDATA[B s0 / mm]]> <![CDATA[H s2 / mm]]> original 1.5 15.5 IVY solution 1.137 12.786 LIVY solution 1.096 13.835 HIVY solution 1.117 13.635

[0162] The four schemes shown in Table 2 are used for torque comparison. The cogging torque of each scheme is as follows: Figure 7 As shown in the figure, it can be seen that the HIVY algorithm improved by hybrid strategy can obtain better results.

[0163] The method of the present invention obtains the optimal solution of slot depth and slot width through an improved IVY algorithm, wherein the IVY intelligent optimization algorithm is improved by introducing cubic chaos mapping and Weibull distribution, thereby overcoming the problems of local optimal trap, slow convergence speed and insufficient search diversity existing in the traditional IVY algorithm in complex optimization problems. Specifically, in the initialization population stage, the cubic chaos mapping can generate more complex nonlinear perturbations, effectively enhance the global exploration ability of the algorithm, and avoid the algorithm from converging to the local optimal solution too early during the optimization process. In addition, the introduction of the cubic chaos mapping enables the algorithm to maintain high randomness and dynamics during the search process, improves its adaptability to complex multi-peak problems, and significantly improves the optimization accuracy and convergence; in the exploration stage, the introduction of Weibull distribution can improve the diversity of the population, improve the global search ability and global development ability, and ensure the convergence speed in the later stage. Therefore, the use of the improved IVY algorithm can better obtain the global optimal solution of slot depth and slot width, thereby improving the performance of the flywheel energy storage motor, thereby effectively improving the performance of the flywheel energy storage system.

[0164] The present invention relates to the technical field of flywheel energy storage motor structure optimization, and specifically to a technical solution for optimizing the motor slot structure using an improved IVY intelligent algorithm to reduce slot torque and improve the operational stability and energy efficiency of the flywheel energy storage system. Specifically, by introducing a cubic chaotic map in the initialization of the IVY intelligent optimization algorithm and using a Weibull distribution in the search strategy, a highly uniformly distributed and well-traversed initial population can be generated within the search space, thereby improving the algorithm's global search, optimization accuracy, and local convergence performance. This not only improves the performance of the IVY algorithm, but also expands its applicability to a wider range of application scenarios.

[0165] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0166] Based on the same inventive concept, the embodiments of the present application also provide a flywheel energy storage motor structure optimization device based on an improved ivy algorithm for implementing the flywheel energy storage motor structure optimization method based on the improved ivy algorithm. The implementation solution provided by this device is similar to the implementation solution described in the above method. Therefore, the specific limitations of one or more embodiments of the flywheel energy storage motor structure optimization device based on the improved ivy algorithm provided below can be found in the above limitations of the flywheel energy storage motor structure optimization method based on the improved ivy algorithm, and will not be repeated here.

[0167] In an exemplary embodiment, Figure 8 As shown, a flywheel energy storage motor structure optimization device based on the improved ivy algorithm is provided, including: an acquisition module, an objective function construction module, a population initialization module, a calculation module, an update module and an optimal solution screening module, wherein:

[0168] An acquisition module is used to obtain the upper and lower limits of the slot depth, the upper and lower limits of the slot width, and the cogging torque of the flywheel energy storage motor;

[0169] An objective function building module is used to build an objective function, which is a peak-to-peak value calculation formula for the cogging torque;

[0170] The population initialization module is used to set the two dimensions of each ivy plant, including the groove depth and groove width. The fitness of each ivy plant is the value of the objective function. The ivy population is randomly generated. The upper and lower limits of the groove depth and groove width are used to form a solution space. The ivy population is subjected to cubic chaos mapping in the solution space to obtain the initial population.

[0171] The calculation module is used to set the maximum number of iterations, calculate the growth rate and fitness of each ivy individual under the current number of iterations, and select the ivy individual with the smallest fitness as the current optimal ivy individual under the current number of iterations;

[0172] An update module is used to substitute the growth rate of each ivy individual into the position update formula to calculate the temporary position of the corresponding ivy individual, and use the adjustment vector, the temporary position and the position of the current optimal ivy individual to calculate the corrected position of each ivy individual. Each ivy individual is updated according to the corrected position and the fitness of each ivy individual after the position update is calculated;

[0173] The optimal solution screening module is used to obtain the fitness of all ivy individuals after position update in the last iteration when the number of iterations reaches the maximum number of iterations, so as to screen out the minimum fitness, and use the ivy individual corresponding to the minimum fitness as the target optimal ivy individual, and the groove depth and groove width corresponding to the target optimal ivy individual as the optimal solution of the optimization variables.

[0174] In an exemplary embodiment, in the population initialization module, a cubic chaos map is performed on the ivy population in the solution space to obtain an initial population, including: obtaining a mathematical expression of the cubic chaos map:

[0175]

[0176] Where, I 0,i is the position of the i-th ivy individual in the randomly generated ivy population; is the position of the i-th ivy individual after cubic chaos mapping; substitute the position of each ivy individual in the randomly generated ivy population into the mathematical expression of cubic chaos mapping to obtain the cubic chaos mapping population; map the cubic chaos mapping population to the solution space to obtain the initial population.

[0177] In an exemplary embodiment, in the population initialization module, mapping the cubic chaos map population to the solution space to obtain the initial population includes: obtaining the mapping formula:

[0178]

[0179] Where, I i is the position of the i-th ivy individual, max is the upper boundary vector of the solution space, min is the lower boundary vector of the solution space, I max is the maximum individual vector of population position; I min is the minimum individual vector of population position; ⊙ is Hadamard multiplication; is Hadamard division; substitute the position of each ivy individual in the cubic chaos mapping population into the mapping formula to obtain the position of all ivy individuals in the initial population.

[0180] In an exemplary embodiment, in the update module, the position update formula is a piecewise function; the growth rate of each ivy individual is substituted into the position update formula to calculate the temporary position of the corresponding ivy individual, including: calculating a growth correction factor, and calculating the product of the growth correction factor and the fitness of the current optimal ivy individual; randomly selecting an ivy individual as an update object, and comparing the fitness of the update object with the product; if the fitness of the update object is less than the product, substituting the growth rate of the update object into the function expression of the first interval to calculate the temporary position of the update object; if the fitness of the update object is greater than or equal to the product, substituting the growth rate of the update object into the function expression of the second interval to calculate the temporary position of the update object.

[0181] In an exemplary embodiment, in the update module, the process of determining the adjustment vector includes: calculating the adjustment vector using the following formula:

[0182]

[0183] Where weibull(D) is the adjustment vector, D is the dimension of the optimization variable, sign(·) is the sign function, rand represents a random number, and wblrnd() represents the Weibull distribution function.

[0184] In an exemplary embodiment, in the update module, the revised position of each ivy individual is calculated using the adjustment vector, the temporary position, and the position of the current optimal ivy individual, including: for each ivy individual, calculating the Hadamard product of the adjustment vector and the temporary position; and calculating the sum of the Hadamard product and the position of the current optimal ivy individual as the revised position of the corresponding ivy individual.

[0185] Each module in the flywheel energy storage motor structure optimization device based on the improved Ivy algorithm can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0186] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 9 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a flywheel energy storage motor structure optimization method based on an improved ivy algorithm is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0187] Those skilled in the art will understand that Figure 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0188] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0189] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0190] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0191] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0192] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile memory and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), a programmable logic unit (PLC), a data processing logic unit based on quantum computing, an artificial intelligence (AI) processor, and the like.

[0193] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0194] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A flywheel energy storage motor structure optimization method based on an improved ivy algorithm, characterized in that: The method comprises: Obtain the upper and lower limits of slot depth, slot width, and cogging torque of the flywheel energy storage motor; Constructing an objective function, wherein the objective function is a peak-to-peak value calculation formula of the cogging torque; Each ivy individual is set to include two dimensions, namely, groove depth and groove width. The fitness of each ivy individual is the value of the objective function. An ivy population is randomly generated. A solution space is formed using the upper and lower limits of the groove depth and the upper and lower limits of the groove width. A cubic chaotic mapping is performed on the ivy population within the solution space to obtain an initial population. Set the maximum number of iterations, calculate the growth rate and fitness of each ivy individual under the current number of iterations, and select the ivy individual with the smallest fitness as the current optimal ivy individual under the current number of iterations; Substituting the growth rate of each ivy individual into a position update formula to calculate a temporary position of the corresponding ivy individual, calculating a revised position of each ivy individual using the adjustment vector, the temporary position, and the position of the current optimal ivy individual, updating each ivy individual according to the revised position, and calculating the fitness of each ivy individual after the position update; When the number of iterations reaches the maximum number of iterations, the fitness of all ivy individuals after position update in the last iteration is obtained to screen out the minimum fitness, and the ivy individual corresponding to the minimum fitness is used as the target optimal ivy individual, and the groove depth and groove width corresponding to the target optimal ivy individual are used as the optimal solution of the optimization variables.

2. The method according to claim 1, characterized in that The performing of cubic chaos mapping on the ivy population in the solution space to obtain an initial population includes: Get the mathematical expression of the cubic chaos map: Where I0,i is the position of the i-th ivy individual in the randomly generated ivy population; is the position of the i-th ivy individual after cubic chaos mapping; Substituting the position of each ivy individual in the randomly generated ivy population into the mathematical expression of the cubic chaos map to obtain a cubic chaos map population; The cubic chaotic map population is mapped to the solution space to obtain an initial population.

3. The method according to claim 2, characterized in that Mapping the cubic chaotic map population to the solution space to obtain an initial population includes: Get the mapping formula: Where, I i is the position of the i-th ivy individual, max is the upper boundary vector of the solution space, min is the lower boundary vector of the solution space, I max is the maximum individual vector of population position; I min is the minimum individual vector of population position; ⊙ is Hadamard multiplication; is Hadamard division; The position of each ivy individual in the cubic chaos mapping population is substituted into the mapping formula to obtain the positions of all ivy individuals in the initial population.

4. The method according to claim 1, wherein The position update formula is a piecewise function; substituting the growth rate of each ivy individual into the position update formula to calculate the temporary position of the corresponding ivy individual includes: calculating a growth correction factor, and calculating the product of the growth correction factor and the fitness of the current optimal ivy individual; Selecting an ivy individual as an update object, and comparing the fitness of the update object with the product; If the fitness of the update object is less than the product, substituting the growth rate of the update object into the function expression of the first interval to calculate the temporary position of the update object; If the fitness of the update object is greater than or equal to the product, the growth rate of the update object is substituted into the function expression of the second interval to calculate the temporary position of the update object.

5. The method according to claim 1, wherein The process of determining the adjustment vector includes: The adjustment vector is calculated using the following formula: Where weibull(D) is the adjustment vector, D is the dimension of the optimization variable, sign(·) is the sign function, rand represents a random number, and wblrnd() represents the Weibull distribution function.

6. The method according to claim 1, characterized in that The calculating of the corrected position of each ivy individual by using the adjustment vector, the temporary position and the position of the current optimal ivy individual includes: For each ivy individual, a Hadamard product of the adjustment vector and the temporary position is calculated; and a sum of the Hadamard product and the position of the current optimal ivy individual is calculated and used as the corrected position of the corresponding ivy individual.

7. A flywheel energy storage motor structure optimization device based on an improved ivy algorithm, characterized in that: The device comprises: An acquisition module is used to obtain the upper and lower limits of the slot depth, the upper and lower limits of the slot width, and the cogging torque of the flywheel energy storage motor; An objective function construction module, configured to construct an objective function, wherein the objective function is a peak-to-peak value calculation formula of the cogging torque; a population initialization module, configured to set each ivy individual to have two dimensions, namely, groove depth and groove width, and the fitness of each ivy individual to be the value of the objective function; randomly generate an ivy population; form a solution space using the upper and lower limits of the groove depth and the upper and lower limits of the groove width; and perform cubic chaotic mapping on the ivy population within the solution space to obtain an initial population; The calculation module is used to set the maximum number of iterations, calculate the growth rate and fitness of each ivy individual under the current number of iterations, and select the ivy individual with the smallest fitness as the current optimal ivy individual under the current number of iterations; an updating module, configured to substitute the growth rate of each ivy individual into a position updating formula to calculate a temporary position of the corresponding ivy individual, calculate a revised position of each ivy individual using the adjustment vector, the temporary position, and the position of the current optimal ivy individual, update each ivy individual according to the revised position, and calculate the fitness of each ivy individual after the position update; The optimal solution screening module is used to obtain the fitness of all ivy individuals after position update in the last iteration when the number of iterations reaches the maximum number of iterations, so as to screen out the minimum fitness, and use the ivy individual corresponding to the minimum fitness as the target optimal ivy individual, and the groove depth and groove width corresponding to the target optimal ivy individual as the optimal solution of the optimization variables.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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