Motor optimization method, computer equipment and storage medium

Abstract

The invention discloses a motor optimization method, computer equipment and a storage medium. The method comprises the steps that N original motor working conditions are analyzed and processed, and M target motor working conditions Ngt are determined; m > = 2; based on the initial motor model, performing simulation analysis processing on the N original motor working conditions, and determining target working condition data corresponding to the M target motor working conditions; based on the target working condition data corresponding to the M target motor working conditions, determining optimization targets corresponding to the M target motor working conditions; and based on the optimization targets corresponding to the M target motor working conditions, performing optimization updating on the initial motor model, and determining a target motor model. The method not only has high motor optimization efficiency, but also can ensure that the target motor model reaches the optimization target, has high optimization accuracy, and has high feasibility.

Description

Technical Field

[0001] The present invention relates to the technical field of motor optimization, and particularly to a motor optimization method, a computer device, and a storage medium. Background Art

[0002] With the rapid development of fields such as electric vehicles and industrial automation, motors (for example, permanent magnet synchronous motors), as key power components, have increasingly high performance requirements. In the prior art, when performing motor optimization design, it is often only optimized for a certain specific working condition, and it is difficult to meet the performance requirements of the motor under multiple working conditions. However, directly simulating and optimizing each working condition has relatively low optimization efficiency. In order to improve the optimization efficiency and keep the data unchanged under multiple working conditions to reduce the data processing complexity, the optimization design result will be inaccurate. Therefore, how to accurately and efficiently perform motor optimization design under multiple working conditions is an urgent problem to be solved in the current motor optimization technology field. Summary of the Invention

[0003] Embodiments of the present invention provide a motor optimization method, a computer device, and a storage medium to solve the problem of how to accurately and efficiently perform motor optimization design under multiple working conditions.

[0004] A motor optimization method includes: Analyzing and processing N original motor working conditions to determine M target motor working conditions, where N > M ≥ 2; Based on an initial motor model, performing simulation analysis on N original motor working conditions to determine target condition data corresponding to the M target motor working conditions; Based on the target condition data corresponding to the M target motor working conditions, determining optimization targets corresponding to the M target motor working conditions; Based on the optimization targets corresponding to the M target motor working conditions, optimizing and updating the initial motor model to determine a target motor model.

[0005] Preferably, the analyzing and processing N original motor working conditions to determine M target motor working conditions includes: Performing clustering analysis on the N original motor working conditions to determine K clustering center working conditions; Based on the K clustering center working conditions, determining M target motor working conditions, where K ≥ M ≥ 2.

[0006] Preferably, the performing simulation analysis on N original motor working conditions based on the initial motor model to determine target condition data corresponding to the M target motor working conditions includes: Analyzing and processing the N original motor working conditions to determine L working conditions to be simulated, where N > L ≥ 2; Based on the initial motor model, perform simulation analysis on the L to-be-simulated working conditions to determine the initial working condition data corresponding to the L to-be-simulated working conditions; Based on the M target motor working conditions, perform interpolation processing on the initial working condition data corresponding to the L to-be-simulated working conditions to determine the target working condition data corresponding to the M target motor working conditions.

[0007] Preferably, the initial working condition data includes initial current data and initial current angle data; The performing interpolation processing on the initial working condition data corresponding to the L to-be-simulated working conditions based on the M target motor working conditions to determine the target working condition data corresponding to the M target motor working conditions includes: Based on the M target motor working conditions, perform interpolation processing on the initial current data corresponding to the L to-be-simulated working conditions to determine the target current data corresponding to the M target motor working conditions; Based on the M target motor working conditions, perform interpolation processing on the initial current angle data corresponding to the L to-be-simulated working conditions to determine the target current angle data corresponding to the M target motor working conditions; Determine the target current data and target current angle data corresponding to the M target motor working conditions as the target working condition data corresponding to the M target motor working conditions.

[0008] Preferably, the determining the optimization objectives corresponding to the M target motor working conditions based on the target working condition data corresponding to the M target motor working conditions includes: Based on the target current data and target current angle data corresponding to the M target motor working conditions, perform simulation processing on the initial motor model corresponding to each target motor working condition to determine the first target efficiency corresponding to each target motor working condition; Perform optimization processing on the M first target efficiencies to determine the second optimized efficiency, and determine the second optimized efficiency as the optimization objective corresponding to the M target motor working conditions.

[0009] Preferably, the initial motor model includes initial decision variables, as well as the target constraints and target optimization functions corresponding to the initial decision variables; The optimizing and updating the initial motor model based on the optimization objectives corresponding to the M target motor working conditions to determine the target motor model includes: Based on the optimization objectives corresponding to the M target motor working conditions, perform optimization processing on the target constraints and target optimization functions corresponding to the initial decision variables to determine at least two initial optimization models; Based on the optimization objectives, perform analysis processing on at least two initial optimization models to determine the target motor model.

[0010] Preferably, based on the optimization objectives corresponding to the M target motor operating conditions, optimize the target constraints and the target optimization function corresponding to the initial decision variables to determine at least two initial optimization models, including: Based on the optimization objectives corresponding to the M target motor operating conditions, use a preset optimization algorithm to optimize the target constraints and the target optimization function corresponding to the initial decision variables, update the initial decision variables, and determine at least two sets of target decision variables; Based on at least two sets of target decision variables, update the initial motor model to determine at least two initial optimization models.

[0011] Preferably, the optimization objective includes the target efficiency; Based on the optimization objective, analyze at least two initial optimization models to determine the target motor model, including: According to a preset sorting index, sort and screen at least two initial optimization models to determine the top P initial optimization models, where P≥1; Conduct simulation analysis on the P initial optimization models respectively to determine the initial optimization efficiencies corresponding to the P initial optimization models; Compare and analyze the initial optimization efficiencies corresponding to the P initial optimization models with the target efficiency respectively to determine the target motor model.

[0012] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned motor optimization method is implemented.

[0013] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned motor optimization method is implemented.

[0014] The above motor optimization method, computer device, and storage medium analyze and process N original motor operating conditions to determine M target motor operating conditions that can effectively represent the overall situation of the N original motor operating conditions and have a much smaller number of operating conditions than N, so as to improve the motor optimization efficiency. Based on the initial motor model, the N original motor operating conditions are simulated and analyzed to determine the target condition data corresponding to the M target motor operating conditions. Without having to re-establish the motor model corresponding to the target motor operating conditions, the target condition data can be determined. This method can efficiently obtain the target condition data. The initial motor model is optimized and updated to obtain a target motor model that meets the optimization goal, which can ensure the optimization quality and reliability of the motor optimization. This method can obtain a target motor model that meets the optimization goal without a large amount of model establishment and data processing. This method not only has a high motor optimization efficiency but also can ensure that the target motor model reaches the optimization goal, has a high optimization accuracy, and has high feasibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments of the present invention. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0016] Figure 1 is a flowchart of the motor optimization method in an embodiment of the present invention; Figure 2 is another flowchart of the motor optimization method in an embodiment of the present invention; Figure 3 is another flowchart of the motor optimization method in an embodiment of the present invention; Figure 4 is another flowchart of the motor optimization method in an embodiment of the present invention; Figure 5 is another flowchart of the motor optimization method in an embodiment of the present invention; Figure 6 is another flowchart of the motor optimization method in an embodiment of the present invention; Figure 7 is another flowchart of the motor optimization method in an embodiment of the present invention; Figure 8 is another flowchart of the motor optimization method in an embodiment of the present invention; Figure 9 is a schematic diagram of a computer device in an embodiment of the present invention; Figure 10 is a schematic diagram of clustering N original motor operating conditions in an embodiment of the present invention. Specific Embodiments

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0018] The motor optimization method provided by the embodiments of the present invention is used to achieve the purpose of accurately and efficiently performing motor optimization design under multiple working conditions.

[0019] In one embodiment, as Figure 1 shown, a motor optimization method is provided. Taking the computer device in Figure 9 as an example for illustration, the method includes the following steps: S101: Analyze and process N original motor working conditions to determine M target motor working conditions, where N > M ≥ 2; S102: Based on the initial motor model, perform simulation analysis and processing on N original motor working conditions to determine the target working condition data corresponding to the M target motor working conditions; S103: Based on the target working condition data corresponding to the M target motor working conditions, determine the optimization objectives corresponding to the M target motor working conditions; S104: Based on the optimization objectives corresponding to the M target motor working conditions, optimize and update the initial motor model to determine the target motor model.

[0020] Among them, the original motor working condition refers to the motor working condition existing in actual applications. The target motor working condition refers to the working condition used for motor optimization design.

[0021] As an example, in step S101, the computer device analyzes and processes N original motor working conditions to obtain M target motor working conditions for optimization design. It can be understood that in the process of motor optimization design, it is necessary to consider as many working conditions as possible to improve the accuracy of motor optimization design. However, the number of original motor working conditions is relatively large. If motor optimization design is carried out under each original motor working condition, the efficiency of motor optimization design is relatively low. In this example, by analyzing and processing N original motor working conditions, M target motor working conditions that can effectively represent the overall situation of N original motor working conditions are obtained, so as to facilitate subsequent motor optimization design based on the M target motor working conditions, while improving the optimization efficiency and ensuring the accuracy of motor optimization design.

[0022] Among them, the initial motor model refers to the motor model without optimization. The target operating condition data refers to the data used to determine the optimization objective. Among them, the optimization objective refers to the objective of the motor optimization design. For example, the maximization of the motor efficiency, or the minimization of the heat generation of the motor.

[0023] As an example, in step S102, the computer device establishes initial motor models corresponding to N original motor operating conditions in the finite element simulation software according to the preset motor power size equation and parametric model. According to the N original motor operating conditions, the finite element simulation analysis of the initial motor models is sequentially performed in the finite element simulation software to determine the simulation analysis results of the initial motor models under each original motor operating condition. The simulation analysis results of the initial motor models under each original motor operating condition are used to analyze and process the M target motor operating conditions to determine the target operating condition data corresponding to the M target motor operating conditions. It can be understood that the motor operating conditions are composed of speed and torque. If the speed and torque of a target motor operating condition are different from those of the N original motor operating conditions, and the initial motor model is the model corresponding to the N original motor operating conditions, if the target motor operating condition is directly simulated through the initial motor model corresponding to the N original motor operating conditions, it will cause a certain error in the simulation results. Therefore, based on the initial motor model, the finite element simulation analysis of the N original motor operating conditions is performed to obtain the simulation analysis results corresponding to the N original motor operating conditions, and the simulation analysis results corresponding to the N original motor operating conditions are analyzed and processed to determine the target operating condition data corresponding to the M original motor operating conditions to ensure the accuracy of the target operating condition data and improve the accuracy of the motor optimization design.

[0024] As an example, in step S103, the computer device processes the target operating condition data corresponding to the M target motor operating conditions to determine the optimization objectives corresponding to the M target motor operating conditions. It can be understood that the target operating condition data is used to determine the optimization objective. Processing the target operating condition data corresponding to the M target motor operating conditions to determine the optimization objective is used to realize the optimization design of the motor.

[0025] Among them, the target motor model refers to the optimized and updated model.

[0026] As an example, in step S104, the computer device optimizes and updates the initial motor model to obtain the optimized and updated initial motor model, and real-time monitors whether the optimized and updated initial motor model reaches the optimization objective. The initial motor model that reaches the optimization objective is determined as the target motor model to ensure the optimization quality and reliability of the motor optimization.

[0027] In this embodiment, N original motor operating conditions are analyzed and processed to determine M target motor operating conditions that can effectively represent the overall situation of the N original motor operating conditions and whose number of operating conditions is much less than N, so as to improve the motor optimization efficiency. Based on the initial motor model, the N original motor operating conditions are subjected to simulation analysis and processing to determine the target operating condition data corresponding to the M target motor operating conditions. Without having to re-establish the motor model corresponding to the target motor operating conditions, the target operating condition data can be determined. This method can efficiently obtain the target operating condition data. The initial motor model is optimized and updated to obtain a target motor model that meets the optimization goal, which can ensure the optimization quality and reliability of the motor optimization. This method can obtain a target motor model that can meet the optimization goal without a large amount of model establishment and data processing. This method not only has a high motor optimization efficiency, but also can ensure that the target motor model reaches the optimization goal, has a high optimization accuracy, and has high feasibility.

[0028] In one embodiment, as Figure 2 shown, step S101, that is, analyzing and processing N original motor operating conditions to determine M target motor operating conditions, includes: S201: Performing clustering analysis on N original motor operating conditions to determine K clustering center operating conditions; S202: Based on the K clustering center operating conditions, determining M target motor operating conditions, where K≥M≥2.

[0029] Among them, the clustering center operating condition refers to the operating condition corresponding to the clustering center generated by clustering the original motor operating conditions.

[0030] As an example, in step S201, the computer device uses a preset clustering algorithm to perform clustering analysis on N original motor operating conditions to determine K clustering center operating conditions. Among them, the preset clustering algorithm includes but is not limited to the K-means clustering algorithm. As Figure 10 shown, it is a schematic diagram of clustering N original motor operating conditions, Figure 10 where K = 5 in this example. In this example, performing clustering analysis on N original motor operating conditions to determine a relatively small number of K clustering center operating conditions effectively reduces the number of operating conditions of the motor for optimization design, so as to improve the optimization efficiency.

[0031] As an example, in step S202, the computer device uses a preset optimization algorithm to optimize the K clustering center operating conditions to determine M target motor operating conditions. For example, the computer device uses an optimization algorithm to exclude the clustering center operating conditions with relatively large offsets among the K clustering center operating conditions to obtain M target motor operating conditions, where K≥M≥2.

[0032] In this embodiment, clustering analysis is performed on N original motor operating conditions to determine K clustering center operating conditions, and based on the K clustering center operating conditions, M target motor operating conditions are determined. This method can obtain a smaller number of target motor operating conditions, so as to improve the efficiency of motor optimization design. Moreover, this method performs clustering analysis and other processing on the N original motor operating conditions to obtain M representative target motor operating conditions, so that the optimization objectives determined based on the target motor operating conditions are relatively close to the optimization objectives corresponding to the N original motor operating conditions, improving the accuracy of motor optimization design.

[0033] In one embodiment, as Figure 3 shown, step S102, that is, based on the initial motor model, perform simulation analysis on N original motor operating conditions to determine the target operating condition data corresponding to M target motor operating conditions, including: S301: Analyze and process the N original motor operating conditions to determine L operating conditions to be simulated, where N > L ≥ 2; S302: Based on the initial motor model, perform simulation analysis on the L operating conditions to be simulated to determine the initial operating condition data corresponding to the L operating conditions to be simulated; S303: Based on the M target motor operating conditions, perform interpolation processing on the initial operating condition data corresponding to the L operating conditions to be simulated to determine the target operating condition data corresponding to the M target motor operating conditions.

[0034] Among them, the operating condition to be simulated refers to the operating condition used for simulation.

[0035] As an example, in step S301, the computer device analyzes and processes the N original motor operating conditions to determine L operating conditions to be simulated. For example, if the rotational speed range corresponding to the N original motor operating conditions is [a, b] and the torque range is [c, d], and the interval of rotational speed is x and the interval of torque is y (where x ≠ 0, y ≠ 0), perform equidistant extraction on the N original motor operating conditions to obtain L = operating conditions to be simulated for simulation analysis.

[0036] Among them, the initial operating condition data refers to the data generated by performing simulation analysis on the initial motor model under the operating condition to be simulated.

[0037] As an example, in step S302, the computer device inputs the L operating conditions to be simulated into the finite element simulation software in batches, and under each operating condition to be simulated, performs simulation analysis on the initial motor model and respectively outputs the initial operating condition data corresponding to each operating condition to be simulated. For example, the computer device uses the initial motor model to perform simulation analysis on the L operating conditions to be simulated, outputs the initial operating condition data corresponding to each operating condition to be simulated, and stores each operating condition to be simulated and the initial operating condition data corresponding to this operating condition to be simulated in a corresponding manner to obtain the initial operating condition data Map.

[0038] As an example, in step S303, the computer device identifies a target motor operating condition, determines the interval of the operating condition to be simulated corresponding to the target motor operating condition, and within the interval of the operating condition to be simulated, interpolates the initial operating condition data corresponding to the operating condition to be simulated using a preset interpolation algorithm to obtain the target operating condition data corresponding to the target motor operating condition. The above method is used to process M target motor operating conditions to obtain the target operating condition data corresponding to the M target motor operating conditions. For example, if each operating condition to be simulated and the initial operating condition data corresponding to the operating condition to be simulated are stored correspondingly to form an initial operating condition data Map, the computer device interpolates the initial operating condition data Map based on the M target motor operating conditions using a preset interpolation method to determine the target operating condition data corresponding to each target motor operating condition. This method does not require complex data processing and can relatively quickly determine the target operating condition data corresponding to each target motor operating condition. In this example, the preset interpolation algorithm includes, but is not limited to, the Kriging interpolation algorithm.

[0039] In this embodiment, based on the M target motor operating conditions, the initial operating condition data corresponding to the L operating conditions to be simulated is interpolated to determine the target operating condition data corresponding to the M target motor operating conditions. This method can determine the target operating condition data corresponding to the M target motor operating conditions in less time, improve the acquisition efficiency of the target operating condition data, and facilitate further improving the efficiency of determining the optimization target based on the target operating condition data.

[0040] In one embodiment, the initial operating condition data includes initial current data and initial current angle data; Among them, the initial current data refers to the current data generated during the simulation analysis of the operating condition to be simulated. The initial current angle data refers to the current angle data generated during the simulation analysis of the operating condition to be simulated.

[0041] In one embodiment, as Figure 4 shown, step S303, that is, based on the M target motor operating conditions, interpolating the initial operating condition data corresponding to the L operating conditions to be simulated to determine the target operating condition data corresponding to the M target motor operating conditions, includes: S401: Based on the M target motor operating conditions, interpolate the initial current data corresponding to the L operating conditions to be simulated to determine the target current data corresponding to the M target motor operating conditions; S402: Based on the M target motor operating conditions, interpolate the initial current angle data corresponding to the L operating conditions to be simulated to determine the target current angle data corresponding to the M target motor operating conditions; S403: Determine the target current data and target current angle data corresponding to the M target motor operating conditions as the target operating condition data corresponding to the M target motor operating conditions.

[0042] Among them, the target current data refers to the current data corresponding to the target motor operating condition. The target current angle data refers to the current angle data corresponding to the target motor operating condition.

[0043] As an example, in step S401, the computer device identifies a target motor operating condition, determines the interval of the to-be-simulated operating condition corresponding to the target motor operating condition, and within the interval of the to-be-simulated operating condition, uses a preset interpolation algorithm to perform interpolation processing on the initial current data corresponding to the to-be-simulated operating condition to obtain the target current data corresponding to the target motor operating condition. Using the above method to process M target motor operating conditions, the target current data corresponding to the M target motor operating conditions is obtained. For example, if in step S302, the computer device uses an initial motor model to perform simulation analysis on L to-be-simulated operating conditions, outputs the initial current data corresponding to each to-be-simulated operating condition, and stores each to-be-simulated operating condition and the initial current data corresponding to the to-be-simulated operating condition in a corresponding manner to form a current Map, then in step S401, the computer device, based on the M target motor operating conditions, uses a preset interpolation method to perform interpolation processing on the current Map to determine the target current data corresponding to each target motor operating condition. This method does not require complex data processing and can relatively quickly obtain the target current data corresponding to each target motor operating condition. In this example, the preset interpolation algorithm includes, but is not limited to, the Kriging interpolation algorithm.

[0044] As an example, in step S402, the computer device identifies a target motor operating condition, determines the interval of the to-be-simulated operating condition corresponding to the target motor operating condition, and within the interval of the to-be-simulated operating condition, uses a preset interpolation algorithm to perform interpolation processing on the initial current angle data corresponding to the to-be-simulated operating condition to obtain the target current angle data corresponding to the target motor operating condition. Using the above method to process M target motor operating conditions, the target current angle data corresponding to the M target motor operating conditions is obtained. For example, if in step S302, the computer device uses an initial motor model to perform simulation analysis on L to-be-simulated operating conditions, outputs the initial current angle data corresponding to each to-be-simulated operating condition, and stores each to-be-simulated operating condition and the initial current angle data corresponding to the to-be-simulated operating condition in a corresponding manner to form a current angle Map, then in step S402, the computer device, based on the M target motor operating conditions, uses a preset interpolation method to perform interpolation processing on the current angle Map to determine the target current angle data corresponding to each target motor operating condition. This method does not require complex data processing and can relatively quickly obtain the target current angle data corresponding to each target motor operating condition. In this example, the preset interpolation algorithm includes, but is not limited to, the Kriging interpolation algorithm.

[0045] As an example, in step S403, the computer device determines the target current data and target current angle data corresponding to the M target motor operating conditions as the target operating condition data corresponding to the M target motor operating conditions, which is used to determine the optimization objective for motor optimal design according to the target operating condition data.

[0046] In this embodiment, based on the M target motor operating conditions, interpolation processing is performed on the initial current data corresponding to the L simulation-to-be operating conditions to determine the target current data corresponding to the M target motor operating conditions. Based on the M target motor operating conditions, interpolation processing is performed on the initial current angle data corresponding to the L simulation-to-be operating conditions to determine the target current angle data corresponding to the M target motor operating conditions. This method can determine the target current data and target current angle data corresponding to the M target motor operating conditions in less time, improve the acquisition efficiency of the target operating condition data, and facilitate further improving the determination efficiency of the optimization objective.

[0047] In one embodiment, as Figure 5 shown, step S103, that is, based on the target operating condition data corresponding to the M target motor operating conditions, determining the optimization objective corresponding to the M target motor operating conditions, includes: S501: Based on the target current data and target current angle data corresponding to the M target motor operating conditions, perform simulation processing on the initial motor model corresponding to each target motor operating condition to determine the first target efficiency corresponding to each target motor operating condition; S502: Perform optimization processing on the M first target efficiencies to determine the second optimized efficiency, and determine the second optimized efficiency as the optimization objective corresponding to the M target motor operating conditions.

[0048] Among them, the first target efficiency refers to the efficiency after performing simulation processing on the initial motor model corresponding to each target motor operating condition.

[0049] As an example, in step S501, the computer device inputs the target current data and target current angle data corresponding to the M target motor operating conditions into the finite element simulation software corresponding to the initial motor model in batches, performs simulation processing on the initial motor model corresponding to each target motor operating condition, and outputs the first target efficiency corresponding to each target motor operating condition.

[0050] Among them, the second optimized efficiency refers to the efficiency obtained by optimizing the M first target efficiencies.

[0051] As an example, in step S502, the computer device optimizes the M first target efficiencies to obtain a second optimized efficiency, and determines the second optimized efficiency as the optimization target corresponding to the M target motor operating conditions. For example, the computer device obtains the average value corresponding to the M first target efficiencies and determines the average value as the second optimized efficiency. As another example, the computer device weights the M first target efficiencies using a preset weight coefficient to obtain a weighted efficiency value, and the weighted efficiency value is the second optimized efficiency.

[0052] In this embodiment, the M first target efficiencies are optimized to obtain an optimization target, so as to optimize the initial motor model according to the optimization target to obtain a target motor model that can achieve the optimization target, ensuring the optimization accuracy.

[0053] In one embodiment, the initial motor model includes initial decision variables, as well as target constraints and a target optimization function corresponding to the initial decision variables.

[0054] Among them, the initial decision variables refer to decision variables with initial values. The decision variables refer to the parameters that affect the motor optimization design and are used to form the motor. The target constraints refer to the constraint conditions for motor optimization design constructed by the initial decision variables. The target optimization function refers to the objective function for motor optimization design.

[0055] In one embodiment, as Figure 6 shown, step S104, that is, based on the optimization target corresponding to the M target motor operating conditions, optimizing and updating the initial motor model to determine the target motor model, includes: S601: Based on the optimization target corresponding to the M target motor operating conditions, optimize the target constraints and the target optimization function corresponding to the initial decision variables to determine at least two initial optimization models; S602: Based on the optimization target, analyze and process at least two initial optimization models to determine the target motor model.

[0056] Among them, the initial optimization model refers to the model obtained by optimizing the initial motor model.

[0057] As an example, in step S601, the computer device optimizes the initial decision variables in the target constraints and the target optimization function corresponding to the initial decision variables so that the target optimization function reaches the optimization target, obtains at least two sets of optimized initial decision variables, and respectively updates the initial motor model using at least two sets of optimized initial decision variables to obtain the initial optimization model corresponding to each set of optimized initial decision variables. In this example, through the optimization target, the target constraints and the target optimization function are optimized to obtain at least two initial optimization models, so as to determine the target motor model according to the initial optimization models.

[0058] As an example, in step S602, the computer device analyzes at least two initial optimization models, determines whether the initial optimization models meet the optimization goal, and determines the initial optimization models that meet the optimization goal as the target motor models, thereby realizing the optimized design of the motor.

[0059] In this embodiment, the target constraints and the target optimization function are optimized through the optimization goal to determine at least two initial optimization models. The initial optimization models that meet the optimization goal are determined as the target motor models, and an optimized design solution for the motor is obtained, thereby realizing the optimized design of the motor. This method can ensure the reliability and feasibility of the target motor model.

[0060] In one embodiment, as Figure 7 shown, step S601, that is, based on the optimization goals corresponding to M target motor operating conditions, the target constraints and the target optimization function corresponding to the initial decision variables are optimized to determine at least two initial optimization models, including: S701: Based on the optimization goals corresponding to M target motor operating conditions, a preset optimization algorithm is used to optimize the target constraints and the target optimization function corresponding to the initial decision variables, update the initial decision variables, and determine at least two sets of target decision variables; S702: Based on at least two sets of target decision variables, the initial motor model is updated to determine at least two initial optimization models.

[0061] Among them, the preset optimization algorithm refers to an algorithm used to optimize the target constraints and the target optimization function. The target decision variable refers to the optimized decision variable.

[0062] As an example, in step S701, the computer device uses a preset optimization algorithm to optimize the target constraints and the target optimization function corresponding to the initial decision variables, updates the initial decision variables, and obtains at least two sets of updated decision variables, so that the target optimization function corresponding to the updated decision variables meets the optimization goal. The at least two sets of updated decision variables that meet the optimization goal are determined as the target decision variables. In this example, the preset optimization algorithm includes, but is not limited to, the non-dominated sorting genetic algorithm (NSGA).

[0063] As an example, in step S702, the computer device updates the initial decision variables in the initial motor model to the target decision variables to determine at least two initial optimization models. Understandably, the target constraints and the target optimization function are optimized to obtain at least two sets of target decision variables, and the initial decision variables in the initial motor model are updated according to each set of target decision variables respectively to obtain at least two initially optimized models after model update.

[0064] In this embodiment, based on the optimization objective, a preset optimization algorithm is adopted to optimize the objective constraints and the objective optimization function, update the initial decision variables, determine at least two sets of objective decision variables, and based on the at least two sets of objective decision variables, update the initial motor model to determine at least two initial optimization models. This method can effectively accelerate the convergence of the objective optimization function, reduce the number of iterations, and improve the optimization efficiency.

[0065] In one embodiment, the optimization objective includes the target efficiency; Among them, the target efficiency refers to the optimization objective corresponding to the efficiency of the motor. Understandably, when constructing the objective optimization function based on the initial decision variables, the characteristics that the motor is expected to have are usually considered as the optimization objective. In this embodiment, the target efficiency is determined as the optimization objective to facilitate the optimization of the motor efficiency.

[0066] In one embodiment, as Figure 8 shown, step S602, that is, based on the optimization objective, analyze and process at least two initial optimization models to determine the target motor model, including: S801: Sort and screen at least two initial optimization models according to the preset sorting index to determine the top P initial optimization models, where P≥1; S802: Perform finite element simulation analysis on the P initial optimization models respectively to determine the initial optimization efficiencies corresponding to the P initial optimization models; S803: Compare and analyze the initial optimization efficiencies corresponding to the P initial optimization models with the target efficiency respectively to determine the target motor model.

[0067] Among them, the preset sorting index refers to the index used to screen the initial optimization models.

[0068] As an example, in step S801, the computer device sorts and screens at least two initial optimization models according to the preset sorting index to determine the top P (where P≥1) initial optimization models. In this example, the preset sorting index is screened from the decision variables. Understandably, the decision variables include but are not limited to the shape and size of the motor. If more attention is paid to the size of the motor during the motor optimization design, the size is determined as the preset sorting index. If more attention is paid to the shape of the motor, the shape of the motor is determined as the preset sorting index.

[0069] Among them, the initial optimization efficiency refers to the efficiency corresponding to the top P initial optimization models obtained by finite element simulation.

[0070] As an example, in step S802, the computer device inputs the first P initial optimization models into the finite element simulation software respectively for finite element simulation analysis to determine the initial optimization efficiency corresponding to each initial optimization model. In this example, the initial optimization efficiency is obtained to facilitate screening the initial optimization efficiency closer to the target efficiency and determining the target motor model.

[0071] As an example, in step S803, the computer device compares and analyzes the initial optimization efficiencies corresponding to the P initial optimization models with the target efficiency respectively, and determines the initial optimization models corresponding to the first Z initial optimization efficiencies closest to the target efficiency as the target motor model, where Z ≥ 1. In this example, the computer device can use a preset distance algorithm to determine the initial optimization models corresponding to the first Z initial optimization efficiencies closest to the target efficiency, or can also use a similarity algorithm to determine the initial optimization models corresponding to the first Z initial optimization efficiencies closest to the target efficiency.

[0072] In this embodiment, by screening the initial optimization models, the initial optimization models are obtained, finite element simulation analysis is performed on the initial optimization models to obtain the initial optimization efficiencies corresponding to the P initial optimization models, the initial optimization efficiencies are compared and analyzed with the target efficiency, the P initial optimization models are screened, and the target motor model is determined, which can effectively ensure that the target motor model meets the target efficiency. And since the target efficiency is determined by M target motor operating conditions, and the M target motor operating conditions are obtained by clustering N original motor operating conditions and can represent the overall situation of the N original motor operating conditions, the target motor model that meets the target efficiency can have a high efficiency performance under the N original motor operating conditions, completing the optimization design of the motor.

[0073] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution is prior or posterior, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0074] In one embodiment, as Figure 9 shown, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the motor optimization method in the above embodiment, such as Figure 1 shown S101 - S104, or Figures 2 to 8 shown, for the sake of avoiding repetition, it will not be elaborated here.

[0075] In one embodiment, a computer-readable storage medium is provided. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by the processor, it implements the motor optimization method in the above embodiment, such as Figure 1 shown S101 - S104, orFigures 3 to 8 As shown, to avoid repetition, it will not be elaborated here.

[0076] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. This 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 methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0077] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A motor optimization method, characterized in that: include: Analyze and process N original motor operating conditions to determine M target motor operating conditions, N>M≥2; Based on the initial motor model, simulation analysis is performed on N original motor operating conditions to determine target operating condition data corresponding to M target motor operating conditions; Determining optimization targets corresponding to the M target motor operating conditions based on the target operating condition data corresponding to the M target motor operating conditions; Based on the optimization targets corresponding to the M target motor operating conditions, the initial motor model is optimized and updated to determine the target motor model.

2. The motor optimization method according to claim 1, characterized in that: The analyzing and processing of the N original motor operating conditions to determine the M target motor operating conditions includes: Performing cluster analysis on the N original motor operating conditions to determine K cluster center operating conditions; Based on the K cluster center operating conditions, M target motor operating conditions are determined, where K≥M≥2.

3. The motor optimization method according to claim 1, characterized in that: The method of performing simulation analysis on N original motor operating conditions based on the initial motor model to determine target operating condition data corresponding to M target motor operating conditions includes: Analyze and process the N original motor operating conditions to determine L operating conditions to be simulated, where N>L≥2; Based on the initial motor model, simulation analysis is performed on the L working conditions to be simulated to determine initial working condition data corresponding to the L working conditions to be simulated; Based on the M target motor operating conditions, initial operating condition data corresponding to the L operating conditions to be simulated are interpolated to determine target operating condition data corresponding to the M target motor operating conditions.

4. The motor optimization method according to claim 3, characterized in that: The initial operating condition data includes initial current data and initial current angle data; The interpolation processing is performed on the initial operating condition data corresponding to the L operating conditions to be simulated based on the M target motor operating conditions to determine the target operating condition data corresponding to the M target motor operating conditions, including: Based on the M target motor operating conditions, interpolating the initial current data corresponding to the L operating conditions to be simulated, to determine the target current data corresponding to the M target motor operating conditions; Based on the M target motor operating conditions, interpolation processing is performed on the initial current angle data corresponding to the L operating conditions to be simulated to determine the target current angle data corresponding to the M target motor operating conditions; The target current data and the target current angle data corresponding to the M target motor operating conditions are determined as the target operating condition data corresponding to the M target motor operating conditions.

5. The motor optimization method according to claim 4, characterized in that: The determining, based on the target operating condition data corresponding to the M target motor operating conditions, the optimization targets corresponding to the M target motor operating conditions comprises: Based on the target current data and the target current angle data corresponding to the M target motor operating conditions, a simulation process is performed on an initial motor model corresponding to each target motor operating condition to determine a first target efficiency corresponding to each target motor operating condition; The M first target efficiencies are optimized to determine a second optimized efficiency, and the second optimized efficiency is determined as the optimization target corresponding to the M target motor operating conditions.

6. The motor optimization method according to claim 1, characterized in that: The initial motor model includes initial decision variables, and target constraints and target optimization functions corresponding to the initial decision variables; The optimizing and updating the initial motor model based on the optimization targets corresponding to the M target motor operating conditions to determine the target motor model includes: Based on the optimization objectives corresponding to the M target motor operating conditions, optimizing the target constraints and target optimization functions corresponding to the initial decision variables to determine at least two initial optimization models; Based on the optimization target, at least two initial optimization models are analyzed and processed to determine a target motor model.

7. The motor optimization method according to claim 6, characterized in that: Based on the optimization objectives corresponding to the M target motor operating conditions, the target constraints and the target optimization function corresponding to the initial decision variables are optimized to determine at least two initial optimization models, including: Based on the optimization targets corresponding to the M target motor operating conditions, a preset optimization algorithm is used to optimize the target constraints and target optimization functions corresponding to the initial decision variables, update the initial decision variables, and determine at least two groups of target decision variables; Based on at least two groups of target decision variables, the initial motor model is updated to determine at least two initial optimization models.

8. The motor optimization method according to claim 6, characterized in that: The optimization target includes target efficiency; The step of analyzing and processing at least two initial optimization models based on the optimization target to determine a target motor model includes: According to the preset sorting index, at least two initial optimization models are sorted and screened to determine the first P initial optimization models, where P≥1; Performing simulation analysis on the P initial optimization models respectively to determine the initial optimization efficiencies corresponding to the P initial optimization models; The initial optimization efficiencies corresponding to the P initial optimization models are compared and analyzed with the target efficiency to determine the target motor model.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the motor optimization method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the motor optimization method according to any one of claims 1 to 8 is implemented.

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