Motor multi-objective optimization method and device, medium and program product

Abstract

The invention provides a motor multi-target optimization method and device, a medium and a program product, and relates to the technical field of motor design and development, and the method comprises the steps: obtaining a plurality of to-be-optimized targets of a target motor; determining a motor performance index matched with each to-be-optimized target and a motor design parameter variable matched with the motor performance index; a simulation model and a processing script are obtained, and the processing script is used for generating model input based on the motor design parameter variables, generating model output based on the motor performance indexes and controlling the simulation model to operate at multiple working condition points; starting the processing script to run the simulation model, and obtaining model output of a plurality of working condition points; and carrying out joint checking calculation on the multiple to-be-optimized targets based on the model output, and determining an optimization result of the motor design parameter variables based on a checking calculation result. According to the method, manual intervention is reduced through an automatic process, the optimization precision is improved, an exclusive optimization process for a specific motor can be constructed, and the design efficiency is remarkably improved.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of motor design and development, and particularly relates to a multi-objective optimization method, device, medium and program product for motors. Background Art

[0002] As an extremely important power device in people's daily life and production, motors are widely used in industries such as household appliances, automobiles, and aerospace. During the motor development process, motor products usually need to evaluate the torque, power, efficiency, and cost at the rated operating point, peak operating point, and maximum speed operating point respectively. At the same time, there are many variables and objective functions for motor products, and optimization needs to be carried out simultaneously to obtain a more perfect motor design scheme and enhance the competitiveness of motor products. It is the most effective method to improve the optimization degree of motors.

[0003] Currently, most related technologies construct simulation models through finite element analysis tools for multi-objective optimization of motors, see Chinese Patent CN118627384A etc. This multi-objective optimization method requires experienced engineers to repeatedly correct, calculate, correct again, and calculate again, so that the design results gradually approach the performance requirements of the motor. Therefore, it has great blindness, the optimization process takes too long, the optimization efficiency is too low, and the optimization effect is not ideal.

[0004] In related technologies, the multi-objective optimization method based on genetic algorithms constructs a genetic algorithm model according to the target parameters, decision variables, objective functions, and constraint conditions, and obtains the optimal solution of the multi-objective parameters of the motor according to this genetic algorithm model, see Chinese Patent CN109033617B etc. However, this method relies on manually adjusting the variables in the genetic algorithm model, with low efficiency and difficult to comprehensively cover the optimization objectives, resulting in insufficient optimization of the motor scheme and difficult to meet the development requirements of high-performance motors. Summary of the Invention

[0005] The present disclosure provides a multi-objective optimization method, device, medium and program product for motors, aiming to at least to some extent solve the technical problems of low efficiency and difficulty in comprehensively covering the optimization objectives in the multi-objective optimization method of motors in related technologies.

[0006] At least one embodiment of the present disclosure provides a multi-objective optimization method for motors, including:

[0007] Obtain multiple optimization targets of a target motor;

[0008] Determine the motor performance indicators matching each of the optimization targets, and the motor design parameter variables matching the motor performance indicators;

[0009] Obtain a simulation model that matches the target motor, and a processing script for controlling the automatic operation of the simulation model. Among them, the processing script is configured with: a first type of program for generating model inputs based on the motor design parameter variables, a second type of program for generating model outputs based on the motor performance indicators, and a third type of program for controlling the simulation model to run at multiple preset operating points respectively;

[0010] Start the processing script to run the simulation model, and obtain the model outputs at multiple said operating points; and,

[0011] Perform joint verification of multiple said optimization targets based on the model outputs, and determine the first optimization solution of the motor design parameter variables based on the verification results.

[0012] For example, in the method provided by at least one embodiment of the present disclosure, the obtaining of the simulation model that matches the target motor includes:

[0013] Obtain the initial model of the target motor. Among them, the fixed parameters of the initial model are configured to include the pole-slot ratio, pitch, number of flat wire layers, and rotor configuration of the target motor, and the configurable parameters of the initial model are configured to include the motor performance indicators and the motor design parameter variables;

[0014] Limit the motor performance indicators for the initial model, and run the initial model;

[0015] Determine the change range of the motor design parameter variables and the motor current advance angle corresponding to the motor peak torque based on the simulation results of the initial model; and,

[0016] Obtain the optimized model of the target motor based on the change range of the motor design parameter variables and the motor current advance angle, as the simulation model that matches the processing script.

[0017] For example, in the method provided by at least one embodiment of the present disclosure, the model input is further configured to include: motor current parameters, motor speed parameters, and current advance angle parameters; and,

[0018] The processing script is further configured with: a fourth type of program for independently configuring the motor current parameters, the motor speed parameters, and the current advance angle parameters based on different said operating points.

[0019] For example, in the method provided by at least one embodiment of the present disclosure, the operating points are configured to include no-load condition, rated condition, and peak condition; and,

[0020] Under the no-load condition, the motor current parameter is configured to be 0, the motor speed parameter is configured to be the maximum motor speed, the current advance angle parameter is configured to be 0, and under the rated condition, the motor current parameter is configured to be the rated motor current, the motor speed parameter is configured to be the rated motor speed, the current advance angle parameter is configured to be the motor current advance angle, and under the peak condition, the motor current parameter is configured to be the peak motor current, the motor speed parameter is configured to be the motor inflection point speed, and the current advance angle parameter is configured to be the motor current advance angle.

[0021] For example, in the method provided by at least one embodiment of the present disclosure, the processing script is further configured with: a fifth type of program for establishing a logical relationship between the motor performance index and the motor design parameter variable, and a sixth type of program for establishing a solution boundary of the simulation model; and,

[0022] The model output is configured to include motor torque, motor efficiency, motor power, motor back electromotive force, and motor cost;

[0023] The joint verification is configured to include: identifying whether each of the model outputs reaches a specified range corresponding to the to-be-optimized target, and identifying whether different model outputs conform to a preset logical relationship.

[0024] For example, in the method provided by at least one embodiment of the present disclosure, the peak condition is configured to include a peak torque condition, a peak power condition, a rated power condition at peak speed, and a maximum power condition at peak speed; and,

[0025] The to-be-optimized target is configured to include a dynamic target of the target motor, an economic target of the target motor, a reliability target of the target motor, and a cost target of the target motor; wherein, the motor performance indexes matching the dynamic target are configured to include at least one of motor torque, motor power, and motor phase voltage; the motor performance indexes matching the economic target are configured to include at least one of motor efficiency, motor power consumption per 100 kilometers, motor iron loss, and motor AC copper loss; the motor performance indexes matching the reliability target are configured to include at least one of motor back electromotive force, motor temperature rise range, motor demagnetization degree, and motor local stress; and the motor performance indexes matching the cost target are configured to include at least one of magnet cost, copper sheet cost, silicon steel cost, and permanent magnet cost.

[0026] For example, in the method provided by at least one embodiment of the present disclosure, it further includes:

[0027] Screening the model outputs of multiple said operating points;

[0028] Perform joint verification of multiple said optimization targets based on the screening results, and determine a second optimization solution for the motor design parameter variables based on the verification results.

[0029] For example, in the method provided by at least one embodiment of the present disclosure, the screening results include the motor torque, motor efficiency, motor power, motor cost, and motor back electromotive force of multiple said operating points; and,

[0030] The performing joint verification of multiple said optimization targets based on the model output and determining a first optimization solution for the motor design parameter variables based on the verification results includes:

[0031] Identify whether the motor torque, motor efficiency, motor power, motor cost, and motor back electromotive force of each said operating point all reach their respective set ranges. If so, perform the next step. If not, update the model input and re-run the simulation model;

[0032] Identify whether the motor temperature rise range, motor demagnetization degree, and motor local stress of each said operating point all meet their respective set requirements. If so, obtain the motor design parameter variables in the simulation model at the current moment and output them as the first optimal solution. If not, update the model input and re-run the simulation model until the first optimization solution is determined.

[0033] At least one embodiment of the present disclosure further provides a motor multi-objective optimization device, including:

[0034] A data acquisition module configured to acquire multiple optimization targets of a target motor;

[0035] A preprocessing module configured to determine motor performance indicators matching each said optimization target, and motor design parameter variables matching the motor performance indicators;

[0036] A model determination module configured to acquire a simulation model matching the target motor and a processing script for controlling the automatic operation of the simulation model, wherein the processing script is configured with: a first type of program for generating model input based on the motor design parameter variables, a second type of program for generating model output based on the motor performance indicators, and a third type of program for controlling the simulation model to run respectively at multiple preset operating points;

[0037] A data processing module configured to start the processing script to run the simulation model and acquire the model output of multiple said operating points; and,

[0038] A result generation module is configured to perform a joint verification of multiple to-be-optimized targets based on the model outputs of multiple working condition points, and determine a first optimization solution of the motor design parameter variables based on the verification result.

[0039] At least one embodiment of the present disclosure further provides a storage medium storing a program or instructions, and when the program or instructions are executed by a processor, the steps of the method provided in any embodiment of the present disclosure are implemented.

[0040] At least one embodiment of the present disclosure further provides a program product including a program or instructions, wherein when the program or instructions are executed by a processor, the steps of the method provided in any embodiment of the present disclosure are implemented.

[0041] The motor multi-objective optimization method, product and medium provided by the embodiments of the present disclosure can establish an exclusive automated design process for a specific motor (such as a flat wire motor). By combining an automated processing script with a multi-objective optimization algorithm, it can provide effective technical support for motor designs with differential advantages, reduce manual intervention, and improve the optimization accuracy. Among them, the processing script can automatically generate the model inputs and outputs of the simulation model based on all the to-be-optimized targets, and control the operation of the simulation model to perform simulations at multiple working condition points. Based on the simulation results of these multiple working condition points, multi-objective collaborative optimization is automatically performed, and finally the optimization result of the motor design parameter variables, that is, the first optimization solution, is obtained. This method significantly improves the design efficiency, optimization degree, optimization efficiency and comprehensive performance of the motor scheme, and is applicable to the development of fields such as new energy vehicle drive motors. This method also has high flexibility and scalability, and can be customized and optimized exclusively according to different application requirements. In fields such as new energy vehicle drive motors, this method can significantly improve the energy efficiency ratio of the motor, reduce energy consumption, extend the battery life, and thus provide a more efficient and environmentally friendly travel experience for motor designers. At the same time, this method also provides a new design idea for motor design engineers, enabling them to find the optimal motor design scheme more quickly and accurately, greatly shortening the product R & D cycle and improving the market competitiveness. In addition, the application of this method, product and medium can also promote the continuous innovation and development of motor technology and drive the overall technological progress of related industries.

[0042] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] To more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present disclosure. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0044] Figure 1 Flowchart of a multi-objective optimization method for an electric machine provided by at least one embodiment of the present disclosure;

[0045] Figure 2 Flowchart of a scheme for determining the actual remanence of a rotor permanent magnet provided by at least one embodiment of the present disclosure;

[0046] Figure 3 Schematic diagram of the process of another multi-objective optimization method for an electric machine provided by at least one embodiment of the present disclosure;

[0047] Figure 4 Schematic block diagram of a multi-objective optimization device for an electric machine provided by at least one embodiment of the present disclosure;

[0048] Figure 5 Schematic diagram of the composition of a program product provided by at least one embodiment of the present disclosure. Detailed implementation manners

[0049] The following will further describe the present disclosure in detail in conjunction with the drawings and embodiments. It should be specifically noted that the following embodiments are only used to illustrate the present disclosure, but do not limit the scope of the present disclosure. Similarly, the following embodiments are only some embodiments of the present disclosure rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present disclosure.

[0050] The terms "first", "second", and "third" in the embodiments of the present disclosure are only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first", "second", and "third" may explicitly or implicitly include at least one of such features.

[0051] In the description of the present disclosure, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically and clearly defined.

[0052] In the present disclosure, the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0053] The terms "comprising" and "having" and any variations thereof in the embodiments of the present disclosure are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or components inherent to these processes, methods, products, or devices.

[0054] As used herein, "program product" includes, but is not limited to, electronic devices, electronic apparatuses.

[0055] As used herein, "electronic device" includes, but is not limited to, a device configured to receive / transmit communication signals via a wired connection (such as via a public switched telephone network (PSTN), digital subscriber line (DSL), digital cable, direct cable connection, and / or another data connection / network) and / or via a wireless interface (such as for a cellular network, wireless local area network (WLAN), digital television network such as DVB-H network, satellite network, AM-FM broadcast transmitter, and / or another communication terminal). A communication terminal configured to communicate via a wireless interface may be referred to as a "wireless communication terminal", "wireless terminal", or "mobile terminal". Examples of mobile terminals include, but are not limited to, satellite or cellular telephones; personal communication system (PCS) terminals that can combine cellular radiotelephone with data processing, facsimile, and data communication capabilities; PDAs that may include a radiotelephone, pager, Internet / intranet access, web browser, notepad, calendar, and / or global positioning system (GPS) receiver; and conventional laptop and / or palmtop receivers or other electronic devices including radiotelephone transceivers. A mobile phone is an electronic device configured with a cellular communication module.

[0056] The term "motor torque" in the embodiments of the present disclosure is the torque generated by the target motor during operation, and it is the product of the motor output power and the rotational speed.

[0057] The term "motor iron loss" in the embodiments of the present disclosure refers to the energy loss caused by the change of magnetic flux in the iron core during the operation of the target motor.

[0058] The term "motor AC copper loss" in the embodiments of the present disclosure refers to the energy loss generated when current flows through the copper wire during the operation of the target motor. Since the copper wire has a certain resistance, heat is generated when current flows through it, resulting in energy loss.

[0059] The term "motor back EMF" in the embodiments of the present disclosure is an electromotive force generated in the armature winding when the target motor is rotating or moving. The direction of this electromotive force is opposite to the direction of change of the motor current and the motor magnetic flux, and is caused by the principle of electromagnetic induction. When a conductor moves in a magnetic field, a current is generated in the conductor, thereby forming an electromotive force in the opposite direction of the external force.

[0060] The term "motor inflection point speed" in the embodiments of the present disclosure refers to the speed value corresponding to the key point where the relationship between the output torque and the speed of the target motor changes when the target motor is running. Before this inflection point speed, the target motor usually exhibits a constant torque characteristic, that is, the output torque is relatively stable; after exceeding this inflection point speed, the target motor enters a non-constant torque section, and its output motor torque gradually decreases with the increase of the speed. The exact value of the inflection point speed depends on the design characteristics and load conditions of the target motor, and is an important parameter in motor performance evaluation and control strategy formulation.

[0061] The term "high efficiency interval" in the embodiment of the present disclosure refers to an interval with an efficiency greater than or equal to a certain efficiency in the analysis MAP diagram. For example, an interval with an efficiency greater than or equal to 95% can be used as an example.

[0062] The term "motor power consumption per 100 kilometers" in the embodiment of the present disclosure is based on a combination of certain operating points. The target motor works in sequence, and the ratio of the mileage to the power consumed is expressed in kWh / 100km.

[0063] The term "motor current advance angle" in the embodiments of the present disclosure refers to the phase difference between the motor current and the motor voltage waveform when the motor is running. It reflects the dynamic response characteristics of the electromagnetic field inside the motor and has an important impact on the performance of the motor, such as efficiency, power factor, and output torque.

[0064] The term "multi-objective optimization" in the embodiments of the present disclosure refers to the optimization of multiple assessment indicators of the target motor during the indicator determination process, including power objectives, economic objectives, reliability objectives, cost objectives, etc.

[0065] Figure 1 A flowchart of a motor multi-objective optimization method provided by at least one embodiment of the present disclosure. Figure 1As shown, the method may include steps S10 - S50.

[0066] Step S10: Obtain multiple optimization targets of the target motor.

[0067] Step S20: Determine the motor performance indicators matching each optimization target, and the motor design parameter variables matching the motor performance indicators.

[0068] Step S30: Obtain a simulation model matching the target motor and a processing script for controlling the automatic operation of the simulation model. Among them, the processing script is configured with: a first - type program for generating model inputs based on motor design parameter variables, a second - type program for generating model outputs based on motor performance indicators, and a third - type program for controlling the simulation model to run at multiple preset operating points respectively.

[0069] Step S40: Start the processing script to run the simulation model and obtain the model outputs at multiple operating points.

[0070] Step S50: Conduct a joint verification of multiple optimization targets based on the model outputs, and determine the first optimization solution of the motor design parameter variables based on the verification results.

[0071] Some embodiments of the present disclosure also provide a device, a medium (storage medium), and a program product corresponding to the above - mentioned method.

[0072] The method provided by at least one embodiment of the present disclosure is applicable to any existing motor design scenario or motor optimization scenario, and the embodiments of the present disclosure do not limit this. For example, the method can be applied to the optimization of the drive motor of an electric vehicle. By comprehensively optimizing multiple performance indicators such as the efficiency, power density, and temperature rise of the motor, the driving range and power performance of the electric vehicle can be improved. In addition, the method can also be applied to the design optimization of a wind turbine. By adjusting parameters such as blade length, rotational speed, and generator power, the power generation efficiency and stability of the wind turbine can be improved. The embodiments of the present disclosure are not limited to the above - mentioned specific application scenarios, but can be widely applied to various fields requiring motor optimization design.

[0073] Compared with the related art, the method provided by at least one embodiment of the present disclosure can establish an exclusive automated design process for a specific motor (such as a flat wire motor). By combining an automated processing script with a multi-objective optimization algorithm, it can provide effective technical support for motor designs with differential advantages, reduce manual intervention, and improve the optimization accuracy. Among them, the processing script can automatically generate the model input and model output of the simulation model based on all the optimization objectives to be optimized, and control the operation of the simulation model to perform simulations at multiple operating points. Based on the simulation results at these multiple operating points, multi-objective collaborative optimization is automatically carried out, and finally the optimization result of the motor design parameter variables, that is, the first optimization solution, is obtained. This method significantly improves the design efficiency, optimization degree, optimization efficiency, and comprehensive performance of the motor scheme, and is applicable to the development of fields such as new energy vehicle drive motors. This method also has a high degree of flexibility and scalability, and can be customized and optimized exclusively according to different application requirements. In fields such as new energy vehicle drive motors, this method can significantly improve the energy efficiency ratio of the motor, reduce energy consumption, extend the battery life, and thus provide a more efficient and environmentally friendly travel experience for motor designers. At the same time, this method also provides a new design idea for motor design engineers, enabling them to find the optimal motor design scheme more quickly and accurately, greatly shortening the product R & D cycle, and improving the market competitiveness. In addition, the application of this method, product, and medium can promote the continuous innovation and development of motor technology and drive the overall technological progress of related industries.

[0074] For step S10, as a preferred implementation manner, the optimization objectives to be optimized for the target motor can be configured to include the power performance objective of the target motor, the economic objective of the target motor, the reliability objective of the target motor, the cost objective of the target motor, and other objectives that need to be optimized, such as the noise level objective of the target motor, etc. Any combination can be made according to specific optimization requirements, and the embodiments of the present disclosure do not limit this.

[0075] For step S20, as a preferred implementation, the motor performance indicators matching the dynamic performance target are configured to include: motor torque, motor power, and motor phase voltage; the motor performance indicators matching the economic performance target are configured to include: motor efficiency, motor power consumption per 100 kilometers, motor iron loss, and motor AC copper loss; the motor performance indicators matching the reliability target are configured to include: motor back electromotive force, motor temperature rise range, motor demagnetization degree, and motor local stress; the motor performance indicators matching the cost target are configured to include: cost of magnetic steel, cost of copper sheet, cost of silicon steel, and cost of permanent magnet. There are many motor design parameter variables related to each motor performance indicator. For example, the motor design parameter variables related to motor torque include the number of motor pole pairs, the number of motor winding turns, the cross-sectional area of the motor wire, etc.; the motor design parameter variables related to motor power include the number of motor pole pairs, magnetic flux, current, etc.; the motor design parameter variables related to motor phase voltage include the number of winding turns, magnetic flux, motor speed, etc. These design parameter variables can all be used as adjustment objects during the optimization process to meet the requirements of different optimization goals. The motor design parameter variables matching other motor performance indicators can all be obtained by referring to relevant motor design guides and will not be elaborated here.

[0076] For step S30, the newly designed motor simulation model can be constructed through modeling software, while the simulation model of the existing motor can utilize the existing simulation model. The processing script can be designed based on existing simulation software. For example, the tool bar of software such as MAXWELL software has a recording script function. Open the full-parameter model, select the variable, and then set the adjustment range of the variable; alternatively, the relevant functions can also be implemented based on software programming, which can be understood by those skilled in the art, and the embodiments of the present disclosure do not impose any restrictions on this. The operating points can be arbitrarily designed and combined according to actual needs.

[0077] For step S50, the joint verification of multiple said optimization targets is also called multi-objective joint optimization. The multi-objective joint optimization is configured to at least include: identifying whether each said model output reaches the specified range of the corresponding said optimization target.

[0078] In some embodiments, in order to more accurately determine the simulation model matching the target motor, obtaining the simulation model matching the target motor in step S30 is further configured to include sub-steps S201 - sub-step S204, as Figure 2 shown.

[0079] Sub-step S201: Obtain the initial model of the target motor, where the fixed parameters of the initial model are configured to include the pole-slot ratio, pitch, number of flat wire layers, and rotor configuration of the target motor, and the configurable parameters of the initial model are configured to include motor performance indicators and motor design parameter variables.

[0080] Sub-step S202: Limit the motor performance indicators of the initial model and run the initial model.

[0081] Sub-step S203: Determine the variation range of the motor design parameter variables and the motor current advance angle corresponding to the motor peak torque based on the simulation results of the initial model.

[0082] Sub-step S204: Obtain the optimized model of the target motor based on the variation range of the motor design parameter variables and the motor current advance angle, as the simulation model matching the processing script.

[0083] Among them, for sub-step S201, exemplarily, the initial model of the target motor, that is, the initial version of the motor scheme, can be obtained based on the MAXWELL software, and then the variables to be optimized and the motor performance indicators are determined. Among the configurable parameters, the motor design parameter variables are generally configured as model inputs, and the motor performance indicators are generally configured as model outputs. Reverse design or combined design can also be performed. The embodiments of the present disclosure do not limit this.

[0084] For sub-step S202, exemplarily, the motor currents under rated conditions and peak conditions can be selected for limitation, and a motor current advance angle of 0 - 360° is set, and the initial model is run.

[0085] For sub-step S203, based on the simulation results of the initial model, that is, the specific values of the motor performance indicators, the variation range of the motor design parameter variables is determined, and each variable and the range of the variable are recorded. Specifically, this variation range should cover the interval from the current design parameter value to the possibly more optimal parameter value to ensure that the potential improvement space can be comprehensively explored in the subsequent optimization process. The key to this step is to ensure the rationality of the variation range, avoiding both too narrow a range that limits the optimization result and too wide a range that increases the unnecessary computational burden.

[0086] For sub-step S204, the range of the model input of the optimization model is obtained based on the result of sub-step S203. Optimization modeling is carried out based on the variation range of the motor design parameter variables and the motor current advance angle to obtain the optimization model of the target motor. The model output of the optimization model corresponds to the motor performance indicators. By adjusting the motor design parameter variables, the variation of the motor performance indicators can be observed. The motor performance indicator data under different combinations of motor design parameters are obtained. Through the analysis of these data, the optimal combination of motor design parameters can be further determined, so that the motor can achieve higher efficiency or lower energy consumption while meeting the performance requirements. When constructing the optimization model, various performance indicators of the target motor need to be comprehensively considered and these indicators are transformed into quantifiable objective functions. By introducing appropriate constraint conditions, such as current limit, voltage fluctuation range, etc., it is ensured that the optimization process is carried out within reasonable physical boundaries.

[0087] It should be noted that the sequential execution of sub-steps S201 - S204 aims to achieve a comprehensive improvement in motor performance and meet specific application requirements. In the execution process, each sub-step plays an indispensable role. The sequential execution of this series of sub-steps not only realizes the comprehensive improvement of motor performance but also meets diverse application requirements, providing an efficient and practical method for the design and optimization of the target motor.

[0088] In some embodiments, in order to make the simulation effect conform to the measured effect, the motor design parameter variables are configured to include the number of parallel branches of the motor winding parameter, the number of turns of the motor winding parameter, the number of phases of the motor winding parameter, the motor stator lamination type parameter, the motor rotor lamination type parameter, the rotor permanent magnet size parameter, the rotor permanent magnet skew pole parameter, and the motor speed parameter, etc. Through the design of the above configurable parameters, it can be ensured that the simulation results are consistent with the results of the measured prototype. For example, by adjusting the number of parallel branches of the motor winding parameter and the number of turns of the motor winding parameter, the current distribution and power output of the motor can be optimized; by changing the number of phases of the motor winding parameter, the running stability and efficiency of the motor can be affected; the design of the motor stator lamination type and rotor lamination type parameters is directly related to the structural strength and heat dissipation performance of the motor. The adjustment of the rotor permanent magnet size parameter and skew pole parameter can further optimize the magnetic field distribution and torque characteristics of the motor. The setting of the motor speed parameter directly determines the running speed and response ability of the motor.

[0089] In some embodiments, to make the simulation effect conform to the measured effect, the model input of the simulation model is configured to further include: motor current parameters, motor speed parameters, and current advance angle parameters. Moreover, the processing script is further configured with a fourth type of program for independently configuring the motor current parameters, motor speed parameters, and current advance angle parameters based on different operating points. The selection and configuration of these parameters are aimed at more accurately simulating the operating state of the target motor at different operating points. The motor current parameters reflect the current variation of the target motor under different loads, the motor speed parameters represent the rotational speed of the target motor, and the current advance angle parameters are closely related to the electromagnetic performance and efficiency of the target motor. By independently configuring these parameters, fine control of the motor performance can be achieved, thereby improving the accuracy and reliability of the simulation. The introduction of the fourth type of program enables the processing script to automatically adjust the values of these parameters according to different operating points to adapt to different operating environments and requirements. This flexibility not only improves the simulation efficiency but also provides more possibilities for the design and optimization of the motor.

[0090] In some embodiments, to achieve a better parameter optimization effect, the operating points are configured to include no-load operating conditions, rated operating conditions, peak operating conditions, and other operating conditions added according to actual requirements. Among them, under no-load operating conditions, the motor current parameters are configured to be 0, the motor speed parameters are configured to be the maximum motor speed, the current advance angle parameters are configured to be 0, and under rated operating conditions, the motor current parameters are configured to be the rated motor current, the motor speed parameters are configured to be the rated motor speed, the current advance angle parameters are configured to be the motor current advance angle, and under peak operating conditions, the motor current parameters are configured to be the peak motor current, the motor speed parameters are configured to be the inflection point speed of the motor, and the current advance angle parameters are configured to be the motor current advance angle.

[0091] As a more preferred implementation manner, the peak operating conditions are configured to include peak torque operating conditions, peak power operating conditions, rated power operating conditions at peak speed, and maximum power operating conditions at peak speed. Under these more detailed peak operating condition configurations, the system can perform more precise optimization for the motor performance requirements in different application scenarios. For example, under peak torque operating conditions, the motor current parameters and current advance angle parameters will be adjusted to a combination that can generate the maximum torque to meet application scenarios that require high torque output. Similarly, under peak power operating conditions, these parameters will be adjusted to a combination that can output the maximum power to adapt to scenarios that require high power output. And under rated power operating conditions at peak speed and maximum power operating conditions at peak speed, the system will optimize the motor parameters respectively to ensure that the rated power or maximum power can be stably output while reaching the peak speed. This detailed configuration method further enhances the flexibility and practicality of the motor multi-objective optimization method.

[0092] In some embodiments, the processing script is further configured with: a fifth type of program that establishes a logical relationship between the motor performance indicators and the motor design parameter variables, and a sixth type of program that establishes the solution boundary of the simulation model. The collaborative work of these two types of programs greatly enhances the automation and intelligence level of the motor multi-objective optimization process. Among them, the fifth type of program uses an accurate mathematical model or algorithm to establish a direct logical relationship between the performance indicators of the target motor, such as motor efficiency, motor torque, and motor power, and the motor design parameter variables, such as the number of turns of the motor winding, the number of pole pairs of the motor, and the motor core material. This enables motor designers to intuitively predict and evaluate the performance indicators of the motor by adjusting the design parameters, thereby greatly shortening the design cycle and improving the design efficiency. The sixth type of program is responsible for setting the solution boundary conditions of the simulation model, which may include the operating conditions of the motor, external environment parameters, etc. By accurately setting these boundary conditions, it can ensure that the simulation results are closer to the actual application scenarios, thereby improving the accuracy and reliability of the optimization results. At the same time, this program can also automatically adjust the parameters of the solution algorithm according to the design requirements to achieve a more efficient solution process.

[0093] In some embodiments, the model output is configured to include motor torque, motor efficiency, motor power, motor back electromotive force, and motor cost. These output parameters are the most critical performance indicators and economic indicators in the motor design process. Motor torque reflects the output capacity of the motor, motor efficiency reflects the energy conversion efficiency of the motor, motor power determines the usage range of the motor, motor back electromotive force is closely related to the operating stability and electromagnetic compatibility of the motor, and motor cost is an important indicator for measuring motor design. Through the comprehensive analysis and evaluation of these output parameters, it can provide comprehensive design feedback for motor designers, guiding them to conduct targeted design optimizations to achieve the best balance of motor performance, cost, and efficiency.

[0094] In some embodiments, the joint verification is configured to include: identifying whether each model output reaches the specified range of the corresponding target to be optimized, and identifying whether different model outputs conform to the preset logical relationships. This step is a key link in the joint verification, which ensures that multiple objectives of the motor design can be optimized collaboratively. Specifically, identifying whether each model output reaches the specified range of the corresponding target to be optimized means that the system will strictly check each key performance index and economic index to ensure that they meet the preset optimization criteria. This not only helps to improve the overall performance of the motor, but also effectively controls costs and improves economic benefits. At the same time, it is also crucial to identify whether different model outputs conform to the preset logical relationships. As a complex system, the target motor often has internal logical relationships among its various performance indexes. By verifying whether these logical relationships meet the expectations, the rationality and reliability of the motor design can be further ensured. The implementation of this step provides a solid guarantee for the multi-objective optimization of the motor, and helps to achieve the best balance among the performance, cost and efficiency of the target motor.

[0095] To more quickly and accurately determine the optimization results of the motor design parameter variables, the method includes step S60 - step S70.

[0096] Step S60: Screen the model outputs of multiple operating points.

[0097] Step S70: Perform joint verification of multiple targets to be optimized based on the screening results, and determine the second optimization solution of the motor design parameter variables based on the verification results.

[0098] Among them, for step S60, the screening results include the motor torque, motor efficiency, motor power, motor cost, and motor back electromotive force of multiple operating points, as well as other motor performance indexes set as needed. For example, in the no-load condition, the motor phase voltage also needs to be extracted, and in the rated condition and peak condition, the motor torque, motor iron loss, and motor AC copper loss also need to be extracted. In the peak condition, the copper wire area and magnet steel area also need to be further obtained.

[0099] For step S70, the second optimization solution obtained in this step may be more accurate and rapid than the first optimization solution obtained in step S50. By screening and jointly verifying the model outputs of multiple operating points, unnecessary calculations are effectively reduced, and the focus is on the key performance indexes. At the same time, this way of joint verification also comprehensively considers multiple optimization objectives of the motor, making the finally obtained second optimization solution achieve a better balance among performance, cost and efficiency. Therefore, the implementation of this step not only improves the efficiency of the multi-objective optimization of the motor, but also further improves the quality of the optimization.

[0100] In some embodiments, to obtain better optimization effects, step S50 is further configured to include sub-step S501 to sub-step S502.

[0101] Sub-step S501: Identify whether the motor torque, motor efficiency, motor power, motor cost, and motor back electromotive force at each operating point all reach their respective set ranges. If so, proceed to the next step. If not, update the model input and re-run the simulation model.

[0102] Sub-step S502: Identify whether the motor temperature rise range, motor demagnetization degree, and motor local stress at each operating point all meet their respective set requirements. If so, obtain the motor design parameter variables in the simulation model at the current moment and output them as the first optimal solution. If not, update the model input and re-run the simulation model until the first optimization solution is determined.

[0103] Among them, exemplarily, sub-step S501 to sub-step S502 can be implemented on the ModeFrontair platform. Open the ModeFrontair platform, set the driving module. Inside the driving module, set the driving script, the input variables and output variables in the driving script, as well as the corresponding relationships between these variables and the abort refresh time. Use the output variables of the simulation model as the input variables in the driving script, construct the objective function of the object to be optimized, and establish the optimization process. Inside the startup module, select the optimization method and set the number of models. The optimization method can select the NSGA-II algorithm for multi-objective optimization, set the number of iterations to 200 generations, and the population size to 50. After setting, click the start button, and the ModeFrontair platform will automatically run the simulation model and perform iterative optimization according to the set optimization process. During the optimization process, the platform will update the fitness values of the individuals in the population in real time, and continuously evolve the population according to the selection, crossover, mutation, etc. operations of the NSGA-II algorithm to find the optimal solution. At the same time, the platform will also monitor the convergence of the optimization process to ensure the effectiveness and reliability of the optimization results.

[0104] In some embodiments, the method can further add steps for multi-objective optimization. For example, add the objects to be optimized and add the priority selection of the objects to be optimized. Before the start of the optimization process, the motor designer can, according to actual needs, add multiple objects to be optimized, such as the noise level of the motor, etc. At the same time, in order to distinguish the importance of different objects to be optimized, the motor designer is allowed to set priorities for each object to be optimized. The selection of priorities can be in numerical form, and the higher the value, the greater the importance of the object to be optimized in the optimization process. The operation system will give corresponding weights according to the priorities set by the motor designer during the iterative optimization process to ensure that the final optimization result can better meet the actual needs of the motor designer.

[0105] In some embodiments, for example, the target motor is a flat wire motor. Flat wire motors have been widely used in the field of drive motors. Therefore, establishing a multi-objective optimization method for flat wire motors can significantly improve the comprehensive performance of flat wire motors. By comprehensively considering power performance, economy, reliability, cost, and other possible indicators such as noise level, this method can ensure that the flat wire motor reaches the optimal balance state during the design process. In addition, using the set priorities, designers can flexibly adjust the importance of different indicators to meet specific application requirements. This multi-objective optimization method can not only improve the overall efficiency of flat wire motors, but also shorten the design cycle, reduce development costs, and provide strong technical support for the wide application of flat wire motors.

[0106] As an exemplary implementation, this method is configured as the following process, as Figure 3 shown:

[0107] 1) Obtain the initial model based on MAXWELL software, determine the variables to be optimized, then manually adjust the range of each variable and record it; according to the current requirements under rated conditions and peak conditions, set the initial angle from 0 to 360°, perform sensitivity calculations, record the motor current advance angle when the motor torque is the highest, and obtain the simulation model;

[0108] 2) Record the model processing script: Open the MAXWELL software platform, in the tool column, click to record the processing script, open the fully parametric model, select the variables to be designed and changed, and then change the values of the variables;

[0109] 3) Set the motor current parameter to 0, the current advance angle parameter to 0, and the motor speed parameter to the maximum speed, and perform the solution calculation. In the calculation results, extract the motor phase voltage;

[0110] 4) Set the motor current parameter to the motor rated current, the current advance angle parameter to the above-mentioned motor current advance angle, and the motor speed parameter to the motor rated speed, and perform the solution calculation. In the calculation results, extract the motor torque, motor iron loss, and motor AC copper loss;

[0111] 5) Set the motor current parameter to the peak torque current, the current advance angle parameter to the above-mentioned motor current advance angle, and the motor speed parameter to the motor inflection point speed, and perform the solution calculation. In the calculation results, extract the motor torque, motor iron loss, and motor AC copper loss, and then output the copper wire area and the magnet area;

[0112] 6) Close the solution simulation model and stop recording the processing script;

[0113] 7) Open the ModeFrontair software platform, set the drive module, and in the drive module, set the drive script, input and output variables, and the corresponding relationship of the variables;

[0114] 8) Output the simulation model as input variables, and establish an optimization process with the no-load back electromotive force of the motor, the rated torque of the motor, the rated torque ripple of the motor, the rated efficiency of the motor, the peak torque of the motor, the peak torque ripple of the motor, the peak torque efficiency of the motor, and the motor cost as the optimization objectives.

[0115] 9) In the starting module, select the optimization method and set the number of models. In the driving module, set the stop refresh time.

[0116] The embodiment of the present disclosure also provides a motor multi-objective optimization device for implementing the above method embodiment, as Figure 4 shown. The motor multi-objective optimization device 1 includes a data acquisition module 10, a preprocessing module 20, a model determination module 30, a data processing module 40, and a result generation module 50.

[0117] The data acquisition module 10 is configured to acquire multiple optimization objectives of the target motor.

[0118] The preprocessing module 20 is configured to determine the motor performance indicators matching each optimization objective, and the motor design parameter variables matching the motor performance indicators.

[0119] The model determination module 30 is configured to acquire a simulation model matching the target motor and a processing script for controlling the automatic operation of the simulation model. Among them, the processing script is configured with: a first type of program for generating model inputs based on motor design parameter variables, a second type of program for generating model outputs based on motor performance indicators, and a third type of program for controlling the simulation model to run respectively at multiple preset operating points.

[0120] The data processing module 40 is configured to start the processing script to run the simulation model and acquire the model outputs of multiple operating points.

[0121] The result generation module 50 is configured to perform a joint check of multiple optimization objectives based on the model outputs of multiple operating points, and determine the first optimization solution of the motor design parameter variables based on the check results.

[0122] The specific manners of the operations executed by each unit in the above device embodiment have been described in detail in the embodiment of the method related thereto, and will not be elaborated here.

[0123] The embodiment of the present disclosure also provides a storage medium storing a program or instruction, and the program or instruction realizes the steps of the above method embodiment when executed by a processor.

[0124] The embodiment of the present disclosure also provides a program product, as Figure 5As shown, the program product includes one or more processors 21 and a memory 22. Figure 5 Taking one processor 21 as an example.

[0125] The controller may further include: an input device 23 and an output device 24.

[0126] The processor 21, the memory 22, the input device 23, and the output device 24 may be connected through a bus or other means. Figure 5 Taking connection through a bus as an example.

[0127] The processor 21 may be a central processing unit (CPU for short), and the processor 21 may also be other general-purpose processors, digital signal processors (DSP for short), application specific integrated circuits (ASIC for short), field-programmable gate arrays (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. chips, or a combination of the above types of chips. The general-purpose processor may be a microprocessor or any conventional processor.

[0128] The memory 22, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the methods in the embodiments of the present disclosure. The processor 21 executes various functional applications and data processing of the server by running the non-transitory software programs, instructions, and modules stored in the memory 22, that is, implements the steps of the above method embodiments.

[0129] The memory 22 may include a program storage area and a data storage area. Among them, the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created according to the use of the processing device of the server operation, etc. In addition, the memory 22 may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory 22 may optionally include a memory remotely set relative to the processor 21, and these remote memories may be connected to the network connection device through a network. Examples of the above networks include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0130] The input device 23 can receive input digital or character information and generate key signal inputs related to the motor designer settings and function control of the processing device of the server. The output device 24 can include display devices such as a display screen.

[0131] One or more modules are stored in the memory 22 and, when executed by one or more processors 21, perform as Figure 1 shown in the method.

[0132] Those skilled in the art can understand that to implement all or part of the processes in the above method embodiments, it can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. Among them, the storage medium can be a magnetic disk, an optical disc, a read-only memory (ROM), a random access memory (RAM), a flash memory (FM), a hard disk drive (HDD), or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memories.

[0133] Although the embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations all fall within the scope defined by the appended claims.

[0134] Although the embodiments of the present disclosure have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limitations on the present disclosure. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A multi-objective optimization method for an electric machine, characterized in that, Including: Obtaining a plurality of optimization targets of a target motor; Determining motor performance indicators matching each of the optimization targets, and motor design parameter variables matching the motor performance indicators; Obtaining a simulation model matching the target motor, and a processing script for controlling the automatic operation of the simulation model, wherein the processing script is configured with: a first type of program for generating model inputs based on the motor design parameter variables, a second type of program for generating model outputs based on the motor performance indicators, and a third type of program for controlling the simulation model to run at a plurality of preset operating points respectively; Starting the processing script to run the simulation model, and obtaining the model outputs at the plurality of operating points; and Performing a joint verification of the plurality of optimization targets based on the model outputs, and determining a first optimization solution of the motor design parameter variables based on the verification results.

2. The method according to claim 1, characterized in that, The obtaining of the simulation model matching the target motor includes: Obtaining an initial model of the target motor, wherein the fixed parameters of the initial model are configured to include the pole-slot ratio, pitch, number of flat wire layers, and rotor configuration of the target motor, and the configurable parameters of the initial model are configured to include the motor performance indicators and the motor design parameter variables; Limiting the motor performance indicators for the initial model, and running the initial model; Determining the variation range of the motor design parameter variables and the motor current lead angle corresponding to the motor peak torque based on the simulation results of the initial model; and Obtaining an optimized model of the target motor based on the variation range of the motor design parameter variables and the motor current lead angle, as the simulation model matching the processing script.

3. The method according to claim 2, wherein The model inputs are further configured to include: motor current parameters, motor speed parameters, and current lead angle parameters; and The processing script is further configured with: a fourth type of program for independently configuring the motor current parameters, the motor speed parameters, and the current lead angle parameters based on different operating points.

4. The method according to claim 3, wherein The operating points are configured to include no-load operating conditions, rated operating conditions, and peak operating conditions; And Under the no-load operating condition, the motor current parameters are configured to be 0, the motor speed parameters are configured to be the maximum motor speed, the current lead angle parameters are configured to be 0, and under the rated operating condition, the motor current parameters are configured to be the rated motor current, the motor speed parameters are configured to be the rated motor speed, the current lead angle parameters are configured to be the motor current lead angle, and under the peak operating condition, the motor current parameters are configured to be the peak motor current, the motor speed parameters are configured to be the motor inflection point speed, and the current lead angle parameters are configured to be the motor current lead angle.

5. The method according to any one of claims 1-4, characterized in that, The processing script is further configured with: a fifth type of program for establishing a logical relationship between the motor performance indicators and the motor design parameter variables, and a sixth type of program for establishing a solution boundary of the simulation model; And The model outputs are configured to include motor torque, motor efficiency, motor power, motor back electromotive force, and motor cost; The combined verification is configured to include: identifying whether each of the model outputs reaches a specified range corresponding to the to-be-optimized target, and identifying whether different model outputs conform to a preset logical relationship.

6. The method according to claim 4, wherein The peak operating conditions are configured to include peak torque conditions, peak power conditions, rated power conditions at peak speed, and maximum power conditions at peak speed; And, The to-be-optimized target is configured to include the dynamic target of the target motor, the economic target of the target motor, the reliability target of the target motor, and the cost target of the target motor; wherein, the motor performance indicators matching the dynamic target are configured to include at least one of motor torque, motor power, and motor phase voltage; the motor performance indicators matching the economic target are configured to include at least one of motor efficiency, motor power consumption per 100 kilometers, motor iron loss, and motor AC copper loss; the motor performance indicators matching the reliability target are configured to include at least one of motor back electromotive force, motor temperature rise range, motor demagnetization degree, and motor local stress; the motor performance indicators matching the cost target are configured to include at least one of magnet cost, copper sheet cost, silicon steel cost, and permanent magnet cost.

7. The method according to any one of claims 1-4, characterized in that It further includes: Screening the model outputs of multiple said operating points; Performing combined verification of multiple said to-be-optimized targets based on the screening results, and determining a second optimization solution of the motor design parameter variables based on the verification results.

8. The method according to claim 7, characterized in that The screening results include motor torque, motor efficiency, motor power, motor cost, and motor back electromotive force of multiple said operating points; and, Performing combined verification of multiple said to-be-optimized targets based on the model outputs, and determining a first optimization solution of the motor design parameter variables based on the verification results, includes: Identifying whether the motor torque, the motor efficiency, the motor power, the motor cost, and the motor back electromotive force of each said operating point all reach their respective set ranges. If so, execute the next step. If not, update the model input and re-run the simulation model; Identifying whether the motor temperature rise range, the motor demagnetization degree, and the motor local stress of each said operating point all reach their respective set requirements. If so, obtain the motor design parameter variables in the simulation model at the current moment as the first optimal solution output. If not, update the model input and re-run the simulation model until the first optimization solution is determined.

9. A multi-objective optimization device for an electric motor, characterized in that, It includes: A data acquisition module configured to acquire multiple to-be-optimized targets of a target motor; A preprocessing module configured to determine motor performance indicators matching each said to-be-optimized target, and motor design parameter variables matching the motor performance indicators; A model determination module, configured to obtain a simulation model matching a target motor and a processing script for controlling automatic operation of the simulation model, wherein the processing script is configured with: a first type of program for generating model inputs based on the motor design parameter variables, a second type of program for generating model outputs based on the motor performance indicators, and a third type of program for controlling the simulation model to run at a plurality of preset operating points respectively; A data processing module, configured to start the processing script to run the simulation model and obtain the model outputs of the plurality of operating points; and A result generation module, configured to perform joint verification of the plurality of to-be-optimized targets based on the model outputs of the plurality of operating points, and determine a first optimization solution of the motor design parameter variables based on the verification results.

10. A storage medium, characterized in that, The storage medium stores a program or instruction, and the program or instruction, when executed by a processor, implements the steps of the method according to any one of claims 1 to 8.

11. A program product, comprising a program or instructions, characterized in that, The program or instruction, when executed by a processor, implements the steps of the method according to any one of claims 1 to 8.

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