Motor system energy efficiency optimization method based on multi-working-condition gravity center

Abstract

The invention discloses a motor system energy efficiency optimization method based on multi-working-condition gravity centers, and belongs to the field of motor optimization design. Firstly, a whole vehicle simulation platform is called, motor circulation working condition points are collected, and a clustering algorithm is adopted to extract a plurality of typical working condition gravity centers. Secondly, multi-objective optimization is carried out on the motor, high-sensitivity design variables are screened out through parameter correlation and sensitivity analysis in ANSYS, a response surface model is constructed on this basis, an optimization algorithm is adopted to solve a Pareto leading edge, and an optimal solution is selected from feasible solutions; and establishing a finite element model according to a motor design variable, and verifying the energy efficiency optimization effect of the method through performance evaluation. And finally, analyzing and proving the improvement effect of the method on the economy of the whole vehicle through a whole vehicle simulation platform. According to the method, efficient and rapid energy efficiency optimization can be performed on the motor system based on the cyclic working condition characteristics, the overlap ratio of working condition point distribution of a high-efficiency region of the motor is improved, and the operation energy efficiency of the motor system is effectively improved.

Description

Technical Field

[0001] The present invention belongs to the field of motor design optimization, and specifically relates to a motor system energy efficiency optimization method based on multiple working condition centers of gravity. Background Art

[0002] In recent years, electric motors have been widely used in electric transportation due to their high power density, high efficiency, and high output torque. However, because electric transportation equipment must adapt to complex working environments and operating conditions, motors need to operate in different speed and torque ranges, and the overlap between their high-efficiency areas and operating point distributions is relatively low.

[0003] Traditional motor system energy efficiency optimization methods are mainly based on rated points, without considering the overall characteristics of the distribution of operating points. When the operating point concentration area is far away from the rated point, the optimization effect of this method is not ideal. In addition, the distribution of operating points in actual application scenarios is generally more scattered, and the analysis process is relatively complex, which leads to low optimization efficiency and even delays in the production and manufacturing cycle. At present, there are few studies on motor system energy efficiency optimization methods based on operating conditions, and it is difficult to carry out efficient and fast motor system energy efficiency optimization. In order to meet the rapid development of various types of electric transport equipment, such as electric vehicles, electric ships, electric aircraft, electrified rail transit, etc., it is necessary to carry out research and development of motor system energy efficiency optimization methods to further improve the overall operating energy efficiency of the motor system, increase cruising range and reduce energy consumption. Summary of the Invention

[0004] In response to the problems existing in the prior art, the present invention provides a motor system energy efficiency optimization method based on multiple working condition centers of gravity, which is used to solve the problems of low overall energy efficiency and insufficient adaptability to cyclic working conditions of motor systems designed based on rated points. By extracting the centers of gravity of typical working conditions, the overall characteristics of the distribution of working condition points can be fully considered, thereby obtaining better optimization effects and effectively improving the operating energy efficiency of the motor drive system.

[0005] To solve the above technical problems, the present invention provides the following technical solution: a method for optimizing the energy efficiency of a motor system based on a multi-working center of gravity, comprising the following steps:

[0006] S1. Cycle operating condition center of gravity extraction: Call the vehicle simulation platform to collect motor cycle operating points, use the Gaussian mixture model clustering algorithm, and output the typical operating condition center of gravity as the input for the motor multi-objective optimization;

[0007] S2. Motor Multi-Objective Optimization: Construct a multi-objective optimization function for the motor and add constraints. Perform parameter correlation and sensitivity analysis on the design variables. Build a response surface model based on highly sensitive design variables, which is effective for the finite element model. Use a multi-objective optimization algorithm to obtain the Pareto optimal solution, which serves as the input for motor performance evaluation.

[0008] S3. Motor performance evaluation: Build a finite element model of the motor and obtain the external characteristic curve and efficiency map of the optimized motor as input for vehicle simulation platform analysis;

[0009] S4. Vehicle simulation platform analysis: Based on the vehicle simulation platform, the economic performance of the vehicle equipped with the motor is analyzed under cycle conditions to verify the improvement effect of the vehicle's economy.

[0010] Furthermore, the aforementioned step S1 includes the following sub-steps:

[0011] S1.1. In the vehicle simulation platform, perform longitudinal motion force analysis on the vehicle based on the vehicle's basic parameters and cyclic operating condition data, and calculate the operating condition data at a single moment. The formula is as follows:

[0012]

[0013] Where D o is the outer diameter of the wheel; i f is the final transmission ratio of the vehicle; η is the vehicle transmission efficiency; V c is the vehicle's current speed under cyclic conditions;

[0014] S1.2. According to formula (1), the distribution of all operating points is obtained. The Gaussian mixture model clustering algorithm is used to analyze all operating points to form a probability density distribution surface. The scattered operating points are merged into different clusters, and several weighted operating center points are extracted from them to be equivalent to all operating points.

[0015] Furthermore, in the aforementioned step S2, the multi-objective optimization function and constraints constructed are as follows:

[0016]

[0017] Where F(x) is the objective function; x is the design variable matrix; ξ AMC and f AMC (x) are the weight and calculation formula of effective material cost respectively; ξ Loss and f Loss (x) are the weight and calculation formula of working condition loss respectively; T N and T r are the average torque and torque ripple under rated conditions respectively; x min_i and x max_i are the minimum and maximum values ​​of the i-th design variable, respectively.

[0018] Furthermore, in the aforementioned step S2, highly sensitive design variables are screened out through parameter correlation and sensitivity analysis, wherein the definition of the correlation coefficient is:

[0019]

[0020] Where ρ is the correlation coefficient; Cov(x1,x2) is the covariance of x1 and x2; σ(x1) and σ(x2) are the standard deviations of x1 and x2, respectively. The parameter sensitivity is calculated based on the variance model, and the formula is:

[0021]

[0022] Where Var(·) is the variance function; x is the design variable matrix; f j (x) is the jth optimization objective; E(f j (x)|x i )) is the average value of the jth optimization objective when only the i-th design variable changes. The global sensitivity of each design variable to all optimization objectives can be obtained by weighted addition, and its expression is:

[0023] S(x i )=ξ AMC |S AMC (x i )|+ξ Loss |S Loss (x i )|,i=1,2,…,9 (5)

[0024] Where, ξ AMC and ξ Loss are the weights of effective material cost and working condition loss in the multi-objective optimization of hub motors; S AMC (x i ) and S Loss (x i ) are the sensitivities of the i-th design variable to the effective material cost and operating loss, respectively.

[0025] Furthermore, in the aforementioned step S2, after parameter correlation and sensitivity analysis, only the highly sensitive layer is retained for the motor design variables. Based on this, the central composite design method is used to generate experimental design points and establish a response surface model. The fitted standard quadratic polynomial is:

[0026]

[0027] Where, f AMC 、f Loss are effective material cost and working condition loss respectively; H s2 , emb, and skew are the stator slot depth, pole arc coefficient, and skew angle, respectively.

[0028] Furthermore, in the aforementioned step S2, based on the changing trends of the motor optimization objectives with the highly sensitive design variables, a fast non-dominated sorting genetic algorithm with an elite strategy is used to iteratively optimize the highly sensitive design variables to obtain the Pareto frontier, that is, a series of equilibrium solutions that keep the optimization objectives non-degraded. The evaluation function is constructed as follows:

[0029]

[0030] Where G is the evaluation function; f AMC 、max(f AMC )、min(f AMC ) are the current value, maximum value and minimum value of effective material cost respectively; f Loss 、max(f Loss )、min(f Loss ) are the current value, maximum value, and minimum value of the working condition loss respectively. According to the above evaluation function, the optimal solution in the Pareto front is selected.

[0031] Furthermore, in the aforementioned step S3, a finite element model of the motor is established, the power supply excitation, loss coefficient, and initial conditions of the scanning step are set, and transient simulation is performed. The performance indicators such as the speed, torque, and efficiency that the motor can achieve are calculated, and the external characteristic curve and efficiency map of the motor are obtained. The performance of the motor before and after optimization is compared to verify the effectiveness of the proposed method.

[0032] Furthermore, in the aforementioned step S4, the whole vehicle simulation platform analysis is used as the starting and ending points, and the energy efficiency of the motor system is optimized through repeated iterations.

[0033] Furthermore, the aforementioned motor system energy efficiency optimization method based on multiple working condition center of gravity is applicable to various types of electric transport equipment, including but not limited to electric vehicles, electric ships, electric aircraft, and electrified rail transit.

[0034] Furthermore, the aforementioned motor system energy efficiency optimization method based on multiple operating conditions is used in various cyclic operating conditions, including but not limited to idling, acceleration, deceleration and cruise control.

[0035] Compared with the prior art, the beneficial technical effects of the present invention using the above technical solution are as follows:

[0036] This method uses a Gaussian mixture model clustering algorithm to extract multiple typical operating center of gravity conditions and optimizes the motor system's energy efficiency based on these multiple operating center of gravity conditions. This approach achieves high efficiency and significant results. Inputting the optimized motor characteristic curves into a vehicle simulation platform effectively improves the vehicle's economy under cyclic operating conditions and extends its range. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1It is a flow chart of the motor system energy efficiency optimization method.

[0038] Figure 2 It is a data transmission relationship diagram of the motor system energy efficiency optimization method.

[0039] Figure 3 It is a schematic diagram of the clustering area of ​​cyclic operating points.

[0040] Figure 4 It is the efficiency map diagram after the initial design of the motor, rated point optimization and multi-working center of gravity optimization. In the figure, (a) is the efficiency map diagram of the initial design of the motor, (b) is the efficiency map diagram after the rated point optimization of the motor, and (c) is the efficiency map diagram after the multi-working center of gravity optimization of the motor.

[0041] Figure 5 This is a comparison chart of the economy of the whole vehicle equipped with the motor after initial design, rated point optimization and multi-working condition center of gravity optimization. In the figure, (a) is the vehicle distance comparison chart, and (b) is the vehicle SOC comparison chart. DETAILED DESCRIPTION

[0042] In order to better understand the technical content of the present invention, specific embodiments are given and described below with reference to the accompanying drawings.

[0043] Various aspects of the present invention are described herein with reference to the accompanying drawings, which show a number of illustrative embodiments. The embodiments of the present invention are not limited to those described in the accompanying drawings. It should be understood that the present invention can be implemented by any of the various concepts and embodiments described above, as well as the concepts and implementations described in detail below, because the concepts and embodiments disclosed herein are not limited to any particular implementation. In addition, some aspects disclosed herein may be used alone or in any appropriate combination with other aspects disclosed herein.

[0044] refer to Figure 1 The present invention provides a method for optimizing the energy efficiency of a motor system based on a multi-operating-condition center of gravity, comprising the following steps:

[0045] S1. Extraction of the center of gravity of the cyclic working condition: The speed and torque data of the motor in a cycle are collected using the vehicle simulation platform to obtain the distribution of the working condition points. In order to ensure that the energy efficiency optimization of the motor system can cover all areas and involve as few working condition points as possible, it is necessary to use the Gaussian mixture model clustering algorithm to concentrate the distribution of the working condition points, extract a number of weighted working condition centers of gravity from them, and use a small number of working condition centers of gravity to be equivalent to all cyclic working condition points. The cyclic working condition center of gravity data extracted in this step will be used as the input for the multi-objective optimization of the motor. It specifically includes the following sub-steps:

[0046] S1.1. In the vehicle simulation platform, perform longitudinal motion force analysis on the vehicle based on the vehicle's basic parameters and cyclic operating condition data, and calculate the operating condition data at a single moment. The formula is as follows:

[0047]

[0048] Where D o is the outer diameter of the wheel; i f is the final transmission ratio of the vehicle; η is the vehicle transmission efficiency (%); V c is the vehicle's current speed under cycle conditions (km / h);

[0049] S1.2. According to formula (1), the distribution of all operating points is obtained. The Gaussian mixture model clustering algorithm is used to analyze all operating points to form a probability density distribution surface. The scattered operating points are merged into different clusters, and several weighted operating center points are extracted from them to be equivalent to all operating points.

[0050] S2. Motor Multi-Objective Optimization: Construct a motor multi-objective optimization function and add constraints. Perform parameter correlation and sensitivity analysis on the design variables. Build a response surface model equivalent to the finite element model based on highly sensitive design variables. Use a multi-objective optimization algorithm to obtain the Pareto optimal solution as input for motor performance evaluation.

[0051] In the embodiment, parameter correlation and sensitivity analysis of design variables are performed in ANSYS Workbench, and high-sensitivity design variables are screened out based on the global sensitivity of the design variables to the two optimization objectives. The computational overhead of directly calling the finite element model for multi-objective optimization of the motor is large, so a response surface model equivalent to the finite element model is constructed based on the highly sensitive design variables, and it is used as a calculation tool for the motor optimization objectives, which significantly reduces the computational complexity. In MATLAB, multi-objective optimization is carried out based on a fast non-dominated sorting genetic algorithm with an elite strategy. By calling the response surface model to iteratively optimize the motor design variables, the Pareto frontier, i.e., a series of feasible solutions that meet the constraints, is finally obtained. According to the evaluation function, the optimal solution, i.e., the optimization result of the design variables, is selected from the feasible solutions as the input for the motor performance evaluation.

[0052] The constructed multi-objective optimization function and constraints are as follows:

[0053]

[0054] Where F(x) is the objective function; x is the design variable matrix; ξ AMC and f AMC (x) are the weight and calculation formula of effective material cost respectively; ξ Loss and f Loss(x) are the weight and calculation formula of working condition loss respectively; T N and T r are the average torque and torque ripple under rated conditions respectively; x min_i and x max_i are the minimum and maximum values ​​of the i-th design variable, respectively.

[0055] Highly sensitive design variables are screened out through parameter correlation and sensitivity analysis, where the correlation coefficient is defined as:

[0056]

[0057] Where ρ is the correlation coefficient; Cov(x1,x2) is the covariance of x1 and x2; σ(x1) and σ(x2) are the standard deviations of x1 and x2, respectively. Parameter sensitivity is calculated based on the variance model, and its formula is:

[0058]

[0059] Where Var(·) is the variance function; x is the design variable matrix; f j (x) is the jth optimization objective; E(f j (x)|x i )) is the average value of the jth optimization objective when only the i-th design variable changes. The global sensitivity of each design variable to all optimization objectives can be obtained by weighted addition, and its expression is:

[0060] S(x i )=ξ AMC |S AMC (x i )|+ξ Loss |S Loss (x i )|,i=1,2,…,9 (5)

[0061] Where, ξ AMC and ξ Loss are the weights of effective material cost and working condition loss in the multi-objective optimization of hub motors; S AMC (x i ) and S Loss (x i ) are the sensitivities of the i-th design variable to the effective material cost and operating loss, respectively.

[0062] After parameter correlation and sensitivity analysis, only the highly sensitive layer of the motor design variables was retained. Based on this, the central composite design method was used to generate experimental design points and establish a response surface model. The standard quadratic polynomial fitted was:

[0063]

[0064] Where, f AMC 、f Loss are effective material cost and working condition loss respectively; H s2 , emb, and skew are the stator slot depth, pole arc coefficient, and skew angle, respectively.

[0065] According to the changing trend of each optimization objective of the motor with the high-sensitivity design variables, a fast non-dominated sorting genetic algorithm with an elite strategy is used to iteratively optimize the high-sensitivity design variables to obtain the Pareto frontier, that is, a series of equilibrium solutions that keep the optimization objective non-degraded. The evaluation function is constructed as follows:

[0066]

[0067] Where G is the evaluation function; f AMC 、max(f AMC )、min(f AMC ) are the current value, maximum value and minimum value of effective material cost respectively; f Loss 、max(f Loss )、min(f Loss ) are the current value, maximum value, and minimum value of the working condition loss respectively. According to the above evaluation function, the optimal solution in the Pareto front is selected.

[0068] S3. Motor performance evaluation: Establish a finite element model of the motor and obtain the external characteristic curve and efficiency map of the optimized motor as input for the vehicle simulation platform analysis.

[0069] In this example, a finite element model of the motor was built using the ANSYS Maxwell development environment. The optimized motor's performance indicators, such as torque, speed, and efficiency, were analyzed. The proposed method was validated by comparing the motor's performance after initial design, rated point optimization, and multi-operating center of gravity optimization. This motor performance evaluation yielded external characteristic curves and efficiency maps, which served as input for subsequent vehicle economic analysis.

[0070] S4. Vehicle simulation platform analysis: It is necessary to conduct an economic analysis of the vehicle equipped with an electric motor under cyclic operating conditions based on the vehicle simulation platform, and determine the degree of completion of the vehicle's economic indicators.

[0071] The method of the present invention is applicable to various motor systems with cyclic working conditions, especially various electric vehicles, including but not limited to electric vehicles, electric ships, electric aircraft, electrified rail transit, etc.

[0072] Figure 2The data transmission relationship of the motor system energy efficiency optimization method is demonstrated. The center of gravity data of the cycle operating condition is used to accurately calculate the motor operating loss; the design variable optimization results are used to evaluate the optimized motor performance; the external characteristic curve and efficiency map are used to analyze the economy of the optimized vehicle.

[0073] Figure 3 The display shows clusters of operating points in the cycle. The four clusters correspond to frequent start-stop driving, low-speed climbing, low-speed cruising, and high-speed cruising. The operating points within each cluster are linearly weighted to determine the centroid, represented by darker scattered points. Statistics show that approximately 83.5% of the operating points are located in the low-speed range (including frequent start-stop driving, low-speed climbing, and low-speed cruising), while approximately 61.6% are located in the low-torque range (including low-speed cruising and high-speed cruising).

[0074] Figure 4 The efficiency maps after the motor initial design, rated point optimization, and multi-operating center of gravity optimization are shown in Figures (a) to (c). By comparison, it can be found that the high efficiency area of ​​the initial design is at a speed of 1100 r·min. -1 , torque 600N·m working point, the high efficiency area after rated point optimization is at speed 900r·min -1 , torque 400N·m working point, and the high efficiency area after multi-working condition center of gravity optimization is at a speed of 650r·min -1 , near the operating point with a torque of 300 N·m, it is obvious that the high efficiency area of ​​the motor after multi-operating condition center of gravity optimization has a higher degree of overlap with the distribution area of ​​the cyclic operating condition points.

[0075] Figure 5 The economic analysis results of the motor vehicle equipped with the initial design, rated point optimization and multi-operating center of gravity optimization are shown in Figures (a) and (b). After comparison, it can be found that: under the rated point optimization, the vehicle's driving distance is extended by about 1.1%, and its unit energy consumption is increased from 0.272kWh·km -1 Down to 0.269 kWh·km -1 Under the optimization of the center of gravity of multiple working conditions, the vehicle's driving distance was extended by about 4.8%, and the unit energy consumption was increased from 0.272kWh·km -1 Down to 0.259 kWh·km -1 When the vehicle's SOC under the initial design reaches its final value, the rated-point optimization method still has 5.75% of the SOC remaining, while the multi-operating-center-of-gravity optimization method still has 9.71% of the SOC remaining. This suggests that the energy efficiency optimization method based on the rated point achieves relatively low returns, while the multi-operating-center-of-gravity method can significantly improve vehicle economic performance.

[0076] While the present invention has been described above with reference to preferred embodiments, this is not intended to limit the present invention. Persons skilled in the art will readily appreciate that various modifications and variations can be made without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A motor system energy efficiency optimization method based on multiple working condition center of gravity, characterized in that: The following steps are involved: S1. Cycle operating condition center of gravity extraction: Call the vehicle simulation platform to collect motor cycle operating points, use the Gaussian mixture model clustering algorithm, and output the typical operating condition center of gravity as the input for the motor multi-objective optimization; S2. Motor Multi-Objective Optimization: Construct a multi-objective optimization function for the motor and add constraints. Perform parameter correlation and sensitivity analysis on the design variables. Build a response surface model based on highly sensitive design variables, which is effective for the finite element model. Use a multi-objective optimization algorithm to obtain the Pareto optimal solution, which serves as the input for motor performance evaluation. S3. Motor performance evaluation: Build a finite element model of the motor and obtain the external characteristic curve and efficiency map of the optimized motor as input for vehicle simulation platform analysis; S4. Vehicle simulation platform analysis: Based on the vehicle simulation platform, the economic performance of the vehicle equipped with the motor is analyzed under cycle conditions to verify the improvement effect of the vehicle's economy.

2. The method for optimizing the energy efficiency of a motor system based on multiple working condition centers of gravity according to claim 1, characterized in that: Step S1 includes the following sub-steps: S1.

1. In the vehicle simulation platform, perform longitudinal motion force analysis on the vehicle based on the vehicle's basic parameters and cyclic operating condition data, and calculate the operating condition data at a single moment. The formula is as follows: Where D o is the outer diameter of the wheel; i f is the final drive ratio of the vehicle; η is the vehicle transmission efficiency; V c is the vehicle's current speed under cyclic conditions; S1.

2. According to formula (1), the distribution of all operating points is obtained. The Gaussian mixture model clustering algorithm is used to analyze all operating points to form a probability density distribution surface. The scattered operating points are merged into different clusters, and several weighted operating center points are extracted from them to be equivalent to all operating points.

3. The method for optimizing the energy efficiency of a motor system based on multiple working condition centers of gravity according to claim 1, characterized in that: In step S2, the constructed multi-objective optimization function and constraints are as follows: Where F(x) is the objective function; x is the design variable matrix; ξ AMC and f AMC (x) are the weight and calculation formula of effective material cost respectively; ξ Loss and f Loss (x) are the weight and calculation formula of working condition loss respectively; T N and T r are the average torque and torque ripple under rated conditions respectively; x mini and x maxi are the minimum and maximum values ​​of the i-th design variable, respectively.

4. The method for optimizing the energy efficiency of a motor system based on multiple working condition centers of gravity according to claim 1, characterized in that: In step S2, high-sensitivity design variables are screened out through parameter correlation and sensitivity analysis, where the correlation coefficient is defined as: Where ρ is the correlation coefficient; Cov(x1,x2) is the covariance of x1 and x2; σ(x1) and σ(x2) are the standard deviations of x1 and x2, respectively. The parameter sensitivity is calculated based on the variance model, and the formula is: Where Var(·) is the variance function; x is the design variable matrix; f j (x) is the jth optimization objective; E(f j (x)|x i )) is the average value of the jth optimization objective when only the i-th design variable changes. The global sensitivity of each design variable to all optimization objectives can be obtained by weighted addition, and its expression is: S(x i )=ξ AMC |S AMC (x i )|+ξ Loss |S Loss (x i )|,i=1,2,,9 (5) Where, ξ AMC and ξ Loss are the weights of effective material cost and working condition loss in the multi-objective optimization of hub motors; S AMC (x i ) and S Loss (x i ) are the sensitivities of the i-th design variable to the effective material cost and operating loss, respectively.

5. The method for optimizing the energy efficiency of a motor system based on multiple working condition centers of gravity according to claim 1, characterized in that: In step S2, after parameter correlation and sensitivity analysis, only the highly sensitive layer is retained for the motor design variables. Based on this, the central composite design method is used to generate experimental design points and establish a response surface model. The fitted standard quadratic polynomial is: Where, f AMC 、f Loss are effective material cost and working condition loss respectively; H s2 , emb, and skew are the stator slot depth, pole arc coefficient, and skew angle, respectively.

6. The method for optimizing the energy efficiency of a motor system based on multiple working condition centers of gravity according to claim 1, characterized in that: In step S2, according to the changing trend of each optimization objective of the motor with the high-sensitivity design variables, a fast non-dominated sorting genetic algorithm with an elite strategy is used to iteratively optimize the high-sensitivity design variables to obtain the Pareto frontier, that is, a series of equilibrium solutions that keep the optimization objective non-degraded. The evaluation function is constructed as follows: Where G is the evaluation function; f AMC 、max(f AMC )、min(f AMC ) are the current value, maximum value and minimum value of effective material cost respectively; f Loss 、max(f Loss )、min(f Loss ) are the current value, maximum value, and minimum value of the working condition loss respectively. According to the above evaluation function, the optimal solution in the Pareto front is selected.

7. The method for optimizing the energy efficiency of a motor system based on multiple working condition centers of gravity according to claim 1, characterized in that: In step S3, a finite element model of the motor is established, and the initial conditions of power supply excitation, loss coefficient, and scanning step size are set. Transient simulation is performed, and the performance indicators such as speed, torque, and efficiency that the motor can achieve are calculated. The external characteristic curve and efficiency map of the motor are obtained, and the performance of the motor before and after optimization is compared to verify the effectiveness of the proposed method.

8. The method for optimizing the energy efficiency of a motor system based on multiple working condition centers of gravity according to claim 1, characterized in that: In step S4, the whole vehicle simulation platform analysis is used as the starting and ending points, and the energy efficiency of the motor system is optimized through repeated iterations.

9. The method for optimizing the energy efficiency of a motor system based on multiple working condition centers of gravity according to claim 1, characterized in that: Applicable to all types of electric transport equipment, including but not limited to electric vehicles, electric ships, electric aircraft, and electrified rail transit.

10. The method for optimizing the energy efficiency of a motor system based on multiple working condition centers of gravity according to claim 1, characterized in that: Used in various operating cycles, including but not limited to idling, acceleration, deceleration and cruise control.

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