Multivariable optimization design method for improving counter electromotive force of permanent magnet motor for flywheel

Abstract

A multivariable optimization design method for improving counter electromotive force of a permanent magnet motor for a flywheel comprises the steps that firstly, after the size structure of the motor is determined, electromagnetic modeling of the motor is completed, and optimization variable parameters and a counter electromotive force optimization target of the motor are defined; calculating the sensitivity of different optimization variable parameters by using a sensitivity hierarchical calculation method; high-sensitivity parameters are selected for further optimization design, and a response surface equation between the optimization variable parameters and the back electromotive force optimization target is sampled, calculated and optimized; and a genetic algorithm is used to solve the optimal design solution of the back electromotive force performance, and when the amplitude of the back electromotive force fundamental wave is maximum and the amplitudes of the third and fifth harmonic voltages are minimum, the back electromotive force performance is the best. The permanent magnet motor for the flywheel is easy to process and high in pertinence, the electric energy quality of the permanent magnet motor for the flywheel can be obviously improved, and the problems of small counter electromotive force and poor electric energy quality in the permanent magnet motor for the flywheel are solved.

Description

Technical Field

[0001] The invention belongs to the field of permanent magnet motor manufacturing, and in particular relates to a multivariable optimization design method for improving the back electromotive force of a permanent magnet motor for a flywheel. Background Art

[0002] Flywheel energy storage technology is a technology that converts electrical energy into kinetic energy and stores it through a high-speed rotating flywheel, and then converts the kinetic energy back into electrical energy when needed. This energy storage method has broad application prospects in renewable energy grid connection, uninterruptible power supply (UPS), rail transportation, aerospace and other fields due to its high efficiency, long life, and rapid response capabilities. Among them, the permanent magnet motor is one of the core components in the flywheel energy storage system, and its performance directly affects the operating efficiency and reliability of the entire system.

[0003] In order to ensure the high efficiency of the flywheel energy storage system and the high quality of the output power, higher requirements are put forward for the permanent magnet motor for flywheel, especially for the back EMF characteristics of the motor. Permanent magnet motors for flywheel generally have the disadvantages of insufficient output voltage and poor power quality. The ideal motor should have the characteristics of high fundamental back EMF amplitude, low third harmonic back EMF amplitude and low fifth harmonic back EMF amplitude, so as to reduce electromagnetic noise and vibration and improve the overall performance of the system.

[0004] At present, in order to improve the back EMF characteristics of the motor, a variety of methods have been developed, such as rotor segmented skewed poles, stator skewed slots, rotor mixed magnetic poles, and auxiliary holes on the stator and rotor surfaces, etc. However, these methods often have the disadvantages of difficult processing and insufficient improvement of back EMF. Summary of the invention

[0005] The present invention aims to provide a multivariable optimization design method for improving the back electromotive force of a permanent magnet motor for a flywheel, so as to solve the technical problems of small back electromotive force and poor power quality in the permanent magnet motor for a flywheel.

[0006] To achieve the above object, the specific technical solution of a multivariable optimization design method for improving the back electromotive force of a permanent magnet motor for a flywheel of the present invention is as follows:

[0007] A multivariable optimization design method for improving the back electromotive force of a permanent magnet motor for a flywheel comprises the following steps:

[0008] S1. Model the permanent magnet motor and determine the optimization variable parameters and back-EMF optimization target of the permanent magnet motor;

[0009] S2, calculate the sensitivity of the optimized variable parameters;

[0010] S3, selecting highly sensitive optimization variable parameters, and sampling and calculating the response surface equation between the optimization variable parameters and the back-electromotive force optimization target;

[0011] S4, using genetic algorithm to solve the optimal design solution for back EMF performance;

[0012] S5. Extract the parameter values ​​of various structural variables of the permanent magnet motor from the optimal design solution of back-EMF performance to achieve multivariable optimization design.

[0013] As a further improvement of the present invention, the back EMF optimization target includes the fundamental back EMF amplitude, the 3rd harmonic back EMF amplitude and the 5th harmonic back EMF amplitude;

[0014] The optimized variable parameters include permanent magnet thickness, permanent magnet pole arc coefficient, permanent magnet surface eccentricity, stator slot opening size, stator slot width, stator tooth tip height, stator slot shoulder height, and stator slot depth.

[0015] As a further improvement of the present invention, in S1, after determining the size structure of the permanent magnet motor, electromagnetic modeling of the permanent magnet motor is performed according to the operating speed, operating back electromotive force and operating power of the permanent magnet motor.

[0016] As a further improvement of the present invention, in S2, a sensitivity hierarchical calculation method is used to calculate the sensitivity values ​​of different optimization variables relative to the fundamental back EMF amplitude, the third harmonic back EMF amplitude, and the fifth harmonic back EMF amplitude; the sensitivity calculation method is as follows:

[0017]

[0018] Among them, f is the optimized target response, z i is the i-th design variable, Δf is the increment of the optimized target response, Δz i is the increment of the i-th design variable, and the weight coefficient is used to evaluate each variable. The comprehensive sensitivity index is introduced, and the weight coefficients are assigned to the back-EMF optimization target respectively. The sum of the weights of the fundamental back-EMF amplitude, the third harmonic back-EMF amplitude, and the fifth harmonic back-EMF amplitude is 1, and the weights are 0.33 respectively.

[0019] As a further improvement of the present invention, in S3, the sensitivity of each optimized variable parameter relative to the back EMF performance is compared, and the optimized variable parameters with high sensitivity are screened out, and then the response surface equations of the optimized variable parameters and the fundamental back EMF amplitude, the third harmonic back EMF amplitude and the fifth harmonic back EMF amplitude are sampled and calculated; the optimized variable parameters with low sensitivity are directly calculated to obtain the optimal value using the parameter scanning method.

[0020] As a further improvement of the present invention, in S3, a central experimental design method is used to perform sampling calculations of optimization variable parameters and back-EMF optimization targets, and a response surface equation represents a fitting regression equation of optimization variable parameters and back-EMF optimization targets.

[0021] As a further improvement of the present invention, in S3, the high-sensitivity variables are selected from the first three optimized variable parameters with decreasing sensitivity, which are the permanent magnet thickness, the permanent magnet surface eccentricity and the opening size of the stator slot.

[0022] As a further improvement of the present invention, in S4, a multi-objective genetic algorithm is used to solve the optimal design solution of the back EMF performance. When the fundamental back EMF amplitude is the largest, and the third harmonic back EMF amplitude and the fifth harmonic back EMF amplitude are the smallest, the back EMF performance is best.

[0023] As a further improvement of the present invention, in S4, the principle of the multi-objective genetic algorithm is to simulate the evolution process in nature and approach the optimal solution through continuous iterative evolution.

[0024] As a further improvement of the present invention, in S1, the power level of the permanent magnet motor is 3kW, the speed is 2000r / min, the back electromotive force is 140V, the slot-pole combination is 12 slots and 8 poles, the permanent magnet arrangement is surface-mounted, and the winding connection method is concentrated winding.

[0025] Beneficial effects:

[0026] The present invention establishes a mathematical model of a permanent magnet motor and sets a specific back-EMF optimization target, that is, maximizing the fundamental back-EMF amplitude while minimizing the third harmonic back-EMF amplitude and the fifth harmonic back-EMF amplitude. This ensures a clear direction in the design process and avoids poor results caused by blindly adjusting parameters.

[0027] By calculating the sensitivity of the optimized variable parameters, the parameters that have the greatest impact on the back EMF performance can be identified, so that these highly sensitive variables can be considered in the subsequent design. This not only improves the design efficiency, but also enhances the effectiveness of the design solution.

[0028] Based on the results of sensitivity analysis, highly sensitive optimization variable parameters are selected, and the relationship model between the optimization variables and back EMF performance is established through the response surface method. This method can more accurately describe the relationship between the two, making the optimization process more scientific and reasonable.

[0029] Using a multi-objective genetic algorithm to optimize back EMF performance can handle possible conflicts between multiple optimization objectives and find the Pareto optimal solution set. This method can take into account both the maximization of fundamental back EMF and the minimization of harmonic back EMF to achieve the best overall performance.

[0030] By using the parameter scanning method to directly find the optimal value for low-sensitivity variables, the design process is simplified, the difficulty of the production process is reduced, and it is beneficial to improve production efficiency and reduce costs.

[0031] The method of the present invention is applicable to the design of permanent magnet motors of different power levels, especially in flywheel energy storage systems under specific conditions, and can effectively improve the back-electromotive force characteristics of the motor and enhance the stability and efficiency of the system.

[0032] In summary, the present invention provides a systematic multivariable optimization design method, which significantly improves the back-EMF quality of the motor by precisely controlling various structural parameters of the permanent magnet motor, thereby greatly promoting the technological progress and practical application of the flywheel energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A schematic diagram of a multivariable optimization design method for improving the back electromotive force of a permanent magnet motor for a flywheel according to the present invention;

[0034] Figure 2 It is a schematic diagram of the structure of the permanent magnet motor of the present invention;

[0035] Figure 3 The back EMF waveforms before and after optimization;

[0036] Figure 4 This is the back EMF harmonic analysis diagram before and after optimization;

[0037] Explanation of the markings in the figure: 1. Permanent magnet thickness; 2. Permanent magnet pole arc coefficient; 3. Permanent magnet surface eccentricity; 4. Opening size of the stator slot; 5. Width of the stator slot; 6. Tip height of the stator tooth; 7. Shoulder height of the stator slot; 8. Depth of the stator slot. DETAILED DESCRIPTION

[0038] In order to better understand the purpose, structure and function of the present invention, a multivariable optimization design method for improving the back electromotive force of a permanent magnet motor for a flywheel according to the present invention is further described in detail below in conjunction with the accompanying drawings.

[0039] Implementation example:

[0040] In order to improve the shortcomings of insufficient output voltage and poor power quality of permanent magnet motors for flywheels, a multivariable optimization design method for improving the back EMF quality of permanent magnet motors for flywheels is proposed. Figure 1 As shown in the figure, this method is easy to process and has strong pertinence. For permanent magnet motors for flywheels, it has a very significant effect in increasing the amplitude of the fundamental back EMF and suppressing the amplitude of the third and fifth harmonic back EMF. In addition, this optimization method also takes into account the convenience of processing. It improves the performance of the motor without increasing the difficulty of processing. It is an efficient and reliable design method. The specific steps are as follows:

[0041] Step (1), after determining the size and structure of the motor according to the operating speed, operating back electromotive force and operating power of the permanent magnet motor for the flywheel, the electromagnetic modeling of the motor is performed using ANSYS Electronics Desktop finite element simulation software, including establishing the stator core, rotor core, permanent magnet and winding copper wire of the motor. The optimized variable parameters and back electromotive force optimization target of the motor are clarified;

[0042] In this embodiment, the purpose of the permanent magnet motor is flywheel energy storage, the power level of the motor is 3kW, the speed is 2000r / min, the back EMF is 140V, the slot-pole combination is 12 slots and 8 poles, the permanent magnet arrangement is surface-mounted, and the winding connection is concentrated winding. The back EMF optimization target of the motor is the fundamental back EMF amplitude, the third harmonic back EMF amplitude, and the fifth harmonic back EMF amplitude. There are 8 optimized variable parameters, namely, the thickness of the permanent magnet 1, in mm; the permanent magnet pole arc coefficient 2; the eccentricity of the permanent magnet surface 3, in mm; the opening size of the stator slot 4, in mm; the width of the stator slot 5, in mm; the tip height of the stator tooth 6, in mm; the shoulder height of the stator slot 7, in mm; the depth of the stator slot 8, in mm.

[0043] Step (2), using a sensitivity hierarchical calculation method, calculating the sensitivity values ​​of different optimization variable parameters relative to the fundamental back EMF amplitude, the third harmonic back EMF amplitude, and the fifth harmonic back EMF amplitude;

[0044] In this embodiment, the sensitivity calculation method is as follows:

[0045]

[0046] Among them, f is the optimized target response, z i is the i-th design variable, Δf is the increment of the optimized target response, Δz i is the increment of the i-th design variable. The comprehensive sensitivity index is introduced to assign weight coefficients to the three optimizations respectively. Since the three performances of fundamental back EMF amplitude, third harmonic back EMF amplitude and fifth harmonic back EMF amplitude are equally important, their weights are all 0.33, and their sum is 1.

[0047] Step (3) compares the sensitivity of each optimized variable parameter relative to the back EMF performance, and performs comparative screening to select the highly sensitive optimized variable parameters, and then samples and calculates the response surface equations between the optimized variables and the fundamental back EMF amplitude, the third harmonic back EMF amplitude, and the fifth harmonic back EMF amplitude; the low-sensitivity variable parameters are directly optimized using the parameter scanning method.

[0048] The central experimental design method is used to perform sampling calculations on the parameters of each optimized variable and the fundamental back EMF amplitude, the third harmonic back EMF amplitude, and the fifth harmonic back EMF amplitude. The response surface equation represents the fitting regression equation of each variable parameter and the fundamental back EMF amplitude, the third harmonic back EMF amplitude, and the fifth harmonic back EMF amplitude. The highly sensitive variables are the first three variables with decreasing sensitivity, which are the permanent magnet thickness, the permanent magnet surface eccentricity, and the stator slot opening size. Sampling calculations are performed on the three highly sensitive parameters, and the sampling points are shown in Table 1.

[0049]

[0050]

[0051] Table 1 Sampling points required for central experimental design method

[0052] Response surface fitting equation:

[0053] E p =-3.48+10.77×Emb-0.005×Loff-1.43×Bs0

[0054] -0.03×Emb×Loff+0.003×Loff×Loff+0.084×Bs0×Bs0

[0055] +2.48×Emb×Bs0-0.06×Loff×Bs0-7.85×Emb×Emb

[0056] E3=-9.387+34.11×Emb+0.11×Loff+0.350.81×Bs0

[0057] -0.154×Emb×Loff-0.012×Emb×Bs0+0.002×Loff×Bs0

[0058] -16.475×Emb×Emb-0.0049×Loff×Loff-0.098×Bs0×Bs0

[0059] F5=6.5-15.26×Emb+0.028×Loff+1.89×Bs0

[0060] -0.23×Emb×Loff-2.977×Emb×Bs0+0.007×Loff×Bs0

[0061] +94.87×Emb×Emb+0.0021×Loff×Loff+0.1292×Bs0×Bs0

[0062] Among them, E pIt represents the fundamental back EMF amplitude, E3 represents the third harmonic back EMF amplitude, E5 represents the fifth harmonic back EMF amplitude, emb represents the permanent magnet pole arc coefficient, loff represents the permanent magnet surface eccentricity, and Bs0 represents the opening size of the stator slot.

[0063] Step (4), using a multi-objective genetic algorithm to solve the optimal design solution for back EMF performance, when the fundamental back EMF amplitude is the largest, and the third harmonic back EMF amplitude and the fifth harmonic back EMF amplitude are the smallest, the back EMF performance is the best;

[0064] The unit of the fundamental back EMF amplitude is V, the unit of the third harmonic back EMF amplitude is V, and the unit of the fifth harmonic back EMF amplitude is V; In this embodiment, the principle of the multi-objective genetic algorithm is to simulate the evolutionary process in nature and approach the optimal solution through continuous iterative evolution.

[0065] Step (5), extracting the final structural variable parameter values ​​of the permanent magnet motor for the flywheel from the optimal design solution, and finally achieving the preset multivariable optimization design goal.

[0066] Figure 2 This is a schematic diagram of the structure of the permanent magnet motor for the flywheel of the present invention. The motor adopts a surface-mounted structure, and the permanent magnets are evenly distributed on the surface of the rotor.

[0067] Figure 3 The back EMF waveforms before and after the optimization of the present invention are shown in FIG. Among them, the back EMF amplitude after the optimization of the present invention method increases significantly from the original 138V to 159V, which can achieve the goal of increasing the back EMF amplitude.

[0068] Figure 4 It is the back EMF harmonic analysis diagram before and after the optimization of the present invention. Among them, the fundamental back EMF after optimization by the method of the present invention is significantly improved, from the original 137V to 158V. The amplitude of the 3rd harmonic back EMF and the amplitude of the 5th harmonic back EMF are significantly suppressed, the 3rd back EMF drops from the original 5.5V to 2.1V, and the 3rd back EMF drops from the original 3.5V to 0.7V. Therefore, the goal of improving the fundamental back EMF and suppressing the amplitudes of the 3rd and 5th harmonic back EMF can be achieved.

[0069] After the flywheel permanent magnet motor of the present invention adopts the multivariable optimization method, the third harmonic back EMF amplitude and the fifth harmonic back EMF amplitude of the motor are significantly suppressed, the running stability and reliability are significantly increased, and the fundamental back EMF amplitude of the motor is significantly improved. The multivariable optimization design method adopted by the present invention can, on the one hand, increase the output back EMF, and on the other hand, eliminate the back EMF harmonics, thereby realizing high-quality operation of the flywheel permanent magnet motor. The present invention adopts a multivariable optimization design method, which has a simple processing technology and high portability.

[0070] It is to be understood that the present invention is described by some embodiments, and it is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the scope of protection of the present invention.

Claims

1. A multivariable optimization design method for improving the back electromotive force of a permanent magnet motor for a flywheel, characterized in that: The following steps are involved: S1. Model the permanent magnet motor and determine the optimization variable parameters and back-EMF optimization target of the permanent magnet motor; S2, calculate the sensitivity of the optimized variable parameters; S3, selecting highly sensitive optimization variable parameters, and sampling and calculating the response surface equation between the optimization variable parameters and the back-electromotive force optimization target; S4, using genetic algorithm to solve the optimal design solution for back EMF performance; S5. Extract the parameter values ​​of various structural variables of the permanent magnet motor from the optimal design solution of back-EMF performance to achieve multivariable optimization design.

2. The multivariable optimization design method for improving the back electromotive force of a permanent magnet motor for a flywheel according to claim 1, characterized in that: The back-EMF optimization targets include the fundamental back-EMF amplitude, the 3rd harmonic back-EMF amplitude and the 5th harmonic back-EMF amplitude; The optimized variable parameters include permanent magnet thickness, permanent magnet pole arc coefficient, permanent magnet surface eccentricity, stator slot opening size, stator slot width, stator tooth tip height, stator slot shoulder height, and stator slot depth.

3. The multivariable optimization design method for improving the back electromotive force of a permanent magnet motor for a flywheel according to claim 1, characterized in that: In S1, after determining the size structure of the permanent magnet motor, the electromagnetic modeling of the permanent magnet motor is performed according to the operating speed, operating back electromotive force and operating power of the permanent magnet motor.

4. The multivariable optimization design method for improving the back electromotive force of a permanent magnet motor for a flywheel according to claim 2, characterized in that: In S2, the sensitivity hierarchical calculation method is used to calculate the sensitivity values ​​of different optimization variables relative to the fundamental back EMF amplitude, the third harmonic back EMF amplitude, and the fifth harmonic back EMF amplitude; the sensitivity calculation method is as follows: Among them, f is the optimized target response, z i is the i-th design variable, Δf is the increment of the optimized target response, Δz i is the increment of the i-th design variable, and the weight coefficient is used to evaluate each variable. The comprehensive sensitivity index is introduced, and the weight coefficients are assigned to the back-EMF optimization target respectively. The sum of the weights of the fundamental back-EMF amplitude, the third harmonic back-EMF amplitude, and the fifth harmonic back-EMF amplitude is 1, and the weights are 0.33 respectively.

5. The multivariable optimization design method for improving the back electromotive force of a permanent magnet motor for a flywheel according to claim 2, characterized in that: In S3, the sensitivity of each optimized variable parameter relative to the back EMF performance is compared to screen out the optimized variable parameters with high sensitivity, and then sample and calculate the response surface equations of the optimized variable parameters and the fundamental back EMF amplitude, the third harmonic back EMF amplitude and the fifth harmonic back EMF amplitude; the low-sensitivity optimized variable parameters use the parameter scanning method to directly find the optimal value.

6. The multivariable optimization design method for improving the back electromotive force of a permanent magnet motor for a flywheel according to claim 1, characterized in that: In S3, the central experimental design method is used to perform sampling calculations of optimization variable parameters and back-EMF optimization targets, and the response surface equation represents the fitting regression equation of optimization variable parameters and back-EMF optimization targets.

7. The multivariable optimization design method for improving the back electromotive force of a permanent magnet motor for a flywheel according to claim 2, characterized in that: In S3, the high-sensitivity variables are the first three optimized variable parameters with decreasing sensitivity, which are the permanent magnet thickness, the permanent magnet surface eccentricity and the opening size of the stator slot.

8. The multivariable optimization design method for improving the back electromotive force of a permanent magnet motor for a flywheel according to claim 2, characterized in that: In S4, a multi-objective genetic algorithm is used to solve the optimal design solution for back EMF performance. When the fundamental back EMF amplitude is the largest, and the third harmonic back EMF amplitude and the fifth harmonic back EMF amplitude are the smallest, the back EMF performance is the best.

9. The multivariable optimization design method for improving the back electromotive force of a permanent magnet motor for a flywheel according to claim 1, characterized in that: In S4, the principle of the multi-objective genetic algorithm is to simulate the evolutionary process in nature and approach the optimal solution through continuous iterative evolution.

10. The multivariable optimization design method for improving the back electromotive force of a permanent magnet motor for a flywheel according to claim 1, characterized in that: In S1, the power level of the permanent magnet motor is 3kW, the speed is 2000r / min, the back electromotive force is 140V, the slot-pole combination is 12 slots and 8 poles, the permanent magnet arrangement is surface-mounted, and the winding connection method is concentrated winding.

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