A mechanism-driven multi-working-mode hybrid excitation motor optimization method

Abstract

The application discloses a mechanism-driven multi-working-mode hybrid excitation motor optimization method, which is based on a hybrid excitation motor analysis method driven by a motor electromagnetic field mechanism, adopts a simple nonlinear magnetic circuit to describe core physical quantities of the motor electromagnetic field, establishes a core physical quantity index space description method of the hybrid excitation motor driven by the mechanism, and realizes multi-working-mode optimization of the hybrid excitation motor based on the index space description method. The hybrid excitation motor optimization method has clear ideas and simple operation. The mechanism-driven hybrid excitation motor optimization method has the advantages of easy implementation, fast calculation, excellent optimization motor performance compared with traditional methods, generalization and universality, and has superior performance.

Description

Technical Field

[0001] The invention relates to a mechanism-driven multi-working mode hybrid excitation motor optimization method, belonging to the technical field of motor optimization design. Background Art

[0002] Faced with the challenges of global climate change and the energy crisis, developing renewable energy is a key energy strategy for my country. As an alternative to traditional fuel-powered vehicles, new energy vehicles have become a crucial option for driving energy innovation. my country's early development of the new energy vehicle industry has fostered a clustering effect, giving it a first-mover advantage. Continuing research into new energy vehicle drive motors and advancing their power density and efficiency is of great strategic and economic significance.

[0003] Hybrid excitation motors combine the advantages of both electrically excited and permanent magnet motors, containing both permanent magnets and excitation windings. Due to the presence of the field windings, hybrid excitation motors offer far greater magnetic field adjustment capabilities than permanent magnet motors. This characteristic allows for flexible adjustment of the magnetic field amplitude during operation, overcoming the inherent limitations of permanent magnet motors, which suffer from poor magnetic field adjustment and the risk of field weakening failure. However, the presence of the field windings in hybrid excitation motors introduces additional control variables, leading to multiple operating modes. This significantly increases the computational complexity of their optimization design, making rapid optimization of this motor solution challenging. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a mechanism-driven multi-working mode hybrid excitation motor optimization method, which solves the problem that the optimization of the hybrid excitation motor caused by multiple working modes is time-consuming and computationally complex, and improves the optimization efficiency and solution optimization speed of the hybrid excitation motor.

[0005] The present invention adopts the following technical solutions to solve the above technical problems:

[0006] A mechanism-driven multi-mode hybrid excitation motor optimization method comprises the following steps:

[0007] Step 1: Model the nonlinear magnetic circuit structure of the stator excitation winding of the hybrid excitation motor, use nonlinear magnetic resistance to reflect the saturation degree of the core, and obtain a nonlinear magnetic circuit model;

[0008] Step 2: Based on the nonlinear magnetic circuit model, the excitation current of the excitation winding of the motor in the magnetization state and the field weakening state is given respectively, and the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit corresponding to the magnetization state and the field weakening state is obtained through iterative calculation;

[0009] Step 3: constructing a magnetic adjustment index of the hybrid excitation motor based on the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit corresponding to the magnetizing state and the magnetic weakening state;

[0010] Step 4, modeling the nonlinear magnetic circuit structure of the hybrid excitation motor armature when the motor rotor is located at the d-axis position to obtain a d-axis nonlinear magnetic circuit model;

[0011] Step 5, iteratively calculating the d-axis nonlinear magnetic circuit model, and obtaining the d-axis magnetic circuit reluctance after the reluctance in the armature nonlinear magnetic circuit converges;

[0012] Step 6: constructing a torque index of the hybrid excitation motor based on the d-axis magnetic circuit reluctance and the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit in the magnetization state;

[0013] Step 7: If the magnetic adjustment index and the torque index are optimized simultaneously, a multi-objective optimization method is used to obtain the multi-objective optimization Pareto front under the distribution of the magnetic adjustment index and the torque index. The Pareto front solution set under the multi-objective optimization is selected, and the motor performance is verified using electromagnetic finite element analysis. Based on the motor performance verification results, the geometric parameters of the hybrid excitation motor are optimized to complete the hybrid excitation motor optimization.

[0014] Step 8: If one of the magnetic modulation index and the torque index is optimized, the single-objective optimization method is used to obtain the hybrid excitation motor design clusters under the distribution of the magnetic modulation index or the torque index, the design clusters are sorted, and the top N design clusters are selected. The motor performance is verified using electromagnetic finite element analysis. Based on the motor performance verification results, the geometric parameters of the hybrid excitation motor are optimized to complete the hybrid excitation motor optimization.

[0015] As a preferred solution of the present invention, in step 1, modeling is performed based on the Thevenin equivalence theorem.

[0016] As a preferred solution of the present invention, in step 2, based on the nonlinear magnetic circuit model, the excitation current of the excitation winding of the motor in the magnetization state is given, and the nonlinear magnetic circuit of the excitation winding part is iteratively calculated to obtain the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit in the magnetization state; similarly, the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit in the weak magnetic state is obtained.

[0017] As a preferred solution of the present invention, in step 3, the calculation formula of the magnetic adjustment index is as follows:

[0018]

[0019] Among them, U EH Indicates the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit in the magnetizing state, U FW Indicates the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit in the field-weakening state, F pm Represents the permanent magnet magnetomotive force.

[0020] As a preferred solution of the present invention, in step 4, the magnetomotive force-permeance method is used for modeling.

[0021] As a preferred solution of the present invention, in step 6, the calculation formula of the torque index is as follows:

[0022]

[0023] Among them, i q Indicates the motor q-axis current, F pm is the permanent magnet magnetomotive force, U EH It indicates the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit in the magnetizing state.

[0024] As a preferred solution of the present invention, in step 7, the multi-objective optimization method is NSGA-II multi-objective genetic algorithm.

[0025] As a preferred solution of the present invention, in step 8, the single-objective optimization method is a GA genetic algorithm.

[0026] A computer device includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, the steps of the mechanism-driven multi-working mode hybrid excitation motor optimization method are implemented.

[0027] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the mechanism-driven multi-working mode hybrid excitation motor optimization method.

[0028] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0029] 1. The present invention adopts a simple nonlinear magnetic circuit to describe the core physical quantities of the motor's electromagnetic field, and establishes a mechanism-driven index space description method for the core physical quantities of the hybrid excitation motor. Based on this index space description method, multi-working mode optimization of the hybrid excitation motor can be realized, thereby improving the optimization efficiency and solution optimization speed of the hybrid excitation motor.

[0030] 2. The mechanism-driven hybrid excitation motor optimization method involved in the present invention has the advantages of being easy to implement, fast to calculate, better optimized motor performance than traditional methods, and being universal and applicable, thus demonstrating superior performance.

[0031] 3. Considering that the data-driven hybrid excitation motor optimization method is susceptible to saturation, nonlinearity and local optimal interference, the mechanism-driven optimization method of the present invention can achieve better performance than data-driven optimization under complex calculations and multiple working modes; the motor optimized using the method of the present invention has better performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1This is a flow chart of a mechanism-driven multi-working mode hybrid excitation motor optimization method of the present invention;

[0033] Figure 2 The hybrid excitation motor according to the embodiment of the present invention;

[0034] Figure 3 is the distribution of hybrid excitation motor design sets optimized by pure finite element method, where the Pareto front is marked by a solid line;

[0035] Figure 4 It is a simple nonlinear magnetic circuit of the excitation part, where 1 represents the permanent magnet magnetomotive force, 2 represents the permanent magnet reluctance, 3 and 9 represent the stator yoke reluctance, 4 and 8 represent the excitation tooth reluctance, 5 and 7 represent the excitation winding magnetomotive force, 6 represents the excitation winding yoke reluctance, and 10 represents the excitation equivalent open-circuit magnetic voltage drop;

[0036] Figure 5 is a simple nonlinear magnetic circuit of the armature winding, where 11 represents the rotor tooth width reluctance, 12 represents the air gap reluctance, 13 represents the stator armature tooth reluctance, and 14 represents the armature magnetomotive force;

[0037] Figure 6 is the spatial distribution of hybrid excitation motor indicators obtained using the mechanism-driven method, where the circular marks represent the optimization process design set and the star marks represent the Pareto frontier design;

[0038] Figure 7 It is the result of transforming the distribution of the motor design set optimized by pure finite element method into the index space;

[0039] Figure 8 The mechanism-driven Pareto front results are verified by finite element analysis, and the index space distribution is mapped to the distribution diagram of the average torque-magnetic adjustment ability space;

[0040] Figure 9 is the optimized stator structure;

[0041] Figure 10 is the optimized rotor structure. DETAILED DESCRIPTION

[0042] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be interpreted as limiting the present invention.

[0043] like Figure 1As shown, the present invention proposes a mechanism-driven multi-working mode hybrid excitation motor optimization method. Based on the hybrid excitation motor analysis method driven by the motor electromagnetic field mechanism, a simple nonlinear magnetic circuit is used to describe the core physical quantities of the motor electromagnetic field. A mechanism-driven hybrid excitation motor core physical quantity index space description method is established. Based on this index space description method, multi-working mode optimization of the hybrid excitation motor can be achieved. Specifically, the method includes the following steps:

[0044] S1. Model the magnetic circuit of the field winding, using nonlinear magnetic resistance to describe the influence of core saturation. The equivalent open-circuit magnetic voltage drop of the magnetic circuit is the output magnetic voltage drop.

[0045] S2. Based on the working mode of the hybrid excitation motor, the excitation current of the excitation winding is given, and the nonlinear iterative calculation of the excitation magnetic circuit is carried out to give the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit under different working modes;

[0046] S3. Assuming that the rotor is located at the d-axis position, a d-axis nonlinear magnetic circuit model on the air gap side is constructed based on the magnetomotive force-permeability method, which includes both the permanent magnet magnetomotive force and the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit;

[0047] S4, according to the operating mode of the hybrid excitation motor, based on the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit in step S2, iteratively calculating the d-axis flux linkage under different operating modes;

[0048] S5. Based on the d-axis nonlinear magnetic circuit model and the nonlinear magnetic circuit model of the excitation part under different working modes, the magnetic regulation index and torque index of the hybrid excitation motor are constructed, which can be expressed as:

[0049]

[0050] Among them, U EH Represents the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit in the magnetizing state, U FW Represents the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit in the weak magnetic state, i q Represents the motor q-axis current, F pm Represents permanent magnet magnetomotive force.

[0051] S6. Based on the hybrid excitation motor index space established in the previous steps, combined with a multi-objective optimization or single-objective optimization method, the multi-working mode hybrid excitation motor optimization based on the mechanism-driven index space description is realized, and the index space distribution of the hybrid excitation motor under different parameter combinations is obtained;

[0052] S7. Under multi-objective optimization, obtain the multi-objective optimization Pareto front under the indicator space distribution; under single-objective optimization, obtain the optimal design cluster of the hybrid excitation motor under the indicator space distribution; select the Pareto front solution set under multi-objective optimization and the optimal design cluster under single-objective optimization, and use electromagnetic finite element analysis to verify the motor performance;

[0053] S8. Based on the electromagnetic finite element verification results, the design parameters of the hybrid excitation motor are optimized to complete the optimization of the hybrid excitation motor.

[0054] Example

[0055] The motor structure of the specific embodiment involved is as follows Figure 2 To demonstrate the effectiveness and superiority of the technology involved in the present invention, this embodiment also shows the optimization results of the hybrid excitation motor driven by pure finite element, and its design set distribution and Pareto front distribution are shown as follows: Figure 3 shown.

[0056] For the magnetic circuit structure of the stator excitation winding, the nonlinear magnetic circuit structure of the excitation winding is modeled based on the Thevenin equivalent theorem, such as Figure 4 It should be noted that all magnetic resistances in the magnetic circuit are nonlinear, and their magnetic permeability values ​​need to be determined according to the material properties.

[0057] Given the excitation current of the excitation winding under the magnetizing state and the weakening state, the nonlinear iterative calculation of the excitation magnetic circuit under the magnetizing and weakening conditions is carried out, the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit under the magnetizing and weakening conditions is given, and the magnetic regulation index is calculated.

[0058] The magnetomotive force-permeance method is used to model the armature magnetic circuit structure of the hybrid excitation motor with the rotor located at the d-axis position, such as Figure 5 It should be noted that the excitation equivalent magnetic voltage drop in the armature magnetic circuit is connected in series with the armature magnetomotive force.

[0059] The armature nonlinear magnetic circuit is iteratively calculated until the magnetic reluctance converges. The torque specification is calculated based on the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit under magnetizing and field weakening conditions and the motor q-axis current value under the parameter combination.

[0060] Based on the NSGA-II multi-objective genetic algorithm, the above torque index and magnetic regulation index are combined to obtain the spatial distribution of the optimization index of the multi-working mode hybrid excitation motor driven by the mechanism. Figure 6 As shown, the design solution set under pure finite element optimization can also be transformed into the index space, such as Figure 7 It can be seen that the index space distribution of the multi-working mode hybrid excitation motor optimization driven by the mechanism is significantly better than that of the pure finite element optimization.

[0061] By transforming the Pareto frontier of the optimization index space of the multi-working mode hybrid excitation motor driven by the mechanism into the average torque-magnetic adjustment capability space, it can be directly compared with the pure finite element optimization method. Figure 8 As shown in the figure, the Pareto front of the mechanism-driven hybrid excitation motor optimization method is better than that of the pure finite element method.

[0062] Finally, the Pareto frontier design of hybrid excitation motor optimization is driven by the optimization mechanism. In this embodiment, the stator is preferably designed as follows: Figure 9 As shown, the rotor Figure 10 As shown, in order to weaken the influence of cogging torque and torque pulsation, the rotor structure is a 5-section skew pole rotor.

[0063] The method proposed in this invention is applicable not only to stator-excited hybrid motors, but also to all motor structures that contain both excitation windings and permanent magnets or ferrites, such as rotor-excited hybrid motors and axially compound-excited hybrid motors. The technology involved in this invention is applicable not only to hybrid excitation drive motors for new energy vehicles, but also to a variety of applications, including conventional aerospace hybrid excitation motors and hybrid excitation motors for special vehicles.

[0064] Based on the same inventive concept, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the steps of the aforementioned mechanism-driven multi-working mode hybrid excitation motor optimization method are implemented.

[0065] Based on the same inventive concept, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the aforementioned mechanism-driven multi-working mode hybrid excitation motor optimization method.

[0066] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0067] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0068] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0069] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0070] The above embodiments are only for illustrating the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A mechanism-driven multi-working mode hybrid excitation motor optimization method, characterized in that: The steps include: Step 1: Model the nonlinear magnetic circuit structure of the stator excitation winding of the hybrid excitation motor, use nonlinear magnetic resistance to reflect the saturation degree of the core, and obtain a nonlinear magnetic circuit model; Step 2: Based on the nonlinear magnetic circuit model, the excitation current of the excitation winding of the motor in the magnetization state and the field weakening state is given respectively, and the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit corresponding to the magnetization state and the field weakening state is obtained through iterative calculation; Step 3: constructing a magnetic adjustment index of the hybrid excitation motor based on the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit corresponding to the magnetizing state and the magnetic weakening state; In step 3, the calculation formula of the magnetic adjustment index is as follows: Among them, U EH Indicates the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit in the magnetizing state, U FW Indicates the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit in the field-weakening state, F pm represents the permanent magnet magnetomotive force; Step 4, modeling the nonlinear magnetic circuit structure of the hybrid excitation motor armature when the motor rotor is located at the d-axis position to obtain a d-axis nonlinear magnetic circuit model; Step 5, iteratively calculating the d-axis nonlinear magnetic circuit model, and obtaining the d-axis magnetic circuit reluctance after the reluctance in the armature nonlinear magnetic circuit converges; Step 6: constructing a torque index of the hybrid excitation motor based on the d-axis magnetic circuit reluctance and the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit in the magnetization state; In step 6, the calculation formula of the torque index is as follows: Among them, i q Indicates the motor q-axis current, F pm is the permanent magnet magnetomotive force, U EH Indicates the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit in the magnetizing state; Step 7: If the magnetic adjustment index and the torque index are optimized simultaneously, a multi-objective optimization method is used to obtain the multi-objective optimization Pareto front under the distribution of the magnetic adjustment index and the torque index. The Pareto front solution set under the multi-objective optimization is selected, and the motor performance is verified using electromagnetic finite element analysis. Based on the motor performance verification results, the geometric parameters of the hybrid excitation motor are optimized to complete the hybrid excitation motor optimization. Step 8: If one of the magnetic modulation index and the torque index is optimized, the single-objective optimization method is used to obtain the hybrid excitation motor design clusters under the distribution of the magnetic modulation index or the torque index, the design clusters are sorted, and the top N design clusters are selected. The motor performance is verified using electromagnetic finite element analysis. Based on the motor performance verification results, the geometric parameters of the hybrid excitation motor are optimized to complete the hybrid excitation motor optimization.

2. The mechanism-driven multi-working mode hybrid excitation motor optimization method according to claim 1 is characterized in that: In the step 1, modeling is performed based on the Thevenin equivalence theorem.

3. The mechanism-driven multi-working mode hybrid excitation motor optimization method according to claim 1 is characterized in that: In step 2, based on the nonlinear magnetic circuit model, the excitation current of the excitation winding of the motor in the magnetization state is given, and the nonlinear magnetic circuit of the excitation winding part is iteratively calculated to obtain the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit in the magnetization state; similarly, the equivalent open-circuit magnetic voltage drop of the excitation magnetic circuit in the weak magnetic state is obtained.

4. The mechanism-driven multi-working mode hybrid excitation motor optimization method according to claim 1 is characterized in that: In step 4, the magnetomotive force-permeance method is used for modeling.

5. The mechanism-driven multi-working mode hybrid excitation motor optimization method according to claim 1 is characterized in that: In step 7, the multi-objective optimization method is the NSGA-II multi-objective genetic algorithm.

6. The mechanism-driven multi-working mode hybrid excitation motor optimization method according to claim 1 is characterized in that: In step 8, the single-objective optimization method is a GA genetic algorithm.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the computer program, the steps of the mechanism-driven multi-working mode hybrid excitation motor optimization method according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the mechanism-driven multi-working mode hybrid excitation motor optimization method according to any one of claims 1 to 6 are implemented.

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