阅读说明：本技术 一种电动汽车用srm系统转矩-效率多目标优化控制方法 (torque-efficiency multi-objective optimization control method for SRM system for electric vehicle ) 是由 李孟秋 蔡辉 沈仕其 于 2019-08-03 设计创作，主要内容包括：一种电动汽车用SRM系统转矩-效率多目标优化控制方法,包括以下步骤：1)根据直线型转矩分配函数定义,提出给定转矩公式；2)定义转矩分配函数评估指标；3)在直线型转矩分配函数的基础上提出了补偿曲线；4)得出新型转矩分配函数公式；5)选择铜耗和电流变化率作为优化目标；6)构造目标函数；7)采用遗传算法对转矩分配函数进行优化,通过仿真对比直线型转矩分配函数与新型转矩分配函数性能。本文提出的新型转矩分配函数能够有效减小电流峰值,提高了电流跟踪能力,抑制转矩脉动,同时减少了铜耗,提高了电机效率。(A torque-efficiency multi-objective optimization control method for an SRM system for an electric vehicle comprises the following steps: 1) according to the linear type torque distribution function definition, a given torque formula is provided; 2) defining a torque distribution function evaluation index; 3) a compensation curve is provided on the basis of a linear type torque distribution function; 4) obtaining a novel torque distribution function formula; 5) selecting copper consumption and current change rate as optimization targets; 6) constructing an objective function; 7) and optimizing the torque distribution function by adopting a genetic algorithm, and comparing the performance of the linear type torque distribution function with the performance of the novel torque distribution function through simulation. The novel torque distribution function provided by the invention can effectively reduce the current peak value, improve the current tracking capability, inhibit the torque ripple, reduce the copper consumption and improve the motor efficiency.)

1. The method for the torque-efficiency multi-objective optimization control of the SRM system for the electric automobile is characterized by comprising the following steps of:

1) according to the linear type torque distribution function definition, a given torque formula is provided;

2) Defining a torque distribution function evaluation index;

3) a compensation curve is provided on the basis of a linear type torque distribution function;

4) Obtaining a novel torque distribution function formula;

5) selecting an optimization target;

6) constructing an objective function;

7) and optimizing the torque distribution function by adopting a genetic algorithm, and comparing the performance of the linear type torque distribution function with the performance of the novel torque distribution function through simulation.

2. The multi-objective optimization of the torque distribution of the switched reluctance motor based on the genetic algorithm as claimed in claim 1, wherein in the step 1), according to the linear type torque distribution function definition, a larger given torque is distributed at the stage:

in the formula, Tk is phase torque, ik is phase current, and Lk is inductance.

3. the switched reluctance motor torque allocation multiobjective optimization based on the genetic algorithm according to claim 1, wherein in the step 1), the initial linear torque allocation function is:

The formula is an on angle, an overlap angle and an off angle.

4. The method for controlling the SRM system torque-efficiency multi-objective optimization for the electric vehicle according to claim 1, wherein in the step 2), the evaluation indexes are maximum current change rate, torque ripple and copper loss respectively.

The maximum current change rate expression is:

wherein ik (theta) and ik (theta 0) are single-phase currents at the k-th phase position angle and the k-th phase position angle respectively. The position angle change Δ θ is θ — θ 0. The formula for torque ripple is:

In the formula, Tav, Tmax and Tmin are average torque, maximum torque and minimum torque, respectively.

The copper loss of the switched reluctance motor in a commutation interval is defined as:

the formula is an on angle, an overlap angle and an off angle.

5. the method and the controller for controlling the torque-efficiency multi-objective optimization of the switched reluctance motor system for the electric vehicle according to claim 1, wherein in the step 3), the compensation curve is defined as:

wherein is the on angle, the overlap angle, and the off angle. And thetac is thetaov/2 + thetaon.

6. the method for controlling the torque-efficiency multi-objective optimization of the SRM system for the electric vehicle according to claim 1, wherein in the step 4), the formula of the novel torque distribution function is as follows:

。

7. the method as claimed in claim 1, wherein in the step 5), the absolute value h of the minimum value of the compensation curve and the overlap angle of the opening angle are selected as the optimization variables of the genetic algorithm, and the copper consumption and the current change rate are used as the optimization targets to perform the multi-objective optimization.

8. The method for controlling the SRM system torque-efficiency multi-objective optimization for the electric vehicle according to claim 1, wherein in the step 6), the objective function is as follows:

in the formula, omega is the weight coefficient of the optimization target (omega is more than or equal to 0 and less than or equal to 1),

。

Technical Field

the invention belongs to the field of motor control, and relates to a torque-efficiency multi-target optimization control method for an SRM (sequence-related modeling) system for an electric vehicle.

Background

The switched reluctance motor has the advantages of simple structure, strong fault-tolerant capability, adaptability to severe environment and the like, and is very suitable for electric vehicles. However, the switched reluctance motor has a doubly salient structure, and large torque pulsation and noise exist in the operation process of the switched reluctance motor, so that the electric automobile has a driving trembling feeling. At present, a torque distribution function is adopted as an effective control means for inhibiting torque ripple, but the traditional torque distribution function does not consider operation parameters such as copper loss and torque-speed characteristics, and the like.

Disclosure of Invention

the technical problem to be solved by the invention is to overcome the defects in the prior art and provide a multi-objective optimization for the torque distribution of the switched reluctance motor based on a genetic algorithm.

in order to solve the technical problems, the technical scheme adopted by the invention is as follows:

the method for the torque-efficiency multi-objective optimization control of the SRM system for the electric automobile is characterized by comprising the following steps of:

1) According to the linear type torque distribution function definition, a given torque formula is provided;

2) Defining a torque distribution function evaluation index;

3) a compensation curve is provided on the basis of a linear type torque distribution function;

4) obtaining a novel torque distribution function formula;

5) selecting an optimization target;

6) Constructing an objective function;

7) and optimizing the torque distribution function by adopting a genetic algorithm, and comparing the performance of the linear type torque distribution function with the performance of the novel torque distribution function through simulation.

In said step 1), a larger given torque is allocated at this stage, defined by a linear torque allocation function:

in the formula, Tk is phase torque, ik is phase current, and Lk is inductance.

In step 1), the initial linear torque distribution function is:

In the formula, the on angle, the overlap angle and the off angle are

In the step 2), the evaluation indexes are maximum current change rate, torque ripple and copper loss respectively.

the maximum current change rate expression is:

wherein ik (theta) and ik (theta 0) are single-phase currents at the k-th phase position angle and the k-th phase position angle respectively. The position angle change Δ θ is θ — θ 0. The formula for torque ripple is:

in the formula, Tav, Tmax and Tmin are average torque, maximum torque and minimum torque, respectively.

the copper loss of the switched reluctance motor in a commutation interval is defined as:

The formula is an on angle, an overlap angle and an off angle.

In step 3), the compensation curve is defined as:

Wherein is the on angle, the overlap angle, and the off angle. θ c ═ θ ov/2+ θ on

In step 4), the new torque distribution function formula is:

In the step 5), an absolute value h of the overlap angle of the opening angle and the minimum value of the compensation curve is selected as an optimization variable of the genetic algorithm, and the copper consumption and the current change rate are used as optimization targets to carry out multi-objective optimization.

in step 6), the objective function is:

In the formula, omega is the weight coefficient of the optimization target (omega is more than or equal to 0 and less than or equal to 1),

Compared with the background technology, the invention has the following advantages:

(1) the current peak value can be effectively reduced;

(2) the current tracking capability is improved, and the torque ripple is restrained;

(3) the copper consumption is reduced, and the motor efficiency is improved.

Drawings

FIG. 1 is a graph of inductance for a switched reluctance machine;

FIG. 2 is a differential plot of inductance versus rotor position;

FIG. 3 is a linear torque distribution function;

FIG. 4 TSF compensation graph;

FIG. 5 is a block diagram of a torque distribution function control system;

FIG. 6(a) the linear torque distribution function outputs total torque and phase currents, (b) the novel torque distribution function outputs total torque and phase currents;

Table 1 optimal new torque distribution function parameters at different rotational speeds;

table 2 comparing the linear torque distribution function with the novel torque distribution function at different rotation speeds;

Detailed Description

The present invention will be described in detail below with reference to the accompanying drawings and examples.

Referring to FIG. 1, the overall flow chart of the present invention is represented by step 1 for a given torque formula; step 2, defining a torque distribution function evaluation index; step 3, providing a compensation curve; step 4, obtaining a novel torque distribution function formula; step 5, selecting an optimization target; step 6, constructing an objective function; and 7, optimizing the torque distribution function by adopting a genetic algorithm, and comparing the linear type torque distribution function with the novel torque distribution function through simulation.

in step 1, a given torque formula is proposed, and due to the double salient pole structure of the switched reluctance motor, the inductance Lk of the switched reluctance motor is not only related to the magnitude of the phase current, but also related to the position of the rotor. Fig. 1 is a graph of inductance of a switched reluctance motor measured with phase currents of 1-15A, 20A, 25A, 30A, 35A, 40A, 45A, 50A, 55A, and 60A, respectively. The inductance value is small when the position angle is 0-9 deg., and the magnitude of the inductance Lk is substantially independent of the magnitude of the phase current. Fig. 2 is a graph of the differential of the inductance Lk with respect to the rotor position for phase currents 0-20, 25A, 30A, 35A, 40A, 45A, 50A, 55A, 60A, respectively. When the position angle is less than 6 degrees, the inductance change rate is small and the change rate is not more than 0.1. According to the characteristics of the traditional torque distribution function, the opening angle is generally less than 5 °, but according to the definition of the linear torque distribution function, a larger given torque is distributed at this stage:

in the formula, Tk is phase torque, ik is phase current, and Lk is inductance.

Description

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The optimization is performed on the basis of a linear distribution function, and the graph of the optimization is shown in FIG. 3. In FIG. 3, the A phase on angle is an overlap angle, and the A phase off angle is an A-correlation off angle. The angular value when the given torques of two adjacent phases in the phase change area are equal is defined, and epsilon is the phase shift angle between the two adjacent phases.

the initial linear torque distribution function is:

(see FIG. 1, FIG. 2, FIG. 3)

In step 2, in defining the torque distribution function evaluation index, as can be seen from the above analysis, the curve shape of the torque distribution function determines the current tracking capability, the magnitude of the torque ripple and the operating efficiency of the motor. And respectively taking the maximum current change rate, the matrix pulsation and the copper loss as evaluation indexes. Wherein the expression of the maximum current change rate is as follows:

wherein ik (theta) and ik (theta 0) are single-phase currents at the k-th phase position angle and the k-th phase position angle respectively. The position angle change Δ θ is θ — θ 0. The formula for torque ripple is:

In the formula, Tav, Tmax and Tmin are average torque, maximum torque and minimum torque, respectively.

the copper loss of the switched reluctance motor in a commutation interval is defined as:

the formula is an on angle, an overlap angle and an off angle.

in the step 3 of developing the compensation curve, the compensation curve is developed on the basis of the linear torque distribution function. The compensation curve is composed of two quadratic curves, as shown in fig. 4.

as can be seen from FIG. 4, two rotational symmetries are used in the overlapping region range (θ on ≦ θ on + θ ov)

description

The method comprises the steps of (1) selecting a quadratic curve, wherein the quadratic curve is a curve with theta c being theta ov/2+ theta on as a boundary point of the two curves. According to the definition of the quadratic curve, the compensation curve is defined as:

wherein is the on angle, the overlap angle, and the off angle. (see FIG. 4)

in the new torque distribution function formula obtained in step 4, the new torque distribution function formula defined by combining the given torque formula and the compensation curve is:

in the step 5, in selecting the optimization target, the copper loss is determined by the curve shape of the torque distribution function according to the formula (5), and the tracking capability of the current is directly determined by considering the given current change rate, so that the size of the torque ripple is determined. Therefore, the opening angle overlap angle and the absolute value h of the minimum value of the compensation curve are selected as optimization variables of the genetic algorithm, and the copper consumption and the current change rate are used as optimization targets to carry out multi-objective optimization.

In step 6, in constructing the objective function, considering the current change rate and the copper loss as two optimization objectives to optimize the opening angle, the overlap angle and the absolute value of the minimum value h of the compensation curve, therefore, the objective function is constructed as follows:

In the formula, omega is the weight coefficient of the optimization target (omega is more than or equal to 0 and less than or equal to 1),

in step 7, a genetic algorithm is adopted to optimize a torque distribution function, and in the process of comparing the performance of the linear type torque distribution function with the performance of the novel torque distribution function through simulation, a system simulation adopts a switched reluctance motor with 1.5kw and 12/8 poles, the voltage of a direct-current power supply is set to be 72V, and the given torque is set to be. The simulation system block diagram is shown in fig. 5. And (3) setting the population size of the genetic algorithm as 50, setting the iteration times as 50, and performing multi-objective optimization to obtain the absolute values of the optimal opening angle theta on, the optimal overlap angle theta ov and the minimum value h of the compensation curve when the rotating speed is 100r/min, 500r/min and 1000r/min, wherein the absolute values are shown in table 1.

the total torque and phase current obtained when using the unoptimized linear torque distribution function at a rotational speed of 500r/min is shown in FIG. 6 (a); the total torque and phase currents obtained when using the novel torque distribution function herein are shown in fig. 6 (b).

The linear type torque distribution function and the novel torque distribution function have performance comparison shown in table 2 under different rotating speeds through a plurality of experiments. (see FIG. 5, FIG. 6, Table 1, Table 2).