Optimal torque distribution method based on distributed electric drive vehicle

A technology of optimal torque and distribution method, applied in electric vehicles, control drives, vehicle components, etc., can solve problems such as prolonging simulation time, increasing calculation burden, and requiring high objective function gradient and Hessian matrix

Active Publication Date: 2019-05-21

NANCHANG UNIV

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AI-Extracted Technical Summary

Problems solved by technology

Although there are many optimization algorithms that can deal with such problems, there are some shortcomings. The model predictive control algorithm has high requirements on the gradient of the objective function and the Hessian matrix. If it is...

Method used

[0028] Considering that the distributed electric drive vehicle is a highly complex coupled nonlinear time-varying system, the torque optimal distribution control strategy should consider multiple objective functions to meet various performances of the vehicle. The functional relationship between the motor efficiency and the required torque and the wheel speed is established, which is convenient for multi-objective optimization together with the objective function that characterizes the longitudinal driving safety optimization.

[0047] Finally, as shown in Figure 2, considering the group-based search strategy and simple genetic operators, genetic algorithms have powerful global search capabilities, parallelism in information processing, and robustness in applications. ...

Abstract

The invention relates to an optimal torque distribution method based on a distributed electric drive vehicle. The torques of four drive wheels are reasonably distributed, and meanwhile the drive system efficiency and driving safety of the distributed electric drive vehicle are improved. The torque distribution method comprises the following steps of (1) adopting a response surface analysis methodfor conducting regression analysis on test data of a hub motor to obtain a drive motor efficiency function; (2) based on a demand torque value of a distributed electric drive system, establishing objective functions which characterize the efficiency optimization of the drive system and the driving safety optimization of the vehicle respectively; adopting a linear weighting method of a self-adaptive weight coefficient for converting solutions of the two objective functions into a multi-objective optimization problem under constraint conditions; (3) integrating the respective advantages of a genetic algorithm and a taboo search algorithm to put forward a hybrid genetic taboo search algorithm (HGTSA) for solving the multi-objective optimization problem, and obtaining the optimal torque distribution of the distributed electric drive system accordingly.

Application Domain

Speed controllerElectric energy management +1

Technology Topic

Driving safetyOptimization problem +16

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Examples

Experimental program(1)

Example Embodiment

[0026] The present invention will be described in detail below with reference to the drawings and specific embodiments. This embodiment takes the driving demand torque of the distributed electric drive vehicle as the distribution object, and is implemented on the premise of the technical solution of the present invention. A detailed implementation mode is given. The protection scope of the present invention includes but is not limited to the distributed electric drive vehicle .

[0027] As attached figure 1 As shown, the efficiency of the drive motor is related to the required torque and wheel speed. The red line in the figure is the maximum motor efficiency corresponding to a certain speed and the required torque. In order to improve the efficiency of the entire vehicle drive system, the drive motor should be operated as high as possible. The efficiency period is near the red curve.

[0028] Considering that the distributed electric drive vehicle is a highly complex coupled nonlinear time-varying system, the torque optimal distribution control strategy should consider multiple objective functions to meet the various performances of the vehicle. The establishment of a functional relationship between motor efficiency and demand torque and wheel speed is convenient for multi-objective optimization together with the objective function that characterizes longitudinal driving safety optimization.

[0029] The motor efficiency Y is described by a fourth-order regression equation with cross terms, and the expression is as follows:

[0030]

[0031] Among them: β is the regression coefficient, x 1 , X 2 For the design variables, which represent the motor speed and the required torque respectively, ε is a random error vector.

[0032] As attached figure 2 As shown, firstly, the objective functions that characterize the optimization of drive system efficiency and the optimization of vehicle driving safety are established respectively.

[0033] The objective function J that characterizes the efficiency optimization of the drive system 1 , Its expression is as follows:

[0034]

[0035] Where: T dfl , T drr Is the torque of the front and rear wheels, n fl , N rr Is the speed of the front and rear wheels, η fl , Η rr It is the working efficiency of the front and rear motors.

[0036] The objective function J that characterizes the optimization of vehicle driving safety 2 , Its expression is as follows:

[0037]

[0038] Where: R is the tire radius, F zi Is the vertical force received by the wheel, i=fl, fr, rl, rr.

[0039] Then, the linear weighting method of adaptive weight coefficients is used to transform the solution of the above two objective functions into a multi-objective optimization problem under constraint conditions. The adaptive weight coefficient ω is a piecewise function of the road adhesion coefficient μ and the speed u:

[0040]

[0041] Where, ω 1max , Ω 1min , Ω 2max , Ω 2min They are the weight coefficients of the first objective function on the high adhesion road and the low adhesion road respectively.

[0042] The final objective function J to be optimized is:

[0043] J=ω 1 J 1 +ω 2 J 2

[0044] The corresponding constraints are:

[0045]

[0046] Where: T dimax Is the peak torque of the motor, T d Is the total required torque.

[0047] Finally, as attached figure 2 As shown, considering the group search strategy and simple genetic operators, genetic algorithms have powerful global search capabilities, parallelism of information processing, and robustness of applications. The tabu search algorithm has strong local search ability, and the optimal solution can be found out of the loop by setting the length of the tabu table reasonably. Combining the advantages of genetic algorithm and taboo search algorithm, a hybrid genetic taboo search algorithm (Hybrid Genetic Taboo SearchAlgorithmHGTSA) is proposed. First, use the powerful global search power of the genetic algorithm to perform a global search, and use the search result as the initial solution of the tabu search algorithm, and then use the tabu search algorithm to perform a local search to find the optimal solution, so as to reasonably allocate the required torque to Four wheels simultaneously improve drive system efficiency and longitudinal driving safety.

[0048] The description and application of the present invention herein are illustrative, and do not exclusively limit the application scope of the present invention to the embodiments. It should be noted that for those of ordinary skill in the art, modifications and changes to the described embodiments are possible without departing from the principle of the present invention.

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