CN109774493B - Optimal torque distribution method based on distributed electric drive vehicle - Google Patents
Optimal torque distribution method based on distributed electric drive vehicle Download PDFInfo
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- CN109774493B CN109774493B CN201910051644.6A CN201910051644A CN109774493B CN 109774493 B CN109774493 B CN 109774493B CN 201910051644 A CN201910051644 A CN 201910051644A CN 109774493 B CN109774493 B CN 109774493B
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Abstract
The application relates to an optimal torque distribution method based on a distributed electric drive vehicle, which is used for improving the efficiency and the running safety of a driving system of the distributed electric drive vehicle by reasonably distributing the torques of four driving wheels. The torque distribution method comprises the following steps: (1) and carrying out regression analysis on the test data of the hub motor by adopting a response surface analysis method to obtain a driving motor efficiency function. (2) Respectively establishing objective functions representing efficiency optimization of a driving system and vehicle running safety optimization based on a required torque value of a distributed electric driving system; and the solution of the two objective functions is converted into a multi-objective optimization problem under the constraint condition by adopting a linear weighting method of a self-adaptive weight coefficient. (3) And (3) by integrating the advantages of the genetic algorithm and the tabu search algorithm, solving the multi-objective optimization problem by the aid of a Hybrid Genetic Tabu Search Algorithm (HGTSA) to obtain optimal torque distribution of the distributed electric drive system.
Description
Technical Field
The invention belongs to the field of longitudinal dynamics control of pure electric vehicles and vehicles, and relates to an optimal torque distribution method based on a distributed electric drive vehicle.
Background
With energy conservation and environmental protection becoming the subject of the current era, the development of electric vehicles has entered a new era. Pure electric automobile relies on actuating system to satisfy the demand of traveling, and actuating system's good or bad has a big influence to the whole car performance, and electric automobile drive mode has two kinds: centralized driving and distributed driving. The distributed electrically driven wheels do not need mechanical transmission parts, and the space utilization efficiency of the whole vehicle is improved. Most importantly, the fixed torque distribution mode of the traditional automobile is broken through, and the wheel torque can be randomly distributed according to the actual road working condition and the vehicle running state.
The multi-motor driven vehicle has the advantage that the driving braking torque is independent and controllable, and can randomly distribute the wheel torque according to the actual road working condition and the vehicle running state, so that the longitudinal dynamics safety control and the energy-saving control of the electric vehicle are realized. And the distributed electric drive automobile is used as a highly complex coupled nonlinear time-varying system, and a plurality of objective functions are considered in the torque optimization distribution control strategy so as to meet various performances of the automobile. Although many optimization algorithms can deal with the problems, the optimization algorithms have some defects, the model prediction control algorithm has higher requirements on the gradient of a target function and a Hessian matrix, if the model prediction control algorithm is too responsible, the calculation load is increased so as to prolong the simulation time, and the multi-target particle swarm algorithm and the multi-target evolution algorithm meet the precision requirement at the expense of the loss of the operation time.
Therefore, a torque optimal distribution method is needed, when the wheel torque is optimally distributed, the simulation operation time can be reduced, the higher precision requirement can be met, and the purposes of improving the efficiency of the driving system and the longitudinal running safety are achieved.
Disclosure of Invention
The invention aims to provide an optimal torque distribution method based on a distributed electric drive vehicle, which improves the efficiency of a driving system and the longitudinal running safety at the same time by reasonably distributing wheel torque.
An optimal torque distribution method based on a distributed electric drive vehicle is mainly characterized by comprising the following steps: the upper layer controller obtains the required torque of the whole vehicle, the torque distribution layer reasonably distributes the required torque to the four wheels, and meanwhile, the requirements for improving the efficiency of the driving system and the longitudinal driving safety are met. The optimized torque distribution method includes but is not limited to genetic algorithm, tabu search algorithm, and genetic tabu hybrid search algorithm.
The method comprises the following steps:
firstly, regression analysis is carried out on test data of the hub motor by adopting a response surface analysis method to obtain a driving motor efficiency function.
Secondly, respectively establishing objective functions representing efficiency optimization of the driving system and vehicle running safety optimization based on the required torque value of the distributed electric driving system; and the solution of the two objective functions is converted into a multi-objective optimization problem under the constraint condition by adopting a linear weighting method of a self-adaptive weight coefficient.
And thirdly, integrating the advantages of the genetic algorithm and the taboo search algorithm, and providing a genetic taboo hybrid search algorithm (HGTSA) to solve the multi-objective optimization problem so as to obtain the optimal torque distribution of the distributed electric drive system.
In a preferred embodiment of the present application, the regression analysis of the test data of the in-wheel motor by using the response surface analysis method in the step one obtains the efficiency function of the driving motor. The motor efficiency Y is described by a fourth-order regression equation with cross terms, and the expression is as follows:
wherein: beta is a regression coefficient, x1，x2And epsilon is a random error vector for design variables respectively representing the motor rotating speed and the required torque.
In a preferred embodiment of the present application, an objective function J characterizing the optimization of the efficiency of the drive system is first established1The expression is as follows:
in the formula: t isdfl、TdrrTorque of front and rear wheels, nfl、nrrThe rotational speed of the front and rear wheels, ηfl、ηrrThe working efficiency of the front and rear motors is improved.
Then, an objective function J representing vehicle driving safety optimization is established2The expression is as follows:
in the formula: r is the tire radius, FziThe vertical force experienced by the wheel, i ═ fl, fr, rl, rr.
And finally, converting the solution of the two objective functions into a multi-objective optimization problem under the constraint condition by adopting a linear weighting method of a self-adaptive weight coefficient. The adaptive weight coefficient ω is a piecewise function of the road adhesion coefficient μ and the speed u:
in the formula, ω1max、ω1min、ω2max、ω2minThe weighting coefficients of the first objective function on the high-attachment road surface and the low-attachment road surface respectively.
In a preferred embodiment of the present application, the Hybrid Genetic Tabu Search Algorithm (HGTSA) is proposed to solve the above-mentioned multi-objective optimization problem to obtain the optimal torque distribution of the distributed electric drive system, based on the advantages of the comprehensive genetic algorithm and the tabu search algorithm described in the third step. Because of the group search strategy and simple genetic operators, the genetic algorithm has strong global search capability, parallelism of information processing and robustness of application. The taboo search algorithm has extremely strong local search capability, and can jump out of the loop to find the optimal solution by reasonably setting the length of the taboo table. Combines the advantages of Genetic Algorithm and tabu Search Algorithm, and proposes a Hybrid Genetic tabu Search Algorithm (Hybrid Genetic Taboo Search Algorithm HGTSA). The method comprises the steps of firstly, carrying out global search by utilizing the powerful global search capability of a genetic algorithm, taking the searched result as an initial solution of a tabu search algorithm, then carrying out local search by utilizing the tabu search algorithm to find an optimal solution, thereby reasonably distributing required torque to four wheels and simultaneously improving the efficiency of a driving system and the longitudinal driving safety.
Drawings
FIG. 1 is a contour plot of motor efficiency versus torque demand and wheel speed
FIG. 2 is a schematic diagram of an optimized torque distribution method of the present invention
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and the detailed implementation is given, taking the driving demand torque of the distributed electrically-driven vehicle as the distribution target, and the scope of protection of the present invention includes but is not limited to the distributed electrically-driven vehicle.
As shown in fig. 1, the efficiency of the driving motor is related to the required torque and the wheel speed, and the red line in the figure is the maximum motor efficiency corresponding to a certain speed and required torque.
Considering a distributed electric drive vehicle as a highly complex coupled nonlinear time varying system, the torque optimization distribution control strategy should consider multiple objective functions to meet various vehicle performance. And establishing a functional relation of the motor efficiency with respect to the required torque and the wheel rotating speed, so as to facilitate multi-objective optimization together with an objective function representing longitudinal driving safety optimization.
The motor efficiency Y is described by a fourth-order regression equation with cross terms, and the expression is as follows:
wherein: beta is the regression coefficient, x1，x2And epsilon is a random error vector for design variables respectively representing the motor rotating speed and the required torque.
As shown in fig. 2, an objective function characterizing the optimization of the efficiency of the drive system and the optimization of the driving safety of the vehicle are first established, respectively.
Objective function J for characterizing drive system efficiency optimization1The expression is as follows:
in the formula: t isdfl、TdrrTorque of front and rear wheels, nfl、nrrThe rotational speed of the front and rear wheels, ηfl、ηrrFor the working efficiency of front and rear motors。
Objective function J for characterizing vehicle driving safety optimization2The expression is as follows:
in the formula: r is the tire radius, FziThe vertical force experienced by the wheel, i ═ fl, fr, rl, rr.
And then, converting the solution of the two objective functions into a multi-objective optimization problem under the constraint condition by adopting a linear weighting method of a self-adaptive weight coefficient. The adaptive weight coefficient ω is a piecewise function of the road adhesion coefficient μ and the velocity u:
in the formula, omega1max、ω1min、ω2max、ω2minThe weighting coefficients of the first objective function on the high-attachment road surface and the low-attachment road surface respectively.
The final objective function J to be optimized is obtained as follows:
J＝ω1J1+ω2J2
the corresponding constraints are:
in the formula: t isdimaxPeak torque, T, of the motordIs the total required torque.
Finally, as shown in fig. 2, considering the group search strategy and simple genetic operators, the genetic algorithm has strong global search capability, parallelism of information processing, and robustness of application. The taboo search algorithm has extremely strong local search capability, and can jump out of the loop to find the optimal solution by reasonably setting the length of the taboo table. Combines the advantages of Genetic algorithm and tabu search algorithm, and proposes Hybrid Genetic tabu search algorithm (Hybrid Genetic Tabo SearchAlgorithm HGTSA). The method comprises the steps of firstly, carrying out global search by utilizing the global search capability with strong genetic algorithm, taking the searched result as the initial solution of the tabu search algorithm, then carrying out local search by utilizing the tabu search algorithm to find the optimal solution, thereby reasonably distributing the required torque to four wheels and simultaneously improving the efficiency of a driving system and the longitudinal driving safety.
The description and applications of the invention herein are illustrative and not exclusive of limiting the scope of the invention to the described embodiments. It should be noted that variations and modifications of the described embodiments are possible to those skilled in the art without departing from the principles of the present invention.
Claims (5)
1. A method for optimal torque distribution for a distributed electrically driven vehicle, comprising the steps of:
firstly, carrying out regression analysis on test data of the hub motor by adopting a response surface analysis method to obtain a driving motor efficiency function,
secondly, respectively establishing an objective function representing efficiency optimization of the driving system and an objective function representing vehicle running safety optimization based on the required torque value of the distributed electric driving system; and the solution of the objective function for representing the efficiency optimization of the driving system and the objective function for optimizing the driving safety of the vehicle is converted into a multi-objective optimization problem under the constraint condition by adopting a linear weighting method of a self-adaptive weight coefficient,
and thirdly, integrating the advantages of the genetic algorithm and the taboo search algorithm, and proposing a genetic taboo hybrid search algorithm (HGTSA) to solve the multi-target optimization problem, firstly performing global search by using the global search capability of the genetic algorithm, taking the searched result as an initial solution of the taboo search algorithm, and then performing local search by using the taboo search algorithm to find an optimal solution so as to obtain the optimal torque distribution of the distributed electric drive system.
2. The distributed electric drive vehicle-based optimal torque distribution method as set forth in claim 1, wherein the motor efficiency Y is described using a fourth order regression equation with cross terms, the expression being as follows:
wherein:
in order to be the regression coefficient, the method,
for the design variables, representing the motor speed and the required torque respectively,
is a random error vector.
3. The distributed electric drive vehicle-based optimal torque distribution method as claimed in claim 2, wherein an objective function J characterizing optimization of drive system efficiency is established1The expression is as follows:
in the formula: t isdfl、TdrrTorque of front and rear wheels, nfl、nrrThe rotational speed of the front and rear wheels, ηfl、ηrrThe working efficiency of the front and rear motors is improved.
4. Method for optimal torque distribution based on distributed electrically driven vehicles according to claim 3, characterised in that an objective function J is established which characterizes the optimization of the driving safety of the vehicle2The expression is as follows:
in the formula: r is the tire radius, FziI = fl, fr, rl, rr are the vertical forces to which the wheel is subjected.
5. The distributed electric drive vehicle-based optimal torque distribution method according to claim 4, wherein the solution of the objective function for representing the drive system efficiency optimization and the objective function for vehicle driving safety optimization is converted into a multi-objective optimization problem under constraint conditions by adopting a linear weighting method of adaptive weight coefficients
Coefficient of road adhesion
And speed
The piecewise function of (d):
in the formula (I), the compound is shown in the specification,
the weighting coefficients of the first objective function on the high-attachment road surface and the low-attachment road surface respectively.
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| CN110605974B (en) * | 2019-09-18 | 2021-03-23 | 北京理工大学 | Multi-axis distributed type electric drive vehicle driving type switching control method |
| CN110816514B (en) * | 2019-10-30 | 2021-05-28 | 武汉理工大学 | Multi-mode switching-based control method and system for hub motor driven vehicle |
| CN110816291B (en) * | 2019-11-11 | 2021-05-11 | 常熟理工学院 | Distributed driving automobile energy efficiency optimization control method of second-order oscillation particle swarm |
| CN110834548B (en) * | 2019-11-11 | 2021-04-06 | 常熟理工学院 | Distributed driving automobile energy efficiency optimization method of sequential selection genetic algorithm |
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