Multi-Objective Optimization Control of Distributed Electric Drive Vehicles Based on Optimal Torque Distribution

To improve the total efficiency of the drive system and the driving safety of distributed electric drive vehicles, this paper proposes a multi-objective optimization method based on torque allocation optimization. First, in the vehicle nonlinear dynamics model, the response surface method is used to perform regression analysis on the test data of the drive motor to obtain the drive motor efficiency function. Second, based on the demand torque value of the distributed electric drive system, the objective functions that characterize the optimization of the drive system efficiency and the optimization of the vehicle driving safety are established. Moreover, the linear weighting method with adaptive weight coefficients is used to transform the solution of the above two objective functions into a multi-objective optimization problem under constraint conditions. Furthermore, the second-generation nondominated sorting genetic algorithm (NSGA-II) and the hybrid genetic Tabu search algorithm (HGTSA) are used to solve the above multi-objective optimization problem to obtain the optimal torque distribution of the distributed electric drive system. Finally, the NEDC operating conditions were selected to verify NSGA-II, the HGTSA and the commonly used average distribution method. The simulation test results show that NSGA-II and the HGTSA can improve the driving efficiency and vehicle driving safety of distributed electric drive systems relative to the average distribution method. In particular, the optimization effect of the HGTSA is more prominent, and stability is more quickly achieved.