Research on multi-objective optimization torque distribution strategy for distributed drive electric vehicles based on dung beetle optimizer

Abstract Distributed drive electric vehicles (DDEVs), characterized by compact structure, efficient transmission, and flexible control, have gradually become the mainstream in the development of new energy electric vehicles. This study focuses on DDEVs and employs a hierarchical control strategy. At the upper level, a sliding mode controller is used for vehicle yaw stability control, while torque distribution is performed at the lower level. Addressing the shortcomings of conventional average and load-based distribution methods in terms of energy consumption, this paper proposes a multi-objective torque distribution strategy that optimizes tire load ratio, torque deviation, and motor energy consumption. The strategy integrates objective functions using weighting coefficients and imposes constraints based on road adhesion and motor output capabilities. To tackle this optimization problem, the Dung Beetle Optimizer (DBO) algorithm is introduced, known for its efficient global search capabilities and adaptability. By applying the DBO algorithm, the optimal torque distribution scheme under constraints is determined. Finally, through joint simulations using Carsim and Matlab/Simulink, comparative experiments are conducted under different conditions to evaluate the simulation results of average distribution, load-based distribution, and DBO multi-objective optimization distribution. The experimental findings demonstrate that the proposed multi-objective torque distribution strategy effectively balances tire load ratio, torque deviation, and motor energy consumption, thereby enhancing the overall performance of distributed drive electric vehicles.