Research on Multi-Objective Optimization of Clutch Engagement Strategy Based on Deep Reinforcement Learning

Ying Liu, Chengyou Xie, Yongxian Zhang, Cheng Zeng, Yinmin Huang, Tianfu Ai, Lie Yang

The optimization of clutch engagement strategies is of great significance for improving vehicle power performance, fuel economy, and driving comfort. Traditional control strategies are difficult to adapt to complex working conditions and lack coordinated optimization of fuel and clutch. This paper proposes a multi-objective optimization method for clutch engagement strategies based on the Deep Deterministic Policy Gradient (DDPG) algorithm. A simulation environment is constructed, which includes a vehicle longitudinal dynamics model, clutch state switching logic, and a reinforcement learning agent. A multi-dimensional state space and action space are designed, and a composite reward function combining power performance, fuel economy, and comfort is developed to achieve multi-objective optimization of the fuel–clutch coordination curve. Experimental results show that the optimized engagement strategy significantly reduces sliding friction power (by 94.07%), power interruption speed (by 8.75%), and jerk (with a maximum reduction of 35.6%), while the average fuel consumption per distance is reduced by 0.39%. Through weight sensitivity analysis, it is found that when the weight of fuel economy is 0.3 and the weight of power performance is 0.5 (Scheme P5E3), the optimal balance among multiple objectives can be achieved. This study provides a new theoretical framework and engineering practice reference for the intelligent control of clutches.