关键词: Q 学习；商用车；智能换挡；离线仿真；交互学习

Abstract: To balance vehicle power performance and shift smoothness, further unlock fuel economy potential, and enhance the intelligence level of gear decision-making, this study establishes a comprehensive "offline simulation and vehicle online" self-learning shift control framework based on Q-learning. First, a baseline control strategy is trained in a simulation environment using typical road load spectrum data. Subsequently, the converged strategy is deployed to the vehicle's onboard controller, where online updates to the vehicle-side Q-table are performed based on real-time interactive data, enabling continuous optimization of the strategy under actual operating conditions. Real-vehicle test results demonstrate that, compared to traditional rule-based strategies, the proposed method ensures vehicle power performance and shift smoothness while exhibiting superior gear decision-making capabilities under typical starting and climbing conditions. Furthermore, it reduces the average upshift engine speed by approximately 100 r/min and achieves a fuel savings of 1.29% under comprehensive driving conditions. The research findings validate the application potential of reinforcement learning in the field of vehicle control and provide an effective reference for the development of next-generation intelligent shift systems.

Key words: Q-learning; commercial vehicle; intelligent shifting; offline simulation; interactive learning