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

摘要： 为兼顾车辆动力性与换挡平顺性，进一步挖掘燃油经济性潜力并提升挡位决策的智能 化水平，研究构建了一套完整的基于 Q 学习的“离线仿真-车端在线”自学习换挡控制框架。 首先，在仿真环境中利用典型路谱数据训练出基础控制策略；随后，将收敛的策略部署至车 端控制器，通过实时交互数据对车端 Q 表进行在线更新，实现了策略在真实运行环境下的持 续优化。实车测试结果表明，与传统规则策略相比，文章所提方法在保证车辆动力性与换挡 平顺性的基础上，于起步、爬坡典型工况下均表现出良好的挡位决策能力，并使平均升挡转 速降低约 100 r/min，综合工况节油效果达到 1.29%。研究成果验证了强化学习在车辆控制领 域的应用潜力，为下一代智能换挡系统的开发提供了有效参考。

关键词: Q 学习；商用车；智能换挡；离线仿真；交互学习