摘要
预见性巡航控制(predictive cruise control,PCC)在规划层以预测节能为目标进行长时域的速度规划,执行层对规划速度进行短时域的跟踪控制。由于规划层与执行层有着不同时间尺度步长要求,在系统设计中很难将二者置于一个优化控制问题中。因此,本文采用分层控制思想,在规划层基于改进的双延迟深度确定性策略梯度算法(twin delayed deep deterministic policy gradient algorithm,TD3)获得预测时域内长周期的规划速度;在执行层基于模型预测控制(model predictive control,MPC)以规划速度为参考速度,同时考虑发动机油耗特性和变速器换挡规律,对规划速度在短时域内作进一步的经济性优化,并进行跟踪控制。硬件在环验证结果表明,将改进的TD3与MPC相结合可以改善PCC在规划与执行中的时间尺度不一致问题,并有效降低重型商用车在巡航过程中的燃油消耗量和换挡频次。
Predictive cruise control(PCC)performs long-term speed planning at the planning layer with the objective of predicting energy savings and short-term tracking control for the vehicle speed at the execution lay-er.Integrating these layers into a single optimal control problem poses significant challenges in system design due to the different time scale step requirements between the planning layer and the execution layer.To address this chal-lenge,a hierarchical control approach is adopted in this paper.At the planning layer,an improved twin delayed deep deterministic policy gradient(TD3)algorithm is utilized to determine the long-term planning speed over the prediction horizon.Meanwhile,at the execution layer,based on model predictive control(MPC),taking the planned vehicle speed as the reference speed and considering engine fuel consumption characteristics and transmis-sion shift laws,further economic optimization and tracking control of the planned speed are carried out in the short term.The hardware-in-the-loop(HIL)validation results show that combining the improved TD3 algorithm with MPC effectively resolves the time scale inconsistency between planning and execution in PCC,which can significantly re-duce both fuel consumption and shift frequency during the cruising of heavy-duty commercial vehicles.
作者
耿小虎
付尧
王杰
雷雨龙
刘卫东
王玉海
刘科
Geng Xiaohu;Fu Yao;Wang Jie;Lei Yulong;Liu Weidong;Wang Yuhai;Liu Ke(Jilin University,State Key Laboratory of Automotive Simulation and Control,Changchun 130000;FAW Jiefang Qingdao Automobile Co.,Ltd.,Qingdao 266000)
出处
《汽车工程》
EI
CSCD
北大核心
2024年第11期2046-2058,共13页
Automotive Engineering
基金
四川省重点研发项目(2023YFG0068)
青岛市科技计划项目(22-5-1-yfzt-4-jch)
山东省泰山产业领军人才工程项目(tscx202211119)资助。
关键词
预见性巡航
速度规划与控制
深度强化学习
模型预测控制
predictive cruising
speed planning and control
deep reinforcement learning
model predictive control
