重庆理工大学学报(自然科学) ›› 2023, Vol. 37 ›› Issue (1): 47-55.

• “复杂环境智能汽车感知与控制”专栏 • 上一篇    下一篇

网联环境下混合动力汽车分层能量管理策略

张 扬,梁 栋,张鹏飞   

  1. 重庆理工大学 车辆工程学院,重庆 40005
  • 出版日期:2023-02-16 发布日期:2023-02-16
  • 作者简介:张扬,男,硕士研究生,主要从事混合动力汽车能量管理策略研究,Email:306254363@qq.com;通讯作者 胡博, 男,博士,硕士生导师,从事节能与新能源汽车先进动力总成建模及智能控制研究,Email:b.hu@cqut.edu.cn

Research on the layered energy management strategy of hybrid electric vehicles in the networked environment

  • Online:2023-02-16 Published:2023-02-16

摘要: 在城市道路背景下,提出分层式模型预测控制(modelpredictivecontrol,MPC),合理 规划车速,减少混合动力汽车频繁启停,提升交通效率并进行能量管理,提高燃油经济性。上层 模型预测控制利用智能网联技术获取前车速度及位置等状态,结合交通环境数据预测车辆避免 红灯停车的最优车速;下层模型预测控制根据最优车速对混合动力汽车进行能量管理以提高燃 油经济性。对比分析分层式 MPC所得能量管理结果与相同车速下动态规划能量管理结果,表 明:分层式 MPC方法能有效避免车辆红灯停车怠速,提高燃油经济性和道路通行率。

关键词: 智能网联, 混合动力汽车, 模型预测控制, 能量管理策略

Abstract: Based on urban roads, this paper proposes a layered model predictive control (MPC) to plan car speeds to reduce frequent start and stop of hybrid electric vehicles, improve traffic efficiency, and achieve fuel economy through energy management. The upper-layer MPC uses the intelligent network technology to obtain states like speed and position of the vehicle in front, and predicts the optimal speed of the vehicle to avoid stopping at red lights by combining the traffic environment data. The lower-layer MPC manages the energy of hybrid electric vehicles according to the optimal speed to improve fuel economy. The comparison between the energy management results of the layered MPC and those of dynamic programming at the same speed shows that the proposed method can effectively avoid vehicle idling at red lights and improve fuel economy and traffic efficiency.

中图分类号: 

  • U461