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汽车安全与节能学报

JASE ›› 2017, Vol. 08 ›› Issue (01): 87-96.DOI: 10.3969/j.issn.1674-8484.2017.01.011

• 汽车节能与环保 • 上一篇    下一篇

基于行驶工况的插电式混合动力汽车电能消耗最优控制

杨林，胡艳青，闫斌   

  1. 上海交通大学 机械与动力工程学院，上海200240，中国
  • 收稿日期:2016-08-05 出版日期:2017-03-23 发布日期:2017-03-23
  • 作者简介:第一作者 / First author ： 杨林(1969—)，男（汉），四川，教授，E-mail: yanglin @sjtu.edu.cn。 第二作者 / Second author ： 胡艳青(1987—)，男( 汉)，安徽，博士研究生，E-mail: sjtu_huyq@sjtu.edu.cn。
  • 基金资助:

    国家自然科学基金项目(51275291)，上海汽车科技基金项目(1502)

Optimal charge depleting control of plug-in hybrid electric vehicles based on driving condition

YANG Lin, HU Yanqing, YAN Bin   

  1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
  • Received:2016-08-05 Online:2017-03-23 Published:2017-03-23

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摘要：

基于行驶工况以获得插电式混合动力汽车(PHEV) 电池电能的优化消耗规律（即荷电状态（SOC）的优化控制轨迹），以插电式四模混合动力客车为例，利用动态规划(DP) 和相关性分析方法确定了影响SOC 最优控制轨迹的主要行驶工况参数，并据此提出SOC 优化轨迹规划方法。综合循环测试表明：所建轨迹规划方法与现有基于里的线性规划方法相比，可显著降低SOC 轨迹与DP 最优SOC 轨迹的偏差，使平均偏差减小75.2% 达到3.6%以内、最大偏差减小72.3% 达到8.2%以内；轨迹规划耗时从DP 的7.6~8.5 h 降到0.18 s 以内；基于跟随规划的SOC 轨迹的自适应等价燃油消耗最小策略（A-ECMS）燃油经济性仅比DP 的最优值低2.7%，有效解决了SOC 轨迹在线最优规划和能耗最优控制问题，为PHEV能量管理在线优化提供了新的途径。

关键词: 混合动力汽车, 插电式混合动力, 能量管理优化, SOC 轨迹规划, 行驶工况

Abstract:

The optimized battery power consumption law (also means the optimized control trajectory of the stage of charge (SOC) ) of plug-in hybrid electric vehicles (PHEVs) was obtained using a four-mode plug-in hybrid bus based on driving conditions. The main driving condition parameters that have the most influence on optimal SOC control trajectory were given by dynamic programming (DP) and correlation analysis method, and an optimized SOC trajectory planning method was proposed. The results of comprehensive driving cycles tests show that, compared with existing mileage-based linear programming method, the established SOC trajectory
planning method significantly reduced the SOC trajectory deviation from DP-based optimal results. The mean deviation was reduced by 75.2% to less than 3.6%, and the maximum deviation decreased by 72.3% to less than 8.2%. Computing time was reduced from 7.6~8.5 h to less than 0.18 s compared with DP. An adaptive
equivalent consumption minimization strategy (A-ECMS) that trace the planned SOC trajectory can improve the fuel economy, only 2.7% worse than DP, which can effectively solve the key problem of the on-line optimal planning of SOC trajectory and the energy optimal control, and provide a novel way for PHEV on-line energy
management optimization.

Key words: hybrid electric vehicle, plug-in hybrid electric vehicle (PHEV), energy management optimization, SOC trajectory planning, driving condition