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汽车安全与节能学报

汽车安全与节能学报 ›› 2020, Vol. 11 ›› Issue (3): 371-378.DOI: 10.3969/j.issn.1674-8484.2020.03.013

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基于工况自适应的 PHEV 等效燃油最小策略

刘灵芝 1 ，张冰战 2,3，蒋通 2,3   

  1. （1.安徽交通职业技术学院 汽车与机械工程系，合肥 230051，中国；2.合肥工业大学 汽车与交通工程学院， 合肥 230009，中国；3.安徽省数字化设计与制造重点实验室，合肥工业大学，合肥 230009，中国）
  • 出版日期:2020-09-30 发布日期:2020-10-20
  • 作者简介:第一作者 / First author ： 刘灵芝 (1971—)，女 ( 汉 )，安徽，副教授。E-mail: liulingzhi09@163.com。
  • 基金资助:
    国家新能源汽车重点研发计划项目（2017YFB0103204）；中央高校基本科研业务费专项资金资助项目 （PA2019GDPK0067）；安徽省高等学校自然科学研究重点项目（KJ2019A1070）。 

Equivalent consumption minimization strategy for PHEV based on driving condition adaptation

LIU Lingzhi1 , ZHANG Bingzhan2,3, JIANG Tong2,3   

  1. (1. Anhui Communications Vocational & Technical College, Department of Automobile and Mechanical Engineering, Hefei 230051, China; 2. School of Automobile and Traffic Engineering, Hefei University of Technology, Hefei 230009, China; 3. Anhui Key Laboratory of Digit Design and Manufacture, Hefei University of Technology, Hefei 230009, China) 
  • Online:2020-09-30 Published:2020-10-20

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摘要： 为改善插电式混合动力汽车（PHEV）的燃油经济性，提出一种基于瞬时优化能量管理策略的 工况自适应方法。建立基于反向传播（BP）神经网络算法的工况识别模型，以电池电量平衡为约束条件， 利用动态规划算法获取标准工况的等效燃油因子序列。根据基于工况识别模型的实时识别结果及电池 荷电状态（SOC），利用插值法求解此时的等效燃油因子，实现了瞬时等效消耗最低控制策略（ECMS） 的实时应用。结果表明：该文中所提出的方法与未考虑工况识别的传统等效燃油最小能量管理策略比 较能很好改善燃油经济性并保证电池电量均衡，5 种工况燃油经济性分别改善 2.2%、2.5%、3.3%、2.4% 和 4.0%。

关键词: 插电式混合动力汽车（PHEV）, 瞬时等效消耗最低控制策略（ECMS）, 反向传播（BP）神经 网络算法, 动态规划, 工况识别

Abstract: The driving condition adaptability method of instantaneous optimal energy management strategy was proposed to improve the fuel economy of plug-in hybrid electric vehicle (PHEV). The driving condition identification model was established based on back propagation (BP) neural network algorithm. The equivalent fuel factor sequence of standard driving condition was obtained by using dynamic programming algorithm under the constraints of battery power balance. According to the real-time identificated results of the model and the state of charge (SOC) of battery, the real-time application of the equivalent consumption minimization strategy (ECMS) was realized by using the interpolation method to solve the equivalent fuel factor at this time. The results shows that the proposed method can improve fuel economy and ensure battery power balance comparing with  the traditional equivalent consumption minimization strategy without considering the condition identification, and the fuel economy under 5 driving conditions are improved by 2.2%, 2.5%, 3.3%, 2.4% and 4.0%, respectively. 

Key words: plug-in hybrid electric vehicle (PHEV), equivalent consumption minimum strategy (ECMS), back propagation(BP) neural network algorithm, dynamic programming, driving condition identification

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