中国机械工程

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[动力电池]融合车、路、人信息的电动汽车续驶里程估算

高建平1,2;高小杰1;郗建国1   

  1. 1.河南科技大学车辆与交通工程学院,洛阳,471003
    2.河南科技大学机械装备先进制造河南省协同创新中心,洛阳,471003
  • 出版日期:2018-08-10 发布日期:2018-08-06
  • 基金资助:
    国家自然科学基金资助项目(U1604147);
    河南省科技攻关计划资助项目(152102210073);
    河南省高等学校青年骨干教师培养计划资助项目(2015GGJS-046)

Driving Range Estimation for Electric Vehicles through Vehicles,Roads, and Human Information Fusion

GAO Jianping1,2;GAO Xiaojie1;XI Jianguo1   

  1. 1.Vehicle and Transportation College,Henan University of Science and Technology,Luoyang,Henan,471003
    2.Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province,Henan University of Science and Technology,Luoyang,Henan,471003
  • Online:2018-08-10 Published:2018-08-06

摘要:

针对因行驶工况与驾驶风格对剩余续驶里程有显著影响而导致剩余里程估算困难的问题,采用工况识别与驾驶风格识别相结合的方式对电动汽车续驶里程进行估算。通过主成分分析和聚类分析,选出基于大数据、符合郑州市当地交通特征的典型行驶工况片段,并利用选取的典型行驶工况片段,在MATLAB/Simulink下建立主成分分析和学习矢量量化神经网络相结合的工况识别模型、驾驶风格模糊识别模型;通过联合仿真进行剩余续驶里程实时估算,并通过卡尔曼滤波对输出结果进一步优化。仿真分析及半实物测试结果表明,采用融合车、路、人信息的电动汽车续驶里程估算方法,不仅降低了剩余里程估算误差,同时也证实了方法的可行性。

关键词: 电动汽车, 续驶里程, 行驶工况, 驾驶风格

Abstract: Aiming at driving cycle and driving style influenced significantly on remaining range estimation, which resulted in the remaining range estimation difficulty, a estimate method was adopted,which combined driving cycle with driver’s driving style,to estimation the driving range of electric vehicles. The typical driving condition fragments were selected based on large sample and traffic characteristics of Zhengzhou, which through principal component analysis and fuzzy C-means clustering technique.Under MATLAB/Simulink,a model to identify driving patterns was established based on principal component analysis and learning vector quantization by the typical driving condition fragments and driving style fuzzy recognition model, the remaining range estimation were carried out, which adopted joint simulation method,and the results of remaining range estimation were optimized by Kalman filtering. The simulation analysis and hardware in the loop test results show that driving range estimation for electric vehicles through vehicles,roads, and human information fusion method, reduces the remaining range estimation errors, and confirmes the feasibility of the method at the same time.

Key words: electric vehicle, driving range, driving cycle, driving style

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