中国物理B ›› 2012, Vol. 21 ›› Issue (11): 118801-118801.doi: 10.1088/1674-1056/21/11/118801

• INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY • 上一篇    下一篇

State of charge estimation of Li-ion batteries in electric vehicle based on radial-basis-function neural network

毕军a b, 邵赛b, 关伟b, 王璐b   

  1. a School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;
    b MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China
  • 收稿日期:2012-02-22 修回日期:2012-05-21 出版日期:2012-10-01 发布日期:2012-10-01
  • 基金资助:
    Project supported by the National High Technology Research and Development Program of China (Grant No. 2011AA110303) and the Beijing Municipal Science & Technology Project, China (Grant No. Z111100064311001).

State of charge estimation of Li-ion batteries in electric vehicle based on radial-basis-function neural network

Bi Jun (毕军)a b, Shao Sai (邵赛)b, Guan Wei (关伟)b, Wang Lu (王璐 )b   

  1. a School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;
    b MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China
  • Received:2012-02-22 Revised:2012-05-21 Online:2012-10-01 Published:2012-10-01
  • Contact: Bi Jun E-mail:bilinghc@163.com
  • Supported by:
    Project supported by the National High Technology Research and Development Program of China (Grant No. 2011AA110303) and the Beijing Municipal Science & Technology Project, China (Grant No. Z111100064311001).

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摘要： The on-line estimation of the state of charge (SOC) of the batteries is important for reliable running of the pure electric vehicle in practice. Because the nonlinear feature exists in the batteries and the radial-basis-function neural network (RBF NN) has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhances the real-time performance of estimation. Finally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.

关键词: state of charge estimation, battery, electric vehicle, radial-basis-function neural network

Abstract: The on-line estimation of the state of charge (SOC) of the batteries is important for reliable running of the pure electric vehicle in practice. Because the nonlinear feature exists in the batteries and the radial-basis-function neural network (RBF NN) has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhances the real-time performance of estimation. Finally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.

Key words: state of charge estimation, battery, electric vehicle, radial-basis-function neural network

中图分类号:  (Electric vehicles (EVs))

  • 88.85.Hj
88.80.ff (Batteries) 07.05.Mh (Neural networks, fuzzy logic, artificial intelligence)