中国物理B ›› 2015, Vol. 24 ›› Issue (9): 98801-098801.doi: 10.1088/1674-1056/24/9/098801

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

Adaptive Kalman filter based state of charge estimation algorithm for lithium-ion battery

郑宏, 刘煦, 魏旻   

  1. University of Electronic Science and Technology of China, Chengdu 611731, China
  • 收稿日期:2014-07-31 修回日期:2015-03-30 出版日期:2015-09-05 发布日期:2015-09-05
  • 基金资助:

    Project supported by the National Natural Science Foundation of China (Grant Nos. 61004048 and 61201010).

Adaptive Kalman filter based state of charge estimation algorithm for lithium-ion battery

Zheng Hong (郑宏), Liu Xu (刘煦), Wei Min (魏旻)   

  1. University of Electronic Science and Technology of China, Chengdu 611731, China
  • Received:2014-07-31 Revised:2015-03-30 Online:2015-09-05 Published:2015-09-05
  • Contact: Zheng Hong E-mail:macrozheng@uestc.edu.cn
  • Supported by:

    Project supported by the National Natural Science Foundation of China (Grant Nos. 61004048 and 61201010).

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摘要：

In order to improve the accuracy of the battery state of charge (SOC) estimation, in this paper we take a lithium-ion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate. Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.

关键词: state of charge (SOC) estimation, temperature, charge rate, adaptive Kalman filter

Abstract:

In order to improve the accuracy of the battery state of charge (SOC) estimation, in this paper we take a lithium-ion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate. Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.

Key words: state of charge (SOC) estimation, temperature, charge rate, adaptive Kalman filter

中图分类号:  (Electric vehicles (EVs))

  • 88.85.Hj
82.47.Aa (Lithium-ion batteries) 07.05.Mh (Neural networks, fuzzy logic, artificial intelligence) 07.05.Tp (Computer modeling and simulation)