Chinese Journal of Mechanical Engineering >

2020 , Vol. 33 >Issue 4: 51 - 51

DOI: https://doi.org/10.1186/s10033-020-00467-x

Advanced Transportation Equipment

A Comparative Study of Fractional Order Models on State of Charge Estimation for Lithium Ion Batteries

  • Jinpeng Tian ,
  • Rui Xiong ,
  • Weixiang Shen ,
  • Ju Wang
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  • 1. Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China;
    2. Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia

收稿日期: 2020-04-17

  修回日期: 2020-06-23

  网络出版日期: 2020-11-06

基金资助

Supported by Beijing Municipal Natural Science Foundation of China (Grant No. 3182035) and National Natural Science Foundation of China (Grant No. 51877009)

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A Comparative Study of Fractional Order Models on State of Charge Estimation for Lithium Ion Batteries

  • Jinpeng Tian ,
  • Rui Xiong ,
  • Weixiang Shen ,
  • Ju Wang
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  • 1. Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China;
    2. Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia

Received date: 2020-04-17

  Revised date: 2020-06-23

  Online published: 2020-11-06

Supported by

Supported by Beijing Municipal Natural Science Foundation of China (Grant No. 3182035) and National Natural Science Foundation of China (Grant No. 51877009)

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摘要

State of charge (SOC) estimation for lithium ion batteries plays a critical role in battery management systems for electric vehicles. Battery fractional order models (FOMs) which come from frequency-domain modelling have provided a distinct insight into SOC estimation. In this article, we compare fve state-of-the-art FOMs in terms of SOC estimation. To this end, frstly, characterisation tests on lithium ion batteries are conducted, and the experimental results are used to identify FOM parameters. Parameter identifcation results show that increasing the complexity of FOMs cannot always improve accuracy. The model R(RQ)W shows superior identifcation accuracy than the other four FOMs. Secondly, the SOC estimation based on a fractional order unscented Kalman flter is conducted to compare model accuracy and computational burden under diferent profles, memory lengths, ambient temperatures, cells and voltage/current drifts. The evaluation results reveal that the SOC estimation accuracy does not necessarily positively correlate to the complexity of FOMs. Although more complex models can have better robustness against temperature variation, R(RQ), the simplest FOM, can overall provide satisfactory accuracy. Validation results on diferent cells demonstrate the generalisation ability of FOMs, and R(RQ) outperforms other models. Moreover, R(RQ) shows better robustness against truncation error and can maintain high accuracy even under the occurrence of current or voltage sensor drift.

关键词： Electric vehicle; Lithium ion battery; Fractional order model; State of charge

本文引用格式

Jinpeng Tian , Rui Xiong , Weixiang Shen , Ju Wang . A Comparative Study of Fractional Order Models on State of Charge Estimation for Lithium Ion Batteries[J]. Chinese Journal of Mechanical Engineering, 2020 , 33(4) : 51 -51 . DOI: 10.1186/s10033-020-00467-x

Abstract

State of charge (SOC) estimation for lithium ion batteries plays a critical role in battery management systems for electric vehicles. Battery fractional order models (FOMs) which come from frequency-domain modelling have provided a distinct insight into SOC estimation. In this article, we compare fve state-of-the-art FOMs in terms of SOC estimation. To this end, frstly, characterisation tests on lithium ion batteries are conducted, and the experimental results are used to identify FOM parameters. Parameter identifcation results show that increasing the complexity of FOMs cannot always improve accuracy. The model R(RQ)W shows superior identifcation accuracy than the other four FOMs. Secondly, the SOC estimation based on a fractional order unscented Kalman flter is conducted to compare model accuracy and computational burden under diferent profles, memory lengths, ambient temperatures, cells and voltage/current drifts. The evaluation results reveal that the SOC estimation accuracy does not necessarily positively correlate to the complexity of FOMs. Although more complex models can have better robustness against temperature variation, R(RQ), the simplest FOM, can overall provide satisfactory accuracy. Validation results on diferent cells demonstrate the generalisation ability of FOMs, and R(RQ) outperforms other models. Moreover, R(RQ) shows better robustness against truncation error and can maintain high accuracy even under the occurrence of current or voltage sensor drift.

Key words： Electric vehicle; Lithium ion battery; Fractional order model; State of charge

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