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Capacity estimation for lithium-ion battery using experimental feature interval approach

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  • Pei, Pucheng
  • Zhou, Qibin
  • Wu, Lei
  • Wu, Ziyao
  • Hua, Jianfeng
  • Fan, Huimin

Abstract

The estimation of lithium-ion battery capacity is of great importance in electric vehicle battery management system (BMS), its results will contribute to controlling the battery to have both excellent output performance and long life. However, there still lacks generalized approaches for different kinds of batteries with high estimating accuracy. Therefore, an experimental feature interval approach for LiFePO4 (LFP) and LiNixCoyMn1-x-yO2 (NCM) capacity estimating is proposed in this paper. Firstly, two concepts of feature interval and remaining charge electricity (RCE) are defined, then partial charging electricity based on incremental capacity analysis is used to estimate capacity. According to the results, there is a strong linear relationship between RCE and capacity. We can obtain capacity directly through this linear function by calculating RCE from the feature interval to the end of charge. A satisfying estimation performance is verified by the results of another experiment data, where the accuracy is more than 98.5%. Moreover, it is found that this approach can be used to NCM battery by modifying the linear fitting weights. This proposed approach is verified in NASA dataset, with the estimating deviations less than 2.4%. Further, the proposed estimating approach may serve as a reference for batteries from other manufactures.

Suggested Citation

  • Pei, Pucheng & Zhou, Qibin & Wu, Lei & Wu, Ziyao & Hua, Jianfeng & Fan, Huimin, 2020. "Capacity estimation for lithium-ion battery using experimental feature interval approach," Energy, Elsevier, vol. 203(C).
  • Handle: RePEc:eee:energy:v:203:y:2020:i:c:s0360544220308859
    DOI: 10.1016/j.energy.2020.117778
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    References listed on IDEAS

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    1. Zheng, Linfeng & Zhu, Jianguo & Wang, Guoxiu & Lu, Dylan Dah-Chuan & He, Tingting, 2018. "Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter," Energy, Elsevier, vol. 158(C), pages 1028-1037.
    2. Bian, Xiaolei & Liu, Longcheng & Yan, Jinying, 2019. "A model for state-of-health estimation of lithium ion batteries based on charging profiles," Energy, Elsevier, vol. 177(C), pages 57-65.
    3. Lin, Qian & Wang, Jun & Xiong, Rui & Shen, Weixiang & He, Hongwen, 2019. "Towards a smarter battery management system: A critical review on optimal charging methods of lithium ion batteries," Energy, Elsevier, vol. 183(C), pages 220-234.
    4. Li, Zhenhe & Khajepour, Amir & Song, Jinchun, 2019. "A comprehensive review of the key technologies for pure electric vehicles," Energy, Elsevier, vol. 182(C), pages 824-839.
    5. Ren, Hongbin & Zhao, Yuzhuang & Chen, Sizhong & Wang, Taipeng, 2019. "Design and implementation of a battery management system with active charge balance based on the SOC and SOH online estimation," Energy, Elsevier, vol. 166(C), pages 908-917.
    6. Chen, Lin & Lin, Weilong & Li, Junzi & Tian, Binbin & Pan, Haihong, 2016. "Prediction of lithium-ion battery capacity with metabolic grey model," Energy, Elsevier, vol. 106(C), pages 662-672.
    7. Galeotti, Matteo & Cinà, Lucio & Giammanco, Corrado & Cordiner, Stefano & Di Carlo, Aldo, 2015. "Performance analysis and SOH (state of health) evaluation of lithium polymer batteries through electrochemical impedance spectroscopy," Energy, Elsevier, vol. 89(C), pages 678-686.
    8. Zheng, Linfeng & Zhu, Jianguo & Lu, Dylan Dah-Chuan & Wang, Guoxiu & He, Tingting, 2018. "Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries," Energy, Elsevier, vol. 150(C), pages 759-769.
    9. Du, Jiuyu & Ouyang, Minggao & Chen, Jingfu, 2017. "Prospects for Chinese electric vehicle technologies in 2016–2020: Ambition and rationality," Energy, Elsevier, vol. 120(C), pages 584-596.
    10. Lyu, Chao & Lai, Qingzhi & Ge, Tengfei & Yu, Honghai & Wang, Lixin & Ma, Na, 2017. "A lead-acid battery's remaining useful life prediction by using electrochemical model in the Particle Filtering framework," Energy, Elsevier, vol. 120(C), pages 975-984.
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