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Discharge capacity estimation for Li-ion batteries based on particle filter under multi-operating conditions

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  • Li, Junfu
  • Wang, Lixin
  • Lyu, Chao
  • Zhang, Liqiang
  • Wang, Han

Abstract

In recent years, Li-ion rechargeable batteries are well liked to be used in BMS (battery management system) of EV (electrical vehicle) and satellite due to various advantages. As battery is aging during the whole life cycles, it is essential to estimate discharge capacity to ensure high performance. This paper presents a discharge capacity estimation model for Li-ion battery based on PF (particle filter). To discover effects of different operating conditions on capacity, LiCoO2 cells are designed to experience aging and characteristic tests alternatively. The contributions of this paper are listed below: (i) four feature parameters extracted from charging voltage curves are selectively used for modeling; (ii) under certain aging condition, the model verifies the applicability for LiCoO2 battery with high estimation accuracy; (iii) the adoption of ANN (artificial neural network) helps to mine the nonlinear relationship between discharge capacities and multi-operating conditions. Validation result indicates that the proposed method is able to accurately estimate discharge capacity under multi-operating conditions.

Suggested Citation

  • Li, Junfu & Wang, Lixin & Lyu, Chao & Zhang, Liqiang & Wang, Han, 2015. "Discharge capacity estimation for Li-ion batteries based on particle filter under multi-operating conditions," Energy, Elsevier, vol. 86(C), pages 638-648.
    • Handle: RePEc:eee:energy:v:86:y:2015:i:c:p:638-648
    DOI: 10.1016/j.energy.2015.04.021
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    5. Wang, Zengkai & Zeng, Shengkui & Guo, Jianbin & Qin, Taichun, 2019. "State of health estimation of lithium-ion batteries based on the constant voltage charging curve," Energy, Elsevier, vol. 167(C), pages 661-669.
    6. Zhang, Shumei & Qiang, Jiaxi & Yang, Lin & Zhao, Xiaowei, 2016. "Prior-knowledge-independent equalization to improve battery uniformity with energy efficiency and time efficiency for lithium-ion battery," Energy, Elsevier, vol. 94(C), pages 1-12.
    7. Singh, Karanjot & Tjahjowidodo, Tegoeh & Boulon, Loïc & Feroskhan, Mir, 2022. "Framework for measurement of battery state-of-health (resistance) integrating overpotential effects and entropy changes using energy equilibrium," Energy, Elsevier, vol. 239(PA).
    8. Lu, Chen & Zhang, Lipin & Ma, Jian & Chen, Zihan & Tao, Laifa & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou, 2017. "Li-ion battery capacity cycling fading dynamics cognition: A stochastic approach," Energy, Elsevier, vol. 137(C), pages 251-259.
    9. 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.
    10. Meng, Jinhao & Cai, Lei & Stroe, Daniel-Ioan & Luo, Guangzhao & Sui, Xin & Teodorescu, Remus, 2019. "Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles," Energy, Elsevier, vol. 185(C), pages 1054-1062.
    11. Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).
    12. Sohn, Suyeon & Byun, Ha-Eun & Lee, Jay H., 2022. "Two-stage deep learning for online prediction of knee-point in Li-ion battery capacity degradation," Applied Energy, Elsevier, vol. 328(C).
    13. Sun, Jinlei & Tang, Yong & Ye, Jilei & Jiang, Tao & Chen, Saihan & Qiu, Shengshi, 2022. "A novel capacity and initial discharge electric quantity estimation method for LiFePO4 battery pack based on OCV curve partial reconstruction," Energy, Elsevier, vol. 243(C).
    14. Li, Shi & Pischinger, Stefan & He, Chaoyi & Liang, Liliuyuan & Stapelbroek, Michael, 2018. "A comparative study of model-based capacity estimation algorithms in dual estimation frameworks for lithium-ion batteries under an accelerated aging test," Applied Energy, Elsevier, vol. 212(C), pages 1522-1536.
    15. Wu, Zhou & Ling, Rui & Tang, Ruoli, 2017. "Dynamic battery equalization with energy and time efficiency for electric vehicles," Energy, Elsevier, vol. 141(C), pages 937-948.

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