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A novel online method for predicting the remaining useful life of lithium-ion batteries considering random variable discharge current

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  • Shen, Dongxu
  • Wu, Lifeng
  • Kang, Guoqing
  • Guan, Yong
  • Peng, Zhen

Abstract

Lithium-ion batteries are widely used in many electronic and electrical devices, and accurately predicting their remaining useful life is essential to ensure the safe and reliable operation of the systems. The discharge current of lithium-ion batteries in the actual environment changes randomly during one charge and discharge cycle, and the randomly changing current has a greater impact on battery life. Existing prediction methods rarely take this into account. Therefore, this paper proposes a new method for predicting the remaining useful life of lithium-ion batteries with variable discharge current. First, the battery aging experiment under variable discharge current is designed by simulating the operation state of batteries and capacity data is collected. Secondly, a novel two-stage Wiener process model is established to describe the differences in the degradation characteristics of lithium-ion batteries at different stages. Finally, the unscented particle filtering algorithm is introduced so that all parameters in the model and the remaining useful life distribution of lithium-ion batteries are adaptively updated with the latest on-line measurements. The experimental results demonstrate that the proposed method can achieve more accurate and robust results compared with the two previous methods, which verifies the effectiveness and robustness of the proposed method.

Suggested Citation

  • Shen, Dongxu & Wu, Lifeng & Kang, Guoqing & Guan, Yong & Peng, Zhen, 2021. "A novel online method for predicting the remaining useful life of lithium-ion batteries considering random variable discharge current," Energy, Elsevier, vol. 218(C).
    • Handle: RePEc:eee:energy:v:218:y:2021:i:c:s0360544220325974
    DOI: 10.1016/j.energy.2020.119490
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    4. Weijie Liu & Yan Shen & Lijuan Shen, 2022. "Degradation Modeling for Lithium-Ion Batteries with an Exponential Jump-Diffusion Model," Mathematics, MDPI, vol. 10(16), pages 1-18, August.
    5. Wan, Hongri & Shen, Xiran & Jiang, Hao & Zhang, Cheng & Jiang, Kaile & Chen, Teng & Shi, Liluo & Dong, Liming & He, Changchun & Xu, Yan & Li, Jing & Chen, Yan, 2021. "Biomass-derived N/S dual-doped porous hard-carbon as high-capacity anodes for lithium/sodium ions batteries," Energy, Elsevier, vol. 231(C).
    6. Shao-Xun Liu & Ya-Fu Zhou & Yan-Liang Liu & Jing Lian & Li-Jian Huang, 2021. "A Method for Battery Health Estimation Based on Charging Time Segment," Energies, MDPI, vol. 14(9), pages 1-15, May.
    7. Kong, Jin-zhen & Yang, Fangfang & Zhang, Xi & Pan, Ershun & Peng, Zhike & Wang, Dong, 2021. "Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries," Energy, Elsevier, vol. 223(C).
    8. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
    9. Ma, Qiuhui & Zheng, Ying & Yang, Weidong & Zhang, Yong & Zhang, Hong, 2021. "Remaining useful life prediction of lithium battery based on capacity regeneration point detection," Energy, Elsevier, vol. 234(C).
    10. Wu, Lifeng & Zhang, Yu, 2023. "Attention-based encoder-decoder networks for state of charge estimation of lithium-ion battery," Energy, Elsevier, vol. 268(C).
    11. Zhang, Meng & Kang, Guoqing & Wu, Lifeng & Guan, Yong, 2022. "A method for capacity prediction of lithium-ion batteries under small sample conditions," Energy, Elsevier, vol. 238(PC).
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    14. Seo, Minhwan & Song, Youngbin & Kim, Jake & Paek, Sung Wook & Kim, Gi-Heon & Kim, Sang Woo, 2021. "Innovative lumped-battery model for state of charge estimation of lithium-ion batteries under various ambient temperatures," Energy, Elsevier, vol. 226(C).

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