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Data-driven state-of-health estimation for lithium-ion battery based on aging features

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  • Li, Xining
  • Ju, Lingling
  • Geng, Guangchao
  • Jiang, Quanyuan

Abstract

Reliable state-of-health (SOH) estimation is crucial to the safe operation of lithium-ion battery. Data-driven SOH estimation becomes a hot research topic with the booming of high-performance machine learning algorithms. The effectiveness of a data-driven approach will be enhanced significantly if the input features are extracted properly. In order to improve the SOH estimation accuracy, the features highly associated with battery degradation should be utilized in the data-driven model. In this paper, an aging feature extraction method based on electrochemical model (EM) is proposed to account for the battery degradation mechanisms. The aging features of EM such as charge transfer resistance, solid phase diffusion coefficient and electrode volume fraction are defined as internal health features (IHFs) for SOH estimation. Moreover, external health features (EHFs) are directly extracted from the voltage and temperature curves that split into multiple stages. Then, IHFs and multi-stage EHFs are selected appropriately for SOH estimation in offline and online application scenarios. Finally, two well-known machine learning algorithms are employed to construct data-driven SOH estimation model using IHFs and EHFs. Experimental data are used to prove that the proposed method can effectively improve the accuracy of SOH estimation under different application scenarios and battery charge–discharge modes.

Suggested Citation

  • Li, Xining & Ju, Lingling & Geng, Guangchao & Jiang, Quanyuan, 2023. "Data-driven state-of-health estimation for lithium-ion battery based on aging features," Energy, Elsevier, vol. 274(C).
    • Handle: RePEc:eee:energy:v:274:y:2023:i:c:s0360544223007727
    DOI: 10.1016/j.energy.2023.127378
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    References listed on IDEAS

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    1. Jing Xie & Yi-Chun Lu, 2020. "A retrospective on lithium-ion batteries," Nature Communications, Nature, vol. 11(1), pages 1-4, December.
    2. Wen, Jianping & Chen, Xing & Li, Xianghe & Li, Yikun, 2022. "SOH prediction of lithium battery based on IC curve feature and BP neural network," Energy, Elsevier, vol. 261(PA).
    3. Ospina Agudelo, Brian & Zamboni, Walter & Postiglione, Fabio & Monmasson, Eric, 2023. "Battery State-of-Health estimation based on multiple charge and discharge features," Energy, Elsevier, vol. 263(PA).
    4. 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.
    5. Zhang, Cheng & Allafi, Walid & Dinh, Quang & Ascencio, Pedro & Marco, James, 2018. "Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique," Energy, Elsevier, vol. 142(C), pages 678-688.
    6. Li, J. & Adewuyi, K. & Lotfi, N. & Landers, R.G. & Park, J., 2018. "A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation," Applied Energy, Elsevier, vol. 212(C), pages 1178-1190.
    7. Tang, Xiaopeng & Liu, Kailong & Lu, Jingyi & Liu, Boyang & Wang, Xin & Gao, Furong, 2020. "Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter," Applied Energy, Elsevier, vol. 280(C).
    8. Shu Zhang & Wenying Chen, 2022. "Assessing the energy transition in China towards carbon neutrality with a probabilistic framework," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    9. Li, Yi & Zou, Changfu & Berecibar, Maitane & Nanini-Maury, Elise & Chan, Jonathan C.-W. & van den Bossche, Peter & Van Mierlo, Joeri & Omar, Noshin, 2018. "Random forest regression for online capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 232(C), pages 197-210.
    10. Ospina Agudelo, Brian & Zamboni, Walter & Monmasson, Eric, 2021. "Application domain extension of incremental capacity-based battery SoH indicators," Energy, Elsevier, vol. 234(C).
    11. Xu, Zhicheng & Wang, Jun & Lund, Peter D. & Zhang, Yaoming, 2022. "Co-estimating the state of charge and health of lithium batteries through combining a minimalist electrochemical model and an equivalent circuit model," Energy, Elsevier, vol. 240(C).
    12. Xiao-Guang Yang & Teng Liu & Chao-Yang Wang, 2021. "Thermally modulated lithium iron phosphate batteries for mass-market electric vehicles," Nature Energy, Nature, vol. 6(2), pages 176-185, February.
    Full references (including those not matched with items on IDEAS)

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    7. Li, Xiaoyu & Lyu, Mohan & Li, Kuo & Gao, Xiao & Liu, Caixia & Zhang, Zhaosheng, 2023. "Lithium-ion battery state of health estimation based on multi-source health indicators extraction and sparse Bayesian learning," Energy, Elsevier, vol. 282(C).
    8. Chang, Chun & Pan, Yaliang & Wang, Shaojin & Jiang, Jiuchun & Tian, Aina & Gao, Yang & Jiang, Yan & Wu, Tiezhou, 2024. "Fast EIS acquisition method based on SSA-DNN prediction model," Energy, Elsevier, vol. 288(C).
    9. Xiong, Ran & Wang, Shunli & Huang, Qi & Yu, Chunmei & Fernandez, Carlos & Xiao, Wei & Jia, Jun & Guerrero, Josep M., 2024. "Improved cooperative competitive particle swarm optimization and nonlinear coefficient temperature decreasing simulated annealing-back propagation methods for state of health estimation of energy stor," Energy, Elsevier, vol. 292(C).
    10. Wang, Siwei & Xiao, Xinping & Ding, Qi, 2024. "A novel fractional system grey prediction model with dynamic delay effect for evaluating the state of health of lithium battery," Energy, Elsevier, vol. 290(C).
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