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A fast state-of-health estimation method using single linear feature for lithium-ion batteries

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  • Shi, Mingjie
  • Xu, Jun
  • Lin, Chuanping
  • Mei, Xuesong

Abstract

Data-driven methods are commonly used for state of health (SOH) estimation, which is essential to battery energy management. However, complex machine learning models, data gathering, and feature processing hinder its further implementation. A fast SOH estimation method based on linear properties of short-time charging is proposed to overcome these challenges. Only the exceptional single linear health factor (LHF) is required for effective SOH estimation. The LHF is chosen through correlation analysis from short-term feature derived from charging curves. The processing is straightforward. To define the relationship between LHF and SOH, a linear regression model is developed. For the simplicity and effectiveness of the method, it is suitable to be implemented in online applications with low hardware requirements. Finally, experiments show that the SOH estimation method has the highest accuracy of 0.54%, and the biggest estimation error is 2.20%. Furthermore, the data from first 20% cycles of the battery are used to build the model, ensuring that the SOH estimation accuracy is comparable. It is worth noting that the time cost of data acquisition does not exceed 30 s, which is important for fast estimation.

Suggested Citation

  • Shi, Mingjie & Xu, Jun & Lin, Chuanping & Mei, Xuesong, 2022. "A fast state-of-health estimation method using single linear feature for lithium-ion batteries," Energy, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:energy:v:256:y:2022:i:c:s0360544222015559
    DOI: 10.1016/j.energy.2022.124652
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    References listed on IDEAS

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    1. Meng, Jinhao & Cai, Lei & Stroe, Daniel-Ioan & Ma, Junpeng & Luo, Guangzhao & Teodorescu, Remus, 2020. "An optimized ensemble learning framework for lithium-ion Battery State of Health estimation in energy storage system," Energy, Elsevier, vol. 206(C).
    2. Chen, Zheng & Zhao, Hongqian & Shu, Xing & Zhang, Yuanjian & Shen, Jiangwei & Liu, Yonggang, 2021. "Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter," Energy, Elsevier, vol. 228(C).
    3. Khaleghi, Sahar & Hosen, Md Sazzad & Karimi, Danial & Behi, Hamidreza & Beheshti, S. Hamidreza & Van Mierlo, Joeri & Berecibar, Maitane, 2022. "Developing an online data-driven approach for prognostics and health management of lithium-ion batteries," Applied Energy, Elsevier, vol. 308(C).
    4. 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.
    5. Jiang, Yihui & Xu, Jun & Hou, Wenlong & Mei, Xuesong, 2021. "A stack pressure based equivalent mechanical model of lithium-ion pouch batteries," Energy, Elsevier, vol. 221(C).
    6. Cheng, Gong & Wang, Xinzhi & He, Yurong, 2021. "Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network," Energy, Elsevier, vol. 232(C).
    7. Li, Xiaoyu & Yuan, Changgui & Li, Xiaohui & Wang, Zhenpo, 2020. "State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression," Energy, Elsevier, vol. 190(C).
    8. Deng, Yuanwang & Ying, Hejie & E, Jiaqiang & Zhu, Hao & Wei, Kexiang & Chen, Jingwei & Zhang, Feng & Liao, Gaoliang, 2019. "Feature parameter extraction and intelligent estimation of the State-of-Health of lithium-ion batteries," Energy, Elsevier, vol. 176(C), pages 91-102.
    9. 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).
    10. Yang, Jufeng & Xia, Bing & Huang, Wenxin & Fu, Yuhong & Mi, Chris, 2018. "Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis," Applied Energy, Elsevier, vol. 212(C), pages 1589-1600.
    11. 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.
    12. Khaleghi, Sahar & Karimi, Danial & Beheshti, S. Hamidreza & Hosen, Md. Sazzad & Behi, Hamidreza & Berecibar, Maitane & Van Mierlo, Joeri, 2021. "Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network," Applied Energy, Elsevier, vol. 282(PA).
    13. Li, Xiaoyu & Yuan, Changgui & Wang, Zhenpo, 2020. "State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression," Energy, Elsevier, vol. 203(C).
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    Citations

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    Cited by:

    1. Liu, Mengmeng & Xu, Jun & Jiang, Yihui & Mei, Xuesong, 2023. "Multi-dimensional features based data-driven state of charge estimation method for LiFePO4 batteries," Energy, Elsevier, vol. 274(C).
    2. Huang, Kai & Yao, Kaixin & Guo, Yongfang & Lv, Ziteng, 2023. "State of health estimation of lithium-ion batteries based on fine-tuning or rebuilding transfer learning strategies combined with new features mining," Energy, Elsevier, vol. 282(C).
    3. Guo, Yongfang & Yu, Xiangyuan & Wang, Yashuang & Huang, Kai, 2024. "Health prognostics of lithium-ion batteries based on universal voltage range features mining and adaptive multi-Gaussian process regression with Harris Hawks optimization algorithm," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    4. Yang, Jufeng & Li, Xin & Sun, Xiaodong & Cai, Yingfeng & Mi, Chris, 2023. "An efficient and robust method for lithium-ion battery capacity estimation using constant-voltage charging time," Energy, Elsevier, vol. 263(PB).
    5. Jin, Haiyan & Cui, Ningmin & Cai, Lei & Meng, Jinhao & Li, Junxin & Peng, Jichang & Zhao, Xinchao, 2023. "State-of-health estimation for lithium-ion batteries with hierarchical feature construction and auto-configurable Gaussian process regression," Energy, Elsevier, vol. 262(PB).
    6. Hou, Jiayang & Xu, Jun & Lin, Chuanping & Jiang, Delong & Mei, Xuesong, 2024. "State of charge estimation for lithium-ion batteries based on battery model and data-driven fusion method," Energy, Elsevier, vol. 290(C).
    7. Wu, Ji & Fang, Leichao & Dong, Guangzhong & Lin, Mingqiang, 2023. "State of health estimation of lithium-ion battery with improved radial basis function neural network," Energy, Elsevier, vol. 262(PB).

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