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Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation

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  • Chen, Lin
  • Wang, Huimin
  • Liu, Bohao
  • Wang, Yijue
  • Ding, Yunhui
  • Pan, Haihong

Abstract

Accurate estimation of battery state-of-health (SOH) is of great importance for ensuring the safety and reliability of battery energy storage system. Due to the complicated degradation mechanism of batteries, the transfer application of SOH estimation for different types of the batteries with unknown usage levels is challenging. To solve this issue, a novel metabolic extreme learning machine (MELM) framework for SOH estimation is proposed in this study. A degradation state model based on the extreme learning machine (ELM) is developed to describe the complex battery degradation mechanism, and the established model can map the relationship between the degradation features and the degradation dynamics for different batteries. To realize SOH estimation at different usage levels with a few data, the metabolic mechanism is introduced to update the input of the degradation state model and reflect the latest trend of degradation. To reduce the errors caused by the metabolism, the grey model is adopted to extrapolate the trend of error accumulation and correct the estimation results. The prominent performances of the MELM framework are synthetically verified from different aspects, the results indicate the MELM framework can effectively realize the SOH estimation for different types of batteries with unknown usage levels.

Suggested Citation

  • Chen, Lin & Wang, Huimin & Liu, Bohao & Wang, Yijue & Ding, Yunhui & Pan, Haihong, 2021. "Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation," Energy, Elsevier, vol. 215(PA).
    • Handle: RePEc:eee:energy:v:215:y:2021:i:pa:s036054422032185x
    DOI: 10.1016/j.energy.2020.119078
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    References listed on IDEAS

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    1. Ngoc-Tham Tran & Abdul Basit Khan & Woojin Choi, 2017. "State of Charge and State of Health Estimation of AGM VRLA Batteries by Employing a Dual Extended Kalman Filter and an ARX Model for Online Parameter Estimation," Energies, MDPI, vol. 10(1), pages 1-18, January.
    2. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    3. You, Gae-won & Park, Sangdo & Oh, Dukjin, 2016. "Real-time state-of-health estimation for electric vehicle batteries: A data-driven approach," Applied Energy, Elsevier, vol. 176(C), pages 92-103.
    4. Pan, Haihong & Lü, Zhiqiang & Wang, Huimin & Wei, Haiyan & Chen, Lin, 2018. "Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine," Energy, Elsevier, vol. 160(C), pages 466-477.
    5. 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.
    6. Yang, Duo & Wang, Yujie & Pan, Rui & Chen, Ruiyang & Chen, Zonghai, 2018. "State-of-health estimation for the lithium-ion battery based on support vector regression," Applied Energy, Elsevier, vol. 227(C), pages 273-283.
    7. Bi, Jun & Zhang, Ting & Yu, Haiyang & Kang, Yanqiong, 2016. "State-of-health estimation of lithium-ion battery packs in electric vehicles based on genetic resampling particle filter," Applied Energy, Elsevier, vol. 182(C), pages 558-568.
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