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Critical summary and perspectives on state-of-health of lithium-ion battery

Author

Listed:
  • Yang, Bo
  • Qian, Yucun
  • Li, Qiang
  • Chen, Qian
  • Wu, Jiyang
  • Luo, Enbo
  • Xie, Rui
  • Zheng, Ruyi
  • Yan, Yunfeng
  • Su, Shi
  • Wang, Jingbo

Abstract

The rapid development of lithium-ion battery (LIB) technology promotes its wide application in electric vehicle (EV), aerospace, and mobile electronic equipment. During application, state of health (SOH) of LIB is crucial to enhance stable and reliable operation of the battery system. However, accurate estimation of SOH is a tough task, especially in its large-scale application. Thus far, a variety of works on the estimation of SOH of LIB have been proposed, along with several review studies that aim to summarize the current research status. However, there are some deficiencies in prior reviews, such as unclear classification, incomplete summary, and insufficient evaluation of estimation methods. Thus, to resolve the shortcomings, the enumeration method is used to fully screen published works related to SOH estimation, and a total of one hundred and ninety relevant studies are investigated for a thorough review and discussion. Besides, the definition of SOH from different perspectives and three representative battery models are summarized, respectively. Meanwhile, twenty commonly used evaluation criteria and two explicit SOH estimation schemes are comprehensively introduced, which all are tabulated in detail for systematic evaluation and fair comparison. Finally, the main problems and challenges in SOH estimation are fully discussed, meanwhile, three promising future development trends are proposed and some essential SOH public datasets are summarized. In general, this review is envisioned to offer insightful guidance to researchers or engineers working on SOH estimation and related research, thus further promoting the development of SOH estimation technology and exploration of potential research direction.

Suggested Citation

  • Yang, Bo & Qian, Yucun & Li, Qiang & Chen, Qian & Wu, Jiyang & Luo, Enbo & Xie, Rui & Zheng, Ruyi & Yan, Yunfeng & Su, Shi & Wang, Jingbo, 2024. "Critical summary and perspectives on state-of-health of lithium-ion battery," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    • Handle: RePEc:eee:rensus:v:190:y:2024:i:pa:s1364032123009358
    DOI: 10.1016/j.rser.2023.114077
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    References listed on IDEAS

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    Cited by:

    1. Zhou, Yifei & Wang, Shunli & Xie, Yanxing & Zeng, Jiawei & Fernandez, Carlos, 2024. "Remaining useful life prediction and state of health diagnosis of lithium-ion batteries with multiscale health features based on optimized CatBoost algorithm," Energy, Elsevier, vol. 300(C).
    2. Jiang, Nanhua & Zhang, Jiawei & Jiang, Weiran & Ren, Yao & Lin, Jing & Khoo, Edwin & Song, Ziyou, 2024. "Driving behavior-guided battery health monitoring for electric vehicles using extreme learning machine," Applied Energy, Elsevier, vol. 364(C).

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