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Progress in the prognosis of battery degradation and estimation of battery states

电池衰减诊断及状态评估研究进展

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Abstract

Lithium-ion batteries (LIBs) have gained immense popularity as a power source in various applications. Accurately predicting the health status of these batteries is crucial for optimizing their performance, minimizing operating expenses, and preventing failures. In this paper, we present a comprehensive review of the latest developments in predicting the state of charge (SOC), state of health (SOH), and remaining useful life (RUL) of LIBs, and particularly focus on machine learning techniques. This paper delves into the degradation mechanisms of LIBs and their underlying theories, providing an in-depth analysis of the strengths and limitations of various machine learning techniques used to predict SOC, SOH and RUL. Furthermore, this review sheds light on the challenges encountered in the practical application of electric vehicles, especially concerning battery degradation. It also offers valuable insights into the future research directions for LIBs. While machine learning methods hold great promise in enhancing the accuracy of predicting SOC, SOH, and RUL, there remain numerous technical and practical obstacles that must be overcome to make them more applicable in real-world scenarios.

摘要

锂离子电池(LIB)广泛应用于储能及动力输出等领域. 准确预测 电池的健康状态对于优化性能、降低运营费用和防止电池故障等方面 具有重要的意义. 本文对预测LIB的荷电状态(SOC)、健康状态(SOH) 和剩余使用寿命(RUL)方面的最新发展进行了全面回顾, 重点关注机器 学习技术方面的研究进展, 深入分析了LIB的退化机制及其基本理论, 评估了各种传统方法及机器学习技术在预测SOC, SOH和RUL方面的 优势和限制. 此外, 还探讨了电动汽车动力电池在实际应用中面临的挑 战, 特别是性能退化问题.最后提出了对LIB未来研究方向有价值的见 解. 尽管机器学习方法在提高预测SOC, SOH和RUL准确性方面具有巨 大潜力, 但在实际应用中仍然有许多技术和实际障碍需要克服.

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Acknowledgements

This work was supported by the major program funds of State Grid Shaanxi Electric Power Company Limited (5226KY23000P) and the Startup funds of Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China (U03210019).

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Author notes

  1. These authors contributed equally to this work.

Authors and Affiliations

  1. School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Jun Yuan  (袁君), Haikun Huang  (黄海坤), Xingdong Gan  (甘兴栋), Shujiang Liu  (刘书江) & Baihai Li  (李白海)

  2. Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, 313001, China

    Jun Yuan  (袁君), Zhili Qin  (秦之理), Haikun Huang  (黄海坤), Xingdong Gan  (甘兴栋), Shujiang Liu  (刘书江), An Wen  (文安), Chuang Bi  (毕闯) & Baihai Li  (李白海)

  3. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Zhili Qin  (秦之理)

  4. Power Research Institute of State Grid Shaanxi Electric Power Company Limited, Xi’an, 710100, China

    Ziwei Wang  (王子为) & Yichen Yang  (杨毅琛)

  5. Department of Chemistry and Biotechnology, Swinburne University of Technology, Hawthorn, Victoria, 3122, Australia

    Chenghua Sun  (孙成华)

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  1. Jun Yuan  (袁君)
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  11. Chenghua Sun  (孙成华)
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Contributions

Author contributions Yuan J, Huang H, Liu S and Sun C wrote the section of AGING MECHANISM AND INFLUENCING FACTORS. Yuan J, Huang H, Gan X, Liu S and Bi C wrote the section of TRADITIONAL TECHNOLOGIES FOR SOC, SOH AND RUL ESTIMATION. Qin Z, Wang Z, Yang Y and Wen A wrote the section of MACHINE LEARNING METHODS FOR SOC, SOH AND RUL ESTIMATION. Li B and Sun C supervised the project, wrote the INTRODUCTION and section of PROSPECTS AND CONCLUSIONS. All authors contributed to the general discussion and revised the manuscript.

Corresponding authors

Correspondence to Baihai Li  (李白海) or Chenghua Sun  (孙成华).

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Conflict of interest The authors declare that they have no conflict of interest.

Additional information

Jun Yuan received her Bachelor’s degree in materials science and engineering from Chengdu University of Technology and Master’s degree in materials science and engineering from the University of Electronic Science and Technology of China (UESTC) in 2023. She is currently working toward her PhD degree in digital catalysis at the Department of Chemistry, Technical University of Munich in Germany. Her research interests include battery states estimation and early faulty diagnosis using machine learning and electrochemical models.

Zhili Qin received his Bachelor’s degree in computer science from Hefei University of Technology in 2017. He is currently pursuing his PhD degree at UESTC. In 2022, he participated in a one-year academic exchange program supported by China Scholarship Council (CSC) Scholarship at Ludwig Maximilian University of Munich in Germany. His primary research interests focus on the areas of multi-label learning, few-shot learning, and zero-shot learning, etc.

Baihai Li received his PhD degree from the Chinese Academy of Sciences in 2011 and subsequently served as a postdoctoral fellow at the University of Michigan-Ann Arbor. In 2013, Dr. Li joined the School of Materials and Energy, UESTC as an associate professor. His research primarily focuses on energy conversion and storage, especially for battery material surfaces/interfaces and intelligent battery management, such as data-driven estimation of battery states and early faulty diagnosis, employing a combination of machine learning techniques and electrochemical models.

Chenghua Sun received his PhD degree from the Chinese Academy of Sciences in 2007 and then joined the University of Queensland (Brisbane, Australia) as a postdoc fellow. In 2013, Dr. Sun joined the School of Chemistry, Monash University as a lecturer and established his group on computer-aided catalyst design. He was awarded ARC Future Fellow and joined the Swinburne University of Technology in 2017 as an associate professor. His research focuses on catalyst design for clean energy and environment applications, particularly for ammonia synthesis, methane combustion, batteries, and biomass conversion.

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Yuan, J., Qin, Z., Huang, H. et al. Progress in the prognosis of battery degradation and estimation of battery states. Sci. China Mater. 67, 1014–1041 (2024). https://doi.org/10.1007/s40843-023-2665-8

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