电池衰减诊断及状态评估研究进展
Progress in the prognosis of battery degradation and estimation of battery states
袁君 1秦之理 2黄海坤 1甘兴栋 1王子为 3杨毅琛 3刘书江 1文安 4毕闯 4李白海 1孙成华5
作者信息
- 1. School of Materials and Energy,University of Electronic Science and Technology of China,Chengdu 611731,China;Yangtze Delta Region Institute(Huzhou),University of Electronic Science and Technology of China,Huzhou 313001,China
- 2. Yangtze Delta Region Institute(Huzhou),University of Electronic Science and Technology of China,Huzhou 313001,China;School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China
- 3. Power Research Institute of State Grid Shaanxi Electric Power Company Limited,Xi'an 710100,China
- 4. Yangtze Delta Region Institute(Huzhou),University of Electronic Science and Technology of China,Huzhou 313001,China
- 5. Department of Chemistry and Biotechnology,Swinburne University of Technology,Hawthorn,Victoria 3122,Australia
- 折叠
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.
Abstract
Lithium-ion batteries(LIBs)have gained im-mense popularity as a power source in various applications.Accurately predicting the health status of these batteries is crucial for optimizing their performance,minimizing oper-ating 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 the-ories,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 applica-tion of electric vehicles,especially concerning battery de-gradation.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.
关键词
state of charge/state of health/remaining useful life/lithium-ion batteries/equivalent-circuit model/electrochemical model/machine learningKey words
state of charge/state of health/remaining useful life/lithium-ion batteries/equivalent-circuit model/electrochemical model/machine learning引用本文复制引用
基金项目
major program funds of State Grid Shaanxi Electric Power Company Limited(5226KY23000P)
Startup funds of Yangtze Delta Region Institute(Huzhou)()
University of Electronic Science and Technology of China(U03210019)
出版年
2024
NETL
NSTL
万方数据