锂离子电池荷电状态与健康状态估计方法

李沂洹; 李慷; 余渐

doi:10.12096/j.2096-4528.pgt.21074

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锂离子电池荷电状态与健康状态估计方法

碳中和 | 更新时间：2025-07-04

- 锂离子电池荷电状态与健康状态估计方法
- Estimation Approaches for States of Charge and Health of Lithium-ion Battery
- 发电技术 2021年42卷第5期页码：537-546
- 作者机构：
  
  1.利兹大学电子电气工程学院, 英国利兹LS2 9JT
  2.苏格兰电力公司, 英国格拉斯哥G2 5AD
- 作者简介：
  
  [ "李沂洹(1992), 女, 博士研究生, 研究方向为电池管理、状态估计等, elyli2@leeds.ac.uk" ]
  李慷(1968), 男, 博士, 教授, 研究方向为自动控制、智慧能源、能量管理、储能系统等, 本文通信作者, K.LI1@leeds.ac.uk
- 基金信息：
  
  英国工程与自然科学研究理事会项目(EP/R030243/1)
- DOI：10.12096/j.2096-4528.pgt.21074
  中图分类号： TK02
- 收稿：2021-06-07，
  
  纸质出版：2021-10-31
- 稿件说明：
移动端阅览
李沂洹, 李慷, 余渐. 锂离子电池荷电状态与健康状态估计方法[J]. 发电技术, 2021,42(5):537-546.

Yihuan LI, Kang LI, James YU. Estimation Approaches for States of Charge and Health of Lithium-ion Battery[J]. Power Generation Technology, 2021, 42(5): 537-546.
李沂洹, 李慷, 余渐. 锂离子电池荷电状态与健康状态估计方法[J]. 发电技术, 2021,42(5):537-546. DOI： 10.12096/j.2096-4528.pgt.21074.

Yihuan LI, Kang LI, James YU. Estimation Approaches for States of Charge and Health of Lithium-ion Battery[J]. Power Generation Technology, 2021, 42(5): 537-546. DOI： 10.12096/j.2096-4528.pgt.21074.

摘要

电池储能系统是实现碳中和最重要、最有效的手段之一，其大规模应用对电池运行过程中的安全性提出了更高的要求。实时准确的电池状态估计为保障电池的安全稳定运行提供重要信息，是电池管理系统（battery management system，BMS）的一项重要功能。然而，由于复杂的操作条件和电池内部的电化学反应，很难准确地评估电池的内部状态。针对电池荷电状态（state of charge，SOC）和健康状态（state of health，SOH）这2个电池系统中的重要参数，系统回顾了当前常用的SOC和SOH估计方法，总结了各种方法的特点及其在实际应用中所面临的主要挑战，并在此基础上对电池SOC和SOH估计技术未来的发展提出展望，为电池状态估计技术的进一步研究提供参考依据。

Abstract

Battery energy storage system is one of the most important and effective means to achieve carbon neutrality

its large-scale applications put forward higher requirements for the battery operating safety. Accurate and real-time battery state estimation is an important function of battery management systems

and it provides critical information for ensuring safe and reliable operation of batteries. However

it is not an easy task to accurately estimate the battery internal state due to the complex operating conditions and the complicated electrochemical reactions inside the battery. This paper provided a systematic review of the state-of-the-art techniques on the state of charge (SOC) and state of health (SOH) estimation of batteries. The advantages and main challenges existing in practical applications of different techniques were analyzed and compared. Then

the future development needs for battery SOC and SOH estimation techniques were prospected

which provides references for further research in battery state estimation

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