• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2025, Vol. 61 ›› Issue (24): 235-244.doi: 10.3901/JME.2025.24.235

• 运载工程 • 上一篇    

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基于容量增量分析的锂离子电池容量估计方法

彭鹏1,2, 杨瑞鑫1, 孙万洲2, 李正阳1, 赫英浩1, 陈满2, 熊瑞1   

  1. 1. 北京理工大学机械与车辆学院 北京 100081;
    2. 南方电网调峰调频发电有限公司储能科研院 广州 510630
  • 收稿日期:2025-01-15 修回日期:2025-09-15 发布日期:2026-01-26
  • 作者简介:彭鹏,男,1988年出生,硕士,高级工程师。主要研究方向为电池储能技术。E-mail:13926169785@139.com
    杨瑞鑫(通信作者),男,1988年出生,副研究员。主要研究方向为车用及储能用电池管理。E-mail:yangruixin@bit.edu.cn
  • 基金资助:
    国家自然科学基金(52107222,,U23B20139);中国南方电网有限责任公司科技(STKJXM20210097)资助项目。

Capacity Estimation Method of Lithium-ion Battery Based on Capacity Increment Analysis

PENG Peng1,2, YANG Ruixin1, SUN Wanzhou2, LI Zhengyang1, HE Yinghao1, CHEN Man2, XIONG Rui1   

  1. 1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081;
    2. CSG PGC Energy Storage Research Institute, Guangzhou 510630
  • Received:2025-01-15 Revised:2025-09-15 Published:2026-01-26

摘要: 锂离子电池被广泛应用于新能源汽车和电化学储能系统,是实现碳中和目标的重要支撑。准确获取健康状态(State of health,SOH)是锂离子电池安全和高效应用的基础,然而,健康状态是电池内部隐含状态,难以直接测量。针对电池外部表征参数难以准确映射内部老化状态问题,提出一种基于容量增量分析的磷酸铁锂锂离子电池容量在线估计方法。首先,分析容量增量曲线特征在不同老化状态和工作温度下的变化规律;其次,提取与电池容量强相关的曲线特征作为健康因子;随后,构建健康因子与电池老化状态的映射关系;最后,针对充电温度对估计结果的影响引入补偿机制,最终实现不同充电工况下电池最大可用容量的准确估计。验证结果表明,电池容量估计最大误差为0.36 A·h,对应的估计结果为47.747 A·h,最大相对误差为0.75%。

关键词: 锂离子电池, 健康状态, 最大可用容量, 容量增量分析, 充电温度

Abstract: Lithium-ion batteries are widely used in new energy vehicles and electrochemical energy storage systems, and are an important support for achieving the goal of carbon neutrality. Accurately obtaining the state of health(SOH) is the basis for the safe and efficient application of lithium-ion batteries. However, the SOH is an implicit state inside the battery and is difficult to measure directly. Aiming at the problem that the external characterization parameters of the battery are difficult to accurately map the internal aging state, an online capacity estimation method of lithium iron phosphate batteries based on incremental capacity(IC) analysis is proposed. Firstly, the changes of IC curve characteristics under different aging states and operating temperatures are analysed. Secondly, the curve features strongly related to the health state of the battery are extracted as health indicators. Then, the mapping relationship between the health indicators and the aging state of the battery is constructed. Finally, a compensation mechanism is introduced for the impact of charging temperature on the estimation results, ultimately achieving accurate estimation of the maximum available capacity of the battery under different charging conditions. The verification results show that the maximum error in capacity estimation is 0.36 A·h, the corresponding estimation result is 47.747 A·h, and the maximum relative error of 0.75%.

Key words: lithium-ion battery, state of health, maximum available capacity, incremental capacity analysis, charging temperature

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