基于实车运行数据的锂离子电池健康状态估计

doi:10.3901/JME.2023.22.046

机械工程学报 ›› 2023, Vol. 59 ›› Issue (22): 46-58.doi: 10.3901/JME.2023.22.046

• 特邀专栏：动力电池安全应用技术 • 上一篇下一篇

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基于实车运行数据的锂离子电池健康状态估计

何洪文^1,2, 王浩宇^1,2, 王勇^1,2, 李双歧^1,2

1. 北京理工大学电动车辆国家工程研究中心北京 100081;
2. 北京理工大学机械与车辆学院北京 100081

收稿日期:2022-10-25 修回日期:2023-03-06 出版日期:2023-11-20 发布日期:2024-02-19
通讯作者: 王勇(通信作者)，男，1995年出生，博士研究生。主要研究方向为智能网联新能源汽车、数据驱动的复杂系统建模与控制。E-mail：17862709675@163.com
作者简介:何洪文，男，1975年出生，博士，教授，博士研究生导师。主要研究方向为纯电驱动车辆动力传动及其控制、燃料电池汽车系统集成及综合控制、车辆网联化智能控制理论与方法。E-mail：hwhebit@bit.edu.cn；王浩宇，男，1999年出生。主要研究方向为电动汽车动力电池大数据与状态估计。E-mail：3120210355@bit.edu.cn；李双歧，男，1996年出生，博士研究生。主要研究方向为电动汽车动力电池大数据与状态估计。E-mail：sqli9966@gmail.com
基金资助:
国家自然科学基金资助项目(U1864202)。

Lithium-ion Battery State of Health Estimation Based on Real-world Driving Data

HE Hongwen^1,2, WANG Haoyu^1,2, WANG Yong^1,2, LI Shuangqi^1,2

1. National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081;
2. School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081

Received:2022-10-25 Revised:2023-03-06 Online:2023-11-20 Published:2024-02-19

摘要/Abstract

摘要： 准确估计锂离子电池健康状态(State of health， SOH)对电动汽车安全管理具有重要意义，针对实车数据存在电池状态不完整、工况复杂、数据质量差的问题，提出面向实车数据的多工况健康因子提取 SOH 联合估计方法。首先，提出实车运行数据工况重构方法，将数据划分为行驶片段和充电片段，降低电池工况复杂性。然后，分别构建行驶工况和充电工况的 SOH评价模型用于 SOH 估计。对于行驶工况，选择内阻作为 SOH 评价指标，通过等效电路模型辨识内阻参数，基于 Auto-LightGBM的电池内阻建模方法估算 SOH；对于充电工况，选择容量作为 SOH 评价指标并通过提取恒流充电片段计算电池容量，再提取容量的影响特征，建立容量模型并估计电池 SOH。结果表明，基于内阻和容量的建模方法平均绝对百分比误差均小于 9%。最后，建立结合充电与放电的 SOH 综合评价模型，提出融合充放电片段的电池 SOH 联合估计方法，基于实车运行数据的 SOH误差在 2%以内，并在实验室数据和多辆实车数据上验证方法的可靠性和适应性。

关键词: 锂离子电池, 健康状态估计, 健康因子提取, 实车运行数据, 机器学习

Abstract: Accurately estimating the state of health of lithium-ion batteries is significant for the safety management of electric vehicles. Aiming at the problems of incomplete battery states, complex operating conditions, and poor data quality in real vehicle data, a joint SOH estimation method for extracting health factors in multiple operating conditions for real vehicle data is proposed. Firstly, the method of condition reconstruction of real vehicle operating data is proposed, which divided the real vehicle data into driving segments and charging segments to reduce the complexity of battery operating conditions. Then, the SOH evaluation models of driving conditions and charging conditions are constructed respectively for SOH estimation. For driving conditions, the internal resistance is selected as the SOH evaluation index, and SOH is estimated by the battery internal resistance modeling method based on Auto-LightGBM. For charging conditions, the capacity is selected as the SOH evaluation index and the battery capacity is calculated by extracting the constant-current charging segment. Then the influence characteristics of the capacity are extracted to establish the capacity model and estimate the battery SOH. The results show that the average absolute percentage errors of the modeling methods based on internal resistance and capacity are both less than 9%. Finally, a comprehensive evaluation model of SOH combining charging and discharging is established, and a joint estimation method of battery SOH combining charging and discharging segments is proposed. The SOH error based on real vehicle data is within 2%, and the reliability and adaptability of the proposed method are verified on laboratory data and multiple real vehicle data.

Key words: lithium-ion battery, state of health estimation, extraction of health factors, real-world driving data, machine learning

中图分类号:

TM912

何洪文, 王浩宇, 王勇, 李双歧. 基于实车运行数据的锂离子电池健康状态估计[J]. 机械工程学报, 2023, 59(22): 46-58.

HE Hongwen, WANG Haoyu, WANG Yong, LI Shuangqi. Lithium-ion Battery State of Health Estimation Based on Real-world Driving Data[J]. Journal of Mechanical Engineering, 2023, 59(22): 46-58.

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基于实车运行数据的锂离子电池健康状态估计

Lithium-ion Battery State of Health Estimation Based on Real-world Driving Data

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