• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2019, Vol. 55 ›› Issue (20): 60-72,84.doi: 10.3901/JME.2019.20.060

• 特邀专栏:动力电池系统关键技术 • 上一篇    

电动汽车锂离子动力电池健康状态在线诊断方法

姜久春1,2, 高洋1, 张彩萍1, 王宇斌1, 张维戈1, 刘思佳1   

  1. 1. 北京交通大学电气工程学院 北京 100044;
    2. 深圳普瑞赛思检测技术有限公司 深圳 518108
  • 收稿日期:2019-05-19 修回日期:2019-08-10 发布日期:2020-01-07
  • 通讯作者: 张彩萍(通信作者),女,1982年出生,博士,教授,博士研究生导师。主要研究方向为动力电池数字仿真技术、状态估计、故障诊断方法。E-mail:zhangcaiping@bjtu.edu.cn
  • 作者简介:姜久春,男,1973年出生,博士,教授,博士研究生导师。主要研究方向为动力电池成组应用技术。E-mail:jcjiang@bjtu.edu.cn;高洋,男,1992年出生,博士研究生。主要研究方向为动力电池老化机理与健康状态估计方法。E-mail:14121407@bjtu.edu.cn;王宇斌,男,1995年出生,博士研究生。主要研究方向为动力电池故障诊断技术。E-mail:18117024@bjtu.edu.cn;张维戈,男,1971年出生,博士,教授,博士研究生导师。主要研究方向为动力电池失效机理与成组应用技术。E-mail:wgzhang@bjtu.edu.cn;刘思佳,男,1991年出生,博士研究生。主要研究方向为动力电池衰减模型。E-mail:liusijia@bjtu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(U1664255)。

Online Diagnostic Method for Health Status of Lithium-ion Battery in Electric Vehicle

JIANG Jiuchun1,2, GAO Yang1, ZHANG Caiping1, WANG Yubin1, ZHANG Weige1, LIU Sijia1   

  1. 1. School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044;
    2. Shenzhen Presisi Testing Technology Co., Ltd., Shenzhen 518108
  • Received:2019-05-19 Revised:2019-08-10 Published:2020-01-07

摘要: 随着电动汽车的快速发展,高比能锂离子电池的衰减问题日益受到关注,其健康状态是耐久性管理的核心参数,对延长电池寿命提高系统可靠性至关重要。以三元材料锂离子电池为研究对象,基于正负极的开路电压模型,描述正负极和全电池的匹配关系并在全新电池尺度上重构其开路电压-荷电状态曲线,分析正负极匹配关系在电池经历各种老化模式后的演变特性,从而在全新电池尺度上重构老化电池的开路电压-荷电状态曲线,并据此提出了改进的锂离子电池老化模式无损定量诊断方法,克服了现有方法必须以电池的真实开路电压-荷电状态曲线为诊断依据的局限性,从而更加适用于实车在线应用。采用扩展卡尔曼滤波算法,从电池动态电流工况放电数据中辨识开路电压随放电容量的变化曲线,并使用所提出的老化诊断方法拟合该开路电压曲线,可以定量分析电池遭受的正极材料损失、负极材料损失和可用锂离子损失。在此基础上,提出电池最大可用容量的估计方法和真实开路电压-荷电状态曲线的辨识方法,结果表明,在动态工况下容量估计误差在1%以内,开路电压-荷电状态曲线的方均根误差在6 mV以内。该方法应用于电池组,可以实现电池组内各单体电池的最大可用容量和荷电状态一致性估计。

关键词: 锂离子电池, 老化模式, 定量分析, 健康状态, 在线诊断

Abstract: With the rapid development of electric vehicles, the attenuation of high-energy lithium-ion batteries has received increasing attention. Battery health status is a core parameter of durability management, which is essential for extending battery life and improving system reliability. Taking the ternary material lithium ion battery as the research object, based on the open circuit voltage model of positive and negative electrodes, the matching relationship between positive and negative electrodes and the whole battery is described, and the open circuit voltage-state of charge curve is reconstructed on the new battery scale. The evolution characteristics of the positive and negative matching relationship after the battery experiences various aging modes are analyzed, thereby reconstructing the open circuit voltage-state of charge curve of the aging battery on the new battery scale. Based on this, an improved non-destructive quantitative diagnosis method for lithium ion battery aging mode is proposed, which overcomes the limitation that the existing method must take the real open circuit voltage-state of charge curve of the battery as the diagnostic basis, so it is more suitable for real vehicle online application. The extended Kalman filter algorithm is used to identify the curve of the open circuit voltage with the discharge capacity from the battery dynamic current discharge data, and the proposed aging diagnosis method is used to fit the open circuit voltage curve, which can quantitatively analyze the material loss of positive electrode, negative electrode and available lithium ions. On this basis, the estimation method of the maximum available capacity of the battery and the identification method of the true open circuit voltage-state of charge curve are presented. The results show that the capacity estimation error of the method under dynamic conditions is less than 1%, and the root mean square error of the open circuit voltage-state of charge curve is within 6 mV. The method is applied to a battery pack, and can realize the estimation of maximum available capacity and the state of charge consistency of each single battery in the battery pack.

Key words: lithium-ion battery, aging mode, quantitative analysis, health status, online diagnosis

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