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汽车安全与节能学报

汽车安全与节能学报 ›› 2022, Vol. 13 ›› Issue (1): 194-201.DOI: 10.3969/j.issn.1674-8484.2022.01.020

• 汽车节能与环保 • 上一篇    下一篇

基于特征电压模型的锂离子电池容量估计与RUL预测

来鑫1,2(), 孟正1, 韩雪冰2, 郑岳久1,2   

  1. 1.上海理工大学 机械工程学院,上海 200093,中国
    2.汽车安全与节能国家重点实验室,清华大学,北京 100084,中国
  • 收稿日期:2021-08-30 修回日期:2021-10-27 出版日期:2022-03-31 发布日期:2022-04-02
  • 作者简介:来鑫(1983—）,男(汉族）,上海,副教授。E-mail:laixin@usst.edu.cn。
  • 基金资助:
    汽车安全与节能国家重点实验室开放基金项目(KF2020);国家自然科学基金资助项目(51977131);国家自然科学基金资助项目(51877138);上海自然科学基金面上项目(19ZR1435800)

State of health estimation and remaining useful life prediction of lithium-ion battery based on characteristic voltage model

LAI Xin1,2(), MENG Zheng1, HAN Xuebing2, ZHENG Yuejiu1,2   

  1. 1. College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
    2. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
  • Received:2021-08-30 Revised:2021-10-27 Online:2022-03-31 Published:2022-04-02

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摘要：

锂离子电池的容量与剩余使用寿命预测对提高其安全性具有重要的意义。该文提出一种基于改进粒子群滤波(PF)算法与特征电压关联模型的锂离子电池容量估计与剩余使用寿命预测方法。提取放电曲线中的特征电压,建立特征电压-循环次数及特征电压-容量2个关联模型;应用改进PF算法对2个关联模型的参数进行辨识, 以实现容量的在线估计与剩余寿命的离线预测; 利用此方法通过拟合样本电池老化数据来优化建议概率密度的初始值,提高模型参数辨识的准确性以提高所建立关联模型的精度。结果表明：所提出的方法容量估计误差能保持在3%以内,寿命预测误差保持在5%以内。

关键词: 锂离子电池, 容量估计, 剩余寿命预测, 改进粒子群滤波(PF)算法

Abstract:

Capacity estimation and remaining useful life (RUL) prediction of lithium-ion batteries are of great significance to improve their safety. An on-line capacity estimation and off-line prediction of RUL method was proposed based on improved particle filter (PF) algorithm and characteristic voltage model. The characteristic voltage from the discharge curve was extracted, and two correlation models of characteristic voltage-cycle times and characteristic voltage-capacity were established. The characteristic voltage was estimated in real time using an improved PF algorithm, in which the initial value of the probability density was optimized by fitting the sample battery aging data to improve the accuracy of the model parameter identification to improve the accuracy of the established correlation models. The results show that the capacity estimation error can be kept within 3%, and the RUL prediction error can be kept within 5%.

Key words: lithium-ion battery, capacity estimation, remaining useful life prediction, improved particle filter (PF) algorithm

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