基于扩展H∞粒子滤波算法的动力电池寿命预测方法

doi:10.3901/JME.2019.20.036

机械工程学报 ›› 2019, Vol. 55 ›› Issue (20): 36-43.doi: 10.3901/JME.2019.20.036

• 特邀专栏：动力电池系统关键技术 • 上一篇

基于扩展H_∞粒子滤波算法的动力电池寿命预测方法

马彦^1,2, 陈阳², 张帆², 陈虹^1,2

1. 吉林大学汽车仿真与控制国家重点实验室长春 130022;
2. 吉林大学通信工程学院长春 130022

收稿日期:2019-03-04 修回日期:2019-06-28 发布日期:2020-01-07
通讯作者: 陈虹(通信作者),女,1963年出生,博士,教授,博士研究生导师。主要研究方向为汽车电子控制。E-mail:chenh@jlu.edu.cn
作者简介:马彦,女,1970年出生,博士,教授,博士研究生导师。主要研究方向为电池管理系统。E-mail:mayan_maria@163.com
基金资助:
国家自然科学基金（61520106008）、吉林省发改委（2018C035-2）、吉林省教育厅（20190016KJ）和中国汽车产业创新发展联合基金（U1664257）资助项目。

Remaining Useful Life Prediction of Power Battery Based on Extend H_∞ Particle Filter Algorithm

MA Yan^1,2, CHEN Yang², ZHANG Fan², CHEN Hong^1,2

1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022;
2. Department of Control Science and Engineering, Jilin University, Changchun 130022

Received:2019-03-04 Revised:2019-06-28 Published:2020-01-07

摘要/Abstract

摘要： 动力电池的性能随着使用会出现不可避免的老化，直接影响着电动汽车的性能和使用。在动力电池使用过程中对其进行剩余寿命的预测，可以确定动力电池的最佳维修和更换时机，进而有效延长动力电池寿命，增加电动汽车的续驶里程。因此，采用扩展H_∞粒子滤波算法进行动力电池的剩余寿命预测。进行锂离子动力电池循环老化试验，获取其全寿命周期的容量衰减数据。采用双指数拟合的方法建立电池容量衰减模型，并验证其准确性。将模型参数作为状态量，采用扩展H_∞粒子滤波算法对模型参数进行实时估计与更新，获得剩余循环次数以及预测结果的可信度。仿真结果表明，基于扩展H_∞粒子滤波算法得到的动力电池剩余寿命预测结果与基于粒子滤波得到的预测结果相比更加精确。

关键词: 动力电池, 扩展H_∞粒子滤波算法, 剩余寿命, 容量衰减模型

Abstract: The performance of the power battery will inevitably deteriorate with the use, which directly affects the performance and use of the electric vehicle. Predicting the remaining life of the power battery during use can determine the optimal maintenance and replacement timing of the power battery, thereby effectively extending the life of the power battery and increasing the driving range of the electric vehicle. Therefore, the extended particle filter algorithm is used to predict the remaining life of the power battery. First, carry out the cycle aging experiment of lithium ion power battery, and obtain the capacity attenuation data of its life cycle. Then, the battery capacity decay model is established by double exponential fitting method and its accuracy is verified. Finally, using the model parameters as the state quantity, the extended particle filter algorithm is used to estimate and update the model parameters in real time, and the number of remaining cycles and the credibility of the prediction results are obtained. The simulation results show that the prediction results of the remaining life of the power battery based on the extended particle filter algorithm are more accurate than those based on the standard particle filter.

Key words: power battery, extended H_∞ particle filter algorithm, remaining useful life, capacity attenuation model

中图分类号:

TG156

马彦, 陈阳, 张帆, 陈虹. 基于扩展H_∞粒子滤波算法的动力电池寿命预测方法[J]. 机械工程学报, 2019, 55(20): 36-43.

MA Yan, CHEN Yang, ZHANG Fan, CHEN Hong. Remaining Useful Life Prediction of Power Battery Based on Extend H_∞ Particle Filter Algorithm[J]. Journal of Mechanical Engineering, 2019, 55(20): 36-43.

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基于扩展H_∞粒子滤波算法的动力电池寿命预测方法

Remaining Useful Life Prediction of Power Battery Based on Extend H_∞ Particle Filter Algorithm

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基于扩展H∞粒子滤波算法的动力电池寿命预测方法

Remaining Useful Life Prediction of Power Battery Based on Extend H∞ Particle Filter Algorithm

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基于扩展H_∞粒子滤波算法的动力电池寿命预测方法

Remaining Useful Life Prediction of Power Battery Based on Extend H_∞ Particle Filter Algorithm