电工技术学报  2020, Vol. 35 Issue (6): 1181-1188    DOI: 10.19595/j.cnki.1000-6753.tces.190082
电工理论与新技术 |
新陈代谢灰色粒子滤波实现电池剩余寿命预测
韦海燕1, 陈静1, 王惠民1, 安晶晶1, 陈琳1,2
1. 广西大学机械工程学院 南宁 530004;
2. 广西电化学能源材料重点实验室培育基地 可再生能源材料协同创新中心(广西大学) 南宁 530004
Remaining Useful Life Prediction of Battery Using Metabolic Grey Particle Filter
Wei Haiyan1, Chen Jing1, Wang Huimin1, An Jingjing1, Chen Lin1,2
1. School of Mechanical Engineering Guangxi University Nanning 530004 China;
2. Guangxi Key Laboratory of Electrochemical Energy Materials Collaborative Innovation Center of Renewable Energy Materials Guangxi University Nanning 530004 China
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摘要 电池剩余寿命(RUL)预测是电池管理系统的核心技术之一。为了以较少的数据量准确地在线预测电池RUL,提出新陈代谢灰色粒子滤波(MGM-PF)算法。首先利用一阶RC模型在线估算电池容量;然后基于估算的容量数据,利用新陈代谢灰色模型动态更新的灰色发展系数作为模型参数,构建表征电池容量退化的动态状态空间模型;并融合粒子滤波跟踪电池容量退化,实现电池RUL预测并给出预测结果的不确定性表达。实验结果表明,所提出的基于在线容量估算的MGM-PF算法能准确预测电池RUL。
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韦海燕
陈静
王惠民
安晶晶
陈琳
关键词 新陈代谢灰色模型粒子滤波电池剩余寿命在线容量估算    
Abstract:Remaining useful life (RUL) prediction is one of the core technologies of battery management systems. In order to accurately predict the battery RUL with less data volume, a metabolic grey model particle filter (MGM-PF) algorithm is proposed. Firstly, the first-order RC model is used to estimate the battery capacity online. Then, based on the obtained capacity data, the grey development coefficient is dynamically updated by the metabolic grey model, and the coefficient is used as the model parameter to construct a dynamic state space model that characterizes the battery capacity degradation. Finally, the particle filter is used to track the battery capacity degradation to realize the battery RUL prediction and derive the uncertainty expression of the prediction results. The experimental results show that the proposed MGM-PF algorithm based on online capacity estimation can accurately predict the battery RUL.
Key wordsMetabolic grey model    particle filter    remaining useful life of battery    online capacity estimation   
收稿日期: 2019-01-18      出版日期: 2020-03-27
PACS: TM911  
基金资助:国家自然科学基金(51667006)和广西自然科学基金(2015GXNSFAA139287)资助项目
通讯作者: 陈 琳 女,1973年生,教授,博士生导师,研究方向为信号检测与处理和电池管理。E-mail: gxdxcl@163.com   
作者简介: 韦海燕 女 1963年生,副教授,硕士生导师,研究方向为内燃机节能与排放控制,电动汽车。E-mail: gxwhytu@163.com
引用本文:   
韦海燕, 陈静, 王惠民, 安晶晶, 陈琳. 新陈代谢灰色粒子滤波实现电池剩余寿命预测[J]. 电工技术学报, 2020, 35(6): 1181-1188. Wei Haiyan, Chen Jing, Wang Huimin, An Jingjing, Chen Lin. Remaining Useful Life Prediction of Battery Using Metabolic Grey Particle Filter. Transactions of China Electrotechnical Society, 2020, 35(6): 1181-1188.
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