| 基于PF-ARIMA的锂离子电池剩余寿命预测 |
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| 引用本文： | 周亚鹏,郭彪,王一纯.基于PF-ARIMA的锂离子电池剩余寿命预测[J].电池工业,2022,26(1):19-22. |
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| 作者姓名： | 周亚鹏 郭彪 王一纯 |
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| 作者单位： | 招商局检测车辆技术研究院有限公司,重庆401329;电动汽车安全评价重庆市工业和信息化重点实验室,重庆401329;重庆机电职业技术大学信息工程学院,重庆402760;重庆大学机械与运载工程学院,重庆400044 |
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| 基金项目： | 重庆市场监督管理局科技计划项目,CQSJKJ2019024,CQSJKJ2019025;招商局检测车辆技术研究院有限公司自研项目,20AKC3。 |
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| 摘 要： | 针对电池健康状态局部波动增加预测难度,采用粒子滤波和自回归整合移动平均模型分别预测由经验模态分解提取的健康状态趋势项和细节项,实现锂离子电池剩余寿命预测.提出的PF-ARIMA方法相对误差均值约4.0％,表明该方法能够较为准确地预测锂离子电池剩余寿命.
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| 关 键 词： | 锂离子电池 粒子滤波 自回归整合移动平均模型 剩余寿命 预测 |
A remaining useful life prediction method for lithium-ion battery based on PF-ARIMA |
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| ZHOU Yapeng,Guo Biao,Wang Yichun.A remaining useful life prediction method for lithium-ion battery based on PF-ARIMA[J].Chinese Battery Industry,2022,26(1):19-22. |
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| Authors: | ZHOU Yapeng Guo Biao Wang Yichun |
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| Affiliation: | (China Merchants Testing Certification Vehicle Technology Research Institute Co.,Ltd.,Department of Power Test and Research,Chongqing,401329,China;Chongqing Key Laboratory of Industrial and Information Technology of Electric Vehicle Safety Evaluation,Chongqing,401329,China;Information Engineering Institute,Chongqing Vocational and technical university of mechatronics,Chongqing,402760,China;College of Mechanical and Vehicle Engineering,Chongqing University,Chongqing,400044,China) |
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| Abstract: | To improve the prediction accuracy,particle filter and autoregressive integrated moving average model are employed to predict degradation trend and fluctuation details extracted from state of health series by empirical model decomposition,respectively.The proposed PF-ARIMA method has an average relative error of 4.0%,indicating PF-ARIMA method can accurately predict the remaining useful life of lithium-ion battery. |
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| Keywords: | lithium-ion battery particle filter autoregressive integrated moving average model remaining useful life prediction |
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