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基于PCA-NARX的锂离子电池剩余使用寿命预测

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基于PCA-NARX的锂离子电池剩余使用寿命预测
Prediction for the Remaining Useful Life of Lithium-ion Battery Based on PCA-NARX
投稿时间:2018-01-10
DOI:10.15918/j.tbit1001-0645.2019.04.012
中文关键词:锂离子电池剩余使用寿命相关性分析PCA算法NARX神经网络
English Keywords:lithium-ion batteryremaining useful lifecorrelation analysisPCA algorithmNARX neural network
基金项目:山西省重点研发计划资助项目(201703D111011);山西省青年自然科学基金资助项目(201601D021075);山西省研究生教育改革研究资助项目(2018JG62);中北大学自然科学基金资助项目(2016032,2017025)
作者单位
庞晓琼中北大学 大数据学院, 山西, 太原 030051
王竹晴中北大学 大数据学院, 山西, 太原 030051
曾建潮中北大学 大数据学院, 山西, 太原 030051
贾建芳中北大学 电气与控制工程学院, 山西, 太原 030051
史元浩中北大学 电气与控制工程学院, 山西, 太原 030051
温杰中北大学 电气与控制工程学院, 山西, 太原 030051
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中文摘要:
目前基于数据驱动的锂离子电池RUL预测方法不能较好地适应于同类型不同电池的RUL预测,且预测精度易受健康因子冗余或不足的影响.针对以上问题,提出一种结合主成分分析(PCA)特征融合与非线性自回归(NARX)神经网络的锂离子电池RUL间接预测框架.首先提取多个能反映电池性能退化的可测参数,并将PCA去除冗余后的结果作为预测健康因子;然后利用一组电池的全寿命数据构建基于NARX神经网络的健康因子和容量预测模型,对同类型不同电池预测时将该电池寿命前期健康因子作为输入,即可间接预测出其RUL.最后实验结果表明所提框架在同类型不同电池RUL的预测中精度较高且适应性较强.
English Summary:
The current data-driven remaining useful life (RUL) prognostics methods generally have the limitation that cannot be well adapted to the prediction for different batteries, along with low prediction accuracy caused by the redundancy or deficiency of health indictors (HIs).To solve the problem, integrating the PCA-based feature fusion method and NARX neural network, an indirect RUL prognostics framework was proposed for lithium-ion battery. Firstly, multiple measurable parameters that could reflect the performance degradations of lithium-ion battery were selected as candidate HIs, and then the HI was extracted based on PCA to eliminate the redundancy. Furthermore, both health factor and capacity prediction models based on NARX-NN were established using a set of battery life data. Taking the HIs of early stage as the input, the RUL of different batteries with the same type could be predicted indirectly. Finally, sufficient experiments were carried out to validate the high efficiency and adaptability of the proposed method for the same type lithium-ion battery.
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