基于PCA-NARX的锂离子电池剩余使用寿命预测

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. 

Prediction for the Remaining Useful Life of Lithium-ion Battery Based on PCA-NARX

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.