一种基于间接健康因子的锂离子电池剩余使用寿命预测方法

针对锂离子电池实际运行过程中容量数据无法在线获取，退化特征存在局部再生现象，剩余使用寿命（RUL）无法通过单一模型准确预测的问题，提出一种基于间接健康因子（HI）的锂离子电池RUL预测方法。首先根据在线数据构建并筛选最优的HI，然后建立基于遗传算法优化的极限学习机（GA-ELM）关系模型，再通过变分模态分解（VMD）解耦间接HI各分量，并通过相关向量机（RVM）进行趋势跟踪与预测，最后将预测结果输入到GA-ELM关系模型中得到电池容量预测值。与NASA PCoE数据集的验证对比表明，文章所用方法在趋势预测和RUL预测结果上均优于其他对比方法，可以实现锂离子电池RUL的准确预测。

A prediction method for the RUL of lithium-ion battery based on indirect health indicator

In the real operation of lithium-ion batteries, the capacity data are usually not available online and the degradation features are subject to the local regeneration, which makes an accurate prediction based on a single model very difficult for the remaining useful life (RUL) of the lithium-ion battery. To solve this problem, an indirect health indicator (HI)-based method for predicting the RUL of the lithium-ion battery is proposed in this paper. First, the optimal HI is evaluated based on the online data. Then, a relational model based on the genetic algorithm optimization of the extreme learning machine (GA-ELM) is built. The indirectly obtained HI components are decoupled by the variational mode decomposition (VMD), and the trends are tracked as well as predicted by the relevance vector machine (RVM). Finally, the predicted results are fed into the GA-ELM relational model to obtain the capacity prediction value, which can be translated into the RUL of the lithium-ion battery. The comparison between the results of the NASA PCoE dataset show that the performance of the proposed method is better than that of the other candidate methods in predicting both the trend and the RUL, with an accurate predicted value of RUL for the lithium-ion batteries.