多尺度分解下GRU-MLR组合的锂电池剩余使用寿命预测方法

Combined GRU-MLR method for predicting the remaining useful life of lithium batteries via multiscale decomposition

准确预测锂电池的剩余使用寿命(remaining useful life,RUL)可以及时了解电池内部的性能退化情况,降低电池的使用风险并为日常维护提供可靠的理论依据。为了提高预测结果的准确性和稳定性,提出了一种基于集合经验模态分解(ensemble empirical mode decomposition,EEMD)、门控循环单元网络(gated recurrent unit,GRU)和多元线性回归(multiple linear regression,MLR)相结合的锂电池RUL预测模型。该模型首先采用EEMD算法将锂电池容量数据分解为若干个高频分量和低频分量,以此减少容量数据中的噪声干扰,然后针对各个分量的特点,分别利用GRU和MLR网络基于获得的高频和低频序列建立预测子模型,最后叠加融合各个子模型的预测值得到锂电池的RUL结果,通过使用NASA和Oxford提供的锂电池公开数据,并采用不同的预测起点与其他单一模型和组合模型进行对比。实验结果表明,EEMD-GRU-MLR预测模型能够提供准确的RUL结果,相比于LSTM、GRU和EEMD-GRU预测模型,最大平均绝对误差分别降低了0.0311、0.0234、0.0182,最大均方根误差分别降低了0.0235、0.0153、0.0098,证明了本模型具有较好的锂电池RUL预测能力。

Accurately predicting the remaining useful life(RUL) of lithium batteries can ensure timely understanding of the internal performance degradation of the battery,reduce the risks associated with battery use,and provide a reliable theoretical basis for routine maintenance.To improve the accuracy and stability of prediction results,a lithium-battery RUL prediction model based on the combination of ensemble empirical mode decomposition(EEMD) and gated recurrent unit(GRU) with multiple linear regression(MLR) is proposed.First,the model decomposes the lithium-battery capacity data into several high-frequency and low-frequency components using the EEMD algorithm to reduce noise interference in the capacity data.Then,based on the characteristics of each component,the model builds prediction submodels based on the obtained high-frequency and low-frequency sequences using the GRU and MLR networks,respectively.Finally,the predicted values of each submodel are superimposed and fused to obtain the RUL of the battery based on the public data on lithium batteries provided by NASA and Oxford;furthermore,using different prediction starting points,the obtained results are compared with those of other single and combined models.The experimental results show that the EEMD-GRU-MLR prediction model can provide accurate RUL results,compared with LSTM,GRU,and EEMD-GRU prediction models,with the maximum mean absolute error decreased by 0.0311,0.0234,and 0.0182,respectively,and the maximum root mean square error decreased by 0.0235,0.0153,and 0.0098,respectively,This proves the satisfactory ability of the proposed EEMD-GRU-MLR model to predict the RUL of lithium batteries.