Adaptive sliding window LSTM NN based RUL prediction for lithium-ion batteries integrating LTSA feature reconstruction

The extraction and prediction of health indicators (HIs) are two vital aspects in remaining useful life (RUL) prediction of lithium-ion batteries (LIBs). Aiming to estimate the RUL precisely, a novel integrated prediction method is proposed for LIBs on the basis of local tangent space alignment (LTSA) feature extraction and adaptive sliding window long short-term memory neural networks (ASW LSTM NN). In the proposed method, the indirect HI is first extracted by LTSA automatically to replace the unmeasurable capacity, and a strong correlation between them is verified by the Spearman correlation coefficient. Following that, with the extracted HI, an adaptive sliding window LSTM is constructed to conduct the RUL estimation of LIBs in routine environment. For the structured neural network, corresponding inputs are dynamically selected by the sliding window, while a varying length window mechanism is devised to update the window data along with the predicting cycle. Hence, the designed predicting method can learn the long-term dependencies by means of the inherent nature of LSTM and simultaneously capture the local fluctuations via the adaptive sliding window. Eventually, extensive experiments are conducted and corresponding results are compared with those obtained by existed approaches. The effectiveness of the integrated prediction method is validated, and our proposed model is proved to be more accurate in predicting the RUL compared with existed approaches. (c) 2021 Published by Elsevier B.V.

Automated summary

The study proposes a novel integrated prediction method for accurately estimating the RUL of lithium-ion batteries by extracting HIs through LTSA and using ASW LSTM NN for prediction. The method dynamically selects inputs and updates window data in the neural network, learning long-term dependencies and capturing local fluctuations simultaneously.