An Indirect Prediction Method based on Feature Extraction of Lithium-ion Batteries Remaining Useful Life

Abstract

Predicting the remaining useful life (RUL) of lithium-ion batteries is of paramount significance for the lifespan management of electric vehicles. This study delves into a feature extraction-based approach for indirectly predicting RUL in lithium-ion batteries, circumventing the limitations posed by the challenge of measuring capacity during battery operation. Initially, the health feature (HF) exhibiting strong correlations with capacity is extracted from localized charging voltage intervals, and capacity estimates are obtained through polynomial fitting. Subsequently, a dynamic long short-term memory (D-LSTM) neural network is established, iteratively incorporating prediction outcomes into training data to continuously enhance the model and ultimately accomplish RUL prediction. Experimental results conducted on batteries with varying initial states from the NASA dataset highlight the substantial accuracy and robustness of the proposed D-LSTM method. It outperforms other commonly utilized models, offering the capability for online RUL prediction.