A Deep Learning Approach for Predicting the Remaining Useful Lifetime of Lithium-Ion Batteries Using 1-D Convolutional Neural Networks

Abstract

Lithium-ion batteries, among many energy storage systems, offer high energy density, low voltage dips, long lifespan, and wide working temperatures. They have been widely adopted in a variety of applications, including as electric vehicles, aerospace, energy management systems, etc. Accurate prediction of remaining useful lifetime (RUL) and health status of lithium-ion batteries have received lot of attention in the recent years. Machine learning approaches have recently gained popularity as a means of empirically learning and predicting battery behaviour. However, the complex and nonlinear behaviour of lithium-ion batteries pose challenges for traditional machine learning approaches. This paper investigates the application of two non-linear machine learning models, namely artificial neural network (ANN) and 1-D Convolution Neural Network (1-D CNN), for predicting the RUL. NASA prognostics battery dataset is utilized for the present study. Experimental results indicate that the 1-D CNN achieves better prediction accuracy as compared to ANN and other traditional machine learning.