Li-Ion Battery Life Prediction Using Deep Learning

Abstract

Lithium-ion (Li-ion) batteries are widely used in electric vehicles, portable electronics, and renewable energy storage systems due to their high energy density and long lifespan. However, accurately predicting battery life and degradation remains a critical challenge for ensuring reliability, safety, and efficient energy management. Traditional methods for battery life prediction rely on empirical models and statistical techniques, which often fail to capture complex nonlinear degradation patterns under varying operating conditions. This paper presents a deep learning-based approach for predicting the remaining useful life (RUL) of Li-ion batteries. The proposed system utilizes advanced deep learning models such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN) to analyze battery performance data, including voltage, current, temperature, and charge-discharge cycles. These models effectively learn temporal and spatial features from historical data, enabling accurate prediction of battery degradation trends. Experimental results demonstrate that the proposed approach outperforms traditional machine learning methods in terms of prediction accuracy and robustness. This study contributes to improving battery management systems, enhancing operational safety, and optimizing maintenance strategies in energy storage applications.