Predicting Battery Life for Electric Vehicles Using Machine Learning

Accurate battery life estimation is critical for enhancing the performance, cost-efficiency, and sustainability of electric vehicles (EVs). This study employs Long Short-Term Memory (LSTM) models to predict the Remaining Useful Life (RUL) of EV batteries by integrating key variables including temperature, humidity, voltage, and capacity. Traditional models lack the adaptability to real-world operating conditions, often resulting in inaccurate predictions. In contrast, our LSTM-based approach leverages time-series analysis and real-world datasets, such as NASA’s battery degradation data, to deliver proactive maintenance insights. The proposed model enhances predictive accuracy and supports sustainable EV operations through optimized battery management.