Predicting battery life performance using artificial intelligence techniques in electric vehicles - Debani Prasad Mishra; Munavath Pavan Kalyan; Shivam Tyagi; Piyushjeet Piyushjeet; Shiv Grover; Surender Reddy Salkuti (Institute of Advanced Engineering and Science, 2026)
Electric vehicles’ (EVs’ performance and sustainability are significantly influenced by the efficiency and lifespan of their lithium-ion batteries. This paper explores the critical factors affecting battery degradation, focusing on parameters such as charge cycles, thermal management, and voltage dynamics. Utilizing a dataset of 14 batteries, the study employs data-driven machine learning (ML) to predict the remaining useful life (RUL) of batteries. The ensemble-based regression model demonstrated superior predictive accuracy through comprehensive analysis, achieving R² values of 97.89% for training and 94.69% for testing. Feature importance analysis identified cycle index (CI) as the most critical determinant of battery health, followed by discharge time and voltage stability. Visualizations, including correlation heatmaps and residual plots, validate the robustness of the selected model. Additionally, sustainable charging strategies, such as steady current-steady voltage (also known as CC-CV), are highlighted for their role in enhancing battery longevity. This research offers actionable insights into battery management systems, providing a robust foundation for predictive maintenance and the development of sustainable electric mobility solutions.