Predicting Battery Life Performance Using AI Techniques in Electric Vehicles

Debani Prasad Mishra, Munavath Pavan Kalyan, Shivam Tyagi, Piyushjeet Jeet, Shiv Grover, Surender Reddy Salkuti

Abstract


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 ML to predict the 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 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.

Keywords


Machine Learning; Battery Life; Cycle Prediction; Regression Models; Lithium-ion Batteries; Remaining Useful Life; Charge Cycles; Battery Ridge.

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DOI: http://doi.org/10.11591/ijict.v15i2.pp805-812

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