Predicting EV Battery Lifespan Using Machine Learning
DOI:
https://doi.org/10.5281/zenodo.15250347Keywords:
Electric Vehicle Batteries, Machine Learning (ML), Remaining Useful Life (RUL), Random Forest (RF), Support Vector MachineAbstract
The continuous advancement of electric vehicle (EV) technology has heightened the emphasis on sustainable energy storage, making lithium-ion batteries a crucial component. Ensuring battery reliability and longevity is essential for optimizing EV performance and reducing maintenance costs. This study explores the prediction of Remaining Useful Life (RUL) for lithium-ion batteries using advanced Machine Learning (ML) models, specifically Random Forest (RF) and Support Vector Machine (SVM). Accurate RUL estimation enhances battery management, prevents failures, and improves safety.A comprehensive dataset from the NASA Ames Prognostics Center of Excellence is preprocessed, with the One-way ANOVA method applied for optimal feature selection. Data normalization techniques are employed to enhance model consistency, while hyperparameter tuning (HPT) optimizes predictive performance. Real-time factors such as temperature fluctuations and usage cycles are incorporated to analyze their impact on battery degradation. The proposed system provides deeper insights into battery aging trends, enabling proactive maintenance strategies.Model performance is evaluated using R2 score and Mean Squared Error (MSE), where the RF model achieves an R2 score of 0.83 and an MSE of 1.67, demonstrating high reliability. The results contribute to improving battery efficiency and safety through predictive modeling, facilitating better battery management in EVs. By leveraging ML-driven predictive analytics, this research supports the advancement of sustainable and cost-effective energy solutions, promoting wider EV adoption and a greener future.
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Copyright (c) 2025 N Vasavi, A Akshith Reddy, K Poorna Chandra, K S S Ramakrishna, P Prasanthi
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