Quantum Machine Learning for Battery Health and Thermal Risk Prediction

The rapid growth of connected Electric Vehicles (EV) as part of modern Intelligent Transport Systems (ITS) motivates the need for real-time management of Lithium-ion (Li-ion) battery health and thermal risks. Li-ion batteries, although widely used, are prone to degradation and thermal runaway, posing significant challenges for safe and efficient EV operation. We present a Quantum Machine Learning (QML) and Agent-Based Model (ABM) that simulates and predicts EV behaviour under various battery degradation con- ditions. We use a Variational Quantum Neural Network (VQNN) trained on NASA battery datasets to classify EVs into four cate- gories: healthy, degraded for fixed chargers, degraded for mobile chargers and thermal runaway based on their State of Health (SoH) and thermal risk. We simulate a fleet of 50 EVs and demonstrate that the VQNN classifies healthy and batteries at risk and achieves an accuracy of 96%, outperforming an LSTM baseline of 91%. The VQNN outperformed the LSTM on the NASA dataset, achieving a significantly lower RMSE of 0.1562 compared to 0.6455. Our re- sults show that the QML-ABM framework improves battery health and thermal risk prediction, enhances safety, Mobile Charging as a Service (MCaaS), and supports real-time decision-making in ITS.