Health monitoring systems for EV battery

Electric vehicle (EV) battery health monitoring is crucial to guaranteeing dependability, performance, and safety. A data-driven State of Charge (SOC) prediction framework utilising machine learning models assessed across three distinct battery chemistries—Lithium-ion, Lithium Polymer, and Lead-acid in this paper. Different operating conditions were captured using publicly accessible datasets from Mendeley Data, the CALCE Battery Research Group, and open-source GitHub repositories. Due to its robustness and low computational complexity, a Random Forest Regressor (RFR) was employed as the main SOC estimation model. Its performance was compared with that of a Recurrent Conditional Variational Autoencoder (RC-VAE) to analyse modelling limitations and cross-chemistry generalisation. The Random Forest model is evaluated experimentally using Mean Absolute Error, Root Mean Squared Error, and the coefficient of determination (R²). The results show that the Random Forest model offers more consistent and dependable SOC predictions, whereas the RC-VAE performs worse under specific datasets and scaling conditions. Additionally, as a proof-of-concept, a voice-activated, lightweight chatbot interface was incorporated to enable users to ask questions about SOC information and get basic charging-related advice through natural language interaction. The suggested method demonstrates how well cross-chemistry SOC estimation can be combined with user-friendly interfaces for useful EV battery monitoring applications.