AI Based Control System for Electric Vehicles with Predictive Maintenance and Adaptive Energy Management

This paper presents the development and implementation of an advanced electric vehicle (EV) intelligent control system which is designed to resolve critical challenges such as battery degradation, limited driving range, and system optimization. This study aims to address these challenges by developing a comprehensive Simulink-based EV simulation model that integrates artificial intelligence (AI) techniques for the Predictive Maintenance System (PMS) and Adaptive Energy Management System (AEMS). PMS ensures early detection of battery degradation by using Neural Networks (NNs) to monitor and predict the battery state of charge (SOC) and State of Health (SOH) with an accuracy exceeding 90%. Meanwhile, the AEMS dynamically optimizes energy distribution through Fuzzy Logic Control (FLC) to improve driving range by at least 10%. The results of the simulation demonstrate significant improvements in driving performance and prediction accuracy over multiple driving cycles. This research establishes a robust platform for the advancement of EV control technologies, paving the way for more effective and environmentally friendly transportation systems.