AIDE: AI-Integrated Drivetrain Emulator for MATLAB-Simulated Electric Vehicle Ecosystems

In order to increase drivetrain fidelity and adaptability in MATLAB/Simulink environments, this research attempts to create a next-generation electric vehicle (EV) simulation framework that blends artificial intelligence (AI) with physics-based modeling. Neuro-adaptive machine learning algorithms were used in the design of a modular AI-Integrated Drivetrain Emulator (AIDE), which dynamically modifies torque, battery state-of-charge (SOC), and regenerative braking in response to driving patterns, terrain, and environmental conditions. AIDE is a reliable simulation tool for intelligent EV system design in both academic and industrial contexts. The suggested system showed a 22% improvement in drivetrain response accuracy and a 17% increase in energy efficiency when compared to traditional static models.1