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A new simulation tool integrates artificial intelligence to improve energy efficiency and adaptive responses in electric vehicles, indicating significant advancements over static models.
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
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Vivek Ghulaxe (2025) studied this question.
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