<![CDATA[The rapid advancement of electric vehicle (EV) technology has intensified the need for comprehensive theoretical frameworks capable of accurately evaluating energy efficiency and driving range under realistic operating conditions. This study presents an integrated multi-domain modelling approach that combines drivetrain physics, battery dynamics, drive-cycle analysis, control strategy optimization, and data-driven prediction to assess energy consumption in modern EV systems. A mechanistic model was developed to capture longitudinal vehicle dynamics, resistive forces, motor–inverter efficiency, battery behavior, and regenerative braking processes. The model was evaluated under standardized driving cycles, including the New European Driving Cycle (NEDC), Worldwide Harmonized Light Vehicles Test Procedure (WLTP), and Indian Driving Cycle (IDC), to investigate the impact of speed profiles and acceleration patterns on energy performance. The results demonstrate that energy consumption varies significantly across drive cycles, with aerodynamic drag and vehicle mass emerging as dominant influencing factors. Regenerative braking contributes meaningful energy recovery in urban conditions, though its effectiveness depends on control strategy and battery constraints. Comparative analysis between mechanistic modelling and machine learning approaches reveals that data-driven models improve predictive accuracy, while physics-based models provide interpretability and theoretical robustness. Furthermore, advanced control strategies such as Model Predictive Control (MPC) show superior performance in reducing energy consumption and range uncertainty compared to conventional PI-based controllers. Overall, the findings confirm that EV energy efficiency is an emergent property shaped by the interaction of design parameters, operational conditions, and intelligent control. The proposed integrated modelling framework provides a reliable foundation for next-generation EV design optimization, accurate range estimation, and sustainable mobility planning.]]>
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