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This thesis explores the optimization of torque path development for rear-wheel drive (RWD) electric vehicles (EVs), aiming to enhance performance and driving experience. Conventional torque control development methods, reliant on physical prototyping and extensive on-road testing, are often inefficient and costly. To address these challenges, this research introduces a cross-platform validation approach utilizing the Everything-In-the-Loop (XIL) methodology, integrating Model-In-the-Loop (MIL), Hardware-In-the-Loop (HIL), and Vehicle-In-the-Loop (VIL) testing. The proposed torque path control algorithm manages critical driveability aspects—such as acceleration response, transient torque adjustments, and creep torque—through components like the Acceleration Response Map (ARM) and Transient Acceleration Response Map (tARM). Development and refinement occurred using 3-Degrees-of-Freedom (3-DoF) and 14-Degrees-of-Freedom (14-DoF) vehicle models across MIL and HIL platforms, with final validation performed on a Cadillac LYRIQ RWD 2023 in VIL testing. Comparative analysis demonstrates that the higher-fidelity 14-DoF HIL model significantly improves simulation accuracy, aligning vehicle speed predictions with real-world data (MAPE of 31.37%). The optimized algorithm exhibits consistent torque response and acceleration performance across platforms, validating the XIL process’s effectiveness in reducing development time and enhancing system reliability. This work advances EV control system design by demonstrating how cross-platform validation streamlines torque path development, unlocking the potential for other EV process development, and supporting the broader adoption of electric vehicles through improved performance and efficiency.