The automotive industry’s transition from internal combustion engines to electric vehicle (EV) ecosystems has triggered a paradigm shift in powertrains. In this transformation, another factor as critical as battery capacity is the motor control strategy that ensures the most efficient transfer of energy to the wheels. Direct Torque Control (DTC), which has revolutionized industrial traction systems owing to its high dynamic torque response and simple structure without a modulator, faces structural limitations in meeting the demands of modern electric vehicles. This study analyzes modern strategies developed to overcome the fundamental drawbacks of the DTC—high torque ripple and variable switching frequency—based on recent literature. Within the scope of this study, space vector modulation, model predictive, and adaptive architectures based on artificial intelligence were comparatively evaluated in terms of EV performance parameters. Additionally, the study demonstrates how the hardware-level integration of DTC algorithms with field-programmable gate array-based parallel processors and wide-bandgap semiconductor technologies improves the torque response by reducing the control loops to microsecond levels. The findings show that intelligent DTC strategies reduce torque ripples by 40–90% and maximize regenerative braking efficiency, transforming the motor driver from a simple ‘‘motion controller’’ into an integrated ‘‘energy management strategy’’ that extends battery life.
