Intelligent Dual‐Layer Differential Speed Control for Electric Vehicles Using GA‐ANN Adaptive Regulation and ACO‐Based Dynamic Torque Distribution

ABSTRACT This paper proposes a dual‐layer control architecture for a dual‐motor rear‐wheel‐drive electric vehicle (EV) equipped with two permanent magnet synchronous motors (PMSMs) and direct torque control (DTC). In the motor control layer, a GA‐optimized artificial neural network (GA‐ANN) replaces the conventional PI speed regulator to generate an adaptive torque‐related control signal, improving transient performance under nonlinear operating conditions. In the vehicle layer, an ant colony optimization (ACO) module computes real‐time left/right torque distributions to implement electronic differential action and enhance cornering behavior. The proposed scheme is evaluated in simulation under straight‐line driving (including road‐grade disturbance) and cornering maneuvers. Compared with conventional control strategies, the proposed approach achieves faster settling, reduced steady‐state tracking error, and improved wheel‐speed coordination during turning, resulting in improved vehicle stability and traction behavior. The results indicate that combining adaptive motor regulation with optimization‐based torque allocation provides an effective solution for EV drivetrains under variable driving conditions.