Coordinated Torque Distribution and Energy Management in Dual-Motor Electric Vehicle Powertrains with Induction and Permanent Magnet Synchronous Motors

The growing demand for high-efficiency and high-performance electric vehicles underscores the urgent need for advanced research in dual-motor configurations to optimize energy management and enhance dynamic stability under diverse driving conditions. This paper presents a novel torque distribution strategy for dual-motor electric vehicles, leveraging the Grey Wolf Optimizer (GWO) to simultaneously improve energy efficiency and dynamic performance. Unlike conventional fixed-ratio methods, the proposed GWO-based approach dynamically adjusts torque allocation between the front and rear motors in response to real-time driving conditions. Simulation results indicate a notable reduction in total power losses, ranging from 12.7% to 15.3%, compared to baseline strategies. The algorithm demonstrates high computational efficiency, with execution times consistently below 5 milliseconds, making it suitable for real-time implementation. A detailed performance analysis highlights that optimal torque distribution is highly sensitive to varying load demands and motor efficiency profiles, emphasizing the shortcomings of static control approaches. The strategy proves particularly effective in urban driving cycles, reducing energy consumption by 13.2% during frequent acceleration and deceleration phases, while ensuring smooth torque transitions. Additionally, under high-torque operation, the approach enhances induction motor efficiency (85–92%), improves regenerative braking performance with a 23.5% increase in permanent magnet synchronous motor utilization, and promotes balanced motor load sharing during steady-state cruising. These findings confirm the GWO-based strategy as a robust, adaptive, and computationally viable control solution for next-generation electric vehicle powertrains, capable of addressing the demands of diverse real-world driving scenarios.