Energy Management Design of the Dual-Motor System for Electric Vehicle Using Whale Optimization Algorithm

Dual-motor electric vehicles enhance power performance and overall output capability by enabling real-time control of torque distribution between the front and rear wheels, thereby improving handling, stability, and safety. In addition to increased energy efficiency, a dual-motor system provides redundancy, if one motor fails, the other can still supply partial power, further enhancing driving safety. This study aims to optimize the energy management strategies of front and rear axis motors, examining the application effects of rule-based control (RBC), global grid search (GGS) and whale optimization algorithm (WOA). A simulation platform based on Matlab/Simulink® was constructed and validated through hardware-in-the-loop (HIL) testing to ensure the authenticity and reliability of simulation results. Detailed tests and analyses of the dual-motor system were conducted under FTP-75 driving cycles. Compared to the RBC strategy, GGS and WOA achieved energy efficiency improvements of 9.1 % and 8.9 %, respectively, in pure simulation; and 4.2 % and 3.8 %, respectively, in HIL simulation. Overall, GGS and WOA each present distinct advantages, with WOA emerging as a highly promising alternative energy management strategy. Future research should further explore WOA applications to enhance energy savings in real-world vehicle operations.