Research on torque fuzzy distribution strategy for integrated flywheel electric drive vehicles based on particle swarm optimization

The integrated flywheel hybrid electric vehicle (DMF-HEV), characterized by its low-carbon and high-efficiency advantages, represents a promising solution for meeting increasingly stringent future passenger vehicle regulations. In this study, a novel DMF-HEV configuration integrating a flywheel with a dual-motor system is proposed as the primary power source. Six operating modes are designed to accommodate diverse driving conditions, and the corresponding energy transfer paths and operating mechanisms are systematically analyzed, upon which a comprehensive simulation model is developed. Considering the influence of flywheel state of charge and vehicle speed on dual-motor torque distribution, a fuzzy control-based torque allocation strategy, together with a rule-based vehicle control strategy, is established to enable real-time energy management and adaptive mode switching. The feasibility and effectiveness of the proposed approach are validated through co-simulation using AMESim and MATLAB. To address the limitations of conventional fuzzy control, which relies heavily on expert knowledge and is prone to suboptimal performance under complex conditions, a particle swarm optimization (PSO) algorithm is introduced to optimize the membership functions, resulting in a PSO-enhanced torque allocation strategy. Simulation results indicate that battery consumption is reduced by 2.43% and 2.86% under China Heavy-duty Commercial Vehicle Test Cycle and Federal Test Procedure 75 driving cycles, respectively, with significant improvements in overall system performance. The integration of PSO and fuzzy control enables the simultaneous achievement of multiple control objectives, including enhanced dynamic response, reduced energy consumption, and lower peak motor torque, thereby providing a novel and effective approach for hybrid control of flywheel-based electric drive systems.