Adaptive Fuzzy Control for Regenerative Braking System in Dual-Drive Electric Motorcycles

Despite extensive research into regenerative braking technology, balancing braking safety and energy recovery efficiency remains a challenge under complex and varied driving conditions. To address this, this paper proposes an adaptive fuzzy control strategy for the regenerative braking system in dual-drive electric motorcycles. Using braking intensity, vehicle speed, and battery state of charge (SOC) as inputs, the strategy employs fuzzy reasoning to dynamically adjust the regenerative braking force ratio in real-time. This approach maximizes energy recovery efficiency while ensuring braking safety. A co-simulation platform for the electromechanical hybrid braking system of the entire vehicle was built using MATLAB/Simulink and BikeSim. Compared with the conventional constant-regeneration scheme, the proposed adaptive fuzzy control strategy achieves a remarkable improvement in energy recuperation efficiency—25.97% under WMTC and 26.43% under FTP-75, respectively—while simultaneously increasing the terminal battery SOC by 2.1% and 1.3%. These quantitative gains substantiate the superior capability of the strategy to dynamically reconcile braking stability with energy-harvesting objectives across diverse driving conditions. By fully exploiting the regenerative potential of dual-drive architectures, the proposed control approach not only extends the achievable driving range but also provides a scalable framework for high-efficiency regenerative braking control in future lightweight electric vehicles.