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Demonstrates improved energy recovery in electric motorcycles, suggesting better braking efficiency.
To address the challenge of balancing regenerative braking efficiency and braking safety in rear-wheel-drive electric motorcycles, this study proposes a genetic algorithm-based electromechanical hybrid fuzzy braking control strategy. First, a three-dimensional fuzzy controller is designed with braking force, motorcycle speed, and battery state of charge (SOC) as input variables to adjust the regenerative braking ratio in real-time. To further improve the fuzzy logic, which typically relies on engineering experience, a genetic algorithm (GA) is employed to optimize the controller’s parameter space. Co-simulation results using BikeSim 2013.1 and MATLAB/Simulink R2022a demonstrate that, under WMTC and NEDC standard driving cycles, the proposed GA-optimized fuzzy control system increases energy recovery rates by 6.59% and 11.65%, respectively, compared with the unoptimized fuzzy control strategy.
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Lai et al. (2026) studied this question.
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