Regenerative Braking Control Strategy for Electric Vehicle Based on Improved Ant Colony Algorithm

To address low energy recovery efficiency and inaccurate torque distribution of regenerative braking in electric vehicles, this paper proposes an optimized fuzzy control strategy for regenerative braking based on an improved ant colony algorithm. The design focuses on using the battery state of charge, real-time vehicle speed, and braking intensity as fuzzy control inputs, with the regenerative braking ratio as the output. By employing the improved ant colony algorithm to dynamically optimize the parameters of the fuzzy controller, the accuracy of electric motor braking force distribution is effectively improved; Building on this foundation, the introduction of the Elite Ant Guidance Algorithm enhances the speed of adaptive pheromone updates and dynamic path selection, thereby improving regenerative braking energy recovery efficiency. A MATLAB/Simulink simulation model was developed, and comparisons under the NEDC and UDDS test cycles demonstrated that, compared to traditional fuzzy control strategies, the optimized algorithm increases energy recovery efficiency by 12.8% and 8.1%, respectively, confirming the effectiveness and validity of the improved control strategy.