ABSTRACT:- With the increasing adoption of electric vehicles (EVs), efficient energy management systems have become critical for extending driving range and improving overall energy utilization. Regenerative braking systems (RBS), which recover kinetic energy during deceleration, play a vital role in this effort. However, the effectiveness of RBS largely depends on the control strategy employed. This study proposes an optimal Fuzzy Logic Controller (FLC) to enhance energy recovery while ensuring vehicle safety and ride comfort. Conventional braking and traditional RBS methods often face challenges in handling nonlinear braking dynamics and varying driving conditions. Fuzzy logic, with its capability to manage system uncertainties and mimic human decision-making, offers a robust solution. The proposed FLC dynamically adjusts regenerative braking force based on real-time inputs such as vehicle speed, brake pedal pressure, and battery state of charge (SOC), ensuring an optimal blend of mechanical and regenerative braking. A detailed EV simulation model was developed in MATLAB/Simulink, incorporating regenerative braking, battery dynamics, and drivetrain components. The controller’s performance was evaluated across various standard driving cycles, including the New European Driving Cycle (NEDC) and Urban Dynamometer Driving Schedule (UDDS). Key performance metrics such as energy recovery efficiency, braking force distribution, SOC variation, and stopping distance were analyzed. Results show that the FLC achieves up to a 25% improvement in energy recovery over conventional PI-based controllers. It adapts effectively to changes in vehicle dynamics and road conditions, maintaining smooth braking transitions, stable SOC, and minimal stopping distance deviation. Sensitivity analysis further validates the robustness of the fuzzy rule base and membership function design. This research demonstrates that a well-optimized FLC can significantly improve regenerative braking efficiency in EVs, contributing to energy savings, reduced charging needs, and extended battery life—supporting broader goals in sustainable mobility.

Keywords: Regenerative Braking System (RBS), Fuzzy Logic Control (FLC), Electric Vehicles (EVs), Energy Recovery Optimization, Brake Energy Regeneration, Nonlinear Control Systems