DEVELOPMENT OF A REGENERATIVE BRAKING MODEL USING MAMDANI FUZZY LOGIC CONTROL FOR BATTERY MANAGEMENT IN ELECTRIC VEHICLES

Abstract

Electric vehicles are one of the key options on the pathway to sustainable mobility, however, optimising the energy efficiency of EVs is a central engineering challenge. One of the most promising methods for extending driving range to the battery onboard a vehicle is regenerative braking, which recycles kinetic energy when the vehicle brakes, but causes transient charging currents and power peaks for the BMS to manage safely. How and when the braking torque is applied to regeneration is thus a key control strategy in energy performance and battery health. This work presents and tests a full regenerative braking model with battery management for EVs based on a Mamdani fuzzy logic controller that was selected by a principle-based and evidence-based comparative screening that compared this controller with a Takagi-Sugeno alternative. Both drivers took the full 1180-second New European Driving Cycle (NEDC). The comparative screening results have shown that the Mamdani controller has lower total battery SOC depletion for the entire NEDC (0.65%) when compared to Takagi-Sugeno (0.70%), which makes it the controller used for the entire model. The selected Mamdani-controlled model was evaluated with regards to the measured engineering outputs and showed: a net consumption of battery energy of 292.5 Wh over the complete NEDC test; an estimated regenerative energy of 73 Wh (ca. 20% regenerative efficiency) over the whole NEDC test; armature current between 0–35 A; stable motor speed tracking of the NEDC reference profile in both the urban (0–680 s) and the extra-urban (681–1180 s) part of the test.