Real-Time Optimization of Regenerative Braking System in Electric Vehicles

Abstract

In the present era, electric vehicles (EV) have revolutionized the world with their dominant features like cleanliness and high efficiency compared to that of the internal combustion (IC) engine-based vehicles. To crave for the higher efficiency of the EV during the braking, the kinetic energy of the EV is converted into electrical energy, which is harvested into storage system, called regenerative braking. Various techniques such as artificial neural network (ANN) and fuzzy-based controllers consider factors like state of charge of the battery and supercapacitor and brake demand for calculating the regenerative braking energy. A force distribution curve is designed to ensure that the braking force is distributed and applied on the four wheels simultaneously. In real-time optimization, an operating area is formed for maximizing the regenerative force which is evaluated by linear programming. It is proved that the drive range of the vehicle is increased by 25.7% compared to the one with non-RBS. In this work, RTO-based control loop for regenerative braking system is simulated in MATLAB/Simulink.