Artificial Neural Networks: A Promising Tool for Regenerative Braking Control in Electric Vehicles

Abstract

Regenerative braking systems (RBS) are a promising technology for recovering wasted kinetic energy during the braking process of electric vehicles. This energy can be stored in the vehicle’s battery for later use, reducing fuel consumption, prolonging travel distances, and reducing maintenance costs. RBS is particularly beneficial in heavy traffic, where the brakes are used more frequently. In this research, an artificial neural network (ANN) model was developed to predict the amount of the recovered current and stoppage time needed for different braking scenarios. The ANN model was trained using data from a developed MATLAB Simulink model that was used to investigate the effects of braking force capacity and vehicle running speed on RBS performance. The performance of the RBS was evaluated in terms of the amount of recovered current and the time needed for the vehicle to come to rest. The outputs from the Simulink model were validated statistically using Design Expert ANOVA analysis before being implemented in the ANN model. The results of this study showed that the ANN model was able to accurately predict the amount of the recovered current and the stoppage time needed for different braking scenarios. Hence ANN models can be considered an accurate flexible model that can be used to develop efficient and effective RBS controllers for electric vehicles.