Comparative Machine Learning-Based Techniques to Provide Regenerative Braking Systems with High Efficiency for Electric Vehicles

Electric vehicles rely on regenerative braking as a means of improving energy efficiency and extending driving range. However, the optimization of torque distribution between regenerative and mechanical braking remains a challenging aspect. This study investigates machine learning techniques for predicting braking torque in light EVs with a view to improving energy recovery and reducing mechanical brake usage. For this purpose, a simulation model was developed in MATLAB/Simulink to generate a data set of 113,622 points based on speed, acceleration, road grade, vehicle weight, and road condition. Four supervised ML algorithms—Linear Regression, K-Nearest Neighbors, Decision Tree, and Random Forest—were trained and evaluated using R 2 , MSE, RMSE, and MAE metrics. To verify the results under WLTP Class 1 driving conditions, a test was conducted on a hardware test platform for the best model. The findings indicate that Random Forest achieved the highest level of accuracy with an R 2 value of 0.97 in the simulation and an R 2 value of 0.98 in the experimental validation. These findings support the hypothesis that ML-based torque prediction is a promising approach for real-time EV braking control. Also, this study supports sustainable transportation by improving energy recovery and reducing environmental impact through advanced AI-based braking strategies.