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 R2, 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 R2 value of 0.97 in the simulation and an R2 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.