In engineering education, traditional teaching approaches cannot sufficiently help students to learn electric vehicle (EV) concepts. In this study, the design of an educational test setup including all the components and all dynamics of EVs is implemented, and case studies for engineering education with practical applications are discussed. The proposed test and training platform not only provides hands-on experience for engineering students, but also the opportunity for expert users to test their own designed algorithms on the test setup through a computer and human–machine interface device. The aim of the study is to show students the effects of road slope, vehicle weight and energy recovery parameters on a light EV. In this context, five case studies have been carried out by the students, and a survey was conducted with them. The survey results show that the test setup can help them better comprehend any EV system and develop their professional knowledge and skills.

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.