There are a lot of parameters to improve the efficiency of squirrel cage induction motors. Most of the studies deal with only one parameter at a time. However, Optimization of all parameters are needed to get the most efficient motor. This paper shows that three phase induction motor parameters including rotor slot type, stator slot type, steel sheet and rotor material optimized by using the module Rmxprt in Maxwell. The Results show that 157% increasing torque and 4.1% increasing efficiency are obtained.

Recently, while electric vehicles (EV) have substituted the fossil fuel vehicles, the design of the electrical motors with more efficient and less mechanical converters has become mandatory due to the weighting gears, mechanical differentials, and other cost-increasing parts. To overcome these problems, double electrical motors with low speed and high torque have been designed and used in the rear wheels of the EVs without any gearbox and mechanical differential. In this study, a novel outer rotor line-start hybrid synchronous motor is proposed as another solution. For this aim, four different hybrid rotor types, including magnets and rotor bars, have been designed and analyzed. Calculation and estimation of all parameters to design a motor are introduced. All of the analyses were carried out by Finite Elements Method (FEM). One of the analyzed motors, which is called Type-D was selected and implemented because of the best startup performance and better steady-state behavior under the rated load and overload. While holding this motor at synchronous speed under nominal load, in case of overloading, it remained in asynchronous mode, thus maintaining the sustainability of the system. Obtained results prove that the newly proposed outer rotor LSSM has the advantages of both synchronous motor and asynchronous motor. All of the experimental results validate the simulations well. The effects of the magnet alignments and dimensions on the performance of the motors are presented.

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.