This paper presents the drive control of a Permanent Magnet Synchronous Motor (PMSM) fed by a multi-level inverter for electric vehicle application. In particular, the advantage of torque mobilization of the PMSM engine has been selected for the electric drive of electric cars. In addition, to improve the transmission quality of electric vehicles to ensure requirements, the T-type three-level inverter will be proposed in the control structure of electric vehicles. Moreover, the challenge of torque entails determining the appropriate physical qualities. Therefore, the design of an active damping and current controller to provide rapid and precise torque response to the induced torsional moment was also conducted. Finally, the results of Plecs simulations prove the correctness of the theoretical research. The simulation results demonstrate the research theory.

This study describes the direct torque control-DTC approach, based on the Sliding Mode Control (SMC) technology with chattering reduction, for reducing the torque ripple of the Switched Reluctance Motor (SRM). The SRM torque control loop has been given the SMC treatment to account for the low-frequency fluctuations in the torque output. To maintain a consistent motor speed, the sliding mode controller modifies the value of the reference current. The findings demonstrate that the constant sliding mode controller is superior to PI controllers at lowering the motor's torque ripple, compensating for its nonlinear torque characteristics, and rendering the drive insensitive to parameter changes. MATLAB/SIMULINK simulation has been used to show how well this SMC performs. The performance of the proposed SMC method has been demonstrated by simulation in MATLAB/SIMULINK with a three-phase 8/6 pole, and a 2kW SRM.

This paper presents three regression models that predict the lithium-ion battery life for electric cars based on a supervised machine learning regression algorithm. The linear regression, bagging regressor, and random forest regressor models will be compared for the capacity prediction of lithium-ion batteries based on voltage-dependent per-cell modeling. When sufficient test data are available, three linear regression learning algorithms will train this model to give a promising battery capacity prediction result. The effectiveness of the three linear regression models will be demonstrated experimentally. The experiment table system is built with an NVIDIA Jetson Nano 4 GB Developer Kit B01, a battery, an Arduino, and a voltage sensor. The random forest regressor model has evaluated the model’s accuracy based on the average of the square of the difference between the initial value and the predicted value in the data set (MSE (mean square error)) and RMSE (root mean squared error), which is smaller than the linear regression model and bagging regressor model (MSE is 516.332762; RMSE is 22.722957). The linear regression model with MSE and RMSE is the biggest (MSE is 22060.500669; RMSE is 148.527777). This result allows the random forest regressor model to remain helpful in predicting the life of lithium-ion batteries. Moreover, this result allows rapid identification of battery manufacturing processes and will enable users to decide to replace defective batteries when deterioration in battery performance and lifespan is identified.

This study provides simulation and experimental results on techniques for avoiding static and dynamic obstacles using a deep Q-learning (DQL) reinforcement learning algorithm for a two-wheel mobile robot with independent control. This method integrates the Q-learning (QL) algorithm with a neural network, where the neural networks in the DQL algorithm act as approximators for the Q matrix table for each pair (state–action). The effectiveness of the proposed solution was confirmed through simulations, programming, and practical experimentation. A comparison was drawn between the DQL algorithm and the QL algorithm. Initially, the mobile robot was connected to the control script using the Robot Operating System (ROS). The mobile robot was programmed in Python within the ROS operating system, and the DQL controller was programmed in Gazebo software. The mobile robot underwent testing in a workshop with various experimental scenarios considered. The DQL controller displayed improvements in computation time, convergence time, trajectory planning accuracy, and obstacle avoidance. As a result, the DQL controller surpassed the QL algorithm in terms of performance.

This paper will present why choosing a 7-level reverse voltage source fed to three-phase induction motors to the railway traction motor. In addition, this paper shows the implementation of space vector pulse width modulation (SVPWM) and the math model of induction motor, stator currents, and speed controller design of electric traction drive system based on field-oriented control (FOC). By MATLAB/Simulink method, this multi-level inverter in FOC structure reduces total harmonic distortion (THD) more than other multi-level inverters such as 3 and 5-level inverter. Furthermore, this FOC control structure combined with 7-level inverter improved speed and torque responses required for railway traction motor load.

This paper presents the control design of an in-wheel axial-flux permanent magnet synchronous motor with one stator and one rotor, using a fuzzy logic controller for electric vehicles. In this controller, the surgeon ambiguous inference file is built by two input vectors, the stator current error and the derivative of the stator error. These input variables include five membership functions: Negative Big (NB), Negative Small (NS), Equal Zero (ZE), Positive Small (PS), and Positive Big (PB). The fuzzy logic controller was implemented using a 5×5 matrix to meet the required output stator voltage of the controller. The fuzzy logic torque controller was compared with the PI controller in stator current response, torque, and speed. The proposed controller was evaluated using simulation results from MATLAB/SIMULINK.