This article deals with an intelligent adaptive neural network (ANN) controller for a direct torque controlled (DTC) electric vehicle (EV) propulsion system. With the realization of artificial intelligence (AI) conferred adaptive controllers, the torque control of an electric car (eCAR) propulsion motor can be achieved by estimating the stator reference flux voltage used to synthesize the space vector pulse width modulation (SVPWM) for a DTC scheme. The proposed ANN tool optimizes the parameters of a proportional integral (PI) controller with real-time data and offers splendid dynamic stability. The response of an ANN controller is examined over standard drive cycles to validate the performance of an eCAR in terms of drive range and energy efficiency using MATLAB simulation software.

A photovoltaic array (PVA) simulation model to be used in Matlab-Simelectronic GUI environment will be developed and analyzed in this paper. In Simelectronic environment the simulink signal is converted to physical signal and again physical signal is converted to simulink signal. The model is developed using basic circuit equations of the photovoltaic (PV) solar cells including the effects of solar irradiation and temperature changes. The new model developed will be tested using a directly coupled dc load. A new implemented Photovoltaic Array Simulation model is to be developed by using Matlab/ Simelectronic Simulation Software. Test and validation studies with proper load matching circuits are simulated and results are presented here.

In this paper, an artificial intelligence (AI)-integrated direct torque control (DTC) scheme is developed for an electric vehicle (EV or eCAR) propulsion motor drive. In addition, a comparison is made between adaptive neural network (ANN) and genetic algorithm (GA)-based torque controllers. The integration of AI into EVs has attracted the attention of many researchers in terns if drive control, dynamic stability, speed estimation, and energy management strategies. Amidst the various motor drive control strategies, DTC schemes with space vector pulse width modulation (SVPWM) have gained prominence due to its fast torque (speed) control capability. The smooth control of a DTC-eCAR propulsion motor is accomplished by the use of AI algorithms. The applications of ANN and GA algorithms for tuning the torque controller are tested and the behavior of an eCAR in terms of drive range, percentage of state of charge (SOC), and energy consumption for different driving conditions is observed using MATLAB simulations.