Performance Analysis of Adaptive Integrated Power Conversion System for Electric Vehicles

Abstract

In recent times, research towards more ecological and nonpolluting forms of transportation has been motivated by climate change and the exhaustion offossil resources. With their ability to reduce reliance on non - renewable and extraordinary flexibility, electric vehicles (EVs) can make a substantial contribution to this. An adaptive integrated power conversion system (IPCS) is examined in this research, which could eliminate the need for separate power converters and perform in two modes: battery charging and electric propulsion. In this integrated topology, the cascaded dual active bridge (DAB) bidirectional dc-dc converter along with a dc-ac voltage source inverter (VSI) share common power components. This set-up for the system in electric vehicles decreases the switch count and the volume of the power converter unit. An artificial neural network (ANN) controller is used to implement speed control of a three-phase induction motor (IM) via constant V /f regulation for traction operation in closed loop. The backpropagation method and the Levenberg-Marquardt (LM) algorithm are used to train the network. In this paper the adaptive IPCS is compared to the traditional PI based system. The findings of two control modalities are provided, and the comparison analysis is validated with MATLAB Simulink.