ANN Based Intelligent Control of Bidirectional DC-DC Converter for EV Drive System with Regenerative Braking

Authors:

Maddala Shyam, Maddala Vinod Kumar, Sivanadhan Giri Sankar, Bathula Suvarna, Satapathi Surya ,A. Chakradhar

Abstract

Electric vehicles (EVs) require efficient bidirectional power conversion to support both forward motoring and regenerative braking. This paper presents a novel non-isolated high-gain bidirectional DC-DC converter (HGBDC) integrated with an Artificial Neural Network (ANN)-based intelligent controller for EV drive systems. The proposed converter achieves high voltage gain using a dual duty-cycle control strategy with reduced component count and low voltage stress. An ANN controller is employed to optimize duty cycle selection in real time, enhancing dynamic response, voltage regulation, and energy recovery efficiency during regenerative braking. The system is modeled and simulated in MATLAB/Simulink under various operating conditions, including forward motoring, step speed variations, and regenerative braking. Comparative analysis with a conventional PID controller demonstrates that the ANN-based control significantly improves transient performance, reduces steady state error, and enhances battery charging during energy recovery. Simulation results confirm the effectiveness of the proposed HGBDC with ANN control for next-generation EV powertrains.

Keywords: Bidirectional DC-DC Converter, High-Gain Converter, ANN Control, Electric Vehicle, Regenerative Braking, MATLAB/Simulink, Energy Recovery, Intelligent Control