Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
@article{191021,
author = {Heartwin Samraj V and Mohanavel B and Harish B and Amudhavalli D},
title = {Design of an Optimized BLDC Motor Control and Battery Management System for Electric Vehicles},
journal = {International Journal of Innovative Research in Technology},
year = {2026},
volume = {12},
number = {8},
pages = {4554-4559},
issn = {2349-6002},
url = {https://ijirt.org/article?manuscript=191021},
abstract = {In electric vehicles (EVs), system efficiency and reliability largely depend on optimized motor control and battery management. Conventional Brushless DC (BLDC) motor controllers often face torque ripple, unstable response, and low efficiency under dynamic load conditions. Similarly, traditional Battery Management Systems (BMS) struggle with accurate state-of-charge (SOC) estimation and current balancing. This paper presents a MATLAB/Simulink-based design and simulation of an integrated control architecture combining an optimized BLDC motor controller and intelligent BMS. The system includes a three-phase PWM rectifier, bidirectional DC–DC converter, and voltage source inverter connected via a common DC bus. A proportional-integral (PI) controller regulates motor speed and voltage, while a Recurrent Neural Network (RNN) controller enhances battery health through predictive current control. Simulation results show improved torque smoothness, stable DC-link voltage, reduced overshoot, and more accurate SOC estimation. The proposed design demonstrates a reliable and efficient control strategy suitable for next-generation electric vehicles.},
keywords = {BLDC Motor, Battery Management System, Electric Vehicle, MATLAB/Simulink, RNN Controller, PWM Rectifier, DC–DC Converter.},
month = {January},
}
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