MODA-BASED OPTIMIZATION AF A LANDSMAN JONVERTER FOR EFFICIENT BLDC MOTOR DRIVE IN ELECTRIC VEHICLES

The application of a PID controller, optimized with the Multi-Objective Dragonfly Algorithm
(MODA), for managing a BLDC motor drive system in an electric vehicle (EV) operating
environment has been investigated in this study. In a number of industrial applications and electric
mobility systems, BLDC motors are growing more and more popular because of their high
efficiency, minimal maintenance requirements, small size, and economy. In dynamic drive
systems, precise speed and torque control remains a technical difficulty, as is the requirement for
improved energy economy. In order to overcome these difficulties, a modified Landsman
converter was added to the system, and the MODA algorithm was used to tune the PID controller
in real time. The main goal ofthe suggested approach was to control motor speed while minimizing
torque ripple and declaring quick, steady dynamic reactions. By effectively determining the ideal
gain parameters, the MODA-based PID tuning approach improved current regulation, torque ripple
decrease, and speed reference tracking. The suggested approach generated better performance
metrics than conventional optimization methods like the Crow Search Algorithm (CFA) and
Elephant Herding Optimization (EHO). According to the comparison analysis, the MODAoptimized PID controller performs better than current control methods in terms of robustness,
dependability, and energy efficiency when used in conjunction with the Landsman converter. For
BLDC motor-driven EV systems, the suggested control approach provides an adaptable,
intelligent, and scalable solution. It can be further extended to other efficient motion control
applications in automation, robotics, or renewable energy. For even more system resilience in
dynamic and unpredictable operating situations, future research may focus on integ