Intelligent Control of Electric Vehicle Drives using Swarm Robotics

¹ Peter the Great St. Petersburg Polytechnic University, Saint Petersburg 195251, Russian Federation
² Lovely Professional University, Phagwara, Punjab, India
³ Department of EEE, GRIET, Bachupally, Hyderabad, Telangana, India
⁴ Uttaranchal University, Dehradun 248007, India
⁵ Centre of Research Impact and Outcome, Chitkara University, Rajpura 140417, Punjab, India
⁶ Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh 174103 India

^* Corresponding author: plml@mail.ru
vinaykumaar.a@gmail.com
jayanti_ballabh@uumail.in Centre of
anoop.dev.orp@chitkara.edu.in
sumeet.singh.sarpal.orp@chitkara.edu.in

Abstract

This study investigates the incorporation of swarm robotics into the control mechanism of electric vehicles (EVs), introducing an innovative intelligent control framework that utilizes the concepts of decentralized decision-making. The research entails a methodical inquiry that encompasses the design of system architecture, the creation of a model for swarm robotics, the modeling of electric vehicle drive, the integration of swarm robotics with EV control, the development of algorithms for intelligent control, and the execution of real-world tests. The fleet of electric cars, propelled by a collective of independent robotic entities, displayed remarkable flexibility in adjusting to fluctuating surroundings. Findings demonstrated disparities in operating duration, distance traversed, mean speed, and energy expenditure during several iterations, highlighting the system’s adeptness in promptly reacting to instantaneous inputs. Significantly, the swarm-propelled electric cars successfully attained varied operating durations, showcasing the system’s adaptability in accommodating environmental dynamics. The swarm-driven system demonstrated its navigation effectiveness by effectively covering various distances, highlighting its versatility and extensive coverage capabilities. The system’s ability to effectively balance energy economy and performance is shown by the collective regulation of average velocity. The energy consumption study demonstrated the system’s efficacy in optimizing energy use, with certain experiments showing significant savings. Percentage change studies have yielded valuable insights into the comparative enhancements or difficulties seen in each indicator, so illustrating the influence of decentralized decision-making on operational results. This study is a valuable contribution to the ever-changing field of intelligent transportation systems, providing insight into the immense potential of swarm-driven electric cars to completely transform sustainable and adaptable transportation. The results highlight the remarkable flexibility and optimization skills of swarm robotics in the management of electric vehicles, paving the way for future advancements in the quest for intelligent, energyefficient, and dynamically responsive transportation solutions.