Optimization Strategies for Electric Vehicles: A Review of Evolutionary, Swarm, and Hybrid Algorithms

¹ Department of Electrical and Electronics Engineering, Presidency University, Bengaluru, Karnataka, India
² Department of Electrical and Electronics Engineering, Presidency University, Bengaluru, Karnataka, India

^* Corresponding author: vijay.kumara@presidencyuniversity.in

Abstract

Electric vehicles (EVs) are revolutionizing the automotive industry due to their environmental benefits and economic advantages. However, EV design and control present significant challenges, especially with regard to optimizing battery management, energy efficiency, thermal control, and cost. These challenges frequently feature non-linear relationships that traditional optimization techniques find difficult to handle effectively. This paper benchmarks the performance of selected non-linear optimization algorithms in addressing critical EV problems, including battery charging strategies, energy-efficient driving control, and thermal management. The analysis of optimization techniques is conducted in a chronological manner, highlighting their development from traditional methods to more sophisticated approaches. These techniques are evaluated against key metrics, including solution quality, convergence speed, computational cost, and robustness, illustrating how advancements over time have more effectively tackled the challenges associated with optimizing electric vehicle (EV) operation scenarios. This paper outlines the strengths and weaknesses of various optimization techniques, and offers insights and recommendations for their effective application in EV operation and design. Future research directions include exploring hybrid algorithms and adaptive optimization approaches to further improve EV performance.