Optimal sizing and cost analysis of hybrid energy storage system for EVs using metaheuristic PSO and firefly algorithms

• DOI: 10.1016/j.rineng.2024.102358
• Publication Date: 2024-06-13T01:03:43Z
• Authors: Muhammad Bin Fayy...; Saad Mekhilef; Tey Kok Soon; Muhammad Usama; Marizan Binti Mubin; Mehdi Seyedmahmou...; Alex Stojcevski; Hazlie Mokhlis; Prashant Shrivastava; Obaid Alshammari
• Keywords: Firefly Algorithm; Driving cycle

This paper presents a single-objective function optimization method for the optimal sizing and cost of hybrid energy storage system (HESS) that integrates lithium-ion batteries (LIB) supercapacitors (SC) electric vehicle (EV) applications. The study introduces comprehensive framework EV modeling, incorporating simulated data to enhance accuracy. A key achievement involves adapting modified–WLTC driving cycle, iteratively employed in simulations accurately capture spectrum power profiles within designated range, ensuring adherence BMW-i3's top speed requisites. proposed method's validity is established by comparing results using Particle Swarm Optimization (PSO) Firefly Algorithm (FA), indicating comparable HESS outcomes. Notably, PSO algorithm demonstrates superior accuracy computational efficiency. Through PSO, LIB-SC weight determined at 160 kg with an $27,660, while FA yields 161 $28,270, surpassing LIB-Only models approximately 21 % improved sizing. research significantly contributes establishing robust modeling paradigm, employing optimization, successfully implementing determine parameters, advancing field efficient promoting cost-effective, sustainable transportation.