A Master-Slave Salp Swarm Algorithm Optimizer for Hybrid Energy Storage System Control Strategy in Electric Vehicles. (14th September 2022)

• Record Type: Journal Article
• Authors: Cheruiyot, Fabian; Segera, Davies
• Other Names: Osorio de la Rosa Edith Academic Editor.
• Is Part Of: Journal of energy. Volume 2022(2022)
• Publication Date: 2022-09-14
• Subjects: Power resources -- Periodicals; Power resources -- Environmental aspects -- Periodicals; Power resources -- Social aspects -- Periodicals; Power resources -- Economic aspects -- Periodicals; Energy industries -- Periodicals; 333.7905
• DOI: 10.1155/2022/1648433
• Languages: English
• ISSNs: 2356-735X

Abstract

Abstract : Pure electric vehicles provide an enticing ecofriendly alternative to traditional fossil fuel combustion engine locomotives. Batteries have primarily been used to store energy in electric vehicles; however, peak load demand and transient power leading to decreased battery lifespan have bred interest in hybrid energy storage systems in electric vehicles. Management of energy drawn from a hybrid energy storage system (HESS) in electric vehicles is a real-time multistage optimization problem aimed at minimizing energy consumption while aptly distributing energy drawn from the battery and capacitor to enhance the battery life cycle. This paper explores the feasibility of a master-slave salp swarm optimization algorithm (MSSSA) (metaheuristic algorithm) in a HESS control strategy for electric vehicles. Introducing a master-slave learning approach to the salp swarm algorithm (SSA) improves its performance by increasing its convergence rate while maintaining a balance between exploration and exploitation phases of the algorithm. A comparison of the MSSSA results with the SSA (salp swarm algorithm), DA (dynamic algorithm), WOA (whale optimization algorithm), MFO (moth flame optimization algorithm), GA (genetic algorithm), and PSO (particle swarm optimization algorithm) on benchmark test functions and dynamic program simulation of an electric vehicle's HESS control strategy and shows preeminence of the MSSSA control strategy for HESS.