Optimal hybrid energy system for locomotive utilizing improved Locust Swarm optimizer

Shen Cheng, Gaiju Zhao, Ming Gao, Yuetao Shi, Mingming Huang and Nasser Yousefi

Energy, 2021, vol. 218, issue C

Abstract: A novel methodology for optimum sizing of a hybrid energy (HE) system is presented in this paper to supply the driving force of a locomotive. The HE system includes a lithium-ion battery along with a polymer electrolyte membrane (PEM) fuel-cell. The idea behind this paper is to minimize the HE system’s total cost under the PEM fuel-cell state of charge (SoC) constraint and capacity constraint of the battery. The minimization in this study is performed by an improved version of the Locust Swarm (LS) optimization algorithm (ILS). The algorithm uses oppositional learning and chaos mechanism to resolve the premature convergence and speed of the algorithm along with escaping from the local optimum point. The results of the final case study have been done for analyzing the locomotive speed demand, the average power demand, and the locomotive slant. A comparison of the outcomes of the suggested ILS algorithm with the standard LS algorithm and Particle swarm optimizer (PSO) from the literature and the results showed that in a maximum slope (2%), the total cost of the HE system for the suggested ILS algorithm, the basic LS algorithm, and the PSO algorithm are 3.8×106 $, 4.43×106 $, and 4.86×106 $, respectively which indicated that the achieved overall expense for the suggested ILS gives the best amount and the results are carried out to verify the superiority of the proposed method in solving a challenging real-world problem.

Keywords: PEMFC; Lithium-ion battery; Hybrid energy system; Locomotive; Improved locust swarm optimization algorithm (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:218:y:2021:i:c:s0360544220325998

DOI: 10.1016/j.energy.2020.119492

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