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Article: A charging-scheme decision model for electric vehicle battery swapping station using varied population evolutionary algorithms

TitleA charging-scheme decision model for electric vehicle battery swapping station using varied population evolutionary algorithms
Authors
KeywordsBattery swapping stations
Electric vehicles
Evolutionary algorithms
Varied population
Issue Date2017
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/asoc
Citation
Applied Soft Computing, 2017, v. 61, p. 905-920 How to Cite?
AbstractThis paper proposes a new battery swapping station (BSS) model to determine the optimized charging scheme for each incoming Electric Vehicle (EV) battery. The objective is to maximize the BSS’s battery stock level and minimize the average charging damage with the use of different types of chargers. An integrated objective function is defined for the multi-objective optimization problem. The genetic algorithm (GA), differential evolution (DE) algorithm and three versions of particle swarm optimization (PSO) algorithms have been implemented to solve the problem, and the results show that GA and DE perform better than the PSO algorithms, but the computational time of GA and DE are longer than using PSO. Hence, the varied population genetic algorithm (VPGA) and varied population differential evolution (VPDE) algorithm are proposed to determine the optimal solution and reduce the computational time of typical evolutionary algorithms. The simulation results show that the performances of the proposed algorithms are comparable with the typical GA and DE, but the computational times of the VPGA and VPDE are significantly shorter. A 24-h simulation study is carried out to examine the feasibility of the model.
Persistent Identifierhttp://hdl.handle.net/10722/259321
ISSN
2019 Impact Factor: 5.472
2015 SCImago Journal Rankings: 1.763
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWu, H-
dc.contributor.authorPang, GKH-
dc.contributor.authorChoy, KL-
dc.contributor.authorLam, HY-
dc.date.accessioned2018-09-03T04:05:11Z-
dc.date.available2018-09-03T04:05:11Z-
dc.date.issued2017-
dc.identifier.citationApplied Soft Computing, 2017, v. 61, p. 905-920-
dc.identifier.issn1568-4946-
dc.identifier.urihttp://hdl.handle.net/10722/259321-
dc.description.abstractThis paper proposes a new battery swapping station (BSS) model to determine the optimized charging scheme for each incoming Electric Vehicle (EV) battery. The objective is to maximize the BSS’s battery stock level and minimize the average charging damage with the use of different types of chargers. An integrated objective function is defined for the multi-objective optimization problem. The genetic algorithm (GA), differential evolution (DE) algorithm and three versions of particle swarm optimization (PSO) algorithms have been implemented to solve the problem, and the results show that GA and DE perform better than the PSO algorithms, but the computational time of GA and DE are longer than using PSO. Hence, the varied population genetic algorithm (VPGA) and varied population differential evolution (VPDE) algorithm are proposed to determine the optimal solution and reduce the computational time of typical evolutionary algorithms. The simulation results show that the performances of the proposed algorithms are comparable with the typical GA and DE, but the computational times of the VPGA and VPDE are significantly shorter. A 24-h simulation study is carried out to examine the feasibility of the model.-
dc.languageeng-
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/asoc-
dc.relation.ispartofApplied Soft Computing-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectBattery swapping stations-
dc.subjectElectric vehicles-
dc.subjectEvolutionary algorithms-
dc.subjectVaried population-
dc.titleA charging-scheme decision model for electric vehicle battery swapping station using varied population evolutionary algorithms-
dc.typeArticle-
dc.identifier.emailPang, GKH: gpang@eee.hku.hk-
dc.identifier.authorityPang, GKH=rp00162-
dc.description.naturepostprint-
dc.identifier.doi10.1016/j.asoc.2017.09.008-
dc.identifier.scopuseid_2-s2.0-85029411864-
dc.identifier.hkuros289916-
dc.identifier.volume61-
dc.identifier.spage905-
dc.identifier.epage920-
dc.identifier.isiWOS:000417629800063-
dc.publisher.placeNetherlands-

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