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

Hao Wu, Grantham Kwok Hung Pang, King Lun Tommy Choy, Hoi Yan Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

25 Citations (Scopus)

Abstract

This 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.
Original languageEnglish
Pages (from-to)905-920
Number of pages16
JournalApplied Soft Computing Journal
Volume61
DOIs
Publication statusPublished - 1 Dec 2017

Keywords

  • Battery swapping stations
  • Electric vehicles
  • Evolutionary algorithms
  • Varied population

ASJC Scopus subject areas

  • Software

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