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Optimal design of high efficiency wireless charging system for electric vehicle: AHO–GBDT technique.

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Title: Optimal design of high efficiency wireless charging system for electric vehicle: AHO–GBDT technique.
Authors: Kalaiselvan, K.1 (AUTHOR) kalai123selvan@gmail.com, Venkatesan, M.2 (AUTHOR), Rachananjali, K.3 (AUTHOR), Bhuvaneswari, G.4 (AUTHOR)
Source: Energy & Environment. May2026, Vol. 37 Issue 3, p1543-1567. 25p.
Subject Terms: *Optimization algorithms, *Metaheuristic algorithms, *Wireless power transmission, *Mathematical optimization, *Electric vehicles, *Boosting algorithms, *Mutual inductance
Reviews & Products: MatLab (Computer software)
Abstract: This paper proposed a hybrid technique that optimizes the design of wireless charging systems (WCSs) for electric vehicles (EVs). The proposed technique combines the Archerfish hunting optimizer (AHO) and the gradient boosting decision tree (GBDT) algorithms, hence named the AHO–GBDT technique. The proposed strategy's goal is to enhance the efficiency, minimize cost, and increase coupling coefficient. The AHO algorithm optimizes the design parameters of WCS for EVs. The GBDT dynamically adjusts and optimizes the hyper-parameters, fine-tuning the algorithm for optimal performance. The outcome displays that the proposed strategy can greatly enhances the wireless charging system's efficiency. The proposed strategy is done in MATLAB software and assessed for performance with different existing strategies. The AHO–GBDT algorithm outperforms particle swarm optimizer (PSO), color harmony algorithm (CHA), and owl search algorithm (OSA) in terms of efficiency, which achieves 98.5%, while compared to 97.2%, 96.8%, and 96.4%, respectively. The proposed method shows the highest efficiency, power, and coupling coefficient compared to other existing the PSO, CHA, and OSA methods. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 193364263
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PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
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  Data: Optimal design of high efficiency wireless charging system for electric vehicle: AHO–GBDT technique.
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  Data: <searchLink fieldCode="AR" term="%22Kalaiselvan%2C+K%2E%22">Kalaiselvan, K.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> kalai123selvan@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Venkatesan%2C+M%2E%22">Venkatesan, M.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rachananjali%2C+K%2E%22">Rachananjali, K.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bhuvaneswari%2C+G%2E%22">Bhuvaneswari, G.</searchLink><relatesTo>4</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energy+%26+Environment%22">Energy & Environment</searchLink>. May2026, Vol. 37 Issue 3, p1543-1567. 25p.
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  Data: *<searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Wireless+power+transmission%22">Wireless power transmission</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+vehicles%22">Electric vehicles</searchLink><br />*<searchLink fieldCode="DE" term="%22Boosting+algorithms%22">Boosting algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Mutual+inductance%22">Mutual inductance</searchLink>
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  Label: Abstract
  Group: Ab
  Data: This paper proposed a hybrid technique that optimizes the design of wireless charging systems (WCSs) for electric vehicles (EVs). The proposed technique combines the Archerfish hunting optimizer (AHO) and the gradient boosting decision tree (GBDT) algorithms, hence named the AHO–GBDT technique. The proposed strategy's goal is to enhance the efficiency, minimize cost, and increase coupling coefficient. The AHO algorithm optimizes the design parameters of WCS for EVs. The GBDT dynamically adjusts and optimizes the hyper-parameters, fine-tuning the algorithm for optimal performance. The outcome displays that the proposed strategy can greatly enhances the wireless charging system's efficiency. The proposed strategy is done in MATLAB software and assessed for performance with different existing strategies. The AHO–GBDT algorithm outperforms particle swarm optimizer (PSO), color harmony algorithm (CHA), and owl search algorithm (OSA) in terms of efficiency, which achieves 98.5%, while compared to 97.2%, 96.8%, and 96.4%, respectively. The proposed method shows the highest efficiency, power, and coupling coefficient compared to other existing the PSO, CHA, and OSA methods. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=193364263
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/0958305X241270231
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 25
        StartPage: 1543
    Subjects:
      – SubjectFull: Optimization algorithms
        Type: general
      – SubjectFull: Metaheuristic algorithms
        Type: general
      – SubjectFull: Wireless power transmission
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Electric vehicles
        Type: general
      – SubjectFull: Boosting algorithms
        Type: general
      – SubjectFull: Mutual inductance
        Type: general
      – SubjectFull: MatLab (Computer software)
        Type: general
    Titles:
      – TitleFull: Optimal design of high efficiency wireless charging system for electric vehicle: AHO–GBDT technique.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Kalaiselvan, K.
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          Name:
            NameFull: Venkatesan, M.
      – PersonEntity:
          Name:
            NameFull: Rachananjali, K.
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            NameFull: Bhuvaneswari, G.
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          Dates:
            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 0958305X
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            – Type: volume
              Value: 37
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: Energy & Environment
              Type: main
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