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EV charging and fuel cell vehicle refuelling with distributed energy resources using hybrid approach.

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Title: EV charging and fuel cell vehicle refuelling with distributed energy resources using hybrid approach.
Authors: Senthilkumar, M.1 (AUTHOR) senthilkumar@psgitech.ac.in, Prabhu, Sandeep2 (AUTHOR) sprabhu@sidtm.edu.in, Arun Kumar, U.3 (AUTHOR) arun.udayakumarn@gmail.com, Krishnakumar, R.4 (AUTHOR) krishnakumar.ramasamy@srec.ac.in
Source: Environment, Development & Sustainability. Feb2026, Vol. 28 Issue 2, p4309-4331. 23p.
Subject Terms: *Distributed resources (Electric utilities), *Optimization algorithms, *Electric vehicle charging stations, *Energy storage, *Metaheuristic algorithms, *Artificial neural networks, *Fuel cells, *Cost control
Abstract: This manuscript proposes a hybrid technique for Electric Vehicle (EV) charging and Fuel Cell vehicle refuelling with distributed energy resources. The proposed hybrid approach, known as the BWO-CCG-DLNN method, combines the Beluga Whale Optimization (BWO) algorithm with the Cascade-Correlation Growing Deep Learning Neural Network (CCG-DLNN). The primary goal of the proposed strategy is to reduce reliance on the utility grid while simultaneously reducing the overall cost of distributed energy resources by using battery storage for peak shaving. The EV charging's cost is reduced using the proposed BWO approach, and the ideal outcome of the system is predicted using the CCG-DLNN approach. The proposed strategy is implemented into use on the MATLAB platform, and it is contrasted with current strategys, including the Cuckoo Search Algorithm Color Harmony Algorithm, and Particle Swarm Optimization, The proposed method demonstrates the lowest mean (1.0936) and median (1.0158), indicating its effectiveness. The standard deviation (0.1505) suggests relatively consistent results. The proposed method shows better result when compared to other methods. When compared to other existing approaches, the proposed approach has a high efficiency of 98% and a low cost of 200 ($/kW). [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 192482149
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  Label: Title
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  Data: EV charging and fuel cell vehicle refuelling with distributed energy resources using hybrid approach.
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  Data: <searchLink fieldCode="AR" term="%22Senthilkumar%2C+M%2E%22">Senthilkumar, M.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> senthilkumar@psgitech.ac.in</i><br /><searchLink fieldCode="AR" term="%22Prabhu%2C+Sandeep%22">Prabhu, Sandeep</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> sprabhu@sidtm.edu.in</i><br /><searchLink fieldCode="AR" term="%22Arun+Kumar%2C+U%2E%22">Arun Kumar, U.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> arun.udayakumarn@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Krishnakumar%2C+R%2E%22">Krishnakumar, R.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> krishnakumar.ramasamy@srec.ac.in</i>
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  Data: <searchLink fieldCode="JN" term="%22Environment%2C+Development+%26+Sustainability%22">Environment, Development & Sustainability</searchLink>. Feb2026, Vol. 28 Issue 2, p4309-4331. 23p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Distributed+resources+%28Electric+utilities%29%22">Distributed resources (Electric utilities)</searchLink><br />*<searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+vehicle+charging+stations%22">Electric vehicle charging stations</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+storage%22">Energy storage</searchLink><br />*<searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Fuel+cells%22">Fuel cells</searchLink><br />*<searchLink fieldCode="DE" term="%22Cost+control%22">Cost control</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This manuscript proposes a hybrid technique for Electric Vehicle (EV) charging and Fuel Cell vehicle refuelling with distributed energy resources. The proposed hybrid approach, known as the BWO-CCG-DLNN method, combines the Beluga Whale Optimization (BWO) algorithm with the Cascade-Correlation Growing Deep Learning Neural Network (CCG-DLNN). The primary goal of the proposed strategy is to reduce reliance on the utility grid while simultaneously reducing the overall cost of distributed energy resources by using battery storage for peak shaving. The EV charging's cost is reduced using the proposed BWO approach, and the ideal outcome of the system is predicted using the CCG-DLNN approach. The proposed strategy is implemented into use on the MATLAB platform, and it is contrasted with current strategys, including the Cuckoo Search Algorithm Color Harmony Algorithm, and Particle Swarm Optimization, The proposed method demonstrates the lowest mean (1.0936) and median (1.0158), indicating its effectiveness. The standard deviation (0.1505) suggests relatively consistent results. The proposed method shows better result when compared to other methods. When compared to other existing approaches, the proposed approach has a high efficiency of 98% and a low cost of 200 ($/kW). [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=192482149
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10668-024-05138-8
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 23
        StartPage: 4309
    Subjects:
      – SubjectFull: Distributed resources (Electric utilities)
        Type: general
      – SubjectFull: Optimization algorithms
        Type: general
      – SubjectFull: Electric vehicle charging stations
        Type: general
      – SubjectFull: Energy storage
        Type: general
      – SubjectFull: Metaheuristic algorithms
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Fuel cells
        Type: general
      – SubjectFull: Cost control
        Type: general
    Titles:
      – TitleFull: EV charging and fuel cell vehicle refuelling with distributed energy resources using hybrid approach.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Senthilkumar, M.
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            NameFull: Prabhu, Sandeep
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            NameFull: Arun Kumar, U.
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            NameFull: Krishnakumar, R.
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            – D: 01
              M: 02
              Text: Feb2026
              Type: published
              Y: 2026
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              Value: 28
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            – TitleFull: Environment, Development & Sustainability
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