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A multi-objective optimization of electric vehicles energy flows: the charging process

Author

Listed:
  • Ghimar Merhy

    (University of Picardie Jules Verne)

  • Ahmed Nait-Sidi-Moh

    (University of Picardie Jules Verne)

  • Nazih Moubayed

    (Lebanese University)

Abstract

As the electrification of vehicles keeps being widespread, and facing the impossibility of storing big amounts of electrical energy, the challenge of controlling and adapting the electricity supply and demand has become a necessity. Therefore, electric vehicles could be an optimal solution for the storage and the retrieval of energy depending on the supply and demand of electricity. Thus, this study proposes an energetic strategy based on a multi-objective and multi-criteria optimization algorithm related to the control of the energy flows between the electric vehicles and the grid, home or building depending on the electricity supply and demand. The main focus at this stage involves the optimization of the vehicles’ charging mode. Hence, in this paper, the multi-objective optimization proposed is first implemented through the presentation of its algorithm and the description and modeling of its objective functions and their corresponding constraints. Then, the genetic algorithm optimization approach is adopted for the charging process of the study to find the optimal solution for each objective function. Besides, the weighted sum approach is applied after the normalization of all the fitness functions and several case studies are carried out to find the optimal solutions based on the priority of specific objectives over the others. Once all calculations are done, a simulation via Matlab software is performed and the results are discussed and compared. The simulation of the results has verified the theoretical calculations proving the effectiveness of the proposed methodology. However, as some of the calculated optimal solutions of the system seem to be conflicting, the prioritization of some objectives over others had to be operated in order to figure out a global solution for the multi-objective system. Thus, as a compromise had to be applied for the calculation of quasi-optimal solutions that would converge towards the Pareto-front, the decision maker’s preference would set the final solution using the weighted sum approach.

Suggested Citation

  • Ghimar Merhy & Ahmed Nait-Sidi-Moh & Nazih Moubayed, 2021. "A multi-objective optimization of electric vehicles energy flows: the charging process," Annals of Operations Research, Springer, vol. 296(1), pages 315-333, January.
    • Handle: RePEc:spr:annopr:v:296:y:2021:i:1:d:10.1007_s10479-020-03529-4
    DOI: 10.1007/s10479-020-03529-4
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    References listed on IDEAS

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    1. Konak, Abdullah & Coit, David W. & Smith, Alice E., 2006. "Multi-objective optimization using genetic algorithms: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 992-1007.
    2. Apostolaki-Iosifidou, Elpiniki & Codani, Paul & Kempton, Willett, 2017. "Measurement of power loss during electric vehicle charging and discharging," Energy, Elsevier, vol. 127(C), pages 730-742.
    3. Xu, Liangfei & Mueller, Clemens David & Li, Jianqiu & Ouyang, Minggao & Hu, Zunyan, 2015. "Multi-objective component sizing based on optimal energy management strategy of fuel cell electric vehicles," Applied Energy, Elsevier, vol. 157(C), pages 664-674.
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    Cited by:

    1. Rehman, Waqas ur & Bo, Rui & Mehdipourpicha, Hossein & Kimball, Jonathan W., 2022. "Sizing battery energy storage and PV system in an extreme fast charging station considering uncertainties and battery degradation," Applied Energy, Elsevier, vol. 313(C).
    2. Erdogan, Nuh & Pamucar, Dragan & Kucuksari, Sadik & Deveci, Muhammet, 2021. "An integrated multi-objective optimization and multi-criteria decision-making model for optimal planning of workplace charging stations," Applied Energy, Elsevier, vol. 304(C).
    3. Ejeh, Jude O. & Roberts, Diarmid & Brown, Solomon F., 2023. "Exploring the value of electric vehicles to domestic end-users," Energy Policy, Elsevier, vol. 175(C).

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