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A rough Dombi Bonferroni based approach for public charging station type selection

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  • Deveci, Muhammet
  • Erdogan, Nuh
  • Pamucar, Dragan
  • Kucuksari, Sadik
  • Cali, Umit

Abstract

As the transition to electric mobility accelerates, charging infrastructure is rapidly expanding. Publicly accessible chargers, also known as electric vehicle supply equipment (EVSE), are critical not only for further promoting the transition but also for mitigating charger access anxiety among electric vehicle (EV) users. It is essential to install the proper EVSE configuration that meets the EV user’s various considerations. This study presents a multi-criteria decision-making (MCDM) framework for determining the best performing public EVSE type from multiple EV user perspectives. The proposed approach combines a new MCDM model with an optimal public charging station model. While the optimal model outputs are used to evaluate the quantitative criteria, the MCDM model assesses EV users’ evaluations of the qualitative criteria using nonlinear Bonferroni functions extended by rough Dombi norms. The proposed MCDM has standardization parameters with a flexible rough boundary interval, allowing for flexible and rational decision-making. The model is tested using real public EVSE charging data and EV users’ evaluations from the field. All public EVSE alternatives are studied. Among the five EVSE options, DCFC EVSE is found to be the best performing, whereas three-phase AC L2 is the least performing option. In terms of EV user preferences, the required charging time is found to have the highest degree of importance, while V2G capability is the least important. The comparative analysis with state-of-the-art MCDM methods validates the proposed model results. Finally, sensitivity analysis verified the ranking order.

Suggested Citation

  • Deveci, Muhammet & Erdogan, Nuh & Pamucar, Dragan & Kucuksari, Sadik & Cali, Umit, 2023. "A rough Dombi Bonferroni based approach for public charging station type selection," Applied Energy, Elsevier, vol. 345(C).
    • Handle: RePEc:eee:appene:v:345:y:2023:i:c:s0306261923006220
    DOI: 10.1016/j.apenergy.2023.121258
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    References listed on IDEAS

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    1. 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).
    2. Erdogan, Nuh & Kucuksari, Sadik & Murphy, Jimmy, 2022. "A multi-objective optimization model for EVSE deployment at workplaces with smart charging strategies and scheduling policies," Energy, Elsevier, vol. 254(PA).
    3. Tanrıverdi, Gökhan & Ecer, Fatih & Durak, Mehmet Şahin, 2022. "Exploring factors affecting airport selection during the COVID-19 pandemic from air cargo carriers’ perspective through the triangular fuzzy Dombi-Bonferroni BWM methodology," Journal of Air Transport Management, Elsevier, vol. 105(C).
    4. Du, Jiuyu & Liu, Ye & Mo, Xinying & Li, Yalun & Li, Jianqiu & Wu, Xiaogang & Ouyang, Minggao, 2019. "Impact of high-power charging on the durability and safety of lithium batteries used in long-range battery electric vehicles," Applied Energy, Elsevier, vol. 255(C).
    5. Xu, Min & Meng, Qiang & Liu, Kai & Yamamoto, Toshiyuki, 2017. "Joint charging mode and location choice model for battery electric vehicle users," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 68-86.
    6. Schmidt, Marc & Staudt, Philipp & Weinhardt, Christof, 2020. "Evaluating the importance and impact of user behavior on public destination charging of electric vehicles," Applied Energy, Elsevier, vol. 258(C).
    7. Muhammad Azam & Muhammad Sajjad Ali Khan & Shilin Yang & Musavarah Sarwar, 2022. "A Decision-Making Approach for the Evaluation of Information Security Management under Complex Intuitionistic Fuzzy Set Environment," Journal of Mathematics, Hindawi, vol. 2022, pages 1-30, February.
    8. Paterakis, Nikolaos G. & Gibescu, Madeleine, 2016. "A methodology to generate power profiles of electric vehicle parking lots under different operational strategies," Applied Energy, Elsevier, vol. 173(C), pages 111-123.
    9. Sadeghi-Barzani, Payam & Rajabi-Ghahnavieh, Abbas & Kazemi-Karegar, Hosein, 2014. "Optimal fast charging station placing and sizing," Applied Energy, Elsevier, vol. 125(C), pages 289-299.
    10. Zhou, Guangyou & Zhu, Zhiwei & Luo, Sumei, 2022. "Location optimization of electric vehicle charging stations: Based on cost model and genetic algorithm," Energy, Elsevier, vol. 247(C).
    11. Jia Liu & Jin Huang & Jinzhi Hu, 2022. "Multi-objective optimisation method of electric vehicle charging station based on non-dominated sorting genetic algorithm," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 44(5/6), pages 413-426.
    12. Lin, Haiyang & Bian, Caiyun & Wang, Yu & Li, Hailong & Sun, Qie & Wallin, Fredrik, 2022. "Optimal planning of intra-city public charging stations," Energy, Elsevier, vol. 238(PC).
    Full references (including those not matched with items on IDEAS)

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