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

Deveci, M, Erdogan, N ORCID: https://orcid.org/0000-0003-1621-2748, Pamucar, D, Kucuksari, S and Cali, U, 2023. A rough Dombi Bonferroni based approach for public charging station type selection. Applied Energy, 345: 121258. ISSN 0306-2619

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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.

Item Type:	Journal article
Publication Title:	Applied Energy
Creators:	Deveci, M., Erdogan, N., Pamucar, D., Kucuksari, S. and Cali, U.
Publisher:	Elsevier BV
Date:	1 September 2023
Volume:	345
ISSN:	0306-2619
Identifiers:	Number Type 10.1016/j.apenergy.2023.121258 DOI 1786997 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Ward
Date Added:	22 Feb 2024 09:31
Last Modified:	29 May 2024 03:00
URI:	https://irep.ntu.ac.uk/id/eprint/50912

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