IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i18p6652-d1241328.html
   My bibliography  Save this article

Enhancing Sustainable Assessment of Electric Vehicles: A Comparative Study of the TOPSIS Technique with Interval Numbers for Uncertainty Management

Author

Listed:
  • Aleksandra Kaczyńska

    (Research Team on Intelligent Decision Support Systems, Department of Artificial Intelligence and Applied Mathematics, Faculty of Computer Science and Information Technology, West Pomeranian University of Technology in Szczecin, ul. Żołnierska 49, 71-210 Szczecin, Poland)

  • Piotr Sulikowski

    (Faculty of Information Technology and Computer Science, West Pomeranian University of Technology, ul. Zolnierska 49, 71-210 Szczecin, Poland)

  • Jarosław Wątróbski

    (Institute of Management, University of Szczecin, Cukrowa 8, 71-004 Szczecin, Poland)

  • Wojciech Sałabun

    (Research Team on Intelligent Decision Support Systems, Department of Artificial Intelligence and Applied Mathematics, Faculty of Computer Science and Information Technology, West Pomeranian University of Technology in Szczecin, ul. Żołnierska 49, 71-210 Szczecin, Poland
    National Institute of Telecommunications, Szachowa 1, 04-894 Warsaw, Poland)

Abstract

The subject of electric vehicles (EVs) is constantly relevant from the perspective of climate change and sustainability. Multi-Criteria Decision Analysis (MCDA) methods can be successfully used to evaluate models of such vehicles. In many cases, the MCDA methods are modified to account for uncertainty in the data. There are many ways to express uncertainty, including more advanced ones, such as fuzzy sets, for example, but expressing attributes in terms of interval numbers remains a popular method because it is an easy-to-implement and easy-to-understand technique. This study focuses on interval extensions of the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. It aims to compare the most popular extension proposed by Jahanshahloo and the proposed new modification, which returns the result in an interval form. Certain inconsistencies of the Jahanshahloo extension are discussed, and it is explained how the new extension avoids them. Both extensions are applied to an EV evaluation problem taken from the literature as an example for sustainable assessment. The results are then analyzed, and the question of whether the input data of the interval should receive an evaluation in the form of interval results is addressed.

Suggested Citation

  • Aleksandra Kaczyńska & Piotr Sulikowski & Jarosław Wątróbski & Wojciech Sałabun, 2023. "Enhancing Sustainable Assessment of Electric Vehicles: A Comparative Study of the TOPSIS Technique with Interval Numbers for Uncertainty Management," Energies, MDPI, vol. 16(18), pages 1-17, September.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6652-:d:1241328
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/18/6652/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/18/6652/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guo, Sen & Zhao, Huiru, 2015. "Optimal site selection of electric vehicle charging station by using fuzzy TOPSIS based on sustainability perspective," Applied Energy, Elsevier, vol. 158(C), pages 390-402.
    2. Sukhee Kim & Jungyoon Choi & Yongju Yi & Hyungjun Kim, 2022. "Analysis of Influencing Factors in Purchasing Electric Vehicles Using a Structural Equation Model: Focused on Suwon City," Sustainability, MDPI, vol. 14(8), pages 1-17, April.
    3. Lin Ding & Zhenfeng Shao & Hanchao Zhang & Cong Xu & Dewen Wu, 2016. "A Comprehensive Evaluation of Urban Sustainable Development in China Based on the TOPSIS-Entropy Method," Sustainability, MDPI, vol. 8(8), pages 1-23, August.
    4. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "Decision analysis in energy and environmental modeling: An update," Energy, Elsevier, vol. 31(14), pages 2604-2622.
    5. Zhang, Cheng & Yang, Fan & Ke, Xinyou & Liu, Zhifeng & Yuan, Chris, 2019. "Predictive modeling of energy consumption and greenhouse gas emissions from autonomous electric vehicle operations," Applied Energy, Elsevier, vol. 254(C).
    6. Paweł Ziemba, 2021. "Selection of Electric Vehicles for the Needs of Sustainable Transport under Conditions of Uncertainty—A Comparative Study on Fuzzy MCDA Methods," Energies, MDPI, vol. 14(22), pages 1-25, November.
    7. Giuseppe Aiello & Salvatore Quaranta & Antonella Certa & Rosalinda Inguanta, 2021. "Optimization of Urban Delivery Systems Based on Electric Assisted Cargo Bikes with Modular Battery Size, Taking into Account the Service Requirements and the Specific Operational Context," Energies, MDPI, vol. 14(15), pages 1-17, August.
    8. Meng Zhao & Song-song Qin & Qi-wang Li & Fu-qiang Lu & Zhe Shen, 2015. "The Likelihood Ranking Methods for Interval Type-2 Fuzzy Sets Considering Risk Preferences," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-12, September.
    9. Zhang, Bo & Zhang, Jiangyan & Shen, Tielong, 2022. "Optimal control design for comfortable-driving of hybrid electric vehicles in acceleration mode," Applied Energy, Elsevier, vol. 305(C).
    10. Poullikkas, Andreas, 2015. "Sustainable options for electric vehicle technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1277-1287.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sellak, Hamza & Ouhbi, Brahim & Frikh, Bouchra & Palomares, Iván, 2017. "Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1544-1577.
    2. Gao, Bingzhao & Meng, Dele & Shi, Wentong & Cai, Wenqi & Dong, Shiying & Zhang, Yuanjian & Chen, Hong, 2022. "Topology optimization and the evolution trends of two-speed transmission of EVs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    3. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
    4. Wang, Zhengchao & Perera, A.T.D., 2020. "Integrated platform to design robust energy internet," Applied Energy, Elsevier, vol. 269(C).
    5. Nock, Destenie & Baker, Erin, 2019. "Holistic multi-criteria decision analysis evaluation of sustainable electric generation portfolios: New England case study," Applied Energy, Elsevier, vol. 242(C), pages 655-673.
    6. Danijela Tuljak-Suban & Patricija Bajec, 2022. "A Hybrid DEA Approach for the Upgrade of an Existing Bike-Sharing System with Electric Bikes," Energies, MDPI, vol. 15(21), pages 1-23, October.
    7. Choudhary, Devendra & Shankar, Ravi, 2012. "An STEEP-fuzzy AHP-TOPSIS framework for evaluation and selection of thermal power plant location: A case study from India," Energy, Elsevier, vol. 42(1), pages 510-521.
    8. Yang, Jun & Guo, Fang & Zhang, Min, 2017. "Optimal planning of swapping/charging station network with customer satisfaction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 103(C), pages 174-197.
    9. Weiwei Li & Pingtao Yi & Danning Zhang, 2018. "Sustainability Evaluation of Cities in Northeastern China Using Dynamic TOPSIS-Entropy Methods," Sustainability, MDPI, vol. 10(12), pages 1-15, December.
    10. Csiszár, Csaba & Csonka, Bálint & Földes, Dávid & Wirth, Ervin & Lovas, Tamás, 2020. "Location optimisation method for fast-charging stations along national roads," Journal of Transport Geography, Elsevier, vol. 88(C).
    11. Hicham El Hadraoui & Mourad Zegrari & Fatima-Ezzahra Hammouch & Nasr Guennouni & Oussama Laayati & Ahmed Chebak, 2022. "Design of a Customizable Test Bench of an Electric Vehicle Powertrain for Learning Purposes Using Model-Based System Engineering," Sustainability, MDPI, vol. 14(17), pages 1-22, September.
    12. Woo, Hyeon & Son, Yongju & Cho, Jintae & Kim, Sung-Yul & Choi, Sungyun, 2023. "Optimal expansion planning of electric vehicle fast charging stations," Applied Energy, Elsevier, vol. 342(C).
    13. Andriosopoulos, Kostas & Bigerna, Simona & Bollino, Carlo Andrea & Micheli, Silvia, 2018. "The impact of age on Italian consumers' attitude toward alternative fuel vehicles," Renewable Energy, Elsevier, vol. 119(C), pages 299-308.
    14. Oluwasola O. Ademulegun & Paul MacArtain & Bukola Oni & Neil J. Hewitt, 2022. "Multi-Stage Multi-Criteria Decision Analysis for Siting Electric Vehicle Charging Stations within and across Border Regions," Energies, MDPI, vol. 15(24), pages 1-28, December.
    15. 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).
    16. Paweł Ziemba & Mateusz Piwowarski & Kesra Nermend, 2023. "Remote Work in Post-Pandemic Reality—Multi-Criteria Evaluation of Teleconferencing Software," Sustainability, MDPI, vol. 15(13), pages 1-20, June.
    17. Rahman, Syed & Khan, Irfan Ahmed & Khan, Ashraf Ali & Mallik, Ayan & Nadeem, Muhammad Faisal, 2022. "Comprehensive review & impact analysis of integrating projected electric vehicle charging load to the existing low voltage distribution system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    18. Hamza El Hafdaoui & Hamza El Alaoui & Salma Mahidat & Zakaria El Harmouzi & Ahmed Khallaayoun, 2023. "Impact of Hot Arid Climate on Optimal Placement of Electric Vehicle Charging Stations," Energies, MDPI, vol. 16(2), pages 1-19, January.
    19. Paweł Ziemba, 2022. "Application Framework of Multi-Criteria Methods in Sustainability Assessment," Energies, MDPI, vol. 15(23), pages 1-18, December.
    20. Witsarut Achariyaviriya & Wongkot Wongsapai & Kittitat Janpoom & Tossapon Katongtung & Yuttana Mona & Nakorn Tippayawong & Pana Suttakul, 2023. "Estimating Energy Consumption of Battery Electric Vehicles Using Vehicle Sensor Data and Machine Learning Approaches," Energies, MDPI, vol. 16(17), pages 1-14, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6652-:d:1241328. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.