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An Expert System for Ranking and Matching Electric Vehicles to Customer Specifications and Requirements

Author

Listed:
  • Yossi Hadad

    (Department of Industrial Engineering and Management, SCE—Shamoon College of Engineering, Beer-Shvea 8410802, Israel)

  • Baruch Keren

    (Department of Industrial Engineering and Management, SCE—Shamoon College of Engineering, Beer-Shvea 8410802, Israel)

  • Dima Alberg

    (Department of Industrial Engineering and Management, SCE—Shamoon College of Engineering, Beer-Shvea 8410802, Israel)

Abstract

Electric vehicles (EVs) have become popular in the last decade because of their advantages compared to conventional vehicles. The market offers dozens of EV models in a large range of prices, performances, and specifications. This paper presents an expert system we developed to support sellers and customers in choosing an EV that matches the customers’ specifications. The system enables ranking-specific EVs according to the customers’ specifications and counting the number of mismatches. The paper analyzes a database of 53 different EVs, each with 22 different characteristics, enabling customers to choose the EV that best suits their most important specifications. Based on the customer’s requirements and the principle of fuzzy sets, the system assigns a matching value to each criterion. These matching values are the input matrix for the TOPSIS procedure that ranks all the EVs according to their matching scores for a specific customer. The applicability of the proposed method is demonstrated for one customer with specific preferred EV requirements. A Python code of this method is also available herein.

Suggested Citation

  • Yossi Hadad & Baruch Keren & Dima Alberg, 2023. "An Expert System for Ranking and Matching Electric Vehicles to Customer Specifications and Requirements," Energies, MDPI, vol. 16(11), pages 1-18, May.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4283-:d:1154091
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    References listed on IDEAS

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    1. Shen, Zuo-Jun Max & Feng, Bo & Mao, Chao & Ran, Lun, 2019. "Optimization models for electric vehicle service operations: A literature review," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 462-477.
    2. Wu, Yang Andrew & Ng, Artie W. & Yu, Zichao & Huang, Jie & Meng, Ke & Dong, Z.Y., 2021. "A review of evolutionary policy incentives for sustainable development of electric vehicles in China: Strategic implications," Energy Policy, Elsevier, vol. 148(PB).
    3. Katarzyna Sobiech-Grabka & Anna Stankowska & Krzysztof Jerzak, 2022. "Determinants of Electric Cars Purchase Intention in Poland: Personal Attitudes v. Economic Arguments," Energies, MDPI, vol. 15(9), pages 1-26, April.
    4. Wang, Endong, 2015. "Benchmarking whole-building energy performance with multi-criteria technique for order preference by similarity to ideal solution using a selective objective-weighting approach," Applied Energy, Elsevier, vol. 146(C), pages 92-103.
    5. Charles Lincoln Kenji Yamamura & Harmi Takiya & Cláudia Aparecida Soares Machado & José Carlos Curvelo Santana & José Alberto Quintanilha & Fernando Tobal Berssaneti, 2022. "Electric Cars in Brazil: An Analysis of Core Green Technologies and the Transition Process," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    6. Li, Zhenhe & Khajepour, Amir & Song, Jinchun, 2019. "A comprehensive review of the key technologies for pure electric vehicles," Energy, Elsevier, vol. 182(C), pages 824-839.
    7. Paweł Ziemba, 2020. "Multi-Criteria Stochastic Selection of Electric Vehicles for the Sustainable Development of Local Government and State Administration Units in Poland," Energies, MDPI, vol. 13(23), pages 1-19, November.
    8. Das, Ridoy & Wang, Yue & Putrus, Ghanim & Kotter, Richard & Marzband, Mousa & Herteleer, Bert & Warmerdam, Jos, 2020. "Multi-objective techno-economic-environmental optimisation of electric vehicle for energy services," Applied Energy, Elsevier, vol. 257(C).
    9. Adam Deptuła & Andrzej Augustynowicz & Michał Stosiak & Krzysztof Towarnicki & Mykola Karpenko, 2022. "The Concept of Using an Expert System and Multi-Valued Logic Trees to Assess the Energy Consumption of an Electric Car in Selected Driving Cycles," Energies, MDPI, vol. 15(13), pages 1-24, June.
    10. Rezaei, Jafar, 2016. "Best-worst multi-criteria decision-making method: Some properties and a linear model," Omega, Elsevier, vol. 64(C), pages 126-130.
    11. Rezaei, Jafar, 2015. "Best-worst multi-criteria decision-making method," Omega, Elsevier, vol. 53(C), pages 49-57.
    12. Vasileios Boglou & Christos-Spyridon Karavas & Konstantinos Arvanitis & Athanasios Karlis, 2020. "A Fuzzy Energy Management Strategy for the Coordination of Electric Vehicle Charging in Low Voltage Distribution Grids," Energies, MDPI, vol. 13(14), pages 1-34, July.
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