2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 11 , Issue 1 , PP: 65-74, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Rita A. Díaz Vásquez 1 * , Marcos L. Flores 2 , Esteban L. Espinel 3
Doi: https://doi.org/10.54216/JISIoT.110107
An essential part of electrifying bus networks is deciding on the appropriate charging strategy among the many available alternatives, such as opportunistic (rapid) charging techniques and overnight (slow) charging. The broad usage of electric buses in public transportation networks and the increasing demand for environmentally friendly transportation options have elevated the significance of this step. This research establishes a multi-criteria decision-making (MCDM) fusion method for choosing the optimal electric bus charging strategy by considering various variables, including operational, quality-of-service, social, environmental, and economic factors. To determine what matters most for making decisions in this field, we surveyed electric bus specialists and reviewed the literature extensively. We used the TOPSIS method as an MCDM fusion method to combine the criteria and alternatives. We compute the weights of criteria by the average method. Then, the TOPSIS fusion method selects and ranks the alternatives. We collected the 20 criteria and five alternatives in this study. We show that overnight is the best charging strategy in the electric bus system. We performed a sensitivity analysis to show the different cases in criteria weights, then ranked the alternatives under different weights to establish the stability of the results.
MCDM Fusion , TOPSIS fusion , Electric Bus , Charging Strategy , Selection Problem , Energy.
[1] N. Qin, A. Gusrialdi, R. P. Brooker, and T. Ali, “Numerical analysis of electric bus fast charging strategies for demand charge reduction,” Transportation Research Part A: Policy and Practice, vol. 94, pp. 386–396, 2016.
[2] K. Liu, H. Gao, Z. Liang, M. Zhao, and C. Li, “Optimal charging strategy for large-scale electric buses considering resource constraints,” Transportation Research Part D: Transport and Environment, vol. 99, p. 103009, 2021.
[3] M. M. Hasan, N. Avramis, M. Ranta, A. Saez-de-Ibarra, M. El Baghdadi, and O. Hegazy, “Multi-objective energy management and charging strategy for electric bus fleets in cities using various ECO strategies,” Sustainability, vol. 13, no. 14, p. 7865, 2021.
[4] K. Liu, H. Gao, Y. Wang, T. Feng, and C. Li, “Robust charging strategies for electric bus fleets under energy consumption uncertainty,” Transportation Research Part D: Transport and Environment, vol. 104, p. 103215, 2022.
[5] M. Sadrani, A. Najafi, R. Mirqasemi, and C. Antoniou, “Charging strategy selection for electric bus systems: A multi-criteria decision-making approach,” Applied Energy, vol. 347, p. 121415, 2023.
[6] A. Houbbadi, E. Redondo-Iglesias, R. Trigui, S. Pelissier, and T. Bouton, “Optimal charging strategy to minimize electricity cost and prolong battery life of electric bus fleet,” in 2019 IEEE Vehicle Power and Propulsion Conference (VPPC), IEEE, 2019, pp. 1–6.
[7] Y. He, Z. Liu, and Z. Song, “Optimal charging scheduling and management for a fast-charging battery electric bus system,” Transportation Research Part E: Logistics and Transportation Review, vol. 142, p. 102056, 2020.
[8] H. Chen et al., “Coordinated charging strategies for electric bus fast charging stations,” in 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), IEEE, 2016, pp. 1174–1179.
[9] J. A. Manzolli, J. P. F. Trovao, and C. H. Antunes, “Electric bus coordinated charging strategy considering V2G and battery degradation,” Energy, vol. 254, p. 124252, 2022.
[10] A. Houbbadi, R. Trigui, S. Pelissier, E. Redondo-Iglesias, and T. Bouton, “Optimal scheduling to manage an electric bus fleet overnight charging,” Energies, vol. 12, no. 14, p. 2727, 2019.
[11] Abedallah Z. Abualkishik,Rasha Almajed, Triangular Neutrosophic Multi-Criteria Decision Making AHP Method for Solar Power Site Selection, International Journal of Advances in Applied Computational Intelligence, Vol. 2 , No. 2 , (2022) : 08-15 (Doi : https://doi.org/10.54216/IJAACI.020201)
[12] L. Dymova, P. Sevastjanov, and A. Tikhonenko, “A direct interval extension of TOPSIS method,” Expert Systems with Applications, vol. 40, no. 12, pp. 4841–4847, 2013.
[13] Ahmed Abdelhafeez, Hoda K. Mohamed, Skin Cancer Detection using Neutrosophic c-means and Fuzzy c-means Clustering Algorithms, Journal of Intelligent Systems and Internet of Things, Vol. 8 , No. 1 , (2023) : 33-42 (Doi : https://doi.org/10.54216/JISIoT.080103)
[14] A. Abdelhafeez, H. K. Mohamed, and N. A. Khalil, “Rank and Analysis Several Solutions of Healthcare Waste to Achieve Cost Effectiveness and Sustainability Using Neutrosophic MCDM Model,” Neutrosophic Systems with Applications, vol. 2, pp. 25–37, 2023.
[15] T.-C. Chu and Y.-C. Lin, “A fuzzy TOPSIS method for robot selection,” The International Journal of Advanced Manufacturing Technology, vol. 21, pp. 284–290, 2003.
[16] Alber S. Aziz, Moahmed Emad, Mahmoud Ismail, Heba Rashad, Ahmed M. Ali, Ahmed Abdelhafeez, Shimaa S. Mohamed, An Intelligent Multi-Criteria Decision-Making Model for selecting an optimal location for a data center: Case Study in Egypt, Journal of Intelligent Systems and Internet of Things, Vol. 9 , No. 2 , (2023) : 23-35 (Doi : https://doi.org/10.54216/JISIoT.090202)
[17] Abedallah Z. Abualkishik,Rasha Almajed, Triangular Neutrosophic Multi-Criteria Decision Making AHP Method for Solar Power Site Selection, International Journal of Advances in Applied Computational Intelligence, Vol. 2 , No. 2 , (2022) : 08-15 (Doi : https://doi.org/10.54216/IJAACI.020201)
[18] A. Sleem, N. Mostafa, and I. Elhenawy, “Neutrosophic CRITIC MCDM Methodology for Ranking Factors and Needs of Customers in Product’s Target Demographic in Virtual Reality Metaverse,” Neutrosophic Systems with Applications, vol. 2, pp. 55–65, 2023.
[19] Z. Pavić and V. Novoselac, “Notes on TOPSIS method,” International Journal of Research in Engineering and Science, vol. 1, no. 2, pp. 5–12, 2013.
[20] N. Nabeeh, “Assessment and Contrast the Sustainable Growth of Various 1 Road Transport Systems using Intelligent Neutrosophic 2 Multi-Criteria Decision-Making Model,” Sustainable Machine Intelligence Journal, vol. 2, 2023.
[21] E. K. Zavadskas, A. Mardani, Z. Turskis, A. Jusoh, and K. M. D. Nor, “Development of TOPSIS method to solve complicated decision-making problems—An overview on developments from 2000 to 2015,” International Journal of Information Technology & Decision Making, vol. 15, no. 03, pp. 645–682, 2016.
[22] G. R. Jahanshahloo, F. H. Lotfi, and M. Izadikhah, “Extension of the TOPSIS method for decision-making problems with fuzzy data,” Applied mathematics and computation, vol. 181, no. 2, pp. 1544–1551, 2006.
[23] R. F. de Farias Aires and L. Ferreira, “A new approach to avoid rank reversal cases in the TOPSIS method,” Computers & Industrial Engineering, vol. 132, pp. 84–97, 2019.
