A region-based model for optimizing charging station location problem of electric vehicles considering disruption - A case study

Ahangar, Shahin Sadeghi; Abazari, Seyed Reza; Rabbani, Masoud

doi:10.1016/j.jclepro.2022.130433

Cited by 8 publications

(2 citation statements)

References 45 publications

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“…On the other hand, node-based models are more suitable for people who prefer to recharge their EVs near their home or workplace while they are resting, working, or engaging in other activities. Thus, for example, Ahangar et al [29] conducted a case study in one district of Tehran and implemented a bi-objective mixed-integer linear mathematical model solved by the Lagrangian relaxation method where limited budget, disruption in charging stations and customer dissatisfaction (measured by the traveling distance to charge EVs) are considered. However, node-based and flow-based models are not the only two approaches used to model this problem as can be seen in Jung et al [30], where the authors propose a method based on vehicle trajectory to solve the problem for electric taxis routing.…”

Section: Challenges In Routing Based On Electric Vehiclesmentioning

confidence: 99%

Battery Management in Electric Vehicle Routing Problems: A Review

Martin,

Escoto,

Guerrero

et al. 2024

Energies

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The adoption of electric vehicles (EVs) has gained significant momentum in recent years as a sustainable alternative to traditional internal combustion engine vehicles. However, the efficient utilization of batteries in EVs, coupled with the growing demand for sustainable transportation, has posed complex challenges for battery management in the context of electric vehicle routing problems in a broad sense, which includes vehicle routing problems, team orienteering problems, and arc routing problems, all of them using EVs. This paper presents a comprehensive review of the state-of-the-art approaches, methodologies, and strategies for battery management in each of the aforementioned optimization problems. We explore the relevant factors influencing battery performance and the interplay between routing, charging, and energy management in the context of EVs. The paper also discusses the advances in optimization algorithms, vehicle-to-grid integration, and intelligent decision-making techniques aimed at enhancing the range, reliability, and sustainability of EV operations. Key findings indicate a paradigm shift towards addressing uncertainties, dynamic conditions, and synchronization challenges inherent in large-scale and dynamic routing problems within the context of EVs that require efficient battery management.

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Section: Challenges In Routing Based On Electric Vehiclesmentioning

confidence: 99%

Battery Management in Electric Vehicle Routing Problems: A Review

Martin,

Escoto,

Guerrero

et al. 2024

Energies

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“…Therefore, the evaluation of electric vehicle charging facilities can find problems and summarize experience from the whole implementation process, which has important reference and guidance significance for the subsequent investment and construction of charging facilities. Currently, the research on electric vehicles is mostly focused on charging station location [2], coordinating Electric Vehicles and distributed energy sources [3], and so on. However, research on the evaluation of electric vehicle charging facilities is absent.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of electric vehicle charging facilities by using the MCDM method

Jia²,

Wu³

et al. 2023

J. Phys.: Conf. Ser.

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As one of the candidate carriers of future transportation, electric vehicles are widely considered an important choice to solve traffic derivative problems and environmental issues. In this paper, the evaluation of electric vehicle charging facilities is studied by employing the MCDM method. The fuzzy BWM was utilized to weight the criteria, and the MARCOS method was employed to evaluate different electric vehicle charging facilities. Eight criteria for electric vehicle charging facilities were determined, and five electric vehicle charging facilities were evaluated by using the hybrid MCDM method. The empirical results indicate that the comprehensive performance of electric vehicle charging facility EVCF#5 is best, followed by EES#3, EES#2, and EES#1, while the electric vehicle charging facility EES#4 has the worst comprehensive performance. This research can provide references for the sustainable development of electric vehicle charging facilities.

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