Comparative Performance Analysis of Algorithm Applications Used in Determining EVFCS Locations
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ID: 287736
2025
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Abstract
With the increasing population, using petroleum and its derivatives in transportation has caused serious environmental problems, including global warming and urban air pollution. This situation has led to the widespread adoption of alternative fuel vehicles, especially Electric Vehicles (EVs), and determining station locations has become a popular research topic. Among the contributions of these studies to the literature are identifying the optimal locations for fast charging stations and space planning. Although numerous routing and charging calculation programs exist for EVs, nature-inspired optimization algorithms can be a valuable approach to addressing the challenges in routing and optimal placement. At the current stage of EV technology, when parameters such as vehicle range and available charging station locations are considered, there is a shortage of charging stations to facilitate efficient intercity travel. Therefore, determining the optimal locations for Electric Vehicles Fast Charging Stations (EVFCS) is a vital issue that needs to be addressed. Failure to identify optimal locations and to adequately plan fast charging stations may lead to problems for both EV owners and charging system operators, such as failing to meet charging demand at the desired level or underutilizing the planned fast charging stations. The main objective of station location planning is to obtain an optimal solution that maximizes the flow volume while simultaneously minimizing the installation costs of charging stations. This paper presents a comparative review of various EV optimal positioning techniques and algorithms used. In addition, it aims to determine the most suitable EVFCS points within the boundaries of Kavacik region of Beykoz district of Istanbul province by applying the geographical proximity-based Haversine algorithm and Analytical Hierarchy Process (AHP) algorithms, which are Multi-Criteria Decision-Making (MCDM) methods, separately.
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persistent_1760865400_68f4ac78d1551
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|---|---|
| Authors | Onur Akar |
| Journal | karadeniz fen bilimleri dergisi |
| Year | 2025 |
| DOI |
10.31466/kfbd.1699614
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| URL | |
| Keywords | Keywords not found |
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