Fast-charging station deployment for battery electric bus systems considering electricity demand charges

The optimal location of electric vehicle charging station (EVCS) will promote the rapid development of the electric vehicle (EV) industry. Generally, EVCS location selection is treated as complex uncertain multi-criteria decision-making (MCDM) problem because of the existence of many quantitative and qualitative influencing factors. Moreover, uncertainty is usually occurred in EVCS location selection problem and Fermatean fuzzy set (FFS), as an expansion of Pythagorean fuzzy set, can effectively handle the ambiguity by reducing human intervention. Thus, the aim of the current study is to design an integrated decision making method for solving multi-criteria EVCS location selection problem under FFS context. This method is based on multi-objective optimization based on the ratio analysis with the full multiplicative form (MULTIMOORA) approach, maximizing deviation method and Einstein aggregation operators within Fermatean fuzzy environment. At the same time, the criteria weights are determined through the maximizing deviation method. For this, we introduce a divergence measure for FFSs. To aggregate the decision information, we propose some novel Einstein operations for FFS. In light of these operational laws, we further suggest some Fermatean fuzzy Einstein aggregation operators and their enviable characteristics. To illustrate the potentiality and usefulness of the present methodology, we carry out an illustrative study of EVCS location selection problem with FFS setting. Comparing the present MULTIMOORA framework with the extant methods confirms the strength of the obtained outcomes. The findings conclude that the introduced method is more useful and well-consistent with extant methods.

Fermatean fuzzy Einstein aggregation operators-based MULTIMOORA method for electric vehicle charging station selection

WASPAS and TOPSIS based interval type-2 fuzzy MCDM method for a selection of a car sharing station

Battery electric buses (BEBs) are considered a promising alternative for bus fleets to alleviate the growing environmental problems in urban areas, and fast-charging technology has been introduced to BEB systems to help electric buses provide uninterrupted service without the need to carry a large onboard battery. The general consensus is that fast-charging may lead to high electricity demand charges, thus compromising the competitiveness of electric bus systems. However, a majority of current electric bus fast-charging station deployment models ignore these charges. The present study addresses this gap by explicitly considering the electricity demand charges in the optimal deployment problem of fast-charging stations for battery electric bus systems. The problem is formulated as a mixed integer linear programming model with the objective of minimizing the total cost of vehicle batteries, fast-charging stations, energy storage systems, and electricity demand charges. Numerical studies based on a real-world bus network in Salt Lake City, Utah, are conducted to demonstrate the effectiveness of the proposed model. The results show that the proposed model can effectively determine the deployment of fast-charging stations, the design of vehicle battery sizes, as well as the installation of energy storage systems. This study demonstrates that energy storage systems are a potential remedy for high demand charges from fast-charging.

pdf/interval-type-2-fuzzy-sets-improved-by-simulated-annealing-4kp2g4ecdo.pdf

Interval type-2 fuzzy sets improved by Simulated Annealing for locating the electric charging stations

An interval type-2 fuzzy analysis towards electric vehicle charging station allocation from a sustainable perspective

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An interval type-2 fuzzy analysis towards electric vehicle charging station allocation from a sustainable perspective