Bi-Level Planning of Multi-Functional Vehicle Charging Stations Considering Land Use Types

Abstract

Locating and planning charging stations for Low-Emission Vehicles (LEVs) such as Battery Electric Vehicle (BEV), Hydrogen Fuel-Cell Vehicle (HFCV), and Natural Gas Vehicle (NGV) are becoming increasingly important for LEV users, government, and the automobile industry. Conventional planning approach of charging station usually plans single functional charging station that can only serve one kind of LEVs, and other factors such as fuel type, driving range, initial fuel tank level, and refueling time of the LEV are less considered in the planning stage. In this article, we propose a bi-level planning model to locate and size Multi-Functional Charging Station (MFCS) which can recharge BEV, HFCV, and NGV at the same time in a medium-sized city with different functional areas (e.g., residential area, industrial area, CBD area). We also established a method for generating a daily route considering vehicle attributes and user habits, and we loaded these traveling data into the upper model to select a set of optimal combinations of refueling station locations with a relatively high success ratio. In the lower model, we introduced the mathematical relationship between number of chargers and average user waiting time, and set the total social cost factor, including investment cost and waiting time cost, to evaluate each optimal combination, and then identified the optimum locational result and defined the size of each station. In the case study, we verify the proposed model in several scenarios and conclude that multifunctional refueling station performs better in terms of investment cost and users’ satisfaction level.

Data Driven Approaches for Sustainable Development of E-Mobility in Urban Areas

“…Most of the contributions about land use and EVs deal with the optimal location of charging infrastructures as a function of the different functional areas of the cities or evaluating which facilities or points of interest are more interesting for EV charging [23]. For example, Zhi Wu et al [24] work on locating multi-functional charging stations (MFCS) in a medium-sized city with different functional areas. However, the difference between the land use is only adopted to classify the vehicle travel destinations (assuming, for example, commuting trips mainly between residential areas and industrial or commercial areas and social and recreational trips mainly between commercial and residential areas).…”

Pothole detection for autonomous vehicles using deep learning: a robust and efficient solution

“…Protecting against hacking attempts, securing communication between vehicles and infrastructure, vehicle-to-vehicle connectivity, and ensuring data privacy are significant challenges that demand robust solutions (Rathore et al, 2022). Furthermore, integrating autonomous and non-autonomous vehicles poses challenges regarding coexistence and interaction (Wu et al, 2020). Ensuring smooth transitions, clear communication, and mutual understanding between these vehicles are essential for safe and efficient traffic flow (Wu et al, 2020).…”

Techno-economic optimal planning of an industrial microgrid considering integrated energy resources

“…There are various resources about DG technology and integration. Researchers need to comprehend the problematic situation of DGs in the power system (Wu et al, 2020). There is no specific definition of DG yet owing to developing technology and the increasing scale, which play an essential and fascinating role in the advancement of a power system.…”