Work

An Agent-Based Training System for Optimizing the Layout of AFVs' Initial Filling Stations

Year: 2014

Type: article

Abstract: The availability of refuelling locations for alternative fuel vehicles (AFVs) is an important factor that drivers consider before adopting an AFV; thus, the layout of initial filling stations for AFVs will influence the adoption of AFVs. This paper presents a training system for optimising the layout of initial filling stations for AFVs by linking an agent-based model of the adoption of AFVs with a real city/area's road network, as well as the city/area's social and economic background. In the agent-based model, two types of agents (driver agents and station owner agents) interact with each other in a city/area's road network, stored in a GIS (Geographic Information System). With simulation scenario analyses and a genetic algorithm, the training system presented in this paper can help decision makers determine close-to-optimal layouts for initial AFV filling stations. This paper also presents a case study of the application of the training system that analyses the layout of fast-charging or battery-changing stations for the promotion of electric vehicles adoption in Shanghai.

Source: Journal of Artificial Societies and Social Simulation

DOI: 10.18564/jasss.2570

Authors: Tieju Ma, Jiangjiang Zhao, Shijian Xiang, Ya Zhu, Peipei Liu

Language: English

Topic: Electric Vehicles and Infrastructure

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $1,300

Funders: National Natural Science Foundation of China, Ministry of Education of the People's Republic of China, Science and Technology Commission of Shanghai Municipality, Program for New Century Excellent Talents in University