Multi-Criteria, Co-Evolutionary Charging Behavior: An Agent-Based Simulation of Urban Electromobility
Publication Year: 2021
Author(s): Adenaw L, Lienkamp M
In order to electrify the transport sector, scores of charging stations are needed to incentivize people to buy electric vehicles. Governmental incentives attempt to convince the general public to buy battery electric vehicles (BEV). However, despite all efforts, current adoption rates of BEV continue to remain behind expectations. One of the key drivers of BEV adoption is the availability of adequate charging facilities, close to homes and work places. In urban areas with a high charging demand and little space, decision-makers are in need of planning tools that enable them to efficiently allocate financial and organizational resources to the promotion of electromobility. As with many other city planning tasks, simulations foster successful decision-making. This article presents a novel agent-based simulation framework for urban electromobility aimed at the analysis of charging station utilization and user behavior. The approach presented here employs a novel co-evolutionary learning model for adaptive charging behavior. The simulation framework is tested and verified by means of a case study conducted in the city of Munich. The case study shows that the presented approach realistically reproduces charging behavior and spatio-temporal charger utilization.
Source of Publication: World Electric Vehicle Journal
Vol/Issue: 12(1)): 18): 1-26p.
DOI No.: 10.3390/wevj12010018
Publisher/Organisation: MDPI AG
Rights: Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/)
Theme: Vehicle Technology | Subtheme: Electric vehicles
Tags: Battery electric vehicles, BEV, Charging Infrastructure, Charging stations, Decarbonization, Electric mobility, Electric vehicles, Electromobility, Fast charging, Agent-based simulation, Behavior learning, Charging behavior, Co-evolutionary algorithm, MATSim, Charging (batteries), Urban planning, Co-evolutionary learning, Simulation framework
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