A Cellular Automata Agent-Based Hybrid Simulation Tool to Analyze the Deployment of Electric Vehicle Charging Stations

Abstract

We present a hybrid model combining cellular automata (CA) and agent-based modeling (ABM) to analyze the deployment of electric vehicle charging stations through microscopic traffic simulations. This model is implemented in a simulation tool called SIMTRAVEL, which allows combining electric vehicles (EVs) and internal combustion engine vehicles (ICEVs) that navigate in a city composed of streets, avenues, intersections, roundabouts, and including charging stations (CSs). Each EV is modeled as an agent that incorporates complex behaviors, such as decisions about the route to destination or CS, when to drive to a CS, or which CS to choose. We studied three different CS arrangements for a synthetic city: a single large central CS, four medium sized distributed CSs or multiple small distributed CSs, with diverse amounts of traffic and proportions of EVs. The simulator output is found to be robust and meaningful and allows one to extract a first useful conclusion: traffic conditions that create bottlenecks around the CSs play a crucial role, leading to a deadlock in the city when the traffic density is above a certain critical level. Our results show that the best disposition is a distributed network, but it is fundamental to introduce smart routing measures to balance the distribution of EVs among CSs.

Insights into Simulated Smart Mobility on Roundabouts: Achievements, Lessons Learned, and Steps Ahead

“…Regarding the third key issue, integrating smart roundabouts with sustainable transportation solutions shows promise [85,[116][117][118][119][120][121][122][123][124][125][126][127][128]. Intelligent transportation systems have become crucial for smart city development, necessitating vehicle awareness, especially in high-risk areas like intersections and roundabouts [116,117].…”

“…By evaluating the integration of smart roundabouts with sustainable transportation solutions, researchers can assess their potential benefits in reducing carbon emissions, promoting active transportation, and facilitating the transition toward a more sustainable urban mobility system. García-Suárez et al [125] proposed a hybrid model merging cellular automata and agent-based modeling to analyze electric vehicle (EV) charging station deployment through microscopic traffic simulations. Their study compared three charging station arrangements in a city setting, highlighting the effectiveness of a distributed network.…”

“…Integration with sustainable transportation solutions [85,[116][117][118][119][120][121][122][123][124][125][126][127][128].…”

Machine Learning for Solving Charging Infrastructure Planning Problems: A Comprehensive Review

“…In [86], the authors used the k-nearest neighbors method for charging demand prediction. In [87], the authors applied a neural network to predict the charger occupancy for an EV charging station in an urban area.…”