What question did this study set out to answer?

This model aims to forecast charging demands of electric vehicles at public charging stations within urban settings.

June 13, 2026Open Access

Agent-based simulation to predict the charging demands of electric vehicles in a city

EREhsan Yahyazadeh RinehThe University of Texas at El Paso RCRuey Long CheuThe University of Texas at El Paso

Key Points

This model aims to forecast charging demands of electric vehicles at public charging stations within urban settings.
Developed an agent-based simulation model using the MATSim framework
Forecasted electric vehicle adoption rates at the ZIP-code level for the year 2030
Validated simulation model against existing transportation plans and daily traffic volumes
Hourly charging demands estimated aligned closely across the 24 simulated charging stations
Simulation outputs assist in determining station capacity to meet projected demands
Model contributes to more effective urban planning for electric vehicle infrastructure expansion

Abstract

Purpose This paper presents an agent-based simulation model to predict charging demand for privately owned battery electric vehicles at fixed-point public charging stations within a city. Design/methodology/approach This simulation model is built on the MATSim framework. The highway network and trip demands are adopted from a regional transportation planning model. Travelers are modeled as agents operating electric and internal-combustion-engine vehicles. Electric vehicle adoption rates in the horizon year are forecast at the ZIP-code level. Electric vehicles are divided into home-charging and on-street-parked vehicles, with different accessibility to chargers. Findings The coded simulation model has been validated against the daily volumes at selected links in a metropolitan transportation plan. A case study based on projected travel demand and the electric vehicle population in 2030 has been conducted. The hourly charging demands estimated by both the proposed state-of-charge and likelihood methods were closely aligned across the 24 simulated charging stations. Research limitations/implications This modeling approach enables regional transportation planning agencies to use their planning models and data to estimate future electric vehicle charging demand. In addition to predicting hourly charging demand, the simulation outputs help to determine station capacity. Social implications The simulation model provides a more realistic approach to estimating electric vehicle charging demand, helping to plan the expansion of charging infrastructure to promote electric vehicle adoption that will lead to a more sustainable transport system. Originality/value Other than the use of the original-destination matrix to generate travel demands, the authors have proposed modeling two types of electric vehicles: home-charging and on-street-parked electric vehicles, and how the users of on-street-parked electric vehicles select charging stations based on the shortest detour distance, combined with the state of charge or likelihood of charging.

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