Impacts of Electric Vehicle Charging Regimes and Infrastructure Deployments on System Performance: An Agent-Based Study

2026-03-19 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Advanced, quick

Summary

An agent-based modeling framework was applied to evaluate electric vehicle (EV) charging infrastructure planning in the Melbourne metropolitan area. The study generated trajectory-level latent public charging demand under three charging regimes: destination, en-route, and combined. Two deployment strategies, an optimization-based approach and a utilization-refined approach, were assessed across various infrastructure layouts. Results indicate that utilization-refined deployments significantly reduce total system cost, which includes both infrastructure deployment and user generalized charging costs. This improvement was most pronounced under the combined charging regime, where a more effective allocation of AC slow chargers reshaped destination charging behavior, thereby reducing reliance on en-route charging and associated detour costs.

Key takeaway

For urban planners and energy grid operators designing EV charging networks, understanding the interplay between charging regimes and user behavior is critical. Your infrastructure deployment decisions, particularly the strategic placement of AC slow chargers, can significantly reduce overall system costs and improve user experience by minimizing unnecessary en-route detours. Consider adopting utilization-refined deployment strategies to optimize resource allocation and encourage efficient charging habits.

Key insights

Effective EV charging infrastructure planning requires considering user behavior and multiple charging regimes.

Principles

• Utilization-refined deployments reduce total system cost.
• AC slow chargers influence destination charging behavior.

Method

An agent-based modeling framework generated trajectory-level latent public charging demand for Melbourne, evaluating optimization-based and utilization-refined deployment strategies across different infrastructure layouts.

In practice

• Prioritize utilization-refined deployment strategies.
• Allocate AC slow chargers strategically.

Topics

• Electric Vehicle Charging
• Agent-Based Modeling
• Infrastructure Planning
• Multiagent Systems
• Charging Regimes

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.