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

2026-03-17

AI Summary

This study addresses the optimal planning of public charging infrastructure for electric vehicles, aiming to balance deployment costs with the impact of user charging behavior on system performance. Leveraging an agent-based modeling framework integrated with synthetic population and trajectory-level demand generation, the authors simulate charging demand across three charging mechanisms—destination, en-route, and hybrid—in the Greater Melbourne area. The analysis compares optimization-based deployment against utilization-aware placement strategies, revealing behavioral interdependencies between destination and en-route charging. Findings underscore the critical role of multi-mechanism coordination and user responsiveness in infrastructure planning. Results demonstrate that utilization-aware deployment substantially reduces total system cost, with the greatest benefits observed under the hybrid mechanism, and show that strategic allocation of AC slow chargers can effectively mitigate redundant en-route charging and associated detour costs.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Mechanism Design
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The rapid growth of electric vehicles (EVs) requires more effective charging infrastructure planning. Infrastructure layout not only determines deployment cost, but also reshapes charging behavior and influences overall system performance. In addition, destination charging and en-route charging represent distinct charging regimes associated with different power requirements, which may lead to substantially different infrastructure deployment outcomes. This study applies an agent-based modeling framework to generate trajectory-level latent public charging demand under three charging regimes based on a synthetic representation of the Melbourne (Australia) metropolitan area. Two deployment strategies, an optimization-based approach and a utilization-refined approach, are evaluated across different infrastructure layouts. Results show that utilization-refined deployments reduce total system cost, accounting for both infrastructure deployment cost and user generalized charging cost, with the most significant improvement observed under the combined charging regime. In particular, a more effective allocation of AC slow chargers reshapes destination charging behavior, which in turn reduces unnecessary reliance on en-route charging and lowers detour costs associated with en-route charging. This interaction highlights the behavioral linkage between destination and en-route charging regimes and demonstrates the importance of accounting for user response and multiple charging regimes in charging infrastructure planning.

Problem

• electric vehicle charging
• charging infrastructure planning
• destination charging
• en-route charging
• system performance

Innovation

• agent-based modeling
• charging regimes
• infrastructure deployment
• electric vehicles
• user behavior