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This study uses agent-based modeling to simulate EV charging demand in Melbourne, Australia, under three charging regimes (destination, en-route, and combined). It compares optimization-based and utilization-refined infrastructure deployment strategies, finding that utilization-refined deployments reduce total system cost by better allocating AC slow chargers. This improved allocation reshapes destination charging behavior, reducing reliance on en-route charging and highlighting the behavioral linkage between charging regimes.
Smarter placement of slow chargers can significantly reduce the need for expensive en-route EV charging, leading to lower overall system costs.
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.
