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.

翻译：电动汽车的快速发展要求更高效的充电基础设施规划。基础设施布局不仅决定部署成本，还会重塑充电行为并影响整体系统性能。此外，目的地充电与途中充电作为两种不同充电模式，对应不同的功率需求，可能导致截然不同的基础设施部署结果。本研究采用基于智能体的建模框架，基于墨尔本（澳大利亚）大都市区的合成表征，在三种充电模式下生成轨迹级潜在公共充电需求。两种部署策略——基于优化的方法和利用率优化方法——在不同基础设施布局下进行评估。结果表明，利用率优化的部署可降低总系统成本（涵盖基础设施部署成本与用户广义充电成本），在组合充电模式下改善效果最为显著。具体而言，交流慢速充电桩的更有效配置重塑了目的地充电行为，进而减少了对途中充电的不必要依赖，并降低了与途中充电相关的绕行成本。这种交互作用凸显了目的地充电与途中充电模式之间的行为关联，并证明了在充电基础设施规划中考虑用户响应及多种充电模式的重要性。