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arXiv2026

Infinite Drive: Optimal Urban Location of Dynamic Wireless Charging at Signalized Intersections

Dynamic Wireless Power Transfer (DWPT) could eliminate plug-in charging in cities, but optimal urban deployment is complex. This paper develops a mixed-integer programming model that optimizes DWPT location under signalized intersection dynamics -- acceleration, deceleration, and queue-position-dependent dwell time -- through probabilistic signal patterns and saturation headway-based modeling. A case study of Kawagoe City, Japan, shows that electrifying 1.5% of the road network is sufficient to sustain continuous urban EV operation without plug-in charging for the baseline scenario, and at most 2.9% suffices across all tested assumptions. Monte Carlo simulations of continuous trip chains averaging approximately 600 km and reaching up to approximately 800 km confirm that optimized 12 kWh-battery deployments sustain operation in all simulated runs, revealing an infrastructure-battery tradeoff corresponding to roughly 1.7-3.0 tonnes CO2e of avoided battery manufacturing emissions per vehicle relative to a conventional 40 kWh urban EV. These findings position DWPT deployment as an environmentally efficient pathway for sustainable urban mobility when deployed optimally.

physics.soc-ph↗

arXiv2024

Innovation Diffusion in EV Charging Location Decisions: Integrating Demand & Supply through Market Dynamics

This paper offers a strategic approach to Electric Vehicles (EVs) charging network planning, emphasizing the integration of demand and supply dynamics via continuous-time fluid queue models and discrete flow refueling location modeling, all in the context of innovation diffusion principles. We employ a continuous-time approximation based on Ordinary Differential Equations (ODEs) to design multi-year supply curves, a method that stands in contrast to conventional practices which often overlook inter-year transitions and ongoing processes. For medium-term charging station location planning (CSLP), we apply a flow refueling location model (FRLM) within grid-based multi-level networks, considering both multiple-path networks and capacity constraints. The grid-based network planning strategy uses a three-tier (Macro-Meso-Micro) approach for thorough EV charging station placement, with the macro-level covering entire cities, the meso-level assessing detailed EV routes and bridging the macro to micro levels, and the micro-level focusing on precise station placement for accessibility and efficiency. Our investigation into overutilization and underutilization scenarios delivers valuable insights for policymaking and cost-benefit analyses. Illustrating our approach with the example of the Chicago sketch network, we introduce an integrated demand-supply model suitable for a single region and extendable to multiple regions, thereby addressing a gap in the existing literature. Our proposed methodology focuses on EV station placement, taking into account future needs, geographical capacities, and the importance of scenario analysis, which empowers strategic resource planning for EV charging networks over extended timeframes, thus aiding the transition towards a more sustainable and efficient transportation system.

math.OC↗