The Hidden Lifecycle of Public EV Charging Infrastructure in the United States

Abstract Public electric vehicle charging infrastructure is expanding rapidly, but comparatively little is known about how stations perform, deteriorate, and exit after deployment. This evidence gap is consequential given the substantial costs of deploying and maintaining charging infrastructure and the limited documentation of lifecycle transitions at the individual-station level. In this study, we analyzed national port-level public charging-status data collected at 10-minute intervals from July 2023 through September 2026. We classified stations as newly added, active, transient, or exited and compared utilization, data-reporting quality, port availability, and repair performance across lifecycle stages. We then estimated discrete-time hazard models and developed temporally validated machine-learning models to identify stations at elevated risk of exit. We find that newly added stations generally matured from lower utilization toward more stable performance, whereas transient and exited stations exhibited greater volatility, more frequent port unavailability and reporting disruptions, and heavier repair burdens. Station exit was associated more strongly with persistent reporting gaps, repeated maintenance events, port unavailability, and prolonged outage recovery than with low utilization alone. Machine-learning models successfully concentrated subsequent exits among relatively small groups of high-risk stations, demonstrating their potential to support lifecycle-based monitoring and risk-ranked inspection. These findings show that operational and reporting deterioration can provide early warning of station exit, while underscoring that station-level risk scores are more appropriate for prioritizing inspections.