EVwire brief: Tesla has rolled out a new machine learning model to improve Supercharger wait-time predictions, using 9 million miles of aggregated, anonymized vehicle trajectory data collected within charger geofences worldwide.
The system reduces queue length estimation error to around 20%, allowing Tesla to predict queues of 10+ vehicles within a margin of just 1–2 cars. The new model enhances Tesla’s Trip Planner and is now being deployed globally across the Supercharger network.
Tesla Senior Director of Charging Max de Zegher posted about the update on X:
Context:
While Supercharger wait times are uncommon, accurately forecasting them has been challenging due to how charging locations operate in real-world environments. Many sites are co-located with high-traffic destinations, introducing noise into prediction models.
Key factors that have historically affected accuracy include:
Mixed-use traffic: Some vehicles stop for food, retail, or drive-throughs near chargers
Navigation gaps: Some drivers do not use Trip Planner, limiting visibility
Edge-case congestion: Certain busy sites, like the Tesla Diner, have produced underestimates of actual wait times
Tesla’s new machine learning model should deliver more accurate expectations in the rare cases when Supercharger wait times do happen.
Tesla Senior Director of Charging Max de Zegher shared a comment about the improvement:
“Waiting at Superchargers is rare, but when it happens, customers should be able to plan with confidence… This new model is much better, will keep improving, and is now being deployed at Superchargers globally.”
The Supercharger network has a reported average uptime of 99.95%, as of the latest data available in April 2026
Vertical integration and charging intelligence
Building on these improvements, Tesla emphasized that its ability to deliver increasingly accurate charging forecasts stems from its vertically integrated ecosystem, which connects vehicles, software, and infrastructure into a single feedback loop.
This integration allows Tesla to deploy models like this at scale while continuously refining them with real-world data.
“We are uniquely positioned to deliver this level of charging intelligence through our vertical integration. There is still more work needed to nail these forecasts and we're already working on the next release.”
Source: Max de Zegher, Tesla Charging on X
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