New AI Tool Calculates EV Dwell Time at Charging Stations

Why It Matters

Accurate dwell‑time forecasts reduce driver uncertainty and boost station utilisation, accelerating the shift to electric mobility.

Key Takeaways

• AI predicts EV charging point availability within five minutes
• Tool integrates location, time, weather, and amenities data
• API and outdoor screen deliver real‑time driver information
• Improves station utilization, customer loyalty, and price transparency
• Enables operators to sell ads and dynamic pricing

Pulse Analysis

The emergence of AI‑powered forecasting tools like SIQMA FlowMax.AI marks a turning point for electric‑vehicle infrastructure. By ingesting granular data—such as day of week, weather conditions, and nearby retail activity—the system can anticipate when a charger will become free, delivering predictions within five minutes. This level of precision mirrors the predictability of conventional fuel stations, addressing a core friction point for EV owners who often waste time searching for an open plug. The technology also standardises price visibility, allowing drivers to compare costs instantly and plan trips more efficiently.

For charging‑station operators, the benefits extend beyond improved user experience. Real‑time occupancy insights enable dynamic pricing models, optimizing revenue during peak demand while offering discounts during off‑peak periods. The integrated advertising module on the SIQMA Sign creates an additional monetisation channel, turning idle screen time into targeted promotions for nearby businesses. Moreover, the open API facilitates seamless integration with third‑party navigation apps and vehicle infotainment systems, expanding the ecosystem and fostering data‑driven partnerships across the mobility sector.

Strategically, the deployment of such AI solutions accelerates the broader electrification agenda. Predictable charging reduces range anxiety, encouraging higher EV adoption rates and supporting policy goals for sustainable transport. As more operators adopt predictive analytics, the industry can expect a shift toward data‑centric business models, where operational efficiency, customer loyalty, and ancillary revenue streams are tightly interwoven. In this context, Scheidt & Bachmann’s initiative exemplifies how legacy energy retailers can reinvent themselves as digital mobility service providers.