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Тоҷикӣ
КыргызчаAI for Grid-Aware EV Fleet Routing with Dynamic Pricing
Route EV fleets while optimizing charging around grid constraints and dynamic prices to cut cost, reduce congestion, and protect reliability.
EV fleets can overwhelm feeders and face volatile tariffs. AI blends routing, charging, and grid signals to pick when/where to charge, respecting SLAs, SOC, and feeder limits.
DR/TOU/spot price integration for cost minimization.
Safety caps during grid stress events.
SOC forecasting; charger availability and queuing.
Fleet/charger telemetry via APIs (OCPP/OCPI/vehicle).
Tariff/price feeds; grid constraints from utility/DSO.
Traffic/ETA services; task schedules.
Frequently asked questions
What APIs are available for integrating with TMS or utility systems to coordinate operations?
APIs to TMS/utility for coordination; reports on savings.
How can fleet and charger telemetry be integrated with tariff data, and what baseline costs and constraints should be considered?
Integrate fleet/charger telemetry and tariffs; baseline costs and constraints.
What forecasting methods are used to predict price and SOC levels for both the grid and overall risk mitigation strategies?
Deploy forecasts for price/SOC and grid risk; advisory routing+charging.
Can you describe the pilot program for implementing grid-aware schedules on a smaller scale, including monitoring of service level agreements and feeder events?
Pilot grid-aware schedules on a subset; monitor SLA and feeder events.
▶ Try it live
Everything above runs in your browser — open Earthquake Wave Propagation Simulation and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.