plan_ev_route

ai.mapmap/mapmap · Verify this server

Plan a whole electric-vehicle journey, charge stops included. Give `origin` and `destination` (plus optional `waypoints`) and a `vehicle`, a published profile ("small_hatch", "saloon", "suv", "van") and/or inline figures (battery_kwh, mass_kg, drag_area_m2, aux_kw, connectors), with `start_soc` (default 0.9), `min_arrival_soc` (default 0.1), `reserve_soc` (default 0.1, the floor the charge must never drop below mid-route), optional `connectors` and `min_kw` filters and `ambient_temperature_c`. Energy comes from a published road-load physics model over the route's own legs; charge times are integrated over the vehicle's charging curve capped by the charge point, NOT energy divided by peak power, which is the single biggest error in naive EV planners. Returns the stops with arrive/depart state of charge, charge time and detour, a per-leg state-of-charge trace, and the journey's driving and charging time. IMPORTANT: when no plan exists, a charger desert, a connector mismatch, a gap wider than the car's range, the answer comes back with `feasible: false`, a `reason` and the furthest point on the route the car can actually reach. That is an ANSWER, not an error to retry: report the reason and never describe it as a plan. `gradient_data` says whether elevation was available: "absent" means consumption was modelled on the flat and under-reads a hilly route. There is no national charge-point registry, so ALWAYS show the returned `coverage_note`, an infeasible plan means "none from these operators", never "there are no chargers here", and statuses are current only when `availability_live` is true. Requires the MapMap gateway; answers a clear error when the deployment has no charge-point dataset. Display the returned charging_attribution with the plan.