A charging schedule that treats all vehicles as equally important is leaving efficiency on the table. If you have 40 vans plugged in and your grid connection allows you to charge them at an average of 8 kW each, the obvious approach is to distribute that power evenly. But van number 12 leaves at 06:00 with 120 km of routes, while van number 31 leaves at 09:00 with a 45 km local delivery loop. Giving them the same charging priority overnight means van 31 gets more charge than it needs by 06:00, while you could have let van 12 charge faster and arrived at 100 percent rather than 88 percent.
That kind of optimization requires two data inputs that only your fleet telematics system holds: departure time and planned route distance. When those flow into the charging scheduler, priority assignment stops being static ("vans in bays 1 to 10 go first") and becomes dynamic ("rank by urgency, update every 15 minutes as sessions evolve").
What Telematics Data the Charging Optimizer Actually Needs
The integration does not need to be complex. Most of the sophisticated-sounding features in telematics-aware charging schedulers reduce to a handful of data fields per vehicle per day:
The departure time, typically sourced from your route planning or dispatch software rather than from the vehicle unit itself. A timestamp like 06:15 for van registration DE-456-NK is all the scheduler needs to know that this vehicle has a higher urgency coefficient than a van not departing until 09:00.
The planned route distance or estimated energy consumption for the day. This can come from your routing software as an estimated kWh value if the system supports it, or it can be approximated from planned kilometers and a vehicle-specific consumption factor. For a eSprinter class vehicle running Amsterdam urban deliveries, 0.28 to 0.35 kWh per km is a reasonable consumption range. A 90 km route therefore requires roughly 25 to 32 kWh of usable battery.
Current state of charge at plug-in. This comes from the vehicle itself via the OCPP session data when the charging session starts, or it can be polled from a telematics unit that reads the battery management system. Knowing that van DE-456-NK plugged in at 19:00 with 38 percent SoC gives the scheduler exactly what it needs to calculate required charge, available window, and minimum charging rate to meet the target SoC by departure time.
How Departure Priority Scoring Works
Once the scheduler has departure time, required energy, and current SoC for each connected vehicle, it can calculate a simple but powerful metric: the minimum charging rate required to reach target SoC by departure, given available time.
For a van at 38 percent SoC with a 75 kWh usable battery, needing to reach 90 percent by 06:15 (about 10 hours away at 20:00 plug-in time), the math is: 52 percent of 75 kWh = 39 kWh needed. Over 10 hours, that requires a sustained average of just 3.9 kW. That van has plenty of time and can be deprioritized during peak demand hours, receiving elevated priority only if it falls behind target SoC during the night.
A second van at 42 percent SoC needing 90 percent by 06:00 with the same battery capacity has a similar calculation, except if it plugs in at 22:00 instead of 19:00: now the window is only 8 hours and the minimum required rate is 4.5 kW. Still not urgent. But if a third van arrives at 23:30 with 25 percent SoC and a 06:00 departure, that vehicle needs 48.75 kWh in 6.5 hours, requiring a sustained 7.5 kW minimum. That vehicle gets priority over the other two.
The scheduler runs this calculation continuously as new vehicles plug in, as the night progresses, and as the grid demand budget fluctuates. Vehicles that fall behind their SoC target get current increases; vehicles that are comfortably ahead get throttled to free up capacity for more urgent cases.
Integration Architecture: How the Data Actually Flows
For operators already running a fleet management platform that includes telematics (Webfleet, Quartix, Trimble, or comparable systems used by Dutch logistics operators), the integration pathway is usually a REST API pull. The charging scheduler queries the fleet management API at a configurable interval, typically every 15 to 30 minutes, to fetch current dispatch assignments: vehicle ID, planned departure time, estimated route distance, and assigned driver.
On the charging side, OCPP handles the real-time session data. When a vehicle connects to a charger, the session start notification includes the connector ID and, if the charger and vehicle support it, the initial SoC via the OCPP MeterValues message. The scheduler matches the OCPP session to a known vehicle ID through the charger's bay assignment or a manual vehicle-to-bay mapping that the fleet manager sets up once per depot.
The combined data model looks like this: vehicle ID is the key that ties together the fleet management record (departure time, route distance) and the OCPP session (current SoC, connector ID, session energy delivered). From those inputs, the priority score is calculated and fed back to the charger as a maximum current setpoint via OCPP's SetChargingProfile command.
We built this pipeline to be tolerant of data gaps. If the telematics API goes offline for an interval, the scheduler falls back to a static priority model based on historical departure time patterns for each bay or vehicle. If SoC data is unavailable from the vehicle, the scheduler uses a conservative estimate based on average fleet return SoC for that time of day. The schedule keeps running; it just uses slightly less precise inputs.
Where the Integration Adds the Most Value
The biggest operational benefit appears in two specific scenarios. The first is split-shift operations, where some vehicles leave at 05:30 for early morning routes and others leave at 08:00 or 09:00 for later shifts. Without departure data, the scheduler has no way to distinguish these vehicles. With departure data, the early-shift vehicles get priority through the night and arrive at target SoC before the later-shift vehicles begin their more intensive charging phase.
The second scenario is cold weather. Battery charging acceptance rates drop significantly below 5 degrees Celsius for most lithium iron phosphate and NMC battery chemistries. In January, a van that would normally charge to 90 percent in 8 hours might need 10 to 11 hours because the battery management system is restricting charge current to protect cell chemistry. The scheduler, seeing SoC progress falling behind the expected curve, can flag the vehicle early and increase its priority weight before the shortfall becomes unrecoverable by morning.
This is something we pay particular attention to in the Netherlands, where winter depot temperatures in unheated warehouses regularly hit minus 2 to minus 8 degrees overnight. The margin for error in charging schedules is tighter in winter, which is exactly when telematics-informed priority scoring provides the most protection against departures with insufficient charge.
What You Do Not Need the Integration to Do
There is a common misconception that telematics integration requires sharing detailed route data with the charging system. You do not need that. The charging optimizer does not care where the van is going, what stops it will make, or what the driver's name is. It needs a single number per vehicle: expected energy required for tomorrow. That can come from a planned route distance estimate, from a flat daily energy target set by the fleet manager, or from historical average consumption for that vehicle on that day of the week.
We are not suggesting that deep integration is impossible or useless. If your routing software can export per-vehicle kWh estimates directly, that is more accurate than a distance-based approximation and worth using. But the integration does not need to be deep to deliver most of the benefit. A daily CSV export of vehicle IDs, planned departure times, and route distances is enough to run departure-priority charging across an entire depot.
The teams who get the most value early on are the ones who start simple: connect the dispatch spreadsheet or TMS export to the scheduler, set reasonable target SoC values per vehicle class, and let the priority engine run. Refinements can follow once you see how the schedule performs in practice.
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