How Smart Charging Schedules Keep Every Van Topped Up Overnight

A fleet manager running 45 electric delivery vans has a fairly concrete overnight goal: every vehicle should leave the depot in the morning with enough charge to complete its route, plus a reasonable buffer. In practice, routes vary in length from 80 km to over 200 km, drivers depart between 06:30 and 08:30, and a subset of vehicles arrive back late with very low state of charge because of route extensions or traffic delays.

The first instinct is to solve this with hardware: add more chargers, upgrade to faster DC units, or install a second feed from the utility. These are valid approaches at some scale, but they all share a common flaw: they address capacity rather than scheduling. A depot with 45 chargers and no schedule management is just a more expensive version of the unmanaged problem.

The sequencing constraint is not obvious until you model it

Consider a depot where all 45 vans return by 19:30 and the first departures are at 06:30. That gives an 11-hour charging window. At 22 kW AC, an 80 kWh battery takes about 3.5 to 4 hours to charge from 15% to 95%. So, in principle, 11 hours is more than enough time to fully charge every vehicle even with a limited number of chargers operating in sequence.

The problem is that available electricity cost is not uniform across those 11 hours. If your tariff has peak pricing from 17:00 to 21:00 and off-peak from 23:00 to 07:00, charging a van immediately at 19:30 (when it arrives) means paying peak rates. Waiting until 23:00 to begin charging off-peak saves on energy cost, but now you have a tighter window: 7.5 hours to complete charging across all 45 vehicles before the first departure.

At 22 kW per charger and 45 vehicles needing an average of 3 hours each, you need the equivalent of 135 charger-hours in a 7.5-hour window, which means 18 chargers running continuously or a mix. If you only have 12 AC chargers, you cannot finish all 45 vehicles in the off-peak window without prioritization.

This is the sequencing constraint. It is not about peak versus off-peak in isolation. It is about matching your departure schedule, the off-peak window, your charger capacity, and the actual state of charge of each incoming vehicle simultaneously.

What the optimizer is actually doing

A smart charging scheduler takes several inputs and solves them together. The core inputs are: current state of charge per vehicle (from telematics or the charger session data), target departure time per vehicle, target state of charge at departure, available charger capacity, and the tariff schedule for the next 24 hours.

Given these inputs, the scheduler's job is to assign start times and charging rates to each vehicle such that every vehicle hits its target SoC before its departure time, total power draw stays within the depot's contracted grid connection limit at all times, and the total energy cost (combining energy tariff and demand charge impact) is minimized subject to those constraints.

In practice, this looks like a constraint optimization problem. Vehicles with early departures and low SoC are given priority charging slots, potentially starting during the evening peak window if the math shows they cannot reach target SoC otherwise. Vehicles with late departures and higher incoming SoC are scheduled entirely within the off-peak window. Vehicles in between are sorted by the tightness of their charging deadline relative to their energy need.

OCPP-compliant chargers (Open Charge Point Protocol) allow the scheduler to send start, stop, and power-level commands to individual charging sessions. A charger that supports OCPP 1.6 or 2.0.1 can be throttled to partial power, for instance 11 kW instead of 22 kW, which gives the scheduler finer control over the total depot power draw profile without stopping and starting sessions.

The departure readiness guarantee

Fleet managers reasonably worry about a charging system that defers charging and might leave a vehicle short. The scheduler has to treat departure readiness as a hard constraint, not an optimization target.

This means that if a vehicle is assigned a departure at 07:00 and needs 40 kWh to reach its target SoC, the schedule will always ensure that 40 kWh is delivered before 07:00, even if that requires starting the charging session during the evening peak window and paying a higher tariff. Cost optimization only operates within the feasible space defined by the readiness constraints.

The implication for operations is that the scheduler needs accurate departure data. If a vehicle that is scheduled to depart at 08:30 is moved to 06:45 because of a route change, the optimizer needs to know. This is where the telematics integration matters: when route assignments change in the transport management system, that information propagates to the charging scheduler so it can resequence if necessary.

In early-access pilots, we have seen that departure time variance within a 30-minute window does not typically require resequencing because the schedule already builds in buffer. Larger changes, say a vehicle moving from an 08:30 departure to a 06:30 departure, do trigger schedule updates.

Handling the edge cases: late returns and incomplete charging nights

No schedule survives contact with real fleet operations perfectly. Vehicles return late. A driver has a breakdown and the van comes back at 01:00 instead of 19:00. Another vehicle has a charging session that fails because of a connector fault that is not detected until the system checks at 03:00.

The scheduler needs to handle these gracefully rather than silently. When a vehicle registers a late return, the system recalculates its feasibility: given the remaining time until departure and the charger power available, can it reach target SoC? If yes, it is added to the schedule queue at appropriate priority. If no, the system generates an alert: this vehicle cannot reach target SoC before departure, here is what it will have at departure time, and here is an option to flag it for route reassignment.

This alert behavior is important. A charging system that silently fails to charge a vehicle to the required level and sends a driver out with insufficient range is worse than one that flags the issue early enough for operations to act. The overnight charging window is actually a substantial buffer: a failure detected at 03:00 is still three hours before the first departure, which may be enough time to transfer the route or arrange alternate charging.

What smart scheduling does not replace

Smart scheduling optimizes the use of the hardware and grid capacity you have. It does not replace the need for adequate physical infrastructure. A depot with 45 vans and only 6 AC chargers cannot charge all vehicles overnight no matter how intelligently the schedule is built. The scheduler will make the best possible use of 6 charger slots, prioritizing correctly and minimizing cost, but it will report that the configuration is infeasible for full fleet readiness and flag which vehicles will not make their targets.

The practical planning question for most depot operators is: how many chargers do I need for a smart scheduling approach to achieve full fleet readiness while staying within my grid connection limit? The answer depends on your departure spread, typical incoming SoC, and off-peak window length, and it is usually substantially fewer chargers than a naive one-charger-per-vehicle approach would suggest.