EV Transition Planning

Optrak takes your real schedule of work and sorts it by what an EV could do with it: which rounds run on overnight depot charging, which need one charge mid-route, which still need diesel or another fuel. The e-HGV transition page sets out what those groups mean for an operator and why the decision is a planning one rather than a purchasing one. What follows is how the sorting is done.

How the Rounds Are Sorted

Optrak runs the existing schedule of work through the same optimiser that produces daily plans, configured against a candidate fleet that includes one or more EV classes. Three things do the work:

• The EV consumption model — physics-based per-leg energy against mass, gradient, drive cycle, start time and weather.
• The in-route charging model — charging stops as constraints inside route construction, against charger availability and tariff data.
• The standard data model — orders, products, vehicles, drivers and loading — so every round is judged against the operation’s actual structure, not a simplified version of it.

Every round comes back in one of four groups: depot-only, one-in-route-charge, multi-charge or marginal, and diesel-or-alternative-fuel-only. Where the first boundary falls is a setting, not a fixed figure. A round counts as depot-only when it completes within usable single-charge range with the configured safety margin intact, and tightening that margin moves rounds across the line.

Conditions matter enough to run the schedule more than once. The consumption model that underwrites operational planning underwrites this too, so the same work can be run against summer, winter and heaviest-payload profiles. That separates the rounds whose feasibility depends on the day from the ones that are settled either way.

The second thing the exercise tells you is infrastructure-shaped: how many depot chargers at what power, and where the in-route charges the optimiser placed cluster geographically. The e-HGV transition page sets out how that reads as an investment case.

Running It Yourself

The exercise can be done as a modelling engagement, or in the strategic workbench by the team that owns the transition. There they import the candidate vehicle specifications from a spreadsheet, clone the live dataset, run the production optimiser across it, and read the result back against the diesel baseline — with an experimental AI agent window over the same steps.

Worked Example: A Regional Distributor’s Transition Plan

A regional distributor runs 50 vehicles over roughly 40 routes a day — mixed urban and regional work, payloads from light multi-drop to heavier full-load consolidations. It is committing to electrification over five years and has to defend both the EV fleet size and the depot spend to its finance director.

Optrak runs the actual annual schedule against three candidate EV classes: 7.5t, 18t and 44t artic. Most of the routes come back depot-only, mostly the urban multi-drop work, feasible on overnight charging at 11 kW per vehicle. A smaller set of regional routes need one in-route charge, and those charges cluster around two motorway-services locations. A handful of long-haul rounds sit on the margin — feasible in summer, not in winter — so they are modelled both ways. The longest hauls and the heaviest consolidations stay on diesel.

That is the plan the finance director gets: an EV fleet sized against the first two groups, depot chargers sized to the overnight window, a charging agreement aimed at the two clustered locations, a continuing diesel or HVO commitment for the long hauls, and a date to re-run the exercise as batteries and the public network improve.

[Note: the numbers here are illustrative, pending case-study data from a transition engagement we can publish.]