Planning Electrification with Fleet Data: From Operating Profiles to Decisions

Planning electrification with fleet data means translating real assignments into requirements for electric vehicles and charging organisation. These include daily distances, energy demand, payload, parked time and operational reserves. Fleet software helps you analyse this information systematically. The decision remains tied to demonstrated suitability for your specific tasks.

A monthly average often misses exceptionally long operating days or tight time windows. It is therefore worth looking at individual days or shifts. You want to identify which assignments are already well understood, where reliable data is missing and which assumptions need testing in a pilot.

Create an operating profile for each vehicle group

Group vehicles by task, such as predictable regional appointments, changing field-service assignments or short trips between sites. Do not use a single average for groups with different requirements.

For each group, record at least the following information:

• Daily distance and the frequency of longer assignments.

• Departure, return and the parked time actually available.

• Location while parked and existing charging opportunities.

• Required payload, passenger seats and special equipment.

• Seasonal or assignment-specific factors.

• Operational reserve needed for unplanned tasks.

FEMP explicitly identifies vehicle location and duty cycle as important factors in finding suitable candidates for replacement with electric vehicles. This provides a professional basis for choosing data, not a blanket assurance that individual vehicles are suitable. Source: Fleet Management Framework.

Check the data before calculating range

A missing daily mileage reading is not a day without trips. A vehicle change must not silently merge two operating profiles. And if return times are entered manually, you should know whether they represent actual arrival or simply the planned end of the shift.

Flag incomplete days and document which conclusions remain provisional as a result. Choose a period that includes typical working days and known peaks in demand. A single month may be unsuitable for seasonal tasks. A limited dataset must remain clearly identified as such.

The article on data quality in fleet software explains rules for required fields and plausibility checks. For electrification planning, add operating characteristics that affect range or charging windows.

Worked example: Bringing energy demand and parked time together

Illustrative planning example: On one working day under review, a vehicle group covers 150 kilometres. For an initial scenario, you explicitly assume consumption of 22 kWh per 100 kilometres. The calculated energy demand for the trip is 150 × 22 ÷ 100 = 33 kWh.

With an arbitrarily chosen planning buffer of 20 per cent, the target becomes 39.6 kWh. This buffer is not a general recommendation and does not replace measurement. For actual system design, you need to check suitable consumption data, usable battery capacity and operational requirements.

An eight-hour parked period would mathematically allow 39.6 kWh to be replenished at an average of 4.95 kW. The required connection capacity must also account for factors including charging losses, simultaneous vehicles and the charging time that can actually be used. The calculation only describes the average energy demand in this example; it is not a complete charging plan.

Build scenarios with transparent assumptions

Create a normal operating scenario, a known demanding assignment and a case with restricted charging availability. In each case, change documented inputs: distance, assumed consumption, available parked time or energy to be replenished.

The software should show which inputs were measured and which were assumed. Every result needs a data cutoff and the rules used. If an assumption changes later, it must remain clear why the assessment is different.

You might label results “plausible on existing data”, “to be checked in the pilot” and “requirement still unresolved”. Avoid automatic yes-or-no approval based solely on average daily mileage. Suitability also covers the transport task, workflow and availability of suitable charging windows.

Worksheet: One decision sheet per vehicle group

Use a concise sheet with these fields for your next discussion:

1. Operational task and number of vehicles needed.

2. Period reviewed and known data gaps.

3. Typical and demanding operating days.

4. Energy demand with named assumptions.

5. Usable parked periods and charging locations.

6. Unresolved technical or organisational requirements.

7. Pilot question, responsible people and measurable acceptance criterion.

An acceptance criterion might be: planned assignments are completed within the agreed charging windows during the defined pilot period, and every additional interruption is recorded with its cause. Choose the number of pilot days to match the operating profile. A rare but important special case requires a separate check.

Keep using the plan after the first vehicle

After getting started, check whether the assumptions match actual operations. Record additional charging interruptions, changes in parked time and unexpected restrictions. This turns the initial plan into a foundation for the next vehicle group.

For the broader choice of charging locations, see the existing electric fleet charging strategy guide. This article focuses on the data used to prepare a specific suitability decision.

Bring your vehicle groups, representative operating days and known parked periods to StromNow for fleets. This allows you to discuss charging requirements and identify which data or checks are still needed for the next planning step.