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Fleet Electrification Feasibility

This page is about a question asked before any vehicle is purchased: can our existing routes be run by electric vehicles? It is an analytical question, not a dispatch question. The distinction matters, because the analytical version is far cheaper to answer than most teams assume.

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The problem

A fleet operator has 400 routes, run daily, by diesel vehicles. Someone — a customer, a regulator, a board — asks about electrification. The questions that follow:
  • Which routes fit within an EV’s range today?
  • Which fit if we charge mid-route, and where would that be?
  • What does charging time do to the schedule?
  • Which depots need charging infrastructure, and how much?
  • What breaks first: range, charging time, or charger availability?
None of these require dispatching a vehicle. They require analysing history against vehicle specifications.
Do not buy an EV routing API to answer a feasibility question. Historical route distances from your telematics, compared against vehicle range and duty cycle, answers most of it. Buy the API when you need to dispatch, not when you need to analyze.

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Who this is for

Fleet operators evaluating electrification. Sustainability and operations teams. Consultancies producing feasibility studies. Logistics companies assessing Class 8 electrification. Anyone who has been asked for a number by a board. Tier 1 costs nothing and classifies most routes. Only the marginal band justifies API calls.

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Tier 1: the analysis you already have data for

Take your historical trips. For each, compute total daily distance per vehicle per shift. Compare against a candidate EV’s usable range, derated for:
  • Temperature. Cold weather reduces usable range materially.
  • Load. A fully laden vehicle consumes more.
  • Auxiliary load. Refrigeration on a delivery van in July is not negligible.
  • Terrain. Sustained grade dominates consumption.
  • Depth-of-discharge policy. Nobody runs a battery to zero.
Classify:
  • Feasible — comfortably within derated range. Electrify.
  • Marginal — within range under favourable conditions only. Requires analysis.
  • Infeasible — exceeds range with no realistic charging stop. Not today.
In most fleets this classification alone answers 70–80% of the question at zero API cost. Present that first. It buys credibility for the harder analysis on the marginal band, and it may be all anyone needed.
The derating factors are where the honesty lives. A linear consumption assumption is optimistic on highways and catastrophically optimistic on grades.

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Tier 2: relevant HERE APIs, and why

Catchment Area with consumption range type — reachability on a charge. Why: “everywhere this vehicle can reach on a full battery, from this depot” is a reachability polygon with consumption as the range type, not time or distance. It answers the depot-siting question directly: which of our stops fall inside, which fall outside. HERE EV Charge Points API — corridor coverage. Why: station locations, connector types, power feeds. It is a separate product from EV routing, at v2 and v3, with a documented migration path and a separate entitlement. EV Routing — the marginal band only. Why: where a route requires a mid-route charge, the vehicle’s ability to reach charger three depends on whether it stopped at charger one. That is a routing problem and cannot be approximated by filtering a route against a charger table. Matrix Routing — matrix responses can include consumption alongside time and distance. Useful for scoring many candidate routes at once.
EV routing and EV Charge Points are different APIs with different entitlements and separate bills. A key that routes EVs may return 403 from the charge points endpoint. Confirm both before you architect. See Getting a HERE API Key.

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The commercial vehicle problem

Truck charging is not car charging, and this is not a solved problem in the market.
HERE documents EV truck charging locations as a distinct concern within the EV Charge Points product. Connector types, power levels, and physical site access differ from passenger charging. A Class 8 vehicle routed against passenger charging infrastructure is routed to sites it cannot physically enter — no pull-through, no clearance, no capacity. Charging corridors for commercial vehicles are sparse. Any feasibility study for heavy trucks that treats the passenger charging network as available infrastructure is producing a number that will not survive contact with a driver. Be honest with whoever commissioned the study about what the data supports.

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Implementation flow

  1. Extract historical trips. Distance, duration, dwell time, shift boundaries, per vehicle.
  2. Derate range by temperature, load, auxiliary draw, terrain, and depth-of-discharge policy. Document every assumption.
  3. Classify feasible / marginal / infeasible.
  4. For depot siting, compute consumption isolines from each depot. Which stops fall inside?
  5. For the marginal band only, run EV routing with a proper consumption curve and realistic starting charge.
  6. Overlay charger network to identify corridor gaps.
  7. Model charging time as schedule time. A 45-minute charge is 45 minutes the vehicle is not delivering.
  8. Present ranges, not point estimates. The uncertainty is real; hiding it is not.

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Cost considerations

Tier 1 is free. Do it before anything else. Restrict Tier 2 to the marginal band. Running EV routing across 400 routes when 280 are trivially feasible is waste. Cache the charger network aggressively. Station locations change over months. Availability changes by the minute — but availability does not matter for a feasibility study, which is analysing a hypothetical future. Do not query it. Batch corridor analysis. Nothing is waiting. Consumption isolines are bounded by depot count. A 12-depot network is 12 calls, refreshed when vehicle specifications change. Do not query charge points on map interaction. Every pan becomes a request. See HERE Pricing Explained.

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Common mistakes

Buying an EV routing API to answer a feasibility question. Range comparison against history costs nothing. Linear consumption models. Highways and grades break them. Ignoring auxiliary load. Refrigerated vans, in summer, with the doors opening forty times. Ignoring temperature derating. Winter range is not summer range. Assuming full charge at shift start. Depot charging capacity constrains this. Filtering a route against a charger table. The vehicle’s ability to reach charger three depends on charger one. Routing commercial EVs against passenger charging data. Inaccessible sites. Treating charging time as free. It is schedule time and it is vehicle downtime. Presenting a point estimate. “62% of routes are electrifiable” implies precision the analysis does not carry. Confusing EV routing with the EV Charge Points API. Separate products, separate entitlements. Analysing routes rather than duty cycles. A vehicle runs several routes a day. Range constrains the day, not the route.

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Production checklist

  • Historical trip data extracted at the duty-cycle level, not the route level
  • Range derating documented: temperature, load, auxiliary, terrain, depth of discharge
  • Tier 1 classification completed and presented before any API spend
  • Consumption curve used, not a linear approximation
  • Starting charge modelled from depot charging capacity, not assumed full
  • EV routing applied to the marginal band only
  • Commercial-vehicle charging infrastructure distinguished from passenger
  • Charging time modelled as vehicle downtime in the schedule
  • Both EV Routing and EV Charge Points entitlements confirmed
  • Results presented as ranges with stated assumptions

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Alternatives and trade-offs

No routing API at all. For most feasibility studies this is the correct answer. Telematics history plus vehicle specifications plus a spreadsheet. Spend the money on better derating assumptions rather than better routing. Google Maps Platform has limited EV routing with charge-state awareness. For consumer navigation the gap has narrowed. For commercial fleet electrification with truck-specific charging, it is not competitive. Charging network operator APIs — ChargePoint, Electrify America — give authoritative data for their network. Better than any aggregator for that network. Worse for coverage. For a fleet standardizing on one network, go direct. Open Charge Map is free and community-maintained. Coverage and freshness vary. Adequate for a coverage map or a prototype. Not adequate for dispatching a vehicle with 40 km of remaining range — but feasibility studies are not dispatch. A consultancy or OEM feasibility service. Vehicle manufacturers publish consumption curves and will run this analysis. If you are buying their vehicles, the analysis is often free and better parameterized than anything you will assemble. Wait. Charging infrastructure for commercial vehicles is expanding. A route that is infeasible today may be feasible in eighteen months without any change to your fleet. The output of this analysis should include which corridors, if built, unlock the most routes — that is a more actionable finding than a percentage.

EV Routing

Consumption models, charge state, and the two-product distinction.

EV Charging Applications

The dispatch-time architecture, once feasibility is established.

Catchment Area

Consumption-range isolines for depot siting.

Fleet Routing

Where electrified routes eventually have to be dispatched.
Also: Matrix Routing · Getting a HERE API Key

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HERE documentation

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Placematic

Need help designing or implementing a production HERE solution? Placematic helps engineering teams select the right HERE APIs, estimate usage, migrate from Google Maps and build production-ready geospatial systems. Talk to us.