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AI-assisted routing can help a commercial fleet choose routes that use less fuel or energy by estimating how a particular vehicle will perform on different roads, then weighing those estimates against traffic, travel time, delivery windows, and other operating needs. It is a tool for improving fleet efficiency—not a guaranteed savings rate. The outcome depends on the vehicles, loads, routes, drivers, and dispatch priorities involved.
What AI route optimization can—and cannot—do
Traditional routing may prioritize distance or arrival time. Energy-aware routing adds an estimate of how much energy a vehicle may use on each route. NLR’s RouteE tools describe estimating vehicle energy use and travel time across proposed routes while accounting for factors such as traffic, speed, road type, lanes, elevation grade, and turns. NLR’s RouteE overview explains the modeling approach.
This matters because the shortest route is not always the least expensive to drive. A shorter path with steep grades, frequent stops, or slow traffic can use more energy than a longer, smoother route. The right choice depends on the vehicle and duty cycle, not just the map.
AI does not eliminate the need for dispatch judgment. A route must remain feasible for the vehicle, meet delivery windows, and satisfy service and safety requirements. Energy estimates are predictions; operational savings have to be measured in the fleet.
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What data an energy-aware routing system needs
Routing recommendations are only as credible as their inputs. NLR’s CoVaR work describes building energy-consumption models from real-world duty-cycle data, including trip duration, road elevation, cargo weight, and vehicle characteristics. Its framework also considers routing factors such as time, distance, grade, and traffic. NLR’s September 2025 account of CoVaR describes the project and its intended fleet applications.
- Vehicle details: vehicle type and relevant powertrain or configuration information. A route that suits one vehicle may not suit another.
- Route conditions: road network, grade, road type, turns, traffic, and expected speeds.
- Work and load: stops, trip duration, cargo weight or other duty-cycle information, and delivery windows.
- Operational priorities: which constraints are firm—such as arrival time, service commitments, or vehicle suitability—and how much flexibility dispatch has to trade time for lower estimated energy use.
Telematics can supply trip and mileage information for assessing how vehicles actually travel. NREL’s Telematics Framework for Federal Fleet Applications describes comparing actual trip mileage with an efficient or optimal route. An OBD-II fleet telematics device may provide useful vehicle and trip data, but hardware alone does not perform AI route optimization; check vehicle compatibility, data access, software integration, and any required subscription.
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How to put AI routing into fleet operations
- Set the operating objective. Decide whether the goal is lower fuel or energy use, lower cost, or a defined balance with travel time. Specify the delivery, service, safety, and vehicle constraints that cannot be relaxed.
- Check the input data. Confirm that vehicle characteristics, route conditions, loads, stops, and arrival requirements are represented accurately enough for the system to make useful comparisons. Identify gaps before treating a recommendation as reliable.
- Compare feasible route options. Review estimated fuel or energy alongside travel time, distance, traffic exposure, grade, turns, vehicle suitability, and schedule feasibility. Avoid selecting a route on miles alone.
- Connect the recommendation to dispatch and drivers. A route has operational value only if dispatch can assign it and drivers can receive and follow it. NLR’s CoVaR account describes a fleet dashboard intended to assign vehicles to routes and send directions to drivers.
- Measure performance against a baseline. Compare similar work before and after a routing change. Track fuel or energy use, miles, delivery performance, idle time, and route adherence. Where possible, account for differences in vehicle, load, traffic, weather, and work mix so those changes are not mistaken for routing effects.
- Review and refine. Check whether actual trips match planned routes and whether service constraints are still being met. Adjust inputs, priorities, or driver communication when the recommended route is impractical or the measured outcome does not improve.
The measurement method above is implementation guidance, not a quantified savings result from NREL’s telematics report. That report’s relevance is its discussion of actual-versus-optimal mileage comparisons.
How to tell whether fuel costs are actually falling
Use a baseline that reflects comparable routes and work, not a simple before-and-after total that may combine different vehicles, loads, or operating conditions. Review both cost and service performance: a reduction in fuel use is not a successful outcome if it causes missed delivery windows or otherwise makes routes infeasible.
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- Review miles driven, idle time, and route adherence alongside fuel or energy.
- Track delivery performance and other service requirements to identify trade-offs.
- Note changes in vehicle, cargo, traffic, weather, and work mix that could affect the result.
Separate modeled estimates from measured results. A routing engine’s predicted energy use can help choose among routes, but it is not proof that a fleet has realized savings. NLR describes CoVaR’s potential as modeled and informed by real-world fleet data; its September 2025 account said the tool package still needed validation and commercial launch. It should not be treated as a generally available fleet product on that basis alone.
Why savings vary from fleet to fleet
Route optimization is one possible efficiency intervention, not a universal fix. NLR commercial vehicle technologies researcher Setayesh Fakhimi put the fleet-specific nature of the problem this way: “The impact of different interventions on energy savings is dependent on the operations of the fleet.” NLR’s report discusses routing alongside other measures, including vehicle configuration and driver coaching.
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That means route changes should be evaluated as part of the operating system around them. Vehicle selection, driver practices, route feasibility, dispatch processes, and the quality of fleet data can all matter. If fuel use changes after a routing rollout, a careful baseline helps determine whether routing was responsible or whether other conditions changed at the same time.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate a fleet routing tool
When comparing tools, ask whether they optimize for energy as well as time, what vehicle and road variables their estimates use, how they handle delivery constraints, and how recommendations reach dispatchers and drivers. Ask what data the fleet must provide and how actual performance can be compared with the route estimate. Treat a claimed savings percentage as fleet- and method-specific unless it is supported by results from comparable operations with a clear baseline.
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NLR’s RouteE materials describe energy-time trade-off analysis, while its CoVaR account describes a research tool package with fleet-focused routing and dashboard capabilities. These sources establish relevant research and capability categories, not the availability, performance, or commercial terms of any particular vendor’s product.
Do not confuse broad map-service results with fleet savings
NLR reported that Google attributed more than 1.2 million metric tons of carbon emissions prevented to fuel-efficient routing in Google’s 2023 Environmental Report, across the United States, Canada, and nearly 40 European countries. NLR’s 2024 account reports that figure. It concerns a broad mapping-service result, not a measured commercial fleet fuel-cost saving or an estimate of routing’s standalone effect in a particular fleet.
The available evidence does not establish a reliable, general percentage by which commercial fleets can expect AI routing to cut fuel costs. Treat projections as estimates until your own fleet’s comparable operating data show an improvement.
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