Summary is AI-generated, newsdesk-reviewed
  • Optrak launches cloud-based EV route platform, aiding fleet decarbonisation and operational efficiency improvement.
  • Platform models EV energy use, integrating en-route charging and alternative fuel support.
  • AI-driven optimisation ensures routing accuracy while balancing cost, efficiency, and decarbonisation.

Optrak Distribution Software has launched a new cloud-based route optimisation platform to help road freight operators manage fleet decarbonisation, while improving efficiency in existing operations.

Optrak, a UK provider of route planning and load optimisation solutions, has developed the system to combine EV consumption modelling, en-route charging and alternative fuel support with advanced load and route optimisation.

New system models EV energy use

The new system models EV energy use by factoring in gradients, load mass, congestion

Operators can use the platform for daily mixed-fleet planning or strategic 'what-if' analysis, when assessing fleet transition options. The new system models EV energy use by factoring in gradients, load mass, congestion, weather and auxiliary systems.

En-route charging is included, with integration into emerging public and private charging networks, such as ZEHID, enabling planners to schedule longer trips with arrival times, charging durations and estimated costs.

Low-carbon vehicles for specific areas

Optrak says the platform can also model alternative fuels, such as HVO, CNG and biofuels alongside diesel and EVs. Operators can apply site- or map-based rules to prioritise low-carbon vehicles for specific areas or customers, while comparative CO₂ reporting supports both Scope 3 and internal emissions reporting.

For operators running primarily diesel fleets, the platform delivers performance gains through advanced optimisation heuristics and integrated load optimisation,” says Tom Pigden, Commercial Director at Optrak.

This includes full modelling of vehicle compartments, temperature zones, stacking and nesting rules, as well as automated sequencing of complex multi-drop deliveries – particularly relevant in food and drink distribution.”

AI-driven self-tuning

The system selects the most effective optimisation methods for a given delivery model

The optimisation engine is built on a detailed real-world model, including time-dependent road speeds, site and vehicle restrictions, driver shifts, overnight rules and split time windows. A cost model aligns with operator pricing structures to identify the lowest cost to serve, while meeting customer requirements.

AI-driven self-tuning ensures routeing accuracy as operations evolve. The system selects the most effective optimisation methods for a given delivery model, continuously incorporating new research, so operators of all sizes benefit from up-to-date solutions without disruption.

Vehicle performance in real-world conditions

The key to making EVs viable is maximising uptime and mileage, while managing charging constraints,” said Tom Pigden.

He adds, “Our modelling allows planners to understand how vehicles perform in real-world conditions and plan routes that balance efficiency, cost and decarbonisation.”

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