However, while almost all major manufacturers are bringing battery-electric production models to market, the development of the necessary charging infrastructure still faces significant challenges.
To design the charging infrastructure effectively and enable demand-oriented planning, specific requirements and data must be taken into account.
There are already several good approaches to this. In June, NOW GmbH presented the plan for 350 rapid charging stations for lorries.
The demand modelling to determine ideal charging locations is based on current data regarding commercial vehicle traffic and market ramp-up figures from discussions with vehicle manufacturers.
What complicates demand-oriented planning, however, is that this current data mostly stems from diesel operation. For optimal utilisation of the charging infrastructure, it must be planned based on the needs and driving profiles of electric vehicles, which differ greatly.
Data-driven planning for tomorrow’s routes
The first step, therefore, is to understand the differences in electric operation. In addition to factors such as delivery windows, shift lengths, and driver rest times, other aspects such as the range of electric lorries, payload restrictions due to heavier batteries, charging locations and times, or grid constraints must be considered.
Balancing all this is only possible with a digital system and the use of artificial intelligence (AI) and machine learning (ML). This requires comprehensive data collection and analysis – both operational data as well as data from the vehicles, driving behaviour, and the charging stations themselves.
One of the greatest challenges is the range and charging times of electric lorries. They influence the entire logistics planning and require precise and dynamic route optimisation.
The range heavily depends on various factors such as load, driving behaviour, and weather conditions. Unlike diesel lorries, which can quickly refuel at any service station, electric lorries require longer and more frequent charging times, making planning more complex.
AI-driven systems can monitor these variables in real-time and make corresponding adjustments. For example, they can predict when and where a vehicle needs to be charged based on current battery depletion and driving conditions. This helps avoid unexpected breakdowns and maximise operational times.
Such models, trained on data from electric operation, can also be used to predict where electric lorries will need to be charged in the future, enabling demand-oriented planning of charging infrastructure.
Another aspect is synchronising charging times with drivers’ legally mandated rest times. AI can be employed here to plan optimal charging and rest times. This not only reduces downtime but also ensures that drivers can use their breaks efficiently.
For charging infrastructure planning, this means that charging facilities must be reachable within driving times and offer drivers space, rest, and infrastructure for their breaks.
Site requirements: more than just power connections
Using digital tools such as AI and ML, it is possible to determine where charging stations will actually be needed in the future. Additionally, the local power grid is, of course, a factor.
It must be ensured that it can handle the increased load, for example, by installing energy storage systems to manage peak demand. The integration of renewable energy sources, such as on-site solar panels, is another way to alleviate part of this burden.
Ideally, charging stations should also be located separately from car areas, such as rest stops. Since there is currently no specific lorry charging infrastructure, tractors and trailers often need to be separated so that the driver can then drive the tractor to the available car charging station. At busy motorway service areas, this poses a significant safety risk.
Once all these aspects are considered, and the ideal locations have been identified, the question of cost remains. At present, charging on the road is considerably more expensive than charging at company premises. To lower the cost of on-the-road charging, government action is required.
The government can work together with businesses that can provide the right data to address this issue. While the government should ensure that subsidies are reflected in charging prices, or lower costs for providers in other ways, such as through the free provision of land, businesses can assist with valuable data and AI-based analyses to help identify the best locations for the highest utilisation. In this way, the greatest benefit can be derived from investments in charging infrastructure.
Author: Robert Ziegler,
Director General EMEA at Einride*
*Einride is a Swedish technology company based in Stockholm that has developed an operating system that enables the transport of goods with electric and autonomous vehicles.