Machine learning is the best answer logistics has to range anxiety

I find it fascinating to witness how society addresses new technologies and reacts to paradigm changes. Questions raised by specialists are picked up by consumers later on. Just as families’ challenges find an echo in day-to-day industrial activities. As the wheels turn and past worries arise in different contexts, we can grab the opportunity to speak openly about innovation and the myths that risk slowing its adoption.

Take range anxiety, for instance, which first came out with private electric cars. This fear of not having enough range (or battery power) to go where one needs started to be put to rest once vehicles began hitting the road. Now, it’s making rounds in the logistics sector.

Let me be blunt here – range anxiety shouldn’t be an issue when it comes to shipping. When you are one person or one household and one car, there’s only so much you can do to get to a destination. Electric fleets, however, have the key advantage of volume. They combine multiple heavy-good vehicles, drivers and loads. The thing is, you need players that know how to master it. And here is where machine learning and, more broadly, AI come in.

With Einride’s freight mobility platform, shippers and carriers have access to an all-encompassing solution that includes connected electric lorries and charging infrastructure. But the high point is Einride Saga, the freight operating system dedicated to improving transport networks, developed by some of the industry’s most talented people. I’ll let the work speak for itself.

It starts with the right source of data. We collect actual information from the vehicles instead of using factory standards. We can analyse battery consumption, routes, external factors, and multiple variables and make accurate decisions and predictions. Knowing when and where a lorry needs to be charged and for how long is just the first step to supporting reliable deliveries.

Suppose a road traffic accident happens or the weather drastically changes. Operations relying on traditional logistics tools are disrupted by bumped times and a snowball effect that can affect multiple flows. Only AI-based technology like Saga will be able to assess the situation, adapt to unforeseen circumstances and provide alternatives in real-time.

Then, we guarantee HGVs have access to charging points with local chargers and a growing network of stations. With the insight Saga has into the electrification potential of a region, our algorithms allow us to understand precisely where we need to build the infrastructure to support current and future customers’ needs. The sooner spades are in the ground, building more infrastructure, the smoother the transition to electric freight is for the entire sector.

No one else in the industry has this much data.

Energy consumption at the center of Einride’s AI models

Because we measure everything, we know that, for instance, a specific lane will consume 20% more energy during the winter, and we can plan schedules accordingly. When we start operations in new regions, we can simulate how specific trucks will behave based on data collected in similar scenarios and deliver the best-localised solution for each route.

As the energy needed for an operation is lowered, there’s a trickle-down effect that saves resources across the chain: it prolongs intervals between charging slots, improves battery life, protects drivers’ time, and reduces electricity use. In one current commercial application, our AI models have been able to shrink the number of trucks in operation by 20% while meeting the same delivery goals. In a market suffering from low margins, efficiency plays a monumental role in financial results.

Einride’s Product Manager Sabina Söderstjerna had a brilliant presentation during Mesh about Saga, if you fancy all the nitty gritty.

This combination of machine learning and artificial intelligence places Einride ahead of everyone in the space. The outcome? Undisrupted HGV flows with a delivery precision of 99.7%, as our track record shows. There was no need for range anxiety after all, eh?

Martin Walsh

Chief Product Officer at Einride

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