Researchers develop tools to streamline EV delivery routes

Researchers from Chalmers University of Technology in Sweden have developed tools designed to help electric delivery vehicles navigate their route so as to use as little energy as possible, capable of using up to 20% less energy.

In an effort to reduce the range anxiety and hesitancy of fleet owners considering transitioning to electric vehicles, researchers from Chalmers University of Technology in Gothenburg, Sweden, teamed up with Volvo Group to see  how electric vehicles can be used for distribution tasks.

The new algorithm learns and plans the optimal path for electric vehicles, and is already so efficient it is being used by Volvo.

“We have developed systematic tools to learn optimal energy usage,” said Balázs Kulcsár, professor at the department of electrical engineering at Chalmers University of Technology. “Additionally, we can ensure that electric vehicles are not running out of battery or charging unnecessarily in complex traffic networks.

The new tools, which were described in a recent article in the journal Transportation Research, are the result of a study into how a fleet of electric trucks can deliver goods in a complex and crowded traffic network.

The researchers had to take into account a delivery vehicle carrying household goods – such as groceries or furniture – to several different addresses, and how best to plan the route.

By working out the optimal order in which to make deliveries, the new tool helps electric delivery vehicles stay out on the road longer without the need to interrupt for recharging.

While much of this sounds obvious – and likely already in use for any delivery fleet – the Chalmers University researchers discovered that the lowest mileage is not necessarily the most energy-efficient route. They focused instead on overall battery usage as the key goal of their tools, looking for routes with the lowest possible energy consumption.

“In real traffic situations a longer distance journey may require less energy than a shorter one, once all the other parameters that affect energy consumption have been accounted for,” Kulcsár explained.

A model was created taking into account a range of factors including speed, load, traffic information, how hilly different routes are, and opportunity charging points.

The subsequent energy consumption model is then entered into a mathematical formula which results in the necessary algorithm for calculating a route that uses the least amount of energy possible. And, if ever charging is needed during the route, the same algorithm helps direct the vehicle along the most efficient route to a fast-charging point.

All of this allows the algorithm to help vehicles reduce their energy consumption anywhere between 5% and 20%.

Moreover, though, the algorithm will continue to be modified and updated so as to take into account the unforeseeable complications that can spring up, helping to further optimise energy usage forecasts through machine learning.

“Taken together, this will allow us to adapt route-planning to uncertain and changing conditions, minimising energy consumption and ensuring successful urban distribution,” Balázs Kulcsár says.