Significant energy savings when electric distribution vehicles take
their best route

Date:
December 13, 2021
Source:
Chalmers University of Technology
Summary:
Range anxiety with electric commercial vehicles is real, since
running out of battery can have serious consequences. Researchers
have developed tools to help electric delivery-vehicles navigate
strategically to use as little energy as possible. The secret lies
in looking beyond just the distance traveled, and instead focusing
on overall energy usage -- and has led to energy savings of up to
20 per cent.



FULL STORY ========================================================================== Range anxiety with electric commercial vehicles is real, since running
out of battery can have serious consequences. Researchers at Chalmers University of Technology, Sweden, have developed tools to help electric delivery-vehicles navigate strategically to use as little energy as
possible. The secret lies in looking beyond just the distance travelled,
and instead focusing on overall energy usage -- and has led to energy
savings of up to 20 per cent.


==========================================================================
"We have developed systematic tools to learn optimal energy usage.

Additionally, we can ensure that electric vehicles are not running out
of battery or charging unnecessarily in complex traffic networks," says
Bala'zs Kulcsa'r, Professor at the Department of Electrical Engineering
at Chalmers University of Technology.

The research is the latest result from a joint project between Chalmers
and Volvo Group that investigates how electric vehicles can be used for distribution tasks, and the new algorithm for learning and planning the
optimal path of electric vehicles is so efficient that it is already
being used by Volvo Group.

Shortest distance not always the least energy In the study, the
researchers investigated how a fleet of electric trucks can deliver goods
in a complex and crowded traffic network. The challenge is how delivery vehicles carrying household goods, such as groceries or furniture to
several different addresses, should best plan their routes. By working
out the optimal order to deliver to customers, the vehicles can be
driven for as long as possible without needing to interrupt the work to recharge unnecessarily.

Route planning for electric vehicles has normally tended to assume that
the lowest mileage is also the most efficient, and therefore focused on
finding the shortest route as the priority. Bala'zs Kulcsa'r and his
colleagues focused instead on overall battery usage as the key goal,
and looked 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," Bala'zs Kulcsa'r explains.

A significant reduction in energy consumption The researchers modeled
the energy consumption of distribution trucks moving in a city by looking
into many factors; speed, load, traffic information, how hilly different
routes were, and opportunity charging points.

The energy consumption model was then entered into a mathematical
formula, resulting in an algorithm for calculating a route that allows the vehicles to make the deliveries using as little energy as possible. And,
if charging is needed out on the road, the vehicle can save time by taking
the most energy efficient route to a fast charging point. By accounting
for extra factors such as these, the researchers' new method allowed the vehicles to reduce their energy consumption by between 5 and 20 per cent.

Because the electric delivery vehicles operate in complex real-world situations, there can often be unforeseen complications that are difficult
to account for even if the algorithm is accurate from the beginning. The
energy usage forecasts will therefore be further optimised through machine learning, with data collected from the vehicles being sent back to the
tool for further input and analysis.

"Taken together, this will allow us to adapt route-planning to uncertain
and changing conditions, minimising energy consumption and ensuring
successful urban distribution," Bala'zs Kulcsa'r says.

========================================================================== Story Source: Materials provided by
Chalmers_University_of_Technology. Note: Content may be edited for style
and length.


========================================================================== Journal Reference:
1. Rafael Basso, Bala'zs Kulcsa'r, Ivan Sanchez-Diaz, Xiaobo
Qu. Dynamic
stochastic electric vehicle routing with safe reinforcement
learning.

Transportation Research Part E: Logistics and Transportation Review,
2022; 157: 102496 DOI: 10.1016/j.tre.2021.102496 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/12/211213084109.htm

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