Researchers at the U.S. Department of Energy’s Argonne National Laboratory are no strangers to accolades.

The laboratory’s reputation as a thought leader in all things energy has garnered its staff countless awards and patents over its nearly seven decades, a trend that shows no signs of slowing down thanks to researchers like Dominik Karbowski.

A principal systems analysis engineer, Karbowski received the Best Paper Award–North America for ​“Vehicle Energy Management Optimisation Through Digital Maps and Connectivity” at the ITS World Congress in Bordeaux, France. The Congress is the world’s largest event focused solely on intelligent transportation, with more than 10,000 researchers, policymakers, and students attending the 2015 conference from October 5–9.

The paper, co-authored by fellow Argonne researchers Vadim Sokolov and Aymeric Rousseau, examines how the efficiency of plug-in hybrid vehicles (PHEVs) can be increased by alternating between their batteries and engines at the most opportune times using digital maps with real-time updates. These types of efficiency studies will be critical as the world transitions to connected and automated vehicles, smart traffic signals, and other intelligent transportation systems.

“If you know the traffic, the route, and other information, you can enhance the efficiency of the vehicle,” said Karbowski.

PHEVs are primed for efficiency improvements. Given that their all-electric range is relatively short (tens of kilometers), their engines are necessary for longer trips. Efficiency is enhanced by having the engine power the vehicle in high-speed driving and during strong accelerations, while the battery is better suited for stop-and-go traffic and other situations requiring low power. And that’s where the gains are made, in the balance between the engine and battery.

To further push this efficiency envelope, Karbowski and his colleagues used two critical tools: prediction and optimal control theory. This meant that first the researchers needed to calculate naturalistic speed profiles by combining data from digital maps, actual recorded speeds from surveys, and Markov chains, a type of stochastic prediction.

From these speed profiles, the researchers then applied a mathematical framework known as optimal control theory to create an algorithm that selects the most efficient means of propulsion at any given time along the route, based on the trip’s forecast.

To evaluate their methods on fuel consumption, the team then ran a large simulation experiment involving 30 total trips in the metropolitan areas of Chicago, San Francisco, and Pittsburgh. While calculations varied depending on trip distance, fuel consumption was significantly improved on longer journeys, thus validating the team’s methodology and winning Karbowski the prestigious Best Paper award.

“Thanks to ever-growing levels of connectivity in cars and trucks, route-based control and energy management has the potential to be applied to a wide variety of vehicles,” said Karbowski. ​“Our research aims at demonstrating how much fuel can be saved as the future of transportation unfolds.”

Fuel savings were evaluated using Argonne’s Autonomie simulation tool, which allows accurate energy consumption prediction for a wide range of transportation technology scenarios.

The research was funded by the U.S. Department of Energy’s Vehicle Technologies Office, and maps were provided by Chicago-based HERE.