Page 13 - ME News Fall 2021

Research
GOING THE DISTANCE WITH ELECTRIFICATION
Researchers in the Thomas E. Murphy Engine Research Lab (MERL)
are working to support the electrification of cars and trucks through a number of parallel methods. Taken together, the methods developed by MERL provide support for further electrification of both consumer and commercial road vehicles in three key ways:
1) Modeling and improving the efficiency of electric vehicles.
2) Guiding the effective distribution and use of charging infrastructure. 3) Quantifying the financial and environmental impacts of electrification.
One recent focus has been working on energy-efficient routing, or eco-routing, algorithms. Given an origin and a destination, the eco-
routing algorithm tries to predict the most energy-efficient paths to drive along, and recommends routes with expected energy savings in exchange for slight time delays.
If the car needs to charge along the way, a complementary method then seeks to predict the minimum charging needed to complete a trip so that owners aren’t stuck at the charging station longer than they need to be.
Another major topic being addressed by the group is electric vehicle range prediction. The already limited ranges of entry-level electric vehicles, which remain a major obstacle to increased electric vehicle adoption, are further reduced in extreme hot or cold weather. The U is well positioned to study the impacts of cold weather on vehicle performance, and MERL has found that Minnesota winters are very taxing on day-to-day range. Developing environment-sensitive range prediction methods can help give drivers a fuller understanding of the effective driving range of their vehicles and help put their minds at ease.
ENERGY SAVINGS IN UNEXPECTED PLACES
Typically, vehicle-based energy savings
measures have focused on the fuel
source. But Professor Zongxuan Sun
is looking at it differently. What if the
energy savings was in how the vehicle
operates, not in how it is fueled? To
answer that question, Sun is developing
a systematic method and necessary
tools to optimize and evaluate energy
savings for connected and autonomous off-road vehicles, including construction and agriculture machines. The outcome includes a system that can intelligently operate the vehicles to achieve 20-40% energy savings over conventional vehicles. 13