University of California, Riverside

Department of Electrical and Computer Engineering

Hybrid Car System Learns Fuel Efficiency

Hybrid Car System Learns Fuel Efficiency

Hybrid Car System Learns Fuel Efficiency

February 23, 2016

A new system that can actually learn from trips driven by the vehicle to balance the use of electricity and fuel in the most fuel-efficient manner is being developed at the University of California, Riverside (UCR).  This futuristic vision of hybrid cars adapting to become more fuel efficient comes from engineers at CE-CERT.

Their special hybrid energy management system uses machine learning software to improve vehicle fuel efficiency based on road and traffic conditions. By comparison, basic “binary” hybrid car modes make the car start driving in all-electric mode and continue until the battery has been depleted. Then the gasoline engine takes over. Driving tests on a 32-kilometer commute in Southern California showed that the new “learning” system achieved average fuel savings of almost 12 percent compared with today’s binary-mode systems.

Most plug-in hybrid cars have just several basic driving modes designed for general driving scenarios on the highway or in city traffic. A new system can actually learn from driving trips to balance the use of electricity and fuel in the most fuel-efficient manner.

“Our current findings have shown how individual vehicles can learn from their historical driving behavior to operate in an energy efficient manner,” said Xuewei Qi, a Ph.D. candidate in electrical and computer engineering at the University of California, Riverside, in a press release. “The next step is to extend the proposed mode to a cloud-based vehicle network where vehicles not only learn from themselves but also each other.”

The idea of networked cars wirelessly sharing information that makes them more fuel efficient could become a reality in the not-so-distant future. But for now, the university has filed patents based on the research published in the February 5th online edition of the journal Transportation Research Record.

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