According to EV-volumes.com, over 500,000 plug-in electric vehicles were sold globally in the first 8 months of 2016, around 40 percent of which were hybrids (PHEVs). Taking inspiration from nature, engineers at the University of California, Riverside (UCR) have found a way to cut the fuel consumption of many of these PHEVs by over 30 percent, just by changing the way the split between combustion engine and battery power is handled.
Deciding whether to draw on power from the internal combustion engine or the battery pack is handled by the vehicle's energy management system (EMS). Although there are some exceptions, most of these operate in a very simple way: prioritizing battery power before battery power levels reach a certain threshold that triggers a switch to the vehicle's engine.
Blended discharge strategies, whereby the choice of power is split throughout the trip so that the battery pack reaches its minimum threshold only at the very end of the trip, have been proposed as a way to reduce fuel consumption and emissions. The UCR team says lab tests have borne such theories out, but the variability of car travel has made it difficult to apply this approach in the real world.
"In reality, drivers may switch routes, traffic can be unpredictable, and road conditions may change, meaning that the EMS must source that information in real-time," says Xuewei Qi, a postdoctoral researcher at the Center for Environmental Research and Technology (CE-CERT) in UCR's Bourns College of Engineering who led the research with Matthew Barth, CE-CERT director and a professor of electrical and computer engineering at UCR.
To address this issue, the team developed a more efficient EMS by combining vehicle connectivity data, such as cellular networks and crowdsourcing platforms like Waze, with evolutionary algorithms, which mathematically describe natural phenomena like evolution and bird flocking.
"By mathematically modeling the energy saving processes that occur in nature, scientists have created algorithms that can be used to solve optimization problems in engineering," says Qi. "We combined this approach with connected vehicle technology to achieve energy savings of more than 30 percent. We achieved this by considering the charging opportunities during the trip—something that is not possible with existing EMS."
This latest work builds on the team's previous research into teaching individual vehicles how to improve fuel efficiency based on their own historical driving records. By combining this with evolutionary algorithms, the team says it will be possible for vehicles to learn and improve their own energy efficiency, with this information then able to be shared with other vehicles in the same traffic network through connected vehicle technology.
"Even more importantly, the PHEV energy management system will no longer be a static device – it will actively evolve and improve for its entire life cycle," says Qi. "Our goal is to revolutionize the PHEV EMS to achieve even greater fuel savings and emission reductions."
The team's paper appears in IEEE Transactions on Intelligent Transportation Systems.
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