Titled "Development and Evaluation of an Evolutionary Algorithm-Based Online
Energy Management System for Plug-In Hybrid Electric Vehicles," a paper
describing the research was recently accepted for publication in the journal
IEEE Transactions on Intelligent Transportation Systems. The work was led
by Xuewei Qi, a postdoctoral researcher at the Center for Environmental Research
and Technology (CE-CERT) in UCR's Bourns College of Engineering, and Matthew
Barth, CE-CERT director and a professor of electrical and computer engineering
PHEVs, which combine a gas or diesel engine with an electric motor and a
large rechargeable battery, offer advantages over conventional hybrids because
they can be charged using mains electricity, which reduces their need for fuel.
However, the race to improve the efficiency of current PHEVs is limited by
shortfalls in their energy management systems (EMS), which control the power split between engine and battery when
they switch from all-electric mode to hybrid mode.
While not all plug-in hybrids work the same way, most models start in
all-electric mode, running on electricity until their battery packs are
depleted, then switch to hybrid mode. Known as binary mode control, this EMS
strategy is easy to apply, but isn't the most efficient way to combine the two
power sources. In lab tests, blended discharge strategies, in which power from
the battery is used throughout the trip, have proven more efficient at
minimizing fuel consumption and emissions. However, their development is complex
and, until now, they have required an unrealistic amount of information
"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," Qi said.
The highly efficient EMS developed and simulated by Qi and his team combines
vehicle connectivity information (such as cellular networks and crowdsourcing
platforms) and evolutionary algorithms—a mathematical way to describe natural
phenomena such as evolution, insect swarming 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," Qi said. "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."
The current paper builds on previous work by the team showing that individual
vehicles can learn how to save fuel from their own historical driving
records. Together with the application of evolutionary algorithms, vehicles
will not only learn and optimize their own energy efficiency, but will also
share their knowledge 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. Our
goal is to revolutionize the PHEV EMS to achieve even greater fuel savings and
emission reductions," Qi said.
The work was done by Qi and Barth, together with Guoyuan Wu, assistant
research engineer at CE-CERT, and Kanok Boriboonsomsin, associate research
engineer at CE-CERT. This project was supported in part by the National Center
for Sustainable Transportation.
The UCR Office of Technology Commercialization has filed patents for the