Plug-in hybrid energy efficiency can be improved by up to at least 12% through the use of “smart” energy management systems — rather than standard binary mode control — according to new research from the University of California, Riverside’s Bourns College of Engineering.
To explain that in plain language, the use of a “blended” discharge strategy — rather than simply running the battery down in all-electric mode and then switching to a hybrid mode after that — allows for greater energy efficiency (fuel efficiency).
“Blended discharge strategies have the ability to be extremely energy efficient, but those proposed previously require upfront knowledge about the nature of the trip, road conditions and traffic information, which in reality is almost impossible to provide,” stated researcher Xuewei Qi.
Here’s more via a press release:
While the UCR EMS does require trip-related information, it also gathers data in real time using onboard sensors and communications devices, rather than demanding it upfront. It is one of the first systems based on a machine learning technique called reinforcement learning (RL), and was published online February 5 in the journal Transportation Research Record.
In comparison-based tests on a 20-mile commute in Southern California, the UCR EMS outperformed currently available binary mode systems, with average fuel savings of 11.9%. Even better, Qi said, the system gets smarter the more it’s used and is not model- or driver-specific, meaning it can be applied to any PHEV driven by any individual.
“In our reinforcement learning system, the vehicle learns everything it needs to be energy efficient based on historical data. As more data are gathered and evaluated, the system becomes better at making decisions that will save on energy,” Qi continued.
“Our current findings have shown how individual vehicles can learn from their historical driving behavior to operate in an energy efficient manner. 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. This will enable them to operate on even less fuel and will have a huge impact on the amount of greenhouse gases and other pollutants released.”