A key component in a PHEV is its energy management system (EMS) that controls when the vehicle switches from 'all-electric' mode, during which stored energy from the batteries is used, to 'hybrid' mode, which utilises both fuel and electricity. As EMS devices are developed, an important consideration is how to combine the power streams in the most energy-efficient way.
While not all PHEVs work the same way, most start in all-electric mode, running on electricity until the battery pack is depleted and then switching 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 to be more efficient at minimising fuel consumption and emissions. But, the researchers say, until now blended discharge strategies haven't been a realistic option for real-world applications,
Xuewei Qi, a graduate student in the Bourns College of Engineering's Centre for Environmental Research and Technology (CE-CERT) who led the research, said: “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.”
While Qi’s EMS does require trip-related information, it also gathers data in real time using onboard sensors and communications devices. It is said to be one of the first systems based on a machine learning technique called reinforcement learning (RL).
In tests on a 20-mile commute, Qi’s EMS outperformed currently available binary mode systems, with average fuel savings of 11.9%. Qi also said that 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 RL 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 explained.
He added: “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.”
