A Statistical Approach to Assess the Impact of Road Events on PHEV Performance using Real World Data 2011-01-0875

Plug in hybrid electric vehicles (PHEVs) have gained interest over last decade due to their increased fuel economy and ability to displace some petroleum fuel with electricity from power grid. Given the complexity of this vehicle powertrain, the energy management plays a key role in providing higher fuel economy. The energy management algorithm on PHEVs performs the same task as a hybrid vehicle energy management but it has more freedom in utilizing the battery energy due to the larger battery capacity and ability to be recharged from the power grid. The state of charge (SOC) profile of the battery during the entire driving trip determines the electric energy usage, thus determining overall fuel consumption. The knowledge of the power requirement or velocity profile during a driving trip is necessary to achieve best fuel economy results; clearly, performance of the energy management algorithm is closely related to the amount of trip information available in the form of road profile, velocity profile, trip distance, and weather conditions. The intelligent transportation system (ITS) allows the vehicle to communicate with other vehicles and the infrastructure to collect data about environment and the expected events in the future, such as traffic density, expected turns, road grade, rain, snow and temperature. The ability to effectively interpret the weather and traffic data to estimate the power demand is important for the energy management and plays crucial role in the battery utilization. This paper presents an important step towards the ITS integration with energy management of PHEVs: an approach to determine correlations/transfer functions between different road events and velocity profile characteristics. This study utilizes a year round data collected from a plug-in Prius and principal component analysis, numerical clustering method to determine the correlations between events and performance. The overarching goal of this study is to identify the factors that have largest impact on the vehicle fuel economy and are most important for subsequent development of energy management strategies in optimizing fuel economy.