Effect of Traffic, Road and Weather Information on PHEV Energy Management 2011-24-0162
Energy management plays a key role in achieving higher fuel economy for plug-in hybrid electric vehicle (PHEV) technology; the state of charge (SOC) profile of the battery during the entire driving trip determines the electric energy usage, thus determining the fuel consumed. The energy management algorithm should be designed to meet all driving scenarios while achieving the best possible fuel economy. The knowledge of the power requirement during a driving trip is necessary to achieve the best fuel economy results; performance of the energy management algorithm is closely related to the amount of information available in the form of road grade, velocity profiles, trip distance, weather characteristics and other exogenous factors. Intelligent transportation systems (ITS) allow vehicles to communicate with one another and the infrastructure to collect data about surrounding, and forecast the expected events, e.g., traffic condition, turns, road grade, and weather forecast. The ability to effectively interpret this traffic and weather 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 ITS integration with energy management of PHEVs: the goal of this research is to determine the correlation (or heuristic relationship) between different road events, weather conditions and PHEV energy management performance. The first step of this study utilizes real world data collected from a plug-in Toyota Prius (after-market conversion kit Hymotion L5) to determine the correlations between events and velocity profile characteristics. The second step finds the impact of power profile characteristics on the performance of equivalent consumption minimization Strategy (ECMS) for PHEV energy management using a high fidelity, validated PHEV simulator. The goal of this study is to identify the impact factors and define qualitative impact on the energy management algorithm and vehicle fuel economy.