Modeling and Measurement to Customer-Specific Fuel Cost Estimation

Due to the sensitivity of hybrid and battery electric vehicles to individual driving behaviour and environmental variability, operators may generally observe different fuel consumptions that differ significantly from the Monroney sticker indicated by the Environmental Protection Agency. This paper investigates a potential measurement and modeling approach to accurately estimate the fuel consumption for specific customers based on their individual driving behaviour. To achieve this, a compact data logger is connected to the vehicle's ODB-II port to record the individual's driving cycle. Once the operating data of the vehicle's typical usage has been recorded, it is analyzed to perform a vehicle monitoring report, indicating the actual energy consumption based on the user's driving pattern. Furthermore, the user's individual driving cycle can be used to accurately predict the energy consumption for various other conventional, hybrid, and battery electric vehicles using a validated computer vehicle model library. In order to accurately estimate the energy consumption based on the user's driving cycle, a computer model library is constructed and validated to within 4% of the real world test data. This paper presents the initial development and testing of the proposed methodology, the results of the validated computer vehicle model, and the potential contribution of this approach.