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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.

This paper seeks to identify the refrigeration load of a hybrid electric truck in order to find the demand power required by the energy management system. To meet this objective, in addition to the power consumption of the refrigerator, the vehicle mass needs to be estimated. The Recursive Least Squares (RLS) method with forgetting factors is applied for this estimation. As an example of the application of this parameter identification, the estimated parameters are fed to the energy control strategy of a parallel hybrid truck. The control system calculates the demand power at each instant based on estimated parameters. Then, it decides how much power should be provided by available energy sources to minimize the total energy consumption. The simulation results show that the parameter identification can estimate the vehicle mass and refrigeration load very well which is led to have fairly accurate power demand prediction.