Validity Assessment and Calibration Approach for Simulation Models of Energy Efficiency of Light-Duty Vehicles 2020-01-1441
Software tools for simulations of vehicle fuel economy/energy efficiency play an important role strategic decision-making in advanced powertrains. In general, there is a trade-off between the level of detail in a numerical model of a vehicle (higher detail provides better simulation accuracy), and the computational time resources to run the model. However, even with detailed models of a vehicle, there remains some uncertainty about how the vehicle performs in the real-world. Calibration of simulation models versus real-world data is a challenging task due to variations in vehicle usage by different owners. This work utilizes datasets of real-world driving in vehicles that have been equipped with OBD/GPS loggers. The loggers record at fairly high frequency the vehicle speed, road slope, cabin heating/air-conditioning loads, as well as energy/fuel consumption. For six advanced powertrain vehicle models (Bolt, Leaf, Model S, C-Max Energi, Prius Prime, Volt), an assessment is made regarding the accuracy of window-sticker ratings derived from standard dynamometer tests. One key observation is that while window-sticker ratings can be reasonably accurate when considering many trips across different vehicle owners, individual trips and/or averages for individual owners can vary quite a bit from the window-sticker ratings. Next, simulation accuracy/validity assessment is conducted for baseline version of FASTSim, which is an open-source software tool originally developed by NREL. Lastly, a calibration approach via mass and power adjustment terms is proposed. Results show success at improving the fidelity of FASTSim simulations.
Citation: Hamza, K., Chu, K., Favetti, M., Benoliel, P. et al., "Validity Assessment and Calibration Approach for Simulation Models of Energy Efficiency of Light-Duty Vehicles," SAE Technical Paper 2020-01-1441, 2020, https://doi.org/10.4271/2020-01-1441. Download Citation
Karim Hamza, Kang-Ching Chu, Matthew Favetti, Peter Benoliel, Vaishnavi Karanam, Ken Laberteaux, Gil Tal
Toyota Motor North America Inc., University of California Davis