High-Fidelity Heavy-Duty Vehicle Modeling Using Sparse Telematics Data
2022-01-0635
03/29/2022
- Event
- Content
- Heavy-duty commercial vehicles tend to have poor energy efficiency due to relatively high mass, poor aerodynamic characteristics, and high auxiliary power loads. These factors lead directly to high fuel cost per mile, perpetuate domestic dependence on imported oil, and contribute to climate change and environmental damage in the form of air pollution. The optimization of energy efficiency in these vehicles presents a way to reduce industry expenses while also taking action on the growing issue of climate change. Vehicle modeling provides a path for energy efficiency improvement by allowing rapid experimentation of different vehicle characteristics on fuel consumption. This research focused on creating higher fidelity models using real-world, sparsely collected telematics data from a large fleet of heavy-duty vehicles. The largest challenge encountered in this research was the use of sparse data, where samples are sporadically collected, leading to infrequent sampling rate and irregularities in time resolution. Captured in the data set was geospatial information, time series measurements, and vehicle-specific metadata from a subset of about 100 vehicles. Vehicle data were processed and analyzed with a custom algorithm to automatically derive vehicle model input parameters and representative drive cycles. These provide a basis on which to simulate the real-world vehicles and iterate on vehicle aerodynamics, auxiliary power loads, transmission shift schedules, and other parameters. These iterations will be used to tune vehicle behavior in order to reduce fuel consumption and increase energy efficiency. The high fidelity models developed through this method also allow for more representative vehicle simulations with increased flexibility regarding vehicle-to-vehicle variations.
- Citation
- Carow, K., Cantwell, N., Ivanco, A., Holden, J. et al., "High-Fidelity Heavy-Duty Vehicle Modeling Using Sparse Telematics Data ," SAE Technical Paper 2022-01-0635, 2022, .