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Optimal Mission-Specific Dynamic Powertrain Calibration from Cloud for Class 8 Linehaul Truck Cruise Operation
Technical Paper
2020-01-5006
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Calibration of control software including engine and transmission is a major step in the integration of the powertrain system in heavy-duty vehicles. This calibration is done through a series of tests performed on the powertrain system in test cell or on the vehicle in the field to meet fuel economy, emissions, drivability and other customer performance requirements. Currently, since the knowledge of the future driving conditions of the vehicle is limited, the calibration is done to meet the requirements under different vehicle operating conditions leading to robust but sub-optimal performance operation of the system under some real-world driving conditions. In this paper, a mission-specific vehicle calibration optimization is presented, in which connectivity to cloud-computing platforms is utilized to run more complex optimization algorithms on a high fidelity digital twin model of the system and dynamically optimize selected calibration parameters of the control software for fuel economy and trip time. The route information including speed limit and elevation, or grade, is available over the entire trip mission, or route. This leads to a framework in which calibration parameters are dynamically updated to optimize fuel (energy) consumption subject to drivability, emissions and other customer requirements. The impact over 49,000 miles (78,900 km) of U.S. highways is further analyzed in simulation. The results indicate that nationwide implementation of the proposed concept to utilize connected and automated vehicle technologies for powertrain efficiency re-calibration has the potential of significant reduction in CO2 emissions and fuel cost spent in line-haul fleet operation.
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Sandoval, J., Tulpule, P., Hodzen, E., and Borhan, H., "Optimal Mission-Specific Dynamic Powertrain Calibration from Cloud for Class 8 Linehaul Truck Cruise Operation," SAE Technical Paper 2020-01-5006, 2020, https://doi.org/10.4271/2020-01-5006.Data Sets - Support Documents
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