On the Validity of Steady-State Gasoline Engine Characterization Methodology for Generation of Optimal Calibrations Used in Real World Driving

2022-01-0579

03/29/2022

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WCX SAE World Congress Experience
Authors Abstract
Content
Vehicle emissions and fuel economy in real-world driving conditions are currently under considerable scrutiny. Key to achieving optimum performance for a given hardware set and control scheme is a calibration that optimizes controller gains such that inputs are scheduled over the operating space to minimize emissions and maximize fuel economy. Generating a suitable calibration requires data that is both precise and accurate, this data is used to generate models that are deployed as part of the calibration optimization process. This paper evaluates the repeatability of typical steady-state measurements used for calibration of engine controllers that will ultimately determine vehicle level emissions for homologation include Real Driving Emissions (RDE).
Stabilization requirements as indicated by three different measurements are evaluated and shown to be different within the same experiment, depending on the metric used. It is shown that this results in emissions and fuel economy measurements that differ, again for the same experiment, in both their mean value and variance. Differences between the measurement results are shown to be a consequence of the dynamics of the system and that the errors introduced by steady-state measurement are likely to be propagated through to the Response Surface Models used within the calibration optimization process. This discrepancy contributes to the differences between real-world and reported values for emissions and fuel economy.
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DOI
https://doi.org/10.4271/2022-01-0579
Pages
9
Citation
Mason, B., Winward, E., Yang, Z., Knowles, J. et al., "On the Validity of Steady-State Gasoline Engine Characterization Methodology for Generation of Optimal Calibrations Used in Real World Driving," SAE Technical Paper 2022-01-0579, 2022, https://doi.org/10.4271/2022-01-0579.
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Publisher
Published
Mar 29, 2022
Product Code
2022-01-0579
Content Type
Technical Paper
Language
English