Machine learning for misfire detection in a dynamic firing level modulation controlled engine of a vehicle
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United States of America
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May 10, 2022
Grant Date -
Jan 13, 2022
app pub date -
Aug 20, 2021
filing date -
Aug 20, 2021
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Abstract
Using machine learning for cylinder misfire detection in a dynamic firing level modulation controlled internal combustion engine is described. In a classification embodiment, cylinder misfires are differentiated from intentional skips based on a measured exhaust manifold pressure. In a regressive model embodiment, the measured exhaust manifold pressure is compared to a predicted exhaust manifold pressure generated by neural network in response to one or more inputs indicative of the operation of the vehicle. Based on the comparison, a prediction is made if a misfire has occurred or not. In yet other alternative embodiment, angular crank acceleration is used as well for misfire detection.
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