Gm Global Technolgoy Operations, Inc.

Jin Shen

Patricia M Laskowsky

Jeffrey M Hutmacher

Dana J Suttman

Scott R Jeffrey

Steven Ward Majors

Iman Mohammadrezazadeh

Ruggero Scorcioni

Jianyang Geng

Vincent DeSapio

In various embodiments, methods, systems, and vehicle apparatuses are provided. In one exemplary embodiment, a method is provided that includes obtaining a set of inputs, by a processor, pertaining to one or more features that are used to predict the purge flow of a purge canister system of an intake system of a vehicle; obtaining data, by the processor, from sensors about the vehicle's intake system for use by a neural network to enable the processor to classify the set of inputs including the one or more features for purge flow control for use in predicting a presence of purge content in the vehicle's intake system; and obtaining, by the processor, an output from the neural network wherein the output is configured as a binary or continuous output to instruct a vehicle controller to execute an action to fueling control by letting fueling controller choose different gain sets and adaption strategy based on the binary output flag in a case of the binary-output model, or apply an adjustment factor to fueling command in case of a continuous model.

Methods, systems, and apparatuses of purge content estimation logic for improved fuel control

Methods and systems are provided for monitoring a fuel injector of an internal combustion engine. In one embodiment, a method includes: receiving a set of feature data, the feature data sensed from a fuel injector during a fuel injection event; processing, by a processor, the set of feature data with a decision tree model to generate a prediction of a fault status; and selectively generating, by the processor, a notification signal based on the prediction.

Diagnostic methods and systems

Methods and systems are provided for monitoring a fuel injector of an internal combustion engine. In one embodiment, a method includes: receiving a set of feature data, the feature data sensed from a fuel injector during a fuel injection event; processing, by a processor, the set of feature data with a machine learning model to generate a prediction of a fault status; and selectively generating, by the processor, a notification signal based on the prediction.

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Northville, MI, United States of America

Jin Shen