Gm Global Technolgoy Operations, Inc.

Layne K Wiggins

Wenbo Wang

Gregory Paul Matthews

Kenneth Paul Dudek

Jeffrey M Kaiser

Chaitanya Sankavaram

Yann G Guezennec

Jason Meyer

Michael Livshiz

James L Worthing

Daniel G Brennan

Shiming Duan

Kurt D McLain

Wajdi B Hamama

Gary Robert Cygan, Jr

Robert J Horner

Dean G Sorrell

Ronald A Davis

Jay S Meldrum

Kevin A Sherwin

Shawn W Murphy

Christopher E Whitney

Paul Alan Bauerle

Richard B Jess

Yiran Hu

Stephen Paul Levijoki

Mark Henry Costin

John F Van Gilder

Ning Jin

Rafat F Hattar

Qi Ma

Mark D Carr

Joseph M Stempnik

Azeem Sarwar

Joseph E Ploucha

Timothy John Hartrey

Stephen Yurkovich

Nicholas John Kalweit

Vivek Mehta

Shawn Midlam-Mohler

Dnyanesh Rajpathak

Marco J Gatti

Anthony L Marks

Xiangxing Lu

J Oscar Aparicio, Jr

Chao F Daniels

Lee C Walker

John A Jacobs

Bryan D Lehman

Wesley Rieves Haney

Paul D Donar

Bryant G Hammond

Roberto De Paula

Kauser Ferdous

Todar Kuzmanov

Antonio E Bartolomeo

Andrew J Kosanka

Martin Leven

Thomas F Porlick

Adam J Heinzen

Jan Andrzej Gatowski

Jill A Slimmer-Velez

Keith M Lindsey, Jr

Brian L O'Hear

Robert Domino

Bastian Maass

David R Kosmalski

Robert D Wagner

Elizabeth Sarah Peterson

Henrik Nordin

A system and method of performing fault diagnosis and analysis for one or more vehicles. The method includes: obtaining design failure mode and effect analysis (DFMEA) data that specifies a plurality of failure modes; receiving diagnostic association data; receiving vehicle operation signals association data; generating augmented DFMEA data that indicates a causal relationship between the diagnostic data and the first set of failure modes, and that indicates a causal relationship between the vehicle operation signals data and the second set of failure modes, wherein the augmented DFMEA data is generated based on the DFMEA data, the diagnostic association data, and the vehicle operation signals association data; and performing fault diagnosis and analysis for the one or more vehicles using the augmented DFMEA data.

Vehicle fault diagnosis and analysis based on augmented design failure mode and effect analysis (DFMEA) data

A physics-based charge temperature model to calculate a charge air temperature for an automobile vehicle includes multiple variables. The multiple variables include: a first variable defining an engine speed of an engine defining revolutions per minute of a crankshaft of the engine; a second variable defining a cam position; a third variable defining an engine coolant temperature; a fourth variable defining an air intake temperature; a fifth variable defining an engine air flow; and a sixth variable defining a firing fraction of the engine. A controller provides multiple lookup tables. The controller controls operation of the engine using the multiple variables and data in the multiple lookup tables to calculate a charge air temperature for individual intake strokes of at least one cylinder of the engine.

Physics-based charge temperature model

An engine control system includes a prediction module that, during an exhaust stroke of a first cylinder of an engine, determines a predicted intake manifold pressure at an end of a next intake stroke of a second cylinder following the first cylinder in a firing order of the cylinders. An air per cylinder (APC) module determines a predicted mass of air that will be trapped within the second cylinder at the end of the next intake stroke of the second cylinder based on the predicted intake manifold pressure. A fueling module controls fueling of the second cylinder during the next intake stroke based on the predicted mass of air.

Intake manifold and cylinder airflow estimation systems and methods

A method for compressor surge detection to enable model based air estimation includes determining if an intake air compressor pressure ratio is within a predetermined surge pressure range. Then the differences between mass air flow sensor signals measured at a start and finish of each count of a predetermined string length counter when the compressor pressure ratio is within the predetermined surge pressure range. Next, a transition is made to an air mass estimation model output signal from a mass air flow sensor signal when a sum of the air flow differences is greater than a predetermined compressor surge threshold.

Method for compressor surge detection to enable model base air estimation

A method of conducting prognosis for an airflow management system for a combustion engine includes adjusting a throttle body valve position control signal in response to a detected airflow variation and monitoring an airflow variation compensation (AVC) value corresponding to a degree of adjustment of the control signal. The method also includes generating a throttle body coking severity metric based on at least a plurality of residual error values and the AVC value, and executing at least one response action based on the throttle body coking severity metric exceeding a predetermined threshold.

Combustion engine airflow management systems and methods

Computational models and calculations relating to trapped and scavenged air per cylinder (APC) improve scavenging and non-scavenging operational modes of internal combustion engines as well as the transition there-between. Data from sensors which include engine speed, manifold air pressure, barometric pressure, crankshaft position, and valve state are provided to a pair of artificial neural networks. A first neural network utilizes this data to calculate the nominal volume of gas, i.e., air trapped in the cylinder. A second neural network utilizes this data to calculate the trapping ratio. The output of the first network is utilized with the ideal gas law to calculate the actual mass of trapped APC. The actual mass of trapped APC is also divided by the trapping ratio calculated by the second network to determine the total APC and is further utilized to calculate the scavenged APC by subtracting the trapped APC from the total APC.

Method of continuously calculating trapped and scavenged air per cylinder

An engine airflow management system includes an inlet portion to receive ambient air and a mass airflow (MAF) sensor to sense mass flow rate of air passed through the inlet portion. The airflow management system includes a throttle body to selectively restrict airflow and a throttle position sensor (TPS) to sense an opening value of the throttle body. The airflow management system includes an intake manifold in fluid connection with the throttle body configured to direct airflow to a number of combustion cylinders. A manifold air pressure (MAP) sensor detects air pressure at the intake manifold. A controller is programmed to monitor signals from each of the MAF sensor, TPS, and the MAP sensor and generate a residual error value based on a difference between a model-based value and a corresponding monitored signal. A response action is based on a trend of at least two residual error values.

A fault diagnostic system of a vehicle includes a noise module that determines a noise value based on a plurality of differences between samples of a pressure signal generated by a pressure sensor located in a positive crankcase ventilation (PCV) system of an engine. A signal module determines a signal value based on the samples of the pressure signal generated by the pressure sensor located in the PCV system of the engine. A diagnostic value module determines a diagnostic value based on one of: (i) a product of the noise value and the signal value; and (ii) a sum based on the noise value and the signal value. A fault module selectively diagnoses a fault in the PCV system based on the diagnostic value and generates a malfunction indicator within a passenger cabin of the vehicle in response to the diagnosis of the fault in the PCV system.

Positive crankcase ventilation system diagnostic systems and methods

An engine control system of a vehicle includes a manifold temperature module, a runner temperature module. The manifold temperature module determines a first temperature of gas in an intake manifold of an engine. The runner temperature module determines a second temperature of gas in an intake runner associated with a cylinder based on the first temperature of the gas in the intake manifold. The engine control system further includes at least one of: a fuel control module that controls fueling of the cylinder based on the second temperature of the gas in the intake runner; and a spark control module that controls spark of the cylinder based on the second temperature of the gas in the intake runner.

Intake runner temperature determination systems and methods

A cylinder control system of a vehicle, includes a cylinder control module and a volumetric efficiency (VE) module. The cylinder control module determines a desired cylinder activation/deactivation sequence. The cylinder control module also activates and deactivates valves of cylinders of an engine based on the desired cylinder activation/deactivation sequence. The VE module determines a volumetric efficiency based on a cylinder activation/deactivation sequence of the last Q cylinders in the firing order. Q is an integer greater than one.

Volumetric efficiency determination systems and methods

A system for a vehicle includes a humidity determination module, a specific humidity module, and a parameter control module. The humidity determination module determines a first specific humidity of air based on a relative humidity of the air measured by a humidity sensor in an intake system of the vehicle. The specific humidity module sets a second specific humidity of the air equal to one of the first specific humidity and a predetermined specific humidity of the air in response to a comparison of a mass air flowrate (MAF) into an engine and a predetermined flowrate. The parameter control module controls at least one operating parameter of an engine based on the second specific humidity.

Engine control systems and methods with humidity sensors

A system according to the principles of the present disclosure includes a starter control module, an engine speed module, and a sensor diagnostic module. The starter control module generates a starter engage signal to engage a starter of an engine. The engine speed module determines a speed of the engine based on input from at least one of a camshaft position sensor and a crankshaft position sensor. The sensor diagnostic module selectively diagnoses a fault in the at least one of the camshaft position sensor and the crankshaft position sensor based on a rate of change in the engine speed before the starter engage signal is generated.

System and method for diagnosing a fault in a camshaft position sensor and/or a crankshaft position sensor

A system for an engine includes an edge detection module and a correlation calibration module. The edge detection module (i) detects edges of a camshaft of the engine using a camshaft position sensor, and (ii) detects edges of a crankshaft of the engine using a crankshaft position sensor. The correlation calibration module calibrates a correlation between the crankshaft and the camshaft based on the detected edges of the crankshaft and the camshaft, respectively.

System and method for calibrating engine crankshaft-camshaft correlation and for improved vehicle limp-home mode

A system according to the principles of the present disclosure includes a fuel volatility module, a temperature generation module, and a fuel control module. The fuel volatility module estimates a volatility of fuel provided to an engine based on an engine torque and an engine speed. The temperature generation module generates a temperature of an intake valve of the engine. The fuel control module selectively increases an amount of fuel provided to the engine based on the temperature of the intake valve, the engine torque, and the fuel volatility.

Controlling fuel injection based on fuel volatility

A system for a parallel intake engine includes first, second, third, and fourth modules. The first module estimates a total mass air flow (MAF) into the engine based on a cross-sectional area of a throttle and a pressure ratio across the throttle. The second module estimates first and second MAFs through first and second induction paths, respectively, based on the estimated total MAF and a factor. The third module calculates first and second differences between the estimated first and second MAFs and first and second MAFs measured by first and second MAF sensors, respectively. The fourth module detects failures of the first and second MAF sensors based on the first and second differences and first and second thresholds, respectively.

System and method for detection failures of mass airflow sensors in a parallel intake engine

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Plymouth, MI, United States of America

Layne K Wiggins