Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.