Abstract: An integrated circuit is provided and includes a multi-bit cell having multiple bit cells disposed in multiple cell rows. The bit cells include M bit cells, M being positive integers. A first bit cell of the bit cells and a M-th bit cell of the bit cells are arranged diagonally in different cell rows in the multi-bit cell. The multi-bit cell includes first to fourth cell boundaries. The first and second boundaries extend in a first direction and the third and fourth boundaries extend in a second direction different from the first direction. The first bit cell and a second bit cell of the bit cells abut the third cell boundary, and the first bit cell and a (M/2+1)-th bit cell of the bit cells abut the first cell boundary.

Abstract: An integrated circuit is provided and includes a multi-bit cell having multiple bit cells disposed in multiple cell rows. The bit cells include M bit cells, M being positive integers. A first bit cell of the bit cells and a M-th bit cell of the bit cells are arranged diagonally in different cell rows in the multi-bit cell. The multi-bit cell includes first to fourth cell boundaries. The first and second boundaries extend in a first direction and the third and fourth boundaries extend in a second direction different from the first direction. The first bit cell and a second bit cell of the bit cells abut the third cell boundary, and the first bit cell and a (M/2+1)-th bit cell of the bit cells abut the first cell boundary.

Abstract: A classifier capable of predicting if cylinder valves of an engine commanded to activate or deactivate failed to activate or deactivate respectively. In various embodiments, the classifier can be binary or multi-class Logistic Regression, or a Multi-Layer Perceptron (MLP) classifier. The variable displacement engine can operate in cooperation with a variable displacement engine using cylinder deactivation (CDA) or skip fire, including dynamic skip fire and/or multi-level skip fire.

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.

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.

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.

Abstract: In one aspect, a skip fire engine controller is described. The skip fire engine controller includes a skip fire module arranged to determine an operational firing fraction and associated cylinder load for delivering a desired engine output. The skip fire engine controller also includes a firing controller arranged to direct firings in a skip fire manner that delivers the selected operational firing fraction. Various methods, modules, lookup tables and arrangements related to the selection of a suitable operational firing fraction are also described.

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.

Abstract: Using machine learning for misfire detection in a Dynamic firing level modulation controlled internal combustion engine is described. A neural network is used to calculate expected crank acceleration from various inputs, including the dynamically defined cylinder skip fire sequence. The output of the neural network is then compared to a signal indicative of the measured crank acceleration. Based the comparison, a prediction is made if a misfire has occurred or not. In alternative embodiment, the neural network is expanded to include the measured crank acceleration as an additional input. With the latter embodiment, the neural network is arranged to directly predict misfire events.

Abstract: In one aspect, a skip fire engine controller is described. The skip fire engine controller includes a skip fire module arranged to determine an operational firing fraction and associated cylinder load for delivering a desired engine output. The skip fire engine controller also includes a firing controller arranged to direct firings in a skip fire manner that delivers the selected operational firing fraction. Various methods, modules, lookup tables and arrangements related to the selection of a suitable operational firing fraction are also described.

Abstract: Using machine learning for misfire detection in a Dynamic firing level modulation controlled internal combustion engine is described. A neural network is used to calculate expected crank acceleration from various inputs, including the dynamically defined cylinder skip fire sequence. The output of the neural network is then compared to a signal indicative of the measured crank acceleration. Based the comparison, a prediction is made if a misfire has occurred or not. In alternative embodiment, the neural network is expanded to include the measured crank acceleration as an additional input. With the latter embodiment, the neural network is arranged to directly predict misfire events.

Abstract: In one aspect, a skip fire engine controller is described. The skip fire engine controller includes a skip fire module arranged to determine an operational firing fraction and associated cylinder load for delivering a desired engine output. The skip fire engine controller also includes a firing controller arranged to direct firings in a skip fire manner that delivers the selected operational firing fraction. Various methods, modules, lookup tables and arrangements related to the selection of a suitable operational firing fraction are also described.

Abstract: A variety of methods and arrangements for detecting misfire and other engine-related errors are described. In one aspect, a window is assigned to a target firing opportunity for a target working chamber. There is an attempt to fire a target working chamber during the target firing opportunity. A change in an engine parameter (e.g., crankshaft angular acceleration) is measured during the window. A model (e.g., a pressure model) is used to help determine an expected change in the engine parameter during the target firing opportunity. Based on a comparison of the expected change and the measured change in the engine parameter, a determination is made as to whether an engine error (e.g., misfire) has occurred.

Abstract: A variety of methods and arrangements for detecting misfire and other engine-related errors are described. In one aspect, a window is assigned to a target firing opportunity for a target working chamber. There is an attempt to fire a target working chamber during the target firing opportunity. A change in an engine parameter (e.g., crankshaft angular acceleration) is measured during the window. A model (e.g., a pressure model) is used to help determine an expected change in the engine parameter during the target firing opportunity. Based on a comparison of the expected change and the measured change in the engine parameter, a determination is made as to whether an engine error (e.g., misfire) has occurred.

Abstract: A variety of methods and arrangements for detecting misfire and other engine-related errors are described. In one aspect, a window is assigned to a target firing opportunity for a target working chamber. There is an attempt to fire a target working chamber during the target firing opportunity. A change in an engine parameter (e.g., crankshaft angular acceleration) is measured during the window. A model (e.g., a pressure model) is used to help determine an expected change in the engine parameter during the target firing opportunity. Based on a comparison of the expected change and the measured change in the engine parameter, a determination is made as to whether an engine error (e.g., misfire) has occurred.

Abstract: Various methods and arrangements for determining a combustion control parameter for a working chamber in an engine are described. In one aspect, an engine controller includes a firing counter that stores a firing history for the working chamber. A combustion control module is used to determine a combustion control parameter, which is used to help manage combustion in the working chamber. The combustion control parameter is determined based at least in part on the firing history.

Abstract: The described embodiments relate generally to skip fire control of internal combustion engines and particularly to mechanisms for determining a desired operational firing fraction. In some embodiments, a firing fraction determining unit is arranged to determine a firing fraction suitable for delivering a requested engine output. The firing fraction determining unit may utilize data structures such as lookup tables in the determination of the desired firing fraction. In one aspect the desired engine output and one or more operational power train parameters such as current engine speed, are used as indices to a lookup table used to select a desired firing fraction. In other embodiments, additional indices to the data structure may include any one of: transmission gear; manifold absolute pressure (MAP); manifold air temperature; a parameter indicative of mass air charge (MAC); cam position; cylinder torque output; maximum permissible manifold pressure; vehicle speed; and barometric pressure.

Abstract: Methods and systems are described for detecting valve actuation faults in internal combustion engines operating in a skip fire operational mode. In one aspect, a torque model is used to estimate an expected net torque during a selected operating window. The torque model considers an expected torque contribution from each of the cylinders and accounts for the effects of specific skip fire firing decisions that affect the expected torque contribution from each cylinder. A parameter indicative of the actual engine torque is also measured. Valve actuation faults can then be identified based at least in part on a comparison of the measured parameter to an expected parameter value that is based at least in part on the expected net torque. With the described approaches, the occurrence of the valve actuation fault can be made within one engine cycle of the initial occurrence of the fault.

Abstract: Various methods and arrangements for determining a combustion control parameter for a working chamber in an engine are described. In one aspect, an engine controller includes a firing counter that stores a firing history for the working chamber. A combustion control module is used to determine a combustion control parameter, which is used to help manage combustion in the working chamber. The combustion control parameter is determined based at least in part on the firing history.

Abstract: The described embodiments relate generally to skip fire control of internal combustion engines and particularly to mechanisms for determining a desired operational firing fraction. In some embodiments, a firing fraction determining unit is arranged to determine a firing fraction suitable for delivering a requested engine output. The firing fraction determining unit may utilize data structures such as lookup tables in the determination of the desired firing fraction. In one aspect the desired engine output and one or more operational power train parameters such as current engine speed, are used as indices to a lookup table used to select a desired firing fraction. In other embodiments, additional indices to the data structure may include any one of: transmission gear; manifold absolute pressure (MAP); manifold air temperature; a parameter indicative of mass air charge (MAC); cam position; cylinder torque output; maximum permissible manifold pressure; vehicle speed; and barometric pressure.