Abstract: A method is provided for monitoring an emission device coupled to an engine. In one example approach, the method comprises: following a deceleration fuel shut-off duration, indicating degradation of the emission device based on an amount of rich products required to cause a sensor to become richer than a threshold. The amount of rich products required may be correlated to an amount of oxygen stored in the emission device.

Abstract: A method of monitoring catalyst performance may comprise applying a set of parameter readings for a given sample to a support vector machine to generate a classification output, clustering the set of parameter readings to reduce a number of support vectors, computing a total fuel mass following a deceleration fuel shut-off event and an exhaust gas oxygen sensor switch and indicating catalyst degradation based on the total fuel mass.

Abstract: Methods and systems are provided for adjusting an anticipatory controller of an exhaust gas sensor coupled in an engine exhaust. In one embodiment, the method comprises adjusting fuel injection responsive to exhaust oxygen feedback from the anticipatory controller of the exhaust gas sensor and adjusting one or more parameters of the anticipatory controller responsive to a type of oxygen sensor degradation. In this way, the anticipatory controller may be adapted based on the type and magnitude of the degradation behavior to increase performance of the air-fuel control system.

Abstract: Methods and system are provided for identifying unintended closing (or corking) of a mechanical valve coupled to a fuel tank. If tank vent valve corking is identified during a leak test, fuel tank pressure data collected during the leak test is disregarded and not used to determine a fuel system leak. Instead, a fuel system leak test is repeated to improve reliability of test results.

Abstract: A method of monitoring an exhaust gas sensor coupled in an engine exhaust is provided. The method comprises indicating exhaust gas sensor degradation based on a difference between a first set of estimated parameters of a rich operation model and a second set of estimated parameters of a lean operation model, the estimated parameters based on commanded lambda and determined lambda values collected during selected operating conditions. In this way, sensor degradation may be indicated with data collected in a non-intrusive manner.