Abstract:

State estimators can be a powerful tool in the development of advanced controls and performance monitoring capabilities for vapor compression cycles, but the nonlinear and numerically stiff aspects of these systems pose challenges for the practical implementation of estimators on large physics-based models. We develop smoothing methods in the extended and ensemble Kalman estimation frameworks that satisfy physical constraints and address practical limitations with standard implementations of these estimators. These methods are tested on a model built in the Julia language, and are demonstrated to successfully estimate unmeasured variables with high accuracy.