Abstract: The present disclosure discloses a system and a method for controlling an operation of a vapor compression cycle based on a hybrid model of dynamics of the vapor compression cycle including a physics-based model and a data-driven model. The method comprises executing a constrained Kalman smoother over the observed variables collected over multiple instances of time to jointly estimate the parameters of the physics-based model and states of the vapor compression cycle, and updating the data-driven model to minimize a difference between the states estimated by executing the constrained Kalman smoother and the states predicted by the physics-based model. The method further comprises updating the hybrid model with the estimated parameters of the physics-based model and the updated data-driven model, and controlling the operation of the vapor compression cycle using the updated hybrid model.

Abstract: The present disclosure discloses a system and a method for controlling an operation of a vapor compression cycle based on a hybrid model of dynamics of the vapor compression cycle including a physics-based model and a data driven model. The method comprises executing a constrained Kalman smoother over the observed variables collected over multiple instances of time to jointly estimate the parameters of the physics-based model and states of the vapor compression cycle, and updating the data driven model to minimize a difference between the states estimated by executing the constrained Kalman smoother and the states predicted by the physics-based model. The method further comprises updating the hybrid model with the estimated parameters of the physics-based model and the updated data driven model, and controlling the operation of the vapor compression cycle using the updated hybrid model.