State estimation concept for a nonlinear melting/solidification problem of a latent heat thermal energy storage

Abstract Latent heat thermal energy storages (LHTES) utilize a material’s phase transition to store energy at an almost constant temperature. To fully exploit their high energy density, reliable state estimation is essential, which requires a suitable model-based observer. In previous works, high-precision and real-time-capable models have been developed to solve the arising coupled Navier-Stokes and energy equations. In the present work, these high-order nonlinear models are applied to predict (simulate) the states of the LHTES one time step ahead. Then, an extended Kalman filter uses a reduced-order observer model derived from the prediction model by linearization and balanced truncation to compute a state update based on measurements. This approach increases both, computational efficiency and performance, since the observer can only update the state of the prediction model compliant to its dominant behavior. Different types of measurements can be accurately combined in the observer, resulting in fast convergence despite model errors.