Extended Kalman filter on sparse identification of nonlinear systems: application to the SoC estimation of a phase change material-based energy storage

Abstract

Recently, phase change material (PCM) has been seen as a promising thermal energy storage (TES) technology for providing energy storage and operation flexibility in buildings. Despite its various applications, there has been a lack of real-time tracking capability of the PCM performance in real-deployed systems due to its complex physics. PCM state of charge (SoC) is a key indicator required for quantifying the remaining energy at any operation condition. Since SoC is not a direct measurement, there is a need for highly accurate prediction models. In this article, we propose solving this challenge by employing sparse identification of nonlinear dynamics (SINDy) to unlock the nonlinear dynamic complexity of PCM-TES. The minor utilization of temperature sensors in real-life applications is mitigated by using an extended Kalman filter (EKF) estimator that tunes, in real-time, any faced model inaccuracy. This framework will make it possible to provide highly accurate estimations for the spatial PCM temperatures based on limited noisy measurements. The proposed approach was successfully applied to experimental data recorded from the operation of a prototypical PCM storage for Domestic Hot water generation. The results show how efficient the proposed EKF-SINDy is in SoC estimation compared to the measurement-only approach.