Extended Kalman filter on sparse identification of nonlinear systems: application to the SoC estimation of a phase change material-based energy storage
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-depl...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | Energy Conversion and Management: X |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590174525003319 |
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| author | Mustapha Habib Youssef Elomari Felix Hochwallner Adam Buruzs Tilman Barz Qian Wang |
| author_facet | Mustapha Habib Youssef Elomari Felix Hochwallner Adam Buruzs Tilman Barz Qian Wang |
| author_sort | Mustapha Habib |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-80b31c0879f846d6975e07d05ee8ae56 |
| institution | Kabale University |
| issn | 2590-1745 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Conversion and Management: X |
| spelling | doaj-art-80b31c0879f846d6975e07d05ee8ae562025-08-22T04:57:30ZengElsevierEnergy Conversion and Management: X2590-17452025-07-012710119910.1016/j.ecmx.2025.101199Extended Kalman filter on sparse identification of nonlinear systems: application to the SoC estimation of a phase change material-based energy storageMustapha Habib0Youssef Elomari1Felix Hochwallner2Adam Buruzs3Tilman Barz4Qian Wang5Division of Building Technology and Design, Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, 11428 Stockholm, Sweden; Corresponding author.Division of Building Technology and Design, Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, 11428 Stockholm, SwedenAIT Austrian Institute of Technology GmbH, Center for Energy, Giefinggasse 2, 1210 Vienna, AustriaAIT Austrian Institute of Technology GmbH, Center for Energy, Giefinggasse 2, 1210 Vienna, AustriaAIT Austrian Institute of Technology GmbH, Center for Energy, Giefinggasse 2, 1210 Vienna, AustriaDivision of Building Technology and Design, Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, 11428 Stockholm, SwedenRecently, 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.http://www.sciencedirect.com/science/article/pii/S2590174525003319Phase change materialSparse identification of nonlinear dynamicsExtended Kalman filter |
| spellingShingle | Mustapha Habib Youssef Elomari Felix Hochwallner Adam Buruzs Tilman Barz Qian Wang Extended Kalman filter on sparse identification of nonlinear systems: application to the SoC estimation of a phase change material-based energy storage Energy Conversion and Management: X Phase change material Sparse identification of nonlinear dynamics Extended Kalman filter |
| title | Extended Kalman filter on sparse identification of nonlinear systems: application to the SoC estimation of a phase change material-based energy storage |
| title_full | Extended Kalman filter on sparse identification of nonlinear systems: application to the SoC estimation of a phase change material-based energy storage |
| title_fullStr | Extended Kalman filter on sparse identification of nonlinear systems: application to the SoC estimation of a phase change material-based energy storage |
| title_full_unstemmed | Extended Kalman filter on sparse identification of nonlinear systems: application to the SoC estimation of a phase change material-based energy storage |
| title_short | Extended Kalman filter on sparse identification of nonlinear systems: application to the SoC estimation of a phase change material-based energy storage |
| title_sort | extended kalman filter on sparse identification of nonlinear systems application to the soc estimation of a phase change material based energy storage |
| topic | Phase change material Sparse identification of nonlinear dynamics Extended Kalman filter |
| url | http://www.sciencedirect.com/science/article/pii/S2590174525003319 |
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