State and state of charge estimation for a latent heat storage

Barz, Tilman; Seliger, Dominik; Marx, Klemens; Sommer, Andreas; Walter, Sebastian F.; Bock, Hans Georg; Körkel, Stefan

doi:10.1016/j.conengprac.2017.11.006

Cited by 28 publications

(25 citation statements)

References 37 publications

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“…An important parameter of the RPW-HEX is its state of charge (SoC), which is used to indicate the extent to which a LHTES module is charged relative to storable latent heat (see definition of SoC (Barz et al, 2018)). The SoC, denoted by Ξ, is calculated as the geometric mean of local (liquid mass) phase fraction fields ξ(x,y,z), where x, y, z represent spatial coordinates of the PCM contained in the LHTES module.…”

Section: Latent Heat Thermal Energy Storage Modulementioning

confidence: 99%

Dynamic Modelling of a Hybrid Solar Thermal/Electric Storage System for Application in Residential Buildings

Frazzica¹,

Palomba²,

Sergi³

et al. 2018

Proceedings of EuroSun 2018

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The present paper presents the dynamic modelling of a hybrid thermal/electric energy storage for residential applications. Two systems are considered, which are suitable for application in Continental and Mediterranean climates. Both systems are suitable for the provision of heating, cooling and domestic hot water, but the Continental system is optimized for heating and domestic hot water production, whereas the Mediterranean system is optimized for the covering of cooling demands. The models are realized in Dymola/Modelica environment and are structured according to a modular approach, using only self-developed components or open source libraries. An adsorption module, used for thermal energy storage purposes, a battery pack, used for electrical storage, latent heat storages and heat pumps are the main components modelled. The integration in subsystem and the typical operation during one day under reference conditions are shown, which prove the successful integration of several sub-modules.

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Section: Latent Heat Thermal Energy Storage Modulementioning

confidence: 99%

Dynamic Modelling of a Hybrid Solar Thermal/Electric Storage System for Application in Residential Buildings

Frazzica¹,

Palomba²,

Sergi³

et al. 2018

Proceedings of EuroSun 2018

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“…Wang et al [20] used a relatively high number of temperature sensors to accurately measure the temperature distributions in the solid and the fluid domain in a small rectangular test cell. Barz et al [21] evaluated a model-based sensor (soft-sensor) for the determination of characteristic temperature and phase fraction fields in the PCM in a lab-scale thermal energy storage with cylindrical PCM shells.…”

Section: Introductionmentioning

confidence: 99%

“…Group 2A techniques focus on the monitoring of changes in the PCM average phase fraction of solid/liquid PCM, while group 2B techniques focus on the monitoring of the amount of absorbed (or released) heat. Accordingly, both groups use a different definition of the SoC (also see Reference [21] for a discussion on that point). For group 2A, the following definition is used:…”

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confidence: 99%

“…In addition, model-based algorithms can increase the accuracy of data obtained from sensors, e.g., to improve the accuracy of approximated temperature fields based on the solution of a heat transfer equation, or to improve the estimation of the mean PCM state using known PCM thermophysical relations to calculate PCM phase fractions from temperature data (e.g., Reference [21]).…”

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Evaluation of the State of Charge of a Solid/Liquid Phase Change Material in a Thermal Energy Storage Tank

et al. 2020

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Monitoring of the state of charge of the thermal energy storage component in solar thermal systems for space heating and/or cooling in residential buildings is a key element from the overall system control strategy point of view. According to the literature, there is not a unique method for determining the state of charge of a thermal energy storage system that could generally be applied in any system. This contribution firstly provides a classification of the state-of-the-art of available techniques for the determination of the state of charge, and secondly, it presents an experimental analysis of different methods based on established sensor technologies, namely temperature, mass flow rates, and pressure measurements, tested using a lab-scale heat exchanger filled with a commercial phase change material for cooling applications. The results indicate that, depending on the expected accuracy and available instrumentation, each of the methods studied here can be used in the present application, the deviations between the methods generally being below 20%. This study concludes that a proper combination of two or more of these methods would be the ideal strategy to obtain a more reliable and accurate estimation of the state of charge of the latent heat thermal energy storage.

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“…35 Moreover, the maximum available power state estimation was conducted by using the Particle-filtering (PF) algorithm 36 and it was also achieved by using the temperature-compensated model. 37 The SOC estimation method was obtained for a latent heat storage 38 and a neural network-based observer was designed. 39 Furthermore, the dual sliding mode observer was built 40 and a comparative study was conducted for different ECMs.…”

Section: Introductionmentioning

confidence: 99%

An improved packing equivalent circuit modeling method with the cell‐to‐cell consistency state evaluation of the internal connected lithium‐ion batteries

Wang

Shi

Fernandez

et al. 2019

Energy Science & Engineering

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The existing equivalent modeling methods reported in literature focuses mainly on the battery cells and do not take the packing consistency state into consideration, which exists on the internal connected cells of the lithium‐ion battery pack. An improved equivalent circuit model is constructed and reported in this manuscript for the first time, which can be used to characterize the working characteristics of the packing lithium‐ion batteries. A new equilibrium concept named as state of balance is proposed as well as the calculation process, which is realized by considering the real‐time detected internal battery cell voltages. In addition, this new equilibrium concept aims to obtain more information on the real‐time consistency characterization of the battery pack. The improved adaptive equivalent circuit model is investigated by using the improved splice modeling method, in which the statistical noise properties are corrected and the additional parallel resistance‐capacitance circuit is introduced. The parameter correction treatment is carried out by comparing the estimated and experimental detected closed circuit voltages. Furthermore, the tracking error is found to be 0.005 V and accounts for 0.119% of the nominal battery voltage. By taking the packing consistency state and temperature correction into consideration, the accurate working characteristic expression is realized in the improved equivalent circuit modeling process. Finally, the model proposed in this manuscript presents a great number of advantages compared to other methods reported so far, like has the high accuracy, and the ability to protect the security of the lithium‐ion battery pack in the power supply application.

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State and state of charge estimation for a latent heat storage

Cited by 28 publications

References 37 publications

Dynamic Modelling of a Hybrid Solar Thermal/Electric Storage System for Application in Residential Buildings

Dynamic Modelling of a Hybrid Solar Thermal/Electric Storage System for Application in Residential Buildings

Evaluation of the State of Charge of a Solid/Liquid Phase Change Material in a Thermal Energy Storage Tank

An improved packing equivalent circuit modeling method with the cell‐to‐cell consistency state evaluation of the internal connected lithium‐ion batteries

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