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Effective estimation of the state-of-charge of latent heat thermal energy storage for heating and cooling systems using non-linear state observers

Bastida, Hector, De La Cruz Loredo, Ivan and Ugalde Loo, Carlos ORCID: https://orcid.org/0000-0001-6361-4454 2023. Effective estimation of the state-of-charge of latent heat thermal energy storage for heating and cooling systems using non-linear state observers. Applied Energy 331 , 120448. 10.1016/j.apenergy.2022.120448

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Abstract

An effective quantification of the energy absorbed and supplied by latent heat thermal energy storage (LHTES) units is critical to maximise their use within thermal systems. An effective control of the charging and discharging processes of these units demands an accurate estimation of the state-of-charge (SoC). However, a direct and reliable SoC estimation requires incorporating internal sensors to monitor the temperature gradient of the phase change material (i.e. the storage medium), resulting in higher instrumentation costs and technical specifications. These issues may be relieved by adopting state observers for SoC estimation to drastically reduce the number of measurements. This paper bridges this gap by presenting a novel and direct method for estimating the SoC of LHTES units, both for heating and cooling applications, based on a non-linear state observer. The observer is based on a simple one-dimensional dynamic model of the thermal store and the thermophysical properties of the storage medium and the heat transfer fluid, which are usually provided by manufacturers. This enables the estimation of the internal temperatures of the LHTES unit and, in turn, SoC calculation. The observer implementation is simple as it requires three measurements only as input variables (i.e. the mass flow rate and the input and output temperatures of the heat transfer fluid). The SoC estimation approach is assessed through dynamic simulations of two LHTES units: one for a heating application and one for a cooling application. The results show that the SoC can be estimated with root mean square and mean absolute errors of less than 4.6% and 3.62%, respectively, compared with experimental measurements.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Elsevier
ISSN: 1872-9118
Funders: CONACyT, EPSRC, WEFO
Date of First Compliant Deposit: 29 November 2022
Date of Acceptance: 27 November 2022
Last Modified: 04 Jan 2023 15:18
URI: https://orca.cardiff.ac.uk/id/eprint/154515

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