Automated identification of a complex storage model and hardware implementation of a model-predictive controller for a cooling system with ice storage

The performance of latent thermal energy storage (LTES) heat exchangers is related to the stored energy (i.e. state of charge) during the (dis)charging of the energy storage system. Furthermore, methods to correlate performance indicators such as the averaged effectiveness and phase change time are based solely on the latent heat stored. Therefore, measuring the stored energy in the separate components is crucial to understand the behavior of LTES systems and expand the correlation methods for performance indicators. However, technical limitations often do not allow to measure the stored energy. This article presents a LTES heat exchanger where only measurements at the in-and outlet of the heat transfer fluid and on the outer surface of the heat exchanger were possible. Based on this sparse set of measurements, estimators for the stored energy are developed. To test these estimators, a finite volume model of the LTES heat exchanger is made and fitted to experiments. The estimators for the stored energy in the heat transfer fluid, metal and phase change material reach a maximum deviation of respectively 1.9 %, 10 % and 2.1 % with the stored energy predicted by the finite volume model. By analyzing the stored energy as a function of time, four charging phases can be distinguished. The present correlating methods for performance indicators focus on the third latent phase. By estimating stored energy, the other phases can also be understood and modeled which can expand correlating methods for LTES heat exchangers in future work.

show abstract

“…[43]) or the latent stored energy (e.g. [44][45][46][47]). The SOC associated with latent heat is also often reported as a liquid fraction (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…A final method directly measures the PCM level as it expands or contracts during phase change. This method is successfully applied in ice storage systems [45]. All techniques based on the density change require a measurable change in PCM level which is not possible for all LTES heat exchanger geometries.…”

Section: Introductionmentioning

confidence: 99%

Estimating the state of charge in a latent thermal energy storage heat exchanger based on inlet/outlet and surface measurements

Beyne

Couvreur

T’Jollyn

et al. 2022

Applied Thermal Engineering

View full text Add to dashboard Cite

show abstract

“…Three model types attempt to fill the gap left by the LMTD and ϵ-NTU method by predicting aspects of the heat exchanger output. These three model types found in literature are average effectiveness-number of transfer unit models [41][42][43][44], state of charge-effectiveness models [45] and models for the charging time [46,47].…”

Section: Introductionmentioning

confidence: 99%

“…State of charge effectiveness models have been developed as control models for ice storage [45]. The model relies on measuring the volumetric state of charge or the volume of water which has solidified.…”

Section: Introductionmentioning

confidence: 99%

A charging time energy fraction method for evaluating the performance of a latent thermal energy storage heat exchanger

Beyne

Couvreur

Jollyn

et al. 2021

Applied Thermal Engineering

View full text Add to dashboard Cite

“…[4] The nonlinear rapid attenuation of cold energy charging rate brings troubles in practical engineering applications. [5] 1) It is difficult for PCM to achieve complete charging in a limited time interval under the rapid attenuation of the charging rate, leading to the low effective utilization rate of the cold energy storage equipment. 2) In the low charging rate, it is hard to make full use of the refrigeration capacity, resulting in the waste of refrigeration equipment resources.…”

Section: Introductionmentioning

confidence: 99%

High‐Performance Charging Method for Cold Energy Storage Using Foam Freezing

et al. 2021

View full text Add to dashboard Cite

Phase change cold energy storage can effectively stabilize the peak value of the power grid, but the cold energy charging rate of phase change material decreases seriously which will reduce the energy efficiency of the system. The cold energy charging performance can be effectively improved by foam freezing, and the foam freezing model is proposed to explore the influence factors and the advantages of this method. Foam freezing is compared with other methods based on the novel concept of cold energy charging coefficient which is carried on the analogy to heat transfer coefficient. It is found that the cold energy charging rate of foam freezing are exponential functions of the height based on e. The foam cold energy charging coefficient is affected by the superposition of convective heat transfer intensity and bubble terminal velocity. The technical path of increasing the cold energy charging coefficient by continuously reducing the bubble size is not feasible. When foam freezing at the radius of 0.6 mm, the cold energy charging coefficient is 237.1 W m−2 K−1, the water equivalent height is 18 mm, and it is the most economical. Foam freezing has advantages over the ice ball and ice‐on‐coil in cold energy charging performance.

show abstract

Automated identification of a complex storage model and hardware implementation of a model-predictive controller for a cooling system with ice storage

Cited by 22 publications

References 22 publications

Estimating the state of charge in a latent thermal energy storage heat exchanger based on inlet/outlet and surface measurements

Estimating the state of charge in a latent thermal energy storage heat exchanger based on inlet/outlet and surface measurements

A charging time energy fraction method for evaluating the performance of a latent thermal energy storage heat exchanger

High‐Performance Charging Method for Cold Energy Storage Using Foam Freezing

Contact Info

Product

Resources

About