Energy

New Salt Hydrates for Thermal Energy Storage

Authors

Abstract

Thermal energy storage (TES) has the potential to improve the efficiency of many applications, but has not been widely deployed. The viability of a TES system depends upon the performance of its underlying storage material; improving the energy density of TES materials is an important step in accelerating the adoption of TES systems. Salt hydrates are a promising class of TES materials due to their relatively high energy densities and their reversibility. Despite their promise, relatively few salt hydrates have been characterized, presenting the possibility that new hydrate compositions with superior properties may exist. Here, the energy densities, turning temperatures, and thermodynamic stabilities of 5292 hypothetical salt hydrates are predicted using high-throughput density functional theory calculations. The hydrates of several metal-fluorides, including CaF2, VF2, and CoF3, are identified as new, stable TES materials with class-leading energy densities and operating temperatures suitable for use in domestic heating and intermediate-temperature applications. The promising performance of these materials is demonstrated at the system level by parameterizing an operating model of a solar thermal TES system with data from the new hydrates. Finally, machine learning models for salt hydrate thermodynamics are developed and used to identify design guidelines for maximizing energy density. In total, the new materials and design rules reported here are expected to foster the adoption of TES systems.

Content

Thumbnail image of Manuscript - Submit.pdf

Supplementary material

Thumbnail image of TES_Hyp SI - Submit.pdf
Supporting Information
Additional details on hypothetical hydrate crystal structure templates; hypothetical salts; compatibility with the Materials Project; quaternary and pressure-temperature phase diagrams of promising hydrates; screening results using relaxed stability criteria; MERITS system analysis; predictive machine learning; interpretable machine learning; machine learning hyperparameters; feature sets used for ML; and the k-NN Matlab code can be found in the Supporting Information.
Thumbnail image of TES_Hyp SI - Submit Tables.xlsx
Supporting Information Screened Hydration Reactions
This file contains tables S5-S8 in the supporting information, with details on the screened hydration reactions using various stability criteria.