Elastocaloric Thermal Battery: Ultrahigh Heat-Storage Capacity Based on Generative Learning-Designed Phase-Change...

Area of Science:

• Materials Science
• Thermodynamics
• Artificial Intelligence

Background:

• Efficient recycling of low-temperature waste heat is crucial for energy sustainability.
• Existing thermal batteries often lack sufficient heat storage capacity and efficiency.
• Developing advanced materials for thermal energy storage remains a significant challenge.

Purpose of the Study:

• To develop an elastocaloric thermal battery utilizing generative learning-designed phase-change alloys.
• To enable efficient storage and on-demand release of thermal energy from low-temperature waste heat.
• To accelerate the discovery of materials with tailored thermal properties using an inverse design framework.

Main Methods:

• Generative learning-enabled inverse design framework for alloy discovery.
• Tailoring alloy compositions and processing parameters for specific transformation characteristics.
• Fabrication and testing of the elastocaloric thermal battery prototype.

Main Results:

• Achieved an ultrahigh figure of merit for heat storage capacity, surpassing existing thermal batteries.
• Demonstrated a work-to-heat efficiency exceeding 9%.
• Successfully converted hand-drawn target heat flow curves into tangible material designs.

Conclusions:

• The developed elastocaloric thermal battery offers a promising solution for low-temperature waste heat recycling.
• The generative learning inverse design framework significantly expedites material development for tailored property curves.
• Potential applications include solar thermal collection, electric vehicles, and data center thermal management.