AI/ML-Optimised High-Temperature Thermal Energy Storage for Next-Gen Energy Efficiency

High-temperature thermal energy storage (HT-TES) is a critical enabler for industrial decarbonization, concentrated solar power (CSP), and waste heat recovery. By storing heat above 300 °C, HT-TES can supply process heat, generate power, or provide grid flexibility. adoption remains limited due to challenges in materials stability, system sizing, charge/discharge control, and long-term performance degradation. Optimizing these complex, non-linear systems using conventional methods is often slow, inaccurate, or impractical. Recent advances in artificial intelligence and machine learning (AI/ML) offer transformative potential to overcome these barriers and unlock the full value of HT-TES.

This Research Topic aims to address the critical gap between HT-TES hardware development and intelligent system optimization. The problem is that current TES design and operation rarely leverage data-driven or adaptive control strategies, leading to suboptimal efficiency, higher costs, and reduced lifetime. Our goal is to gather cutting-edge research that applies AI/ML techniques—such as neural networks, reinforcement learning, genetic algorithms, and hybrid models—to optimise HT-TES across multiple scales: from material selection and component design to real-time operation and system integration. By doing so, we seek to demonstrate measurable improvements in round-trip efficiency, levelized cost of storage, thermal cycling stability, and demand-side response. We encourage contributions that validate AI/ML models with experimental or field data, as well as studies that benchmark against traditional optimization methods. The ultimate objective is to accelerate the commercial deployment of HT-TES in energy-intensive industries, renewable power plants, and district heating networks.

We invite contributions addressing, but not limited to, the following themes:
• ML-assisted design and selection of high-temperature storage materials;
• AI-based predictive control of charging, storage, and discharging cycles for HT-TES;
• Hybrid physics-informed ML models for degradation and lifetime prediction;
• Solar collectors for thermal energy storage integration including Parabolic trough collectors, central receivers and linear Fresnel systems;
• Advanced energy storage materials for high temperature applications;
• Insulation and thermal loss reduction strategies.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Thermal energy storage, high temperature energy storage, AI/ML techniques, optimisation, energy efficiency, renewable integration, industrial decarbonization

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.