The development of artificial intelligence (AI) is continually progressing, resulting in promising performance in the face of unpredictable settings. These approaches give incredible outcomes and are devoted to improving accuracy and performance in a range of challenging fields. Machine learning (ML) has been increasingly popular in recent years, particularly when paired with large amounts of data. Solar, wind, hydro, biomass, geothermal, and other renewable energy sources are critical for creating a clean and sustainable energy future. These renewables are often intermittent, unpredictable, and poorly distributed physically and temporally, and so incorporating them directly would significantly disrupt power networks. As a result, the development of efficient and dependable energy storage technology is critical to the effective adoption of renewable energy.

Despite substantial advancements in advanced energy storage technology (AEST), particularly for large-scale energy storage, the need for smart and efficient energy storage systems is higher than ever. Huge volumes of data on the performance and life of energy storage devices are becoming available, owing to the advent of the Internet of Things (IoT).

The primary goal of this Special Issue is to provide a venue to present the most recent breakthroughs in the use of AI in energy storage systems, particularly large-scale energy storage systems. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Novel energy storage materials and topologies
• Large-scale energy storage system applications and innovative next-generation energy storage technologies
• Energy storage system modelling, simulation, and optimization
• Energy storage systems with advanced energy management systems
• Advanced energy storage control systems
• Energy storage system lifecycle analysis and safety and reliability review
• Artificial intelligence-based looped energy systems
• Renewable energy and smart applications
• AI-enriched sustainable energy conservation technologies