Abstract
ABSTRACT In recent times, artificial intelligence (AI) and machine learning (ML) have emerged as revolutionary technologies with wide-ranging applications across various fields, including energy conversion and storage (ECS) systems. These methods utilise large amounts of data and computational power to predict material properties, optimise energy storage systems, and develop control algorithms for energy conversion devices. This literature analysis focuses on the latest advancements and methodologies in AI/ML applications to ECS systems, encompassing material design and discovery, property prediction, and system optimisation. Furthermore, the study examines the main challenges of integrating ML into these systems. These problems include issues related to data availability and quality, model interpretability, transfer learning, experimental integration, and ethics. Despite the challenges, ML has the potential to revolutionise energy technologies and enhance performance. Advancements in ML-driven sustainable energy technologies are fostering interdisciplinary collaboration and research, offering promising solutions for sustainable energy.
Published Version
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