AI for science in electrochemical energy storage: A multiscale systems perspective on transportation electrification

Abstract

The electric vehicle (EV) industry, crucial for low-emission transportation, is undergoing a significant transformation driven by advancements in battery and electrochemical energy storage technologies. Artificial intelligence (AI) has the potential to revolutionize these technologies by enhancing efficiency and performance while accelerating development cycles. This paper systematically reviews the current state-of-the-art and future perspectives of AI in battery research and applications for EVs. Various AI methodologies, including unsupervised learning, supervised learning, reinforcement learning, and generative AI, are explored to improve battery performance, longevity, and safety. The review identifies key challenges in advancing AI for electrochemical energy storage: data shortages, cyberinfrastructure limitations, data privacy issues, intellectual property obstacles, and ethical complexities. Groundbreaking opportunities presented by AI applications, such as Large Language Models, Foundation Models, Multimodal Learning, and Few-shot Learning, are also highlighted. AI-based technologies offer promising pathways for rapid material discovery, predictive maintenance, and the development of efficient, scalable, and reliable battery systems. Strategic directions for future research are proposed, emphasizing the need for comprehensive data and cyber infrastructures, enhanced interpretability, ethical AI use, and interdisciplinary collaboration. Ultimately, this paper identifies key challenges and opportunities for AI-driven innovations in battery technology, contributing to the advancement of low-emission transportation through electrification.