Borah Manashita, Wang Qiao, Moura Scott, Sauer Dirk Uwe, Li Weihan
Energy, Controls and Application Laboratory, Department of Civil and Environmental Engineering, University of California, Berkeley, CA, 94720, USA.
Department of Electrical Engineering, Tezpur University, Tezpur, Assam, 784028, India.
Commun Eng. 2024 Sep 17;3(1):134. doi: 10.1038/s44172-024-00273-6.
Improving battery health and safety motivates the synergy of a powerful duo: physics and machine learning. Through seamless integration of these disciplines, the efficacy of mathematical battery models can be significantly enhanced. This paper delves into the challenges and potentials of managing battery health and safety, highlighting the transformative impact of integrating physics and machine learning to address those challenges. Based on our systematic review in this context, we outline several future directions and perspectives, offering a comprehensive exploration of efficient and reliable approaches. Our analysis emphasizes that the integration of physics and machine learning stands as a disruptive innovation in the development of emerging battery health and safety management technologies.
改善电池健康状况与安全性促使物理学与机器学习这两大强大力量形成协同效应。通过无缝整合这些学科，数学电池模型的效能能够得到显著提升。本文深入探讨了管理电池健康与安全所面临的挑战及潜力，着重强调整合物理学与机器学习以应对这些挑战所带来的变革性影响。基于我们在此背景下的系统综述，我们概述了若干未来方向与观点，全面探索高效且可靠的方法。我们的分析强调，物理学与机器学习的整合在新兴电池健康与安全管理技术的发展中堪称一项颠覆性创新。
