Gao Tianhan, Lu Wei
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Department of Materials Science & Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
iScience. 2020 Dec 13;24(1):101936. doi: 10.1016/j.isci.2020.101936. eCollection 2021 Jan 22.
Technology advancement demands energy storage devices (ESD) and systems (ESS) with better performance, longer life, higher reliability, and smarter management strategy. Designing such systems involve a trade-off among a large set of parameters, whereas advanced control strategies need to rely on the instantaneous status of many indicators. Machine learning can dramatically accelerate calculations, capture complex mechanisms to improve the prediction accuracy, and make optimized decisions based on comprehensive status information. The computational efficiency makes it applicable for real-time management. This paper reviews recent progresses in this emerging area, especially new concepts, approaches, and applications of machine learning technologies for commonly used energy storage devices (including batteries, capacitors/supercapacitors, fuel cells, other ESDs) and systems (including battery ESS, hybrid ESS, grid and microgrid-containing energy storage units, pumped-storage system, thermal ESS). The perspective on future directions is also discussed.
技术进步需要性能更优、寿命更长、可靠性更高且管理策略更智能的储能设备（ESD）和系统（ESS）。设计此类系统需要在大量参数之间进行权衡，而先进的控制策略需要依赖许多指标的瞬时状态。机器学习可以显著加速计算、捕捉复杂机制以提高预测准确性，并基于全面的状态信息做出优化决策。其计算效率使其适用于实时管理。本文综述了这一新兴领域的最新进展，特别是机器学习技术在常用储能设备（包括电池、电容器/超级电容器、燃料电池、其他ESD）和系统（包括电池ESS、混合ESS、含电网和微电网的储能单元、抽水蓄能系统、热ESS）中的新概念、方法和应用。还讨论了未来的发展方向。
