Machine learning is a specific application of artificial intelligence that allows computers to learn and improve from data and experience via sets of algorithms, without the need for reprogramming. In the field of energy storage, machine learning has recently emerged as a promising modelling approach to determine the state of charge, state of health and remaining useful life of batteries. First, we review the two most studied types of battery models in the literature for battery state prediction: the equivalent circuit and physics-based models. Based on the current limitations of these models, we showcase the promise of various machine learning techniques for fast and accurate battery state prediction. Finally, we highlight the major challenges involved, especially in accurate modelling over length and time, performing in situ calculations and high-throughput data generation. Overall, this work provides insights into real-time, explainable machine learning for battery production, management and optimization in the future.

机器学习是人工智能的一种特殊应用，它允许计算机通过算法集从数据和经验中学习和改进，而无需重新编程。在能量存储领域，机器学习最近成为一种有前途的建模方法，可以用来确定电池的充电状态，健康状态和剩余使用寿命。首先，我们回顾了用于电池状态预测的文献中研究最多的两种电池模型：等效电路模型和基于物理的模型。基于这些模型的当前局限性，我们展示了各种机器学习技术的前景，可用于快速准确的电池状态预测。最后，我们重点介绍了所涉及的主要挑战，尤其是在长度和时间上进行精确建模时，执行原位计算和高通量数据生成。总的来说，这项工作为将来的电池生产，管理和优化提供了可解释的实时机器学习见解。