The paper "Lithium-ion battery digitalization: Combining physics-based models and machine learning" by Mahshid N. Amiri, Anne Håkansson, Odne S. Burheim, and Jacob J. Lamb reviews the integration of physics-based models and machine learning algorithms to advance the digitalization of lithium-ion batteries (LIBs). The authors highlight the importance of accurate physics-based models in providing deep insights into battery behavior but note that their high computational cost limits their real-time application. Machine learning models, on the other hand, offer computational efficiency and can be used to complement physics-based models. The paper discusses various modeling methods, including equivalent circuit models (ECMs) and electrochemical models, and machine learning approaches such as neural networks, LSTM, and GPR. It also explores the development of hybrid models that combine the strengths of both physics-based and machine learning methods to enhance battery performance, design, and control. The authors present a comprehensive review of current trends, challenges, and future research directions, emphasizing the need for standardized datasets and the integration of physical insights to improve the accuracy and efficiency of hybrid models. The paper concludes by discussing the potential of hybrid models in achieving intelligent LIB digital twins, which can be used for real-time monitoring, control, and design optimization.The paper "Lithium-ion battery digitalization: Combining physics-based models and machine learning" by Mahshid N. Amiri, Anne Håkansson, Odne S. Burheim, and Jacob J. Lamb reviews the integration of physics-based models and machine learning algorithms to advance the digitalization of lithium-ion batteries (LIBs). The authors highlight the importance of accurate physics-based models in providing deep insights into battery behavior but note that their high computational cost limits their real-time application. Machine learning models, on the other hand, offer computational efficiency and can be used to complement physics-based models. The paper discusses various modeling methods, including equivalent circuit models (ECMs) and electrochemical models, and machine learning approaches such as neural networks, LSTM, and GPR. It also explores the development of hybrid models that combine the strengths of both physics-based and machine learning methods to enhance battery performance, design, and control. The authors present a comprehensive review of current trends, challenges, and future research directions, emphasizing the need for standardized datasets and the integration of physical insights to improve the accuracy and efficiency of hybrid models. The paper concludes by discussing the potential of hybrid models in achieving intelligent LIB digital twins, which can be used for real-time monitoring, control, and design optimization.