This paper reviews the integration of physics-based models and machine learning algorithms to accelerate the digitalization of lithium-ion batteries (LIBs). The digitalization of LIBs can improve performance through smarter control strategies and reduce design and development risks. Physics-based models provide in-depth understanding of battery systems but are computationally expensive. Machine learning models offer computational efficiency and can be combined with physics-based models to create hybrid models that balance accuracy and efficiency. The paper discusses current trends in hybrid modeling, including explicit modeling methods and machine learning algorithms used in battery research. It presents a thorough investigation of contemporary hybrid models for battery design, development, and real-time monitoring and control. The objective is to provide details of hybrid methods, including their applications, model types, machine learning algorithms, and outcomes. Challenges and research gaps are discussed to inspire future work in this field. The paper also discusses the importance of accurate models for digital twin (DT) development, which can replicate the behavior of physical batteries. DTs can be used for system control strategy determination and system design. However, the underlying models of a LIB DT must be highly detailed to calculate local battery parameters accurately. Electrochemical-thermal models are among the most accurate simulation methods for studying battery behavior. These models can be developed into battery DTs. The paper reviews the current state-of-the-art in battery modeling and machine learning for DT development. It explores the possibilities for developing a fully functional battery DT that can optimize battery design. The paper presents a review of studies on the application of machine learning for advancing battery digitalization. The novelty of this work allows the consolidation of studies in battery digitalization to focus on future research into digital experimentation. The work also guides focus towards real-time battery monitoring and control, and digital battery research and development. The paper highlights clear gaps and perspectives in the field where future studies and opportunities can lead to innovation. The paper discusses the use of physics-based models and machine learning algorithms for battery monitoring and control. Equivalent circuit models (ECMs) are simple and computationally efficient but lack detail. Electrochemical models provide more detailed insights into battery behavior but are computationally expensive. Hybrid models combine the strengths of both approaches, offering high accuracy and computational efficiency. The paper also discusses the use of machine learning algorithms for battery design, including parameter estimation and improved energy and power. The paper highlights the potential of combining physics-based models with machine learning algorithms for improved battery performance management. The paper concludes that the integration of physics-based models and machine learning algorithms is a promising approach for the digitalization of lithium-ion batteries.This paper reviews the integration of physics-based models and machine learning algorithms to accelerate the digitalization of lithium-ion batteries (LIBs). The digitalization of LIBs can improve performance through smarter control strategies and reduce design and development risks. Physics-based models provide in-depth understanding of battery systems but are computationally expensive. Machine learning models offer computational efficiency and can be combined with physics-based models to create hybrid models that balance accuracy and efficiency. The paper discusses current trends in hybrid modeling, including explicit modeling methods and machine learning algorithms used in battery research. It presents a thorough investigation of contemporary hybrid models for battery design, development, and real-time monitoring and control. The objective is to provide details of hybrid methods, including their applications, model types, machine learning algorithms, and outcomes. Challenges and research gaps are discussed to inspire future work in this field. The paper also discusses the importance of accurate models for digital twin (DT) development, which can replicate the behavior of physical batteries. DTs can be used for system control strategy determination and system design. However, the underlying models of a LIB DT must be highly detailed to calculate local battery parameters accurately. Electrochemical-thermal models are among the most accurate simulation methods for studying battery behavior. These models can be developed into battery DTs. The paper reviews the current state-of-the-art in battery modeling and machine learning for DT development. It explores the possibilities for developing a fully functional battery DT that can optimize battery design. The paper presents a review of studies on the application of machine learning for advancing battery digitalization. The novelty of this work allows the consolidation of studies in battery digitalization to focus on future research into digital experimentation. The work also guides focus towards real-time battery monitoring and control, and digital battery research and development. The paper highlights clear gaps and perspectives in the field where future studies and opportunities can lead to innovation. The paper discusses the use of physics-based models and machine learning algorithms for battery monitoring and control. Equivalent circuit models (ECMs) are simple and computationally efficient but lack detail. Electrochemical models provide more detailed insights into battery behavior but are computationally expensive. Hybrid models combine the strengths of both approaches, offering high accuracy and computational efficiency. The paper also discusses the use of machine learning algorithms for battery design, including parameter estimation and improved energy and power. The paper highlights the potential of combining physics-based models with machine learning algorithms for improved battery performance management. The paper concludes that the integration of physics-based models and machine learning algorithms is a promising approach for the digitalization of lithium-ion batteries.