This review article discusses the importance of accurate state of charge (SOC) and state of health (SOH) estimation for lithium-ion batteries in automotive applications. The authors highlight the critical role of precise SOC/SOH estimation in ensuring optimal battery management, maximizing battery lifespan, and enhancing safety and performance. They emphasize the increasing demand for reliable algorithms to estimate SOC and SOH due to the widespread use of lithium-ion batteries in various industries. The review provides an in-depth analysis of the state-of-the-art in SOC/SOH estimation algorithms, examining recent and promising theoretical and practical techniques. It also critically evaluates different approaches, highlighting their advantages, limitations, and potential areas for improvement. The goal is to provide a clear understanding of the current landscape and to identify future research directions in this crucial field for technological innovation. The review covers various modeling approaches for battery systems, including empirical models, equivalent circuit models, and other battery models. Empirical models are based on experimental data and are used to predict battery performance and aging. Equivalent circuit models, such as the RC–Thevenin model, are used to describe the dynamic behavior of batteries and are widely used in battery management systems. The review also discusses the PNGV model and the dual polarization (DP) model, which are used for accurate SOC estimation in electric vehicles. The Warburg impedance concept is also discussed, as it is an important element in battery modeling, particularly in the context of electrochemical impedance spectroscopy (EIS) and equivalent circuit models. The review concludes that accurate SOC/SOH estimation is essential for the efficient and safe operation of battery systems in automotive applications. It emphasizes the need for further research and development in this area to improve the accuracy and reliability of battery management systems. The review also highlights the importance of considering various factors, such as battery chemistry, operational requirements, and desired accuracy levels, when selecting an appropriate SOC/SOH estimation algorithm. The authors conclude that the continuous evolution and refinement of SOC/SOH estimation algorithms hold the promise of significant improvements in the accuracy and reliability of battery management systems, leading to optimized energy utilization, extended battery lifespans, enhanced operational safety, and streamlined maintenance practices.This review article discusses the importance of accurate state of charge (SOC) and state of health (SOH) estimation for lithium-ion batteries in automotive applications. The authors highlight the critical role of precise SOC/SOH estimation in ensuring optimal battery management, maximizing battery lifespan, and enhancing safety and performance. They emphasize the increasing demand for reliable algorithms to estimate SOC and SOH due to the widespread use of lithium-ion batteries in various industries. The review provides an in-depth analysis of the state-of-the-art in SOC/SOH estimation algorithms, examining recent and promising theoretical and practical techniques. It also critically evaluates different approaches, highlighting their advantages, limitations, and potential areas for improvement. The goal is to provide a clear understanding of the current landscape and to identify future research directions in this crucial field for technological innovation. The review covers various modeling approaches for battery systems, including empirical models, equivalent circuit models, and other battery models. Empirical models are based on experimental data and are used to predict battery performance and aging. Equivalent circuit models, such as the RC–Thevenin model, are used to describe the dynamic behavior of batteries and are widely used in battery management systems. The review also discusses the PNGV model and the dual polarization (DP) model, which are used for accurate SOC estimation in electric vehicles. The Warburg impedance concept is also discussed, as it is an important element in battery modeling, particularly in the context of electrochemical impedance spectroscopy (EIS) and equivalent circuit models. The review concludes that accurate SOC/SOH estimation is essential for the efficient and safe operation of battery systems in automotive applications. It emphasizes the need for further research and development in this area to improve the accuracy and reliability of battery management systems. The review also highlights the importance of considering various factors, such as battery chemistry, operational requirements, and desired accuracy levels, when selecting an appropriate SOC/SOH estimation algorithm. The authors conclude that the continuous evolution and refinement of SOC/SOH estimation algorithms hold the promise of significant improvements in the accuracy and reliability of battery management systems, leading to optimized energy utilization, extended battery lifespans, enhanced operational safety, and streamlined maintenance practices.