This paper reviews the aging mechanisms and estimation methods for lithium-ion batteries in automotive applications. Lithium-ion batteries have gained significant attention due to their advantages in vehicle applications, but their performance degradation over time remains a major challenge. The paper discusses the complex interactions between various factors, such as temperature, storage conditions, and usage patterns, which contribute to battery aging. It highlights the differences between calendar aging (aging during storage) and cycle aging (aging during use), and explores various estimation methods, including electrochemical models, equivalent circuit models, performance-based models, analytical models with empirical fitting, and statistical approaches. Each method has its strengths and limitations, with electrochemical and equivalent circuit models providing detailed insights but being technology-specific, and statistical methods being adaptable but requiring large datasets. The paper concludes by proposing a hybrid approach that combines the precision of measurements, adaptability of statistical methods, and understanding of physico-chemical processes through modeling, aiming to develop a robust and real-time compatible aging estimation method for electric vehicles.This paper reviews the aging mechanisms and estimation methods for lithium-ion batteries in automotive applications. Lithium-ion batteries have gained significant attention due to their advantages in vehicle applications, but their performance degradation over time remains a major challenge. The paper discusses the complex interactions between various factors, such as temperature, storage conditions, and usage patterns, which contribute to battery aging. It highlights the differences between calendar aging (aging during storage) and cycle aging (aging during use), and explores various estimation methods, including electrochemical models, equivalent circuit models, performance-based models, analytical models with empirical fitting, and statistical approaches. Each method has its strengths and limitations, with electrochemical and equivalent circuit models providing detailed insights but being technology-specific, and statistical methods being adaptable but requiring large datasets. The paper concludes by proposing a hybrid approach that combines the precision of measurements, adaptability of statistical methods, and understanding of physico-chemical processes through modeling, aiming to develop a robust and real-time compatible aging estimation method for electric vehicles.