Artificial Intelligence Approaches for SOH Estimation in Lithium‐Ion Batteries: A Comprehensive Review of Progress, Critical Evaluations, Challenges, and Toward Future Outlooks

In recent years, safety issues with electric vehicles (EVs) have grown. These EVs are powered by rechargeable lithium‐ion batteries (LIBs), which are key energy storage systems. The accurate estimation of these LIBs state of health (SOH) is crucial for reliable performance and reduction in safety issues. This estimation is challenging due to the complex electrochemical dynamics and nonlinear behavior of LIBs and variability in external environmental conditions. Traditional direct and model‐based frameworks significantly produce inaccurate results, resulting in increased interest in artificial intelligence hybrid and coestimation methods that combine multiple models to enhance precision and reliability. This review explores models and AI‐based methods, highlighting methodologies, implementation strategies, and evaluation criteria for SOH estimation. Furthermore, it identifies limitations in previous studies, including unclear classifications and inadequate evaluations, while examining essential operational factors such as battery parameters, model execution, and characteristics. Hybrid methods exhibit significant accuracy across various conditions, whereas coestimation techniques indicate the potential for simultaneous examination of SOH. Challenges including capacity degradation, model limitations, data integrity, and leading toward Green AI are explored and proposed, along with prospective advancements such as advanced algorithms, cloud‐based frameworks, and second‐life applications. This review presents insights for researchers and industries, facilitating the development of effective BMS frameworks to improve EV performance, battery longevity, and sustainability.

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