Battery State of Health Estimation Methods: Implementation and Comparison of 11 Algorithms
Eojin Kim, Sunghun Jung
Abstract
Battery State of Health (SOH) estimation is critical for ensuring the safety, performance, and longevity of batteries, particularly in applications such as electric vehicles and renewable energy systems. This study systematically reviews and implements 11 SOH estimation algorithms, categorized into direct measurement, adaptive, data-driven, and hybrid methods. Unlike previous research, this work emphasizes empirical validation using real-world battery datasets and evaluates each algorithm based on predictive accuracy, computational complexity, and practical applicability. Notably, these foundational algorithms serve as critical building blocks for advanced hybrid approaches that combine their unique strengths to enhance accuracy and robustness. Through detailed comparative analysis, this study not only guides researchers and practitioners in selecting optimal SOH estimation techniques but also lays the groundwork for innovative hybrid algorithm development to address limitations in current battery management systems. These findings contribute to advancing sustainable battery technologies and their integration into modern energy solutions.