Performance Evaluation Of State Estimation Algorithms For Li Ion Battery State Of Health

Abstract

Lithium-ion battery state of health (SOH) represents the battery’s ability to store and deliver charge relative to its nominal condition . Accurate SOH estimation is vital for the safety and reliability of battery systems, preventing unexpected failures and hazards when cells approach end-of-life. This paper provides a comprehensive overview of prominent state estimation algorithms for predicting SOH in Li-ion batteries and evaluates their performance. We discuss traditional model-based techniques (including Kalman filtering and its variants), advanced data-driven approaches (machine learning models such as neural networks and support vector machines), and hybrid strategies. Key performance metrics and evaluation methods are described, and the strengths and limitations of each algorithm category are compared. By reviewing reported estimation accuracies, computational requirements, and robustness, we highlight how modern algorithms can achieve high precision (often within a few percent error) in SOH prediction. No specific application context is assumed, so the findings apply broadly to Li-ion battery management in electric vehicles, grid storage, and other domains. The paper concludes with insights into the trade-offs among algorithms and the importance of combining model fidelity with data-driven learning to enhance SOH estimation performance.