A Detailed Survey on State of Health Estimation Methods

Lithium-ion batteries are widely used in electrical vehicles, renewable energy storage systems, and long cycle life. However, with each charge-discharge cycle, their performance gradually degrades, impacting reliability and safety. A key parameter to evaluate battery degradation is the State of Health (SOH), which cannot be measured directly and must be estimated using advanced Battery Management System (BMS) techniques. This article presents a comprehensive survey of the state of the art in SOH estimation for lithium-ion batteries. It classifies methods into three categories: direct measurement methods, model-based prediction methods and data-driven methods using machine learning. Each method is analyzed in terms of advantages, limitations, and implementation challenges. The study also highlights emerging hybrid approaches combining electrochemical models with AI for improved SOH estimation accuracy.