A Rapid State-of-Health Estimation Method for Lithium-Ion Batteries Without Initial State Restrictions

The rapid estimation of state-of-health (SoH) is essential for the efficient recycling and repurposing of retired batteries. However, most existing methods impose strict constraints on the initial state-of-charge (ISoC) and initial state-of-polarization (ISoP) of batteries, which limits their applicability in real-world scenarios. To overcome these limitations, this article presents a novel SoH estimation method independent of battery initial states. Specifically, the open-circuit voltage (OCV) is employed as the initial-condition feature-of-interest (IC-FoI) and the residual capacity is employed as the capacity feature-of-interest (CAP-FoI), both of which can be obtained under arbitrary ISoC conditions, thus eliminating the restrictions of ISoC in traditional methods. To further mitigate the influence of ISoP on feature extraction, a rapid extraction scheme based on an equivalent circuit model (ECM) and a multiobjective optimization algorithm (MOA) is developed, bypassing the conventional relaxation procedure for 1–3 h. Afterward, a fusion-based framework is introduced, integrating the extracted IC-FoI and CAP-FoI for comprehensive SoH estimation. Experimental results indicate that the proposed method achieves an average absolute error of less than 2.1% among different initial conditions, even when the training dataset is restricted in battery equilibrium state.