Rapid and Non-Invasive SoH Estimation of Lithium-Ion Cells via Automated EIS and EEC Models

Abstract

The growing need for efficient battery reuse and recycling requires rapid, reliable methods to assess the state of health (SoH) of lithium-ion cells. Conventional SoH estimation based on full charge–discharge cycling is slow, energy-intensive, and unsuitable for dismantled cells with unknown histories. This work presents an automated diagnostic approach using Electrochemical Impedance Spectroscopy (EIS) combined with Electrical Equivalent Circuit (EEC) modeling for fast, non-invasive SoH estimation. A correlation between fitted EIS parameters and cell degradation stages was established through controlled aging tests on NMC-based lithium-ion cells. The methodology was implemented in custom software (BaterurgIA) integrated into a robotic testing bench, enabling automatic EIS acquisition, data fitting, and SoH determination. The system achieves SoH estimation with 5–10% accuracy for cells in intermediate and advanced degradation stages, while additional parameters improve sensitivity during early aging. Compared to conventional cycling methods, the proposed approach reduces diagnostic time from hours to minutes, minimizes energy consumption, and offers predictive insights into internal degradation mechanisms. This enables fast and reliable cell grading for reuse, reconditioning, or recycling, supporting the development of scalable solutions for battery second-life applications and circular economy initiatives.