Battery Electrode-Level Diagnostics: Enabled by a 2-Minute Transient Response

Battery health diagnostics are critical for enabling aging-aware control strategies and optimizing operational planning in energy storage systems, yet electrode-level state estimation remains challenging due to time-intensive methods and specialized equipment requirements. This study introduces a rapid diagnostic framework that estimates electrode-level states using only a 2 -minute transient response. By integrating resistance-capacitance parameters—extracted from the voltage response to a step current—into an ensemble learning model, battery capacity, electrode-level capacities, and lithium inventory are estimated. The framework is validated with experimental data from 36 commercial electric vehicle batteries across all state-of-charge (SOC) levels, achieving an average diagnostic accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${9 8. 3 \%}$</tex> for cell capacity, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${9 5. 5 \%}$</tex> for negative electrode capacity, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${9 8. 5 \%}$</tex> for positive electrode capacity, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${9 7. 9 \%}$</tex> for lithium inventory. Moreover, the framework remains effective even when the cell's SOC is uncertain, offering valuable insights for adaptive control and aging-aware battery management.