A Comparison between Electrochemical Impedance Spectroscopy and Incremental Capacity-Differential Voltage as Li-ion Diagnostic Techniques to Identify and Quantify the Effects of Degradation Modes within Battery Management Systems

TLDR

Lithium‑ion battery degradation arises from multiple mechanisms, typically grouped into conductivity loss, loss of active material, and loss of lithium inventory, yet BMS‑derived State of Health metrics do not indicate which degradation modes are responsible. The study aims to compare incremental capacity‑differential voltage and electrochemical impedance spectroscopy on the same dataset to assess whether both techniques reveal similar degradation causes. Both techniques were applied to identical data from parallelized cells aged differently, enabling a direct comparison of their diagnostic outputs. In this experimental case, IC‑DV and EIS identified loss of active material and loss of lithium inventory as the most significant degradation modes, and the techniques were found to be correlated, indicating that this approach could be integrated into BMS to quantify ageing causes and support lifetime control and design.

Abstract

Degradation of Lithium-ion batteries is a complex process that is caused by a variety of mechanisms. For simplicity, ageing mechanisms are often grouped into three degradation modes (DMs): conductivity loss (CL), loss of active material (LAM) and loss of lithium inventory (LLI). State of Health (SoH) is typically the parameter used by the Battery Management System (BMS) to quantify battery degradation based on the decrease in capacity and the increase in resistance. However, the definition of SoH within a BMS does not currently include an indication of the underlying DMs causing the degradation. Previous studies have analysed the effects of the DMs using incremental capacity and differential voltage (IC-DV) and electrochemical impedance spectroscopy (EIS). The aim of this study is to compare IC-DV and EIS on the same data set to evaluate if both techniques provide similar insights into the causes of battery degradation. For an experimental case of parallelized cells aged differently, the effects due to LAM and LLI were found to be the most pertinent, outlining that both techniques are correlated. This approach can be further implemented within a BMS to quantify the causes of battery ageing which would support battery lifetime control strategies and future battery designs.