Degradation Monitoring and Characterization in Lithium-Ion Batteries via the Asymptotic Local Approach

• Published in: IEEE transactions on control systems technology Vol. 33; no. 1; pp. 189 - 206
• Main Authors: Couto, Luis D., Reniers, Jorn, Zhang, Dong, Howey, David A., Kinnaert, Michel
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Asymptotic local approach; Batteries; Computational modeling; Data models; Degradation; Electrodes; fault diagnosis; Hydraulic models; Likelihood ratio; Lithium; lithium-ion (Li-ion); Lithium-ion batteries; modeling; Monitoring; Parameter identification; Reduced order models; Solid modeling; Solids; State observers; Statistical tests
• ISSN: 1063-6536; 1558-0865
• DOI: 10.1109/TCST.2024.3483093

Degradation mechanisms affecting the long-term performance of lithium-ion batteries should be monitored and characterized. Such mechanisms, such as loss of lithium inventory (LLI) or active material, can be translated into parameter variations in electrochemical battery models. Here, a reduced-order model (the equivalent hydraulic model) is considered as it provides a good tradeoff between physical interpretability and complexity. The aim is to detect and characterize degradation, namely, to indicate the parameters subject to change, from standard (dis)charge data. To this end, change indicators (or residuals) are computed by combining a state observer and a local statistical approach. Model parameter changes induce changes in the mean of the residual vector which is asymptotically normally distributed with a specified variance. Degradation detection and characterization is achieved by processing the latter residual by statistical tests relying on log-likelihood ratios between multiple simple hypotheses. Results indicate the long-term changes in the main degradation modes affect battery performance. Most degradation modes considered are active at the 0.1% relative parametric change level, but active material loss reaches the 1% parametric change level over the battery lifetime, and 10% parametric change levels are obtained for sluggish diffusion and impedance rise. We show how the proposed methodology could be a useful alternative to methods based only on parameter identification.