A Comprehensive Single-Particle-Degradation Model for Battery State-Of-Health Prediction
Understanding degradation phenomenon, which is quantified by the State of Health (SOH) in Battery Management Systems (BMSs), is one of the most challenging issues in the usage of Li-ion batteries (LIBs). Models employed for characterizing battery degradation need to be simple enough for on-line estimation in BMSs, very accurate, and comprehensive in that they include several degradation physics in both the battery's anode and cathode. In this paper, a single-particle based battery degradation model is developed that can predict cycling capacity with less than 2% error over the battery life. The model includes the volumetric fraction change due to dissolution and the corresponding changes in the effective transport properties in the cathode. In the anode, the lithium loss caused by mechanical/chemical degradation is included. The predicted battery behavior follows three important stages of capacity fade. At first, the lithium loss dominates the cell capacity loss. Then, the lithium loss becomes stabilized while the metal dissolution accumulates. Lastly, dissolution primarily determines the cell degradation. This behavior is in good agreement with experimental observations. This new single-particle based degradation model addresses two critical requirements for model-based BMSs, which is a rapid and accurate battery response prediction.
J. Li et al., "A Comprehensive Single-Particle-Degradation Model for Battery State-Of-Health Prediction," Journal of Power Sources, vol. 456, Elsevier, Apr 2020.
The definitive version is available at https://doi.org/10.1016/j.jpowsour.2020.227950
Mechanical and Aerospace Engineering
Center for Research in Energy and Environment (CREE)
Second Research Center/Lab
Center for High Performance Computing Research
Keywords and Phrases
Battery degradation; Crack propagation; Lithium loss; Metal dissolution; Single particle
International Standard Serial Number (ISSN)
Article - Journal
© 2020 Elsevier, All rights reserved.
30 Apr 2020
The authors gratefully acknowledge the financial support from the National Science Foundation (award number CMMI-1538415).