The title of the article is:
Modeling capacity fade of lithium-ion batteries during dynamic cycling considering path dependence
The authors are: Alexander Karger, Leo Wildfeuer, Deniz Aygül, Arpit Maheshwari, Jan P. Singer and Andreas Jossen.
Aging models are important tools to optimize the application of lithium-ion batteries. Usually, aging models are parameterized at constant storage or cycling conditions, whereas during application, storage and cycling conditions can change. In the literature, two different methods for modeling capacity fade during such dynamic operation are proposed. These methods use either the cumulated charge-throughput (CCT-method) or the current capacity (CAP-method) as reference points, when aging conditions are changing. In this work, we show that the CAP-method models capacity fade more accurately when applied to dynamic cyclic aging tests with periodically changing mean state-of-charge, depth-of-discharge, ambient temperature and discharge rates for a commercial NCA cell with a silicon-doped graphite anode. However, in cases where the difference between actual and reference charge-throughput of the CAP-method becomes large, the capacity gradient is modeled more accurately with the CCT-method. Because the relative capacity fade error of the CAP-method is small at , we assume that capacity fade behaves path-independently for the dynamic cyclic aging tests since the CAP-method assumes path independence through history independence. Moreover, because the measured capacity fade is non-commutative, which is sometimes labeled path-dependent, we recommend to not consider non-commutative capacity fade as a definitive sign of path-dependent degradation.
Link to the publication: https://doi.org/10.1016/j.est.2022.104718