Aging models are fundamental tools to optimize the application of lithium-ion batteries. 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.
Alexander Karger, Leo Wildfeuer, Deniz Aygül, Arpit Maheshwari, Jan P. Singer, Andreas Jossen.
- Two methods for modeling capacity fade during dynamic operation are compared.
- Current capacity instead of charge-throughput as reference point is more accurate.
- Experimental validation with commercial NCA cell with silicon-doped graphite anode.
- Non-commutative capacity fade revealed as insufficient sign of path dependence.
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 it with 6%, 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.
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Alexander Karger is a battery and machine learning engineer at TWAICE, where he works on developing cutting-edge battery models for the electric, thermal, and aging behavior. At the same time, he pursues a Ph.D. at the Chair of Electrical Energy Storage Technology led by Prof. Dr.-Ing. Andreas Jossen at the Technical University of Munich (TUM), and regularly publishes his latest findings. He holds an M.Sc. in Aerospace Engineering, having studied at TUM and the University of Stuttgart. His career includes experience with Porsche and GE Aviation. He joined TWAICE in 2019, initially to complete his master’s thesis.
Dr. Jan Singer is Engineering Manager at TWAICE, where his primary focus is working on laboratory measurements and battery models. Before he joined TWAICE in 2019, he was a Research Associate at the Chair for Electrical Energy Storage Systems, Institute for Photovoltaics, University of Stuttgart, Germany. During his Ph.D. studies under the supervision of Prof. Dr.-Ing. Kai Peter Birke, he focused on the detection of electrochemical effects induced by volume strains and the development of new non-destructive characterization methods for lithium-ion cells. He started his professional career in 2006 as mechatronic technician at Harman/Becker Automotive Systems. In 2012, he received his Bachelor’s degree from University of Applied Sciences Ulm and graduated with an M.Sc. from University of Stuttgart in 2015.