In this work we present a novel mechanistic calendar aging model for a commercial lithium-ion cell with NCA cathode and silicon-graphite anode. The mechanistic calendar aging model is a semi-empirical aging model that is parameterized on component states of health, instead of capacity.
Research Papers
Mechanistic calendar aging model for lithium-ion batteries
Mechanistic calendar aging model for lithium-ion batteries
Authors: Alexander Karger, Julius Schmitt, Cedric Kirst, Jan Singer, Leo Wildfeuer, Andreas Jossen
Highlights
- Mechanistic calendar aging model is parameterized on component states of health
- Three component states of health are derived from degradation modes
- Model is parameterized on 627 days of calendar aging at 27 storage conditions
- Influence of check-up during testing is compensated in a two-step process
- Check-up compensation increases predicted lifetime by > 150%
In this work we present a novel mechanistic calendar aging model for a commercial lithium-ion cell with NCA cathode and silicon-graphite anode. The mechanistic calendar aging model is a semi-empirical aging model that is parameterized on component states of health, instead of capacity.
Three component states of health are derived from the degradation modes, which are calculated by fitting the electrode potential curves at every check-up measurement. The aging data used for model parameterization spans 672 days of storage at 27 different combinations of ambient temperature (T amb) and state of charge (SOC).
To compensate for the influence of the check-up measurements on cell degradation, the aging data is pre-processed in two steps, considering immediate degradation caused by the check-up cycles and accelerated degradation during subsequent storage. The loss of active anode material is negligible during check-up-compensated calendar aging. For loss of lithium inventory and loss of active cathode material, Tafel and Arrhenius terms are successfully used to model T amb and SOC dependence. The mechanistic calendar aging model predicts the capacity with <1% mean deviation for 7 different storage conditions after 672 days without check-ups. The check-up compensation increases predicted lifetime by >150% for exemplary storage at T amb=60°C and SOC=50%.
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About the Authors
Alexander Karger
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.
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Cedric Kirst
Prof. Jan Singer
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.
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