In this publication we extend a state-of-the-art electrode open circuit potential model for blend electrodes and inhomogeneous lithiation. We introduce a bi-level optimization algorithm to estimate the open parameters of the electrode model using measurements conducted on the full-cell level with state-of-the-art testing equipment.
Research Papers
Non-destructive electrode potential and open-circuit voltage aging estimation for lithium-ion batteries
Non-destructive electrode potential and open-circuit voltage aging estimation for lithium-ion batteries
Authors: Cedric Kirst, Alexander Karger, Jan Singer, Andreas Jossen
Highlights
- Physical motivated model for blend electrodes and inhomogeneous lithiation
- Non-destructive estimation of electrode potential curves
- Validation on commercial LFP-C, NMC-SiC and NCA-SiC Li-ion cells
- Novel non-destructive degradation mode analysis
- Achievement of model-based fitting voltage RMSE < 6 mV over lifetime
In this publication we extend a state-of-the-art electrode open circuit potential model for blend electrodes and inhomogeneous lithiation. We introduce a bi-level optimization algorithm to estimate the open parameters of the electrode model using measurements conducted on the full-cell level with state-of-the-art testing equipment.
As input for the optimization algorithm, we use data from a pseudo open-circuit voltage aging study to estimate the electrode potential curves, the electrode capacities, and the capacity of cyclable lithium over lifetime without opening the cell. We validate our method against measurements on commercial lithium-ion cells with the electrode materials LFP-C, NMC-SiC and NCA-SiC, and compare the estimated electrode pseudo open-circuit potential curves to measurements conducted on harvested half-cells.
We conclude that the presented non-destructive method of half-cell open-circuit potential modeling offers an alternative to complex cell disassembly and electrode material harvesting for degradation mode analysis.
Access the paper here.
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About the Authors
Cedric Kirst
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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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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