Cycling lithium-ion batteries causes capacity fade, but also changes the shape of the open-circuit voltage (OCV) curve, due to loss of active material (LAM) and loss of lithium inventory (LLI). To model this change, we recently proposed a novel empirical calendar aging model that is parameterized on component states of health (s) instead of capacity fade only.
Research Papers
Mechanistic cycle aging model for the open-circuit voltage curve of lithium-ion batteries
Mechanistic cycle aging model for the open-circuit voltage curve of lithium-ion batteries
Authors: Alexander Karger, Julius Schmitt, Cedric Kirst, Jan Singer, Leo Wildfeuer, Andreas Jossen
‍
Highlights
- Empirical algorithm for degradation rates enables first mechanistic cycle aging model
- Degradation rates for all components mainly driven by DOD, SOC and temperature
- Modeling aged OCV curves reduces voltage error by factor 8, compared to no update
- Predicted capacity fade values exhibit an average error of 1.04% on validation data
- Hidden degradation of electrodes does not explain onset of non-linear capacity fade
‍
Cycling lithium-ion batteries causes capacity fade, but also changes the shape of the open-circuit voltage (OCV) curve, due to loss of active material (LAM) and loss of lithium inventory (LLI). To model this change, we recently proposed a novel empirical calendar aging model that is parameterized on component states of health (s) instead of capacity fade only.
In this work, we present a mechanistic aging model for cycle aging, allowing prediction of capacity fade, OCV curve change and component degradation. The model is parameterized on cycling data of 59 commercial lithium-ion batteries with NCA cathode and silicon–graphite anode, which were cycled for 2500 equivalent full cycles under varying conditions. We propose a stepwise approach to identify the most relevant stress parameters causing LLI and LAM, where we also separate between loss of accessible graphite and silicon in the blend anode. Stress parameter dependence is modeled with linear combinations of exponential functions and the model predicts capacity fade with  mean absolute error(MAE).
For all test conditions, LLI is the dominating degradation mode and loss of accessible graphite is negligible. Reconstructed OCV curves reduce the median voltage MAE by a factor of 8, compared to not updating the OCV.
‍
Access the paper here.
Featured Resources · Webinar
Beyond Lithium: Introducing TWAICE's New Sodium-Ion Battery ModelÂ
Explore the fundamentals, growing demand, and advantages of sodium-ion battery technology, including TWAICE's battery model, its potential applications in ESS and EVs, and its environmental and economic benefits.
August 20, 10:00am (ET) | 4:00pm (CET)
Sign upRelated Resources
Insights from EPRI's BESS failure incident database
This report is intended to address the failure mode analysis gap by developing a classification system that is practical for both technical and non-technical stakeholders.
Non-destructive electrode potential and open-circuit voltage aging estimation for lithium-ion batteries
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.
Mechanistic calendar aging model for lithium-ion batteries
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.
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.
Connect on Linkedin
Cedric Kirst
Cedric Kirst is a Battery Engineer at TWAICE Technologies GmbH, where he has worked since August 2018. He also serves as an Associate Researcher at the TUM Chair of Electrical Energy Storage Technology, a role he has held since March 2023.
Cedric’s professional journey includes a significant tenure as a Student Assistant at the Bundeswehr University Munich from January 2017 to October 2019. He also gained valuable industry experience during his internship at IBM Research Dublin, where he participated in the EnableS3 project funded by the European Union.
Cedric holds a Master of Science in Mechatronics, Robotics, and Automation Engineering from the Technical University of Munich, completed in 2020. He also earned his Bachelor of Science in Engineering from the same institution in 2018.
Connect on Linkedin
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.
Connect on Linkedin