The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Comprehensive Modeling of Temperature-Dependent Degradation Mechanisms in Lithium Iron Phosphate Batteries
Open accessAbstract
For reliable lifetime predictions of lithium-ion batteries, models for cell degradation are required. A comprehensive semi-empirical model based on a reduced set of internal cell parameters and physically justified degradation functions for the capacity loss is developed and presented for a commercial lithium iron phosphate/graphite cell. One calendar and several cycle aging effects are modeled separately. Emphasis is placed on the varying degradation at different temperatures. Degradation mechanisms for cycle aging at high and low temperatures as well as the increased cycling degradation at high state of charge are calculated separately. For parameterization, a lifetime test study is conducted including storage and cycle tests. Additionally, the model is validated through a dynamic current profile based on real-world application in a stationary energy storage system revealing the accuracy. Tests for validation are continued for up to 114 days after the longest parametrization tests. The model error for the cell capacity loss in the application-based tests is at the end of testing below 1% of the original cell capacity and the maximum relative model error is below 21%.
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“Most of the existing studies model the battery cyclic and calendar aging independently, and combine the two to make predictions under a dynamic load profile [6,94,95].”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [95]
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“The current rate and DOD dependence on cyclic ageing can be modelled with exponential [95,102] or polynomial [102] functions.”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [95]
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“In contrast, the SOC dependence on calendar ageing lifetime is typically fitted by linear functions [100], exponential functions [99] or by the Tafel equation [95].”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [95]
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“The capacity loss due to calendar ageing is usually proportional to a power law relation with time 𝑡, weighted by the influences of temperature 𝑇 and storage SOC, what can be represented by some stress factor 𝑘 [95]:”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [95]
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Cites
Ageing mechanisms in lithium-ion batteries
Vetter, J., Novák, P., Wagner, M. R., Veit, C., Möller, K.-C., Besenhard, J. O., Winter, M., Wohlfahrt-Mehrens, M., Vogler, C., & Hammouche, A. (2005) · Journal of Power Sources · doi:10.1016/j.jpowsour.2005.01.006 · Cited by 3,987
Cycle-life model for graphite-LiFePO4 cells
Wang, J. J., Liu, P., Hicks-Garner, J., Sherman, E., Soukiazian, S., Verbrugge, M. W., Tataria, H., Musser, J., & Finamore, P. (2010) · Journal of Power Sources · doi:10.1016/j.jpowsour.2010.11.134 · Cited by 1,785
A review on lithium-ion battery ageing mechanisms and estimations for automotive applications
Barré, A., Deguilhem, B., Grolleau, S., Gérard, M., Suard, F., & Riu, D. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.05.040 · Cited by 1,758
Temperature dependent ageing mechanisms in Lithium-ion batteries – A Post-Mortem study
Waldmann, T., Wilka, M. D., Kasper, M., Fleischhammer, M., & Wohlfahrt‐Mehrens, M. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.03.112 · Cited by 1,269
Main aging mechanisms in Li ion batteries
Broussely, M., Biensan, P., Bonhomme, F., Blanchard, P., Herreyre, S., Nechev, K., & Staniewicz, R. J. (2005) · Journal of Power Sources · doi:10.1016/j.jpowsour.2005.03.172 · Cited by 1,162
Calendar and cycle life study of Li(NiMnCo)O2-based 18650 lithium-ion batteries
Ecker, M., Nieto, N., Käbitz, S., Schmalstieg, J., Blanke, H., Warnecke, A., & Sauer, D. U. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.09.143 · Cited by 809
A holistic aging model for Li(NiMnCo)O2 based 18650 lithium-ion batteries
Schmalstieg, J., Käbitz, S., Ecker, M., & Sauer, D. U. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.02.012 · Cited by 755
Lithium plating in a commercial lithium-ion battery – A low-temperature aging study
Petzl, M., Kasper, M., & Danzer, M. A. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.11.065 · Cited by 731
Calendar Aging of Lithium-Ion Batteries: I. Impact of the Graphite Anode on Capacity Fade
Keil, P., Schuster, S. F., Wilhelm, J., Travi, J., Hauser, A., Karl, R. C., & Jossen, A. (2016) · Journal of The Electrochemical Society · doi:10.1149/2.0411609jes · Cited by 669
Self-citationEcker, M., Gerschler, J. B., Vogel, J., Käbitz, S., Hust, F., Dechent, P., & Sauer, D. U. (2012) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.05.012 · Cited by 647
Flagged first, then by citations
Research works printed but not linked: 1
- Yasuda Masayuki , in OREBA 1.0 - International Conference on Olivines for Rechargeable Batteries, (2014).
Cited by
Sorted by citations
A review on the key issues of the lithium ion battery degradation among the whole life cycle
Han, X., Lu, L., Zheng, Y., Feng, X., Li, Z., Li, J., & Ouyang, M. (2019) · eTransportation · doi:10.1016/j.etran.2019.100005 · Cited by 1,745
Hu, X., Xu, L., Lin, X., & Pecht, M. (2020) · Joule · doi:10.1016/j.joule.2019.11.018 · Cited by 1,320
Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
Li, Y., Liu, K., Foley, A. M., Zülke, A., Berecibar, M., Nanini-Maury, E., Van Mierlo, J., & Hoster, H. E. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109254 · Cited by 1,193
Thermally modulated lithium iron phosphate batteries for mass-market electric vehicles
Yang, X., Liu, T., & Wang, C. (2021) · Nature Energy · doi:10.1038/s41560-020-00757-7 · Cited by 583
Wang, J., Huang, W., Pei, A., Li, Y., Shi, F., Yu, X., & Cui, Y. (2019) · Nature Energy · doi:10.1038/s41560-019-0413-3 · Cited by 540
Metadata
- Authors
- Schimpe, M., von Kuepach, M. E., Naumann, M., Hesse, H. C., Smith, K., & Jossen, A.
- Affiliations
- Technical University of Munich (DE)
- National Laboratory of the Rockies (US)
- Venue
- Journal of The Electrochemical Society
- Publisher
- The Electrochemical Society
- Published
- 2018
- Volume
- 165
- Issue
- 2
- Pages
- A181-A193
- ISSN
- 0013-4651 (ISSN-L)
- 1945-7111
- OpenAlex
- W2782996909
- Cited by
- 238
- References
- 26
- Citations by year
- 2026: 40 · 2025: 30 · 2024: 38 · 2023: 28 · 2022: 34 · 2021: 37 · 2020: 17 · 2019: 9 · 2018: 5
- FWCI
- 9.04
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Journal of The Electrochemical Society (publishedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion, other-oa)
- Funders
- Bundesministerium für Wirtschaft und Energie (Federal Ministry for Economic Affairs and Energy) (03ET1205G)
- Department of Energy | National Renewable Energy Laboratory, Office of Energy Efficiency and Renewable Energy (NREL) (AC36-08GO28308)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Materials Science › Metals and Alloys › Extraction and Separation Processes
- Keywords
- lithium iron phosphate batteries, battery degradation modeling, temperature-dependent degradation, capacity fade, calendar aging, cycle aging, battery lifetime prediction, semi-empirical model, stationary energy storage
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