The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data
Abstract
Abstract Battery lifetime prognosis is a key requirement for successful market introduction of electric and hybrid vehicles. This work aims at the development of a lifetime prediction approach based on an aging model for lithium-ion batteries. A multivariable analysis of a detailed series of accelerated lifetime experiments representing typical operating conditions in hybrid electric vehicle is presented. The impact of temperature and state of charge on impedance rise and capacity loss is quantified. The investigations are based on a high-power NMC/graphite lithium-ion battery with good cycle lifetime. The resulting mathematical functions are physically motivated by the occurring aging effects and are used for the parameterization of a semi-empirical aging model. An impedance-based electric-thermal model is coupled to the aging model to simulate the dynamic interaction between aging of the battery and the thermal as well as electric behavior. Based on these models different drive cycles and management strategies can be analyzed with regard to their impact on lifetime. It is an important tool for vehicle designers and for the implementation of business models. A key contribution of the paper is the parameterization of the aging model by experimental data, while aging simulation in the literature usually lacks a robust empirical foundation.
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Citations
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
Stress generation and fracture in lithium insertion materials
Christensen, J., & Newman, J. (2006) · Journal of Solid State Electrochemistry · doi:10.1007/s10008-006-0095-1 · Cited by 727
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Jaguemont, J., Boulon, L., & Dubé, Y. (2016) · Applied Energy · doi:10.1016/j.apenergy.2015.11.034 · Cited by 1,188
Xiong, R., Li, L., & Tian, J. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.10.019 · Cited by 1,017
Waag, W., Käbitz, S., & Sauer, D. U. (2013) · Applied Energy · doi:10.1016/j.apenergy.2012.09.030 · Cited by 987
Self-citationLi plating as unwanted side reaction in commercial Li-ion cells – A review
Waldmann, T., Hogg, B.-I., & Wohlfahrt-Mehrens, M. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.02.063 · Cited by 895
Metadata
- Authors
- Ecker, M., Gerschler, J. B., Vogel, J., Käbitz, S., Hust, F., Dechent, P., & Sauer, D. U.
- Venue
- Journal of Power Sources
- Publisher
- Elsevier BV
- Published
- 2012-10
- Volume
- 215
- Pages
- 248-257
- ISSN
- 0378-7753 (ISSN-L)
- OpenAlex
- W2097760098
- Cited by
- 642
- References
- 2
- FWCI
- 57.76
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ecker, M., Gerschler, J. B., Vogel, J., Käbitz, S., Hust, F., Dechent, P., & Sauer, D. U. (2012). Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data. Journal of Power Sources. https://doi.org/10.1016/j.jpowsour.2012.05.012
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