A4 Refereed article in a conference publication
Capacity loss estimation for li-ion batteries based on a semi-empirical model
Authors: Rabah Mohammed, Immonen Eero, Shahsavari Sajad, Haghbayan Mohammad-Hashem, Murashko Kirill, Immonen Paula
Editors: Khalid Al-Begain, Mauro Iacono, Lelio Campanile, Andrzej Bargiela
Conference name: European Conference on Modelling and Simulation
Publisher: European Council for Modelling and Simulation
Publication year: 2021
Journal: Proceedings: European Conference for Modelling and Simulation
Book title : Proceedings of the 35th ECMS International Conference on Modelling and Simulation ECMS 2021
Journal name in source: Proceedings - European Council for Modelling and Simulation, ECMS
Series title: Communications of the ECMS
Number in series: 1
Volume: 35
First page : 235
Last page: 242
ISBN: 978-3-937436-72-2
ISSN: 2522-2414
DOI: https://doi.org/10.7148/2021-0235
Web address : http://www.scs-europe.net/dlib/2021/ecms2021acceptedpapers/0235_simo_ecms2021_0036.pdf
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/66341359
Understanding battery capacity degradation is instrumental for designing modern electric vehicles. In this paper, a Semi-Empirical Model for predicting the Capacity Loss of Lithium-ion batteries during Cycling and Calendar Aging is developed. In order to redict the Capacity Loss with a high accuracy, battery operation data from different test conditions and different Lithium-ion batteries chemistries were obtained from literature for parameter optimization (fitting). The obtained models were then compared to experimental data for validation. Our results show that the average error between the estimated Capacity Loss and measured Capacity Loss is less than 1.5% during Cycling Aging, and less than 2% during Calendar Aging. An electric mining dumper, with simulated duty cycle data, is considered as an application example.
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