Mechanistic modelling of cyclic voltage-capacity response for lithium-ion batteries

One of the challenging tasks related to lithium-ion batteries (LIBs) remains a comprehensive approach for battery behaviour modelling. An approach is presented that enables modelling the voltage-capacity response of LIBs that are subjected to variable temperature and current load histories. A detail...

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Bibliographic Details
Published in:Energy (Oxford) 2019-11, Vol.186, p.115791, Article 115791
Main Authors: Nagode, Marko, Gosar, Aleš, Sweeney, Caoimhe A., Jaguemont, Joris, Van Mierlo, Joeri, Šeruga, Domen
Format: Article
Language:English
Subjects:
Batteries
Battery model
Calibration
Computer simulation
Continuum modelling
Cyclic loading
Electric potential
Embedding
Equivalent circuits
Lithium
Lithium-ion batteries
Lithium-ion battery
Load history
Model accuracy
Modelling
Prandtl operator
Rechargeable batteries
Thermo-mechanical
Topology
Voltage
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Summary:One of the challenging tasks related to lithium-ion batteries (LIBs) remains a comprehensive approach for battery behaviour modelling. An approach is presented that enables modelling the voltage-capacity response of LIBs that are subjected to variable temperature and current load histories. A detailed presentation of the developed macro-scale phenomenological model embedding the mechanistic properties of the Prandtl type hysteresis operator and the concept of the force-voltage analogy is made. The necessary input data preparation for the model calibration is also presented. Accuracy of the model is confirmed with experimental observations for both nested current load history at two different temperatures and for arbitrary current load history. The same measured data is used to calibrate and to simulate response of the first order Thevenin equivalent circuit topology in order to amply compare the obtained results. •A current controlled macro-scale model of lithium-ion battery is proposed.•Battery behaviour under arbitrary cyclic loading could be simulated.•A detailed calculation algorithm of the model is presented.•Input data preparation is described thoroughly.•The model validation is performed for various conditions demonstrating its accuracy.
ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2019.07.121