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Electrolyte Optimisation via Float-Current Measurement for High-Nickel/Graphite-SiOx Cells
Cell characterization
Characterization methods

Electrolytes play a central role in cell development, as they significantly influence the behaviour and ageing of lithium-ion batteries [1, 2]. The identification of optimal electrolyte formulations can thus lead to decisive competitive advantages in cell production. Electrolyte decomposition is indicated by gas formation, drying of the cell and/or the formation of cover layers and reactive intermediates [3, 4]. The quantification of electrolyte degradation through classical ageing tests is a time-consuming measurement procedure that only provides initial results after several weeks or months [5]. In this work, a rapid measurement method for determining the calendrical ageing rate of lithium-ion batteries – float-current measurement (FCM) – is therefore used to determine the electrolyte degradation reactions and applied to the optimisation of electrolytes [6, 7]. The hardware used for the precise measurement of the current was developed at the Institute for Power Electronics and Electrical Drives (ISEA).
In this work, we focus on the ageing behaviour of a high nickel lithium-ion cell. The pouch cell is a 1 Ah lithium nickel cobalt manganese oxide battery (NMC811/Graphit-SiOx) which is provided by the Li-FUN Technology Corporation Limited dry without electrolyte. The cells are filled with different electrolytes to evaluate the influence of additives. FCM and electrochemical impedance spectroscopy (EIS) are used to investigate the influence of the additives after formation. To evaluate the stability of the electrolytes, the cells are tested at different temperatures and voltages using FCM. To gain further information about the influence of the electrolyte formulation, EIS spectra are recorded frequently and analysed using the distribution of relaxation times as well as an equivalent circuit model [8].
Our results show that FCM provides a differentiated overview of the stability of electrolytes over a wide temperature and SOC range in a short time. The ageing behaviour of the investigated cells differs significantly from each other. The degradation of the silicon anode plays a major role in the ageing behaviour of the investigated cells and it mainly influences the ageing of the cells. Limiting these ageing mechanisms can help to improve performance. FCM accelerates the optimisation of electrolytes for lithium-ion cells and the lifetime prediction.

[1] J. Burns, A. Kassam, N. Sinha, L. Downie, L. Solnickova, B. Way und J. Dahn, „Predicting and Extending the Lifetime of Li-Ion Batteries,“ Journal of The Electrochemical Society, p. A1451–56, 2013.
[2] C. Kupper, B. Weißhar, S. Rißmann und W. Bessler, „End-of-Life Prediction of a Lithium-Ion Battery Cell Based on Mechanistic Aging Models of the Graphite Electrode,“ Journal of The Electrochemical Society, Bd. 165, pp. A3468-80, 2018.
[3] M. Winter, „The Solid Electrolyte Interphase – The Most Important and the Least Understood Solid Electrolyte in Rechargeable Li Batteries,“ Zeitschrift für Physikalische Chemie, pp. 1395-1406, 2009.
[4] B. Berkes, A. Schiele, H. Sommer, T. Brezesinski und J. Janek, „On the gassing behavior of lithium-ion batteries with NCM523 cathodes,“ Journal of Solid State Electrochemistry, p. 2961–2967, 2016.
[5] A. Tornheim, C. Peebles, J. Gilbert, R. Sahore, J. Garcia, J. Bareno, H. Iddir, C. Liao und D. Abraham, „Evaluating electrolyte additives for lithium-ion cells: A new Figure of Merit,“ Journal of Power Sources, pp. 201-209, 23 August 2017.
[6] M. Theiler, C. Endisch und M. Lewerenz, „Float Current Analysis for Fast Calendar Aging Assessment of 18650 Li(NiCoAl)O2/Graphite Cells,“ Batteries, 01 April 2021.
[7] J. Li, L. Xing, L. Zhang, L. Yu, W. Fan, M. Xu und W. Li, „Insight into self-discharge of layered lithium-rich oxide cathode in carbonate-based electrolytes with and without additive,“ Journal of Power Sources, pp. 17-25, 30 August 2016.
[8] H. Witzenhausen, Elektrische Batteriespeichermodelle : Modellbildung, Parameteridentifikation und Modellreduktion, Aachen: RWTH Aachen University, 2017.

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Dirk Uwe Sauer