Study of the influence of mechanical pressure on the performance and aging of Lithium-ion battery cells

Müller, Verena; Scurtu, Rares-George; Memm, Michaela; Danzer, Michael A.; Wohlfahrt‐Mehrens, Margret

doi:10.1016/j.jpowsour.2019.227148

Cited by 120 publications

(68 citation statements)

References 41 publications

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“…Other potential applications of microstructure-resolved models for characterizing the deformation of electrodes subject to external loads include modeling the effect of stack pressure on battery performance, [242][243][244][245] predicting the initiation of mechanical failure, [234,246] and characterizing the mechanical tests such as nanoindentation. [217][218][219]247] There have not been many existing publications on these aspects using microstructure-resolved models, but more usage can be expected.…”

Section: Prediction Of Mechanical Propertiesmentioning

confidence: 99%

Guiding the Design of Heterogeneous Electrode Microstructures for Li‐Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning

Zhu

Finegan

et al. 2021

Advanced Energy Materials

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Electrochemical and mechanical properties of lithium‐ion battery materials are heavily dependent on their 3D microstructure characteristics. A quantitative understanding of the role played by stochastic microstructures is critical for the prediction of material properties and for guiding synthesis processes. Furthermore, tailoring microstructure morphology is also a viable way of achieving optimal electrochemical and mechanical performances of lithium‐ion cells. To facilitate the establishment of microstructure‐resolved modeling and design methods, a review covering spatially and temporally resolved imaging of microstructure and electrochemical phenomena, microstructure statistical characterization and stochastic reconstruction, microstructure‐resolved modeling for property prediction, and machine learning for microstructure design is presented here. The perspectives on the unresolved challenges and opportunities in applying experimental data, modeling, and machine learning to improve the understanding of materials and identify paths toward enhanced performance of lithium‐ion cells are presented.

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Section: Prediction Of Mechanical Propertiesmentioning

confidence: 99%

Guiding the Design of Heterogeneous Electrode Microstructures for Li‐Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning

Zhu

Finegan

et al. 2021

Advanced Energy Materials

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“…It should be noted that the calculated stack pressure is the theoretically applied stack pressure. Due to the compressibility of the cell components, the real applied stack pressure can reasonably be assumed to be smaller [12] . Measuring the real applied stack pressure is not trivial and typically requires a more extensive setup including a load cell or force sensor, which was not further explored here [12–13] .…”

Section: Figurementioning

confidence: 99%

“…Due to the compressibility of the cell components, the real applied stack pressure can reasonably be assumed to be smaller [12] . Measuring the real applied stack pressure is not trivial and typically requires a more extensive setup including a load cell or force sensor, which was not further explored here [12–13] . Homogeneity in the stack pressure can be reasonably assumed due to the rigidity of the glassy carbon windows and the flat compressed area.…”

Section: Figurementioning

confidence: 99%

Design and Operation of an Operando Synchrotron Diffraction Cell Enabling Fast Cycling of Battery Materials

Gustafsson

Schökel

Brant

2021

Batteries & Supercaps

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Operation of a battery typically involves dynamic and non‐equilibrium processes, making real time operando techniques crucial for understanding their nature. Operando X‐ray diffraction is an important technique for investigating metastable intermediates and non‐equilibrium phase transitions in crystalline electrode materials. Currently employed experimental setups often apply a disruptive approach to cell design, whereby the integrity of standard electrochemical cells is compromised to facilitate collection of high‐quality diffraction data. Here, we present a non‐disruptive approach to adapting the use of a standard pouch cell that enables fast and long‐term cell cycling. Suitability of the setup is demonstrated on the well‐studied cathode material LiNi0.5Mn1.5O4. While exhibiting comparable electrochemical behavior to a standard pouch cell up to a current rate of 8 C (∼6.6 mA cm−2), phase transitions could be monitored accurately. Thus, the cell provides a new alternative to investigating non‐equilibrium transitions and long‐term aging effects in battery materials.

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“…It has been shown that the measurement of the mechanical response either as stress or strain can improve the accuracy of SOC estimation [8], and can be used for estimating SOH defined as capacity fade [9,10]. The relationship between the evolution of capacity fade, cell thickness, and effects of the external pressure on the aging and cell performance has been the subject of a number of studies [11][12][13][14][15]. A shared conclusion of these studies is the importance of monitoring and ideally controlling the external pressure.…”

Section: Introductionmentioning

confidence: 99%

Reversible and Irreversible Expansion of Lithium-ion Batteries Under a Wide Range of Stress Factors

Mohtat¹,

Lee²,

Stefanopoulou³

et al. 2021

Preprint

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Lithium-ion batteries cell thickness changes as they degrade. These changes in thickness consist of a reversible intercalation-induced expansion and an irreversible expansion. In this work, we study the cell expansion evolution under variety of conditions such as temperature, charging rate, depth of discharge, and pressure.A specialized ?fixture was used to keep the cells at a constant pressure during cycling, while measuring the thickness change both within a cycle and the cumulative growth over many cycles. The changes in positive and negative electrode capacity and stoichiometric range can be diagnosed from the evolution of the reversible expansion. The changes in the reversible expansion if combined with the voltage, lead to a higher-confi?dence estimation of cell health parameters important for lifetime prediction and adoptive battery management such as asymmetric charge/discharge power limits. This study raises the importance of monitoring the expansion for enabling advanced and more-informed health diagnostics of lithium-ion batteries.

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Study of the influence of mechanical pressure on the performance and aging of Lithium-ion battery cells

Cited by 120 publications

References 41 publications

Guiding the Design of Heterogeneous Electrode Microstructures for Li‐Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning

Guiding the Design of Heterogeneous Electrode Microstructures for Li‐Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning

Design and Operation of an Operando Synchrotron Diffraction Cell Enabling Fast Cycling of Battery Materials

Reversible and Irreversible Expansion of Lithium-ion Batteries Under a Wide Range of Stress Factors

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