4.7 Article

Revealing the mechanism of stress rebound during discharging in lithium-ion batteries

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JOURNAL OF ENERGY STORAGE
卷 58, 期 -, 页码 -

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ELSEVIER
DOI: 10.1016/j.est.2022.106454

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Lithium-ion battery; Discharging process; Stress increasing; Mechanism; Diffusion coefficient

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Research shows that lithium-ion batteries, especially those with silicon-based anodes, undergo significant volume changes during charge and discharge. This contradicts the expected decrease in stress as the battery volume decreases. By combining experimental measurements and an improved single-particle model, this study reveals that the large volume change of the anode material and the limited diffusion of lithium-ion in the active particles result in uneven concentration distribution and anomalous stress changes. The rupture of particles during cycling also contributes to increased diffusion channels and diffusion coefficient, enabling the prediction of battery health and reducing prediction complexity.
Lithium-ion batteries especially with silicon-based anodes, exhibit high energy density but experience huge volume changes during charge and discharge. Research shows that multiple types of lithium-ion batteries un-dergo stress rise during the discharge process, which seems to contradict the sense that the battery volume ought to be reduced and the stress should decrease. To insight into this phenomenon, this work combines experimental measurements and an improved single-particle model to analyse the stress rise in detail. The results demonstrate that the large volume change of the anode material during (de)lithiation and the severe limitation of the solid phase diffusion of lithium-ion in the active particles lead to an uneven concentration distribution and cause anomalous stress changes. Accordingly, the diffusion coefficient of the anode material has a critical influence. Furthermore, the rupture of particles during cycling contributes to the increase in lithium-ion diffusion channels and the diffusion coefficient, making the stress characteristics show regular changes, which can be used as a means to predict the health state of the battery and reduce the complexity of prediction.

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