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The parameterization of a physico-chemical model constitutes a critical part in model development. Conclusions about the internal state of a battery can only be drawn if a correct set of material parameters is provided for the material combination under consideration. In this work, parameters to fully parameterize a physico-chemical model for a 7.5 Ah cell produced by Kokam are determined and are compared with existing literature values. The paper presents parameter values and procedures to determine the parameters. Cells were opened under argon atmosphere and the geometrical data were measured. Hg-porosimetry was conducted to determine porosity, particle radius as well as tortuosity of the electrodes and the separator. Conductivity and diffusion constants of the electrolyte as well as the electronic conductivity of the active material were measured detecting the voltage response to a dc current. Physico-chemical models are based on fundamental equations describing migration and diffusion processes as well as intercalation kinetics. They can be used to gain understanding of internal processes of batteries and to optimize material development. Several papers have been published developing physico-chemical simulation models that are based on the work of Newman and Tiedemann 1975, 1 amongst others. 2-6However, one crucial part of physico-chemical models is the model parameterization. Especially if conclusions about the internal state of the battery are drawn, it is of utmost importance to choose the right parameters for the materials under consideration. To the knowledge of the authors no work exists where a simulation model was completely parameterized using the geometric data and the parameters of the materials of one commercial cell in total. In most works dealing with physico-chemical models, values from supplementary literature sources were used; parameters were fitted or even guessed, e.g. [2][3][4][5][6] There are publications focusing on the determination of certain parameters for certain material combinations. Park et al. 7 for example gathered diffusion constants as well as conductivities investigated for different materials used in lithium-ion batteries in literature. Several authors also determined exchange currents for graphite materials. 8-11The problem here is that these parameters are usually not measured with the purpose to parameterize a battery model. This leads to measurement setups and finally to parameters that are not applicable in battery models. The exchange current, for example, is usually not scaled with the active surface area, which makes a transfer to a material with a different surface structure impossible. Another example is the determination of the electronic conductivity of active materials. The conductivity in literature is usually not measured for a whole electrode setup, including the filler, binder and porous structure. This makes it difficult to apply these values in models.Furthermore, there are parameters that have (to the knowledge of the authors) not yet been investigated ...

Lithium-ion batteries can last many years but sometimes exhibit rapid, nonlinear degradation that severely limits battery lifetime. Here, we review prior work on “knees” in lithium-ion battery aging trajectories. We first review definitions for knees and three classes of “internal state trajectories” (termed snowball, hidden, and threshold trajectories) that can cause a knee. We then discuss six knee “pathways”, including lithium plating, electrode saturation, resistance growth, electrolyte and additive depletion, percolation-limited connectivity, and mechanical deformation, some of which have internal state trajectories with signals that are electrochemically undetectable. We also identify key design and usage sensitivities for knees. Finally, we discuss challenges and opportunities for knee modeling and prediction. Our findings illustrate the complexity and subtlety of lithium-ion battery degradation and can aid both academic and industrial efforts to improve battery lifetime.

This paper proposes a testing method that allows the monitoring of the development of volume expansion of lithium-ion batteries. The overall goal is to demonstrate the impact of the volume expansion on battery ageing. The following findings are achieved: First, the characteristic curve shape of the diameter change depended on the state-of-charge and the load direction of the battery. The characteristic curve shape consisted of three areas. Second, the characteristic curve shape of the diameter change changed over ageing. Whereas the state-of-charge dependent geometric alterations were of a reversible nature. An irreversible effect over the lifetime of the cell was observed. Third, an s-shaped course of the diameter change indicated two different ageing effects that led to the diameter change variation. Both reversible and irreversible expansion increased with ageing. Fourth, a direct correlation between the diameter change and the capacity loss of this particular lithium-ion battery was observed. Fifth, computer tomography (CT) measurements showed deformation of the jelly roll and post-mortem analysis showed the formation of a covering layer and the increase in the thickness of the anode. Sixth, reproducibility and temperature stability of the strain gauges were shown. Overall, this paper provides the basis for a stable and reproducible method for volume expansion analysis applied and established by the investigation of a state-of-the-art lithium-ion battery cell. This enables the study of volume expansion and its impact on capacity and cell death.

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In this paper, the origin of the jelly roll deformation in 18650 lithium-ion batteries is examined in more detail by combining volume expansion measurements, accelerated lifetime testing, and CT imaging. Based on the presented research, a theory is developed to determine the cause of the jelly roll deformation at low states of charge (0%–20% SOC). The diameter of the cell is increasing during ageing, which reflects the increase of the internal pressure. Continuously growing cover layers of chemical degradation explain the latter. It is concluded that for releasing internal pressure, the jelly roll starts to deform. This deformation accelerates the ageing processes by electrically isolating active material from the current collector and the formation of fresh SEI. In addition, the CT images are used to determine the time of the deformation. Conclusively, it is shown that the jelly roll deformation is driven by cyclic ageing. Due to the correlation of the mean diameter change and the capacity curve, the deformation of the jelly roll and thus the sudden cell death can be predicted by measuring the mean diameter change of the battery.

Ageing of lithium-ion batteries results in irreversible reduction in performance. Intrinsic variability between cells, caused by manufacturing differences, occurs throughout life and increases with age. Researchers need to know the minimum number of cells they should test to give an accurate representation of population variability, since testing many cells is expensive. In this paper, empirical capacity versus time ageing models were fitted to various degradation datasets for commercially available cells assuming the model parameters could be drawn from a larger population distribution. Using a hierarchical Bayesian approach, we estimated the number of cells required to be tested. Depending on the complexity, ageing models with 1, 2 or 3 parameters respectively required data from at least 9, 11 or 13 cells for a consistent fit. This implies researchers will need to test at least these numbers of cells at each test point in their experiment to capture manufacturing variability.

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