Experimental investigation of parametric cell-to-cell variation and correlation based on 1100 commercial lithium-ion cells

Rumpf, Katharina; Naumann, Maik; Jossen, Andreas

doi:10.1016/j.est.2017.09.010

Cited by 166 publications

(74 citation statements)

References 40 publications

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“…These values were small compared to previous studies and in the literature [8,16,18,22]. The deviations in loading ratio values were small, below 2.5%, and no offset variations were measured (Figure 2b).…”

Section: Cell-to-cell Variationsmentioning

confidence: 64%

“…The extent of initial cell-to-cell variations within a batch of cells, although not reported often enough, has been the topic of multiple studies in the literature [4][5][6][8][9][10][11][12][13]. Researchers have reported statistical studies on 12 [14], 50 [5,15,16], 100 [17][18][19][20], and 1000 [8][9][10]21] cells.…”

Section: Introductionmentioning

confidence: 99%

“…Researchers have reported statistical studies on 12 [14], 50 [5,15,16], 100 [17][18][19][20], and 1000 [8][9][10]21] cells. All of these studies determined that cells within a batch are different and that these differences need to be quantified and taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

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Intrinsic Variability in the Degradation of a Batch of Commercial 18650 Lithium-Ion Cells

2018

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Abstract:The use of lithium batteries for power and energy-hungry applications has risen drastically in recent years. For such applications, it is necessary to connect the batteries in large assemblies of cells in series and parallel. With a large number of cells operating together, it is necessary to understand their intrinsic variabilities, not only at the initial stage but also upon aging. In this study, we studied a batch of commercial cells to address their initial cell-to-cell variations and also the variations induced by cycling. To do so, we not only tracked several metrics associated with cell performance, the maximum capacity, the resistance, and the rate capability but also the degradation mechanism via a non-invasive quantification of the loss of lithium inventory (LLI), the loss of active material (LAM) and the kinetic degradation on both electrodes. We found that, even with small initial cell-to-cell variations, significant variations will be observed upon aging because the cells degrade at a different pace. We also observed that these variations were not correlated with the initial variations.

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Section: Cell-to-cell Variationsmentioning

confidence: 64%

Section: Introductionmentioning

confidence: 99%

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Intrinsic Variability in the Degradation of a Batch of Commercial 18650 Lithium-Ion Cells

2018

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“…In order to achieve a stable configuration of a battery module or pack, a screening process is needed to ensure high homogeneity of the cells therein 11,22,23 . Several screening processes have been previously proposed 12,20,[24][25][26] . Multiple parameters, including capacity, mass, electrochemical impedance, direct current resistance and voltage, were employed to index the cell-to-cell variation.…”

mentioning

confidence: 99%

“…Multiple parameters, including capacity, mass, electrochemical impedance, direct current resistance and voltage, were employed to index the cell-to-cell variation. Rumpf et al 12 conducted a statistical analysis of parametric cell-to-cell variation of 15 different cell parameters and the correlations between parameters based on 1100 examined cells, and found it necessary to characterize variation in ohmic impedance when determining cell-to-cell variation. Dubarry et al 27,28 have conducted both statistical and electrochemical analyses to characterize cell-to-cell variations, and found that the cell capacity, resistance and rate capability can be regarded as three independent parameters for cell variations evaluation.…”

mentioning

confidence: 99%

A Facile Approach to High Precision Detection of Cell-to-Cell Variation for Li-ion Batteries

Xie

Ren

Wang

et al. 2020

Sci Rep

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over the past decade, it has been repeatedly demonstrated that homogeneity in electrochemical performance of lithium-ion cells plays a major role in determining the life and safety of lithium-ion battery modules or packs. Generally, the homogeneity of a battery pack is evaluated by characterizing the cells individually in terms of capacity, mass, impedance. particularly, high quality electrochemical data heavily relies on the availability of high precision current source to minimize the discrepancy induced by the channel-to-channel variation. Here, a facile and precise measurement method is reported for screening cell-to-cell variations, in which voltage is the only indicator parameter independent of high precision current source. in detail, by connecting the cells in series (ciS), the measurement error of electrochemical data caused by stability and discrepancy of current sources among different charge/discharge equipment can be effectively avoided. The findings of this work showed that the cell-to-cell variations can be simply and sensitively detected with CiS configuration. for example, the relative standard deviation, which is the evaluation criterion of battery homogeneity, was 2.14% based on CiS while it was 0.43% based on individual measurements. The simple and precise ciS measurement is promising for evaluation of cell quality or module integration quality. in addition, this work can also provide a solid foundation for the development of detection algorithms for battery management systems to rapidly monitor battery homogeneity.Lithium ion batteries (LIBs) have to be integrated into modules and packs for large-scale applications such as electric vehicles (EVs) and stationary energy storage systems 1-7 . However, a reliable and long-lasting power system is determined mostly by the cell homogeneity rather than the performance of individual cells 8,9 . The cell-to-cell variations in terms of capacity and impedance will subject individual cells to different levels of state of charge (SOC), current, temperature and aging, which in turn would accelerate the degradation of electrochemical performance or even lead to safety performance of the power system 2,10-13 . For instance, a weak cell with a smaller capacity than the average in a series-connected module will be repeatedly overcharged (over-discharged) during charging (discharging) process if the charge/discharge cutoff condition is determined according to the total voltage 14 . The weaker cell will then have faster decay than others and will be susceptible to failure under harsh conditions. The available energy of the module will also be limited by the weakest cell 15 . Therefore, there is an urgent need to understand and minimize the cell-to-cell variations in a battery module.There are different factors leading to variations among cells 11 , including manufacturing factors (manufacturing tolerances, quality control and process design), and environmental factors such as temperature gradient within the battery pack 16 . Rumpf et al. 12 has evaluated the cel...

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Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries

et al. 2021

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performance and cost are not satisfactory while the energy density of conventional LIBs have almost reached the theoretical maximum. To further advance current LIBs, numerous efforts have been devoted to the exploration of new electrode and electrolyte materials. [3] The historical material research has heavily relied on either "trial-and-error" processes or serendipity, both of which require the vast numbers of tedious experiments (Figure 1a). Such intuition-based approaches are often time-consuming and inefficient, which cannot avoid the consumption of many manpower and material resources. In the past 50 years, computational chemistry, such as firstprinciples (FP) calculations, [4,5] quantum mechanics, [6] molecular dynamics (MD) [7] and Monte Carlo techniques, [8] has become a mature approach to complement and aid experimental studies for predicting and designing new materials. With the rapid development of high-performance computations, density functional theory (DFT) has been widely applied to high-throughput property prediction, which is conducive to the development of materials databases, such as Inorganic Crystal Structure Database (ICSD), [9] Cambridge Structural Database, [10] the Materials Project [11] database, AFLOWLIB consortium, [12] Open Quantum Materials Database, [13] Harvard Clean Energy Project, [14] Electronic Structure Project, [15] MaterialGo, [16] and so on. However, Lithium-ion batteries (LIBs) are vital energy-storage devices in modern society. However, the performance and cost are still not satisfactory in terms of energy density, power density, cycle life, safety, etc. To further improve the performance of batteries, traditional "trial-and-error" processes require a vast number of tedious experiments. Computational chemistry and artificial intelligence (AI) can significantly accelerate the research and development of novel battery systems. Herein, a heterogeneous category of AI technology for predicting and discovering battery materials and estimating the state of the battery system is reviewed. Successful examples, the challenges of deploying AI in real-world scenarios, and an integrated framework are analyzed and outlined. The state-of-the-art research about the applications of ML in the property prediction and battery discovery, including electrolyte and electrode materials, are further summarized. Meanwhile, the prediction of battery states is also provided. Finally, various existing challenges and the framework to tackle the challenges on the further development of machine learning for rechargeable LIBs are proposed.

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Experimental investigation of parametric cell-to-cell variation and correlation based on 1100 commercial lithium-ion cells

Cited by 166 publications

References 40 publications

Intrinsic Variability in the Degradation of a Batch of Commercial 18650 Lithium-Ion Cells

Intrinsic Variability in the Degradation of a Batch of Commercial 18650 Lithium-Ion Cells

A Facile Approach to High Precision Detection of Cell-to-Cell Variation for Li-ion Batteries

Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries

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