Quantification of aging mechanisms and inhomogeneity in cycled lithium-ion cells by differential voltage analysis

Fath, Johannes Philipp; Dragičević, Daniel; Bittel, Laura; Nuhic, Adnan; Sieg, Johannes; Hahn, Severin; Alsheimer, Lennart; Spier, Bernd; Wetzel, Thomas

doi:10.1016/j.est.2019.100813

Cited by 55 publications

(24 citation statements)

References 47 publications

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“…Although some studies seem to fit the full constant current voltage data [33], most studies favor the use of features of interest (FOI) and only focus on a specific part of the electrochemical response. This could be capacity and resistance evolution [10,[34][35][36][37], curvature [38,39], sections of the voltage response [8,[40][41][42], electrochemical impedance spectroscopy [43,44], variance [5,11], or EVS [33,[45][46][47][48]. The latter has attracted a lot of attention in recent years since the early work on the technique [22, 49,50].…”

Section: Of 20mentioning

confidence: 99%

Big data training data for artificial intelligence-based Li-ion diagnosis and prognosis

Dubarry

Beck

2020

Journal of Power Sources

104

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Accurate lithium battery diagnosis and prognosis is critical to increase penetration of electric vehicles and grid-tied storage systems. They are both complex due to the intricate, nonlinear, and path-dependent nature of battery degradation. Data-driven models are anticipated to play a significant role in the behavioral prediction of dynamical systems such as batteries. However, they are often limited by the amount of training data available. In this work, we generated the first big data comprehensive synthetic datasets to train diagnosis and prognosis algorithms. The proof-of-concept datasets are over three orders of magnitude larger than what is currently available in the literature. With benchmark datasets, results from different studies could be easily equated, and the performance of different algorithms can be compared, enhanced, and analyzed extensively. This will expend critical capabilities of current AI algorithms, tools, and techniques to predict scientific data.

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Section: Of 20mentioning

confidence: 99%

Big data training data for artificial intelligence-based Li-ion diagnosis and prognosis

Dubarry

Beck

2020

Journal of Power Sources

104

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“…The origin of the apparent RDF increase is still unclear. In our approach, it was treated as a kinetic limitation, but, in light of recent literature [62], part of it could also be associated with inhomogeneities in the electrode degradation that could have led to localized plating. This concept is currently under investigation in our laboratory and will be reported in future works.…”

Section: Discussionmentioning

confidence: 99%

Durability and Reliability of EV Batteries Under Electric Utility Grid Operations: Impact of Frequency Regulation Usage on Cell Degradation

2020

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The usage of electric vehicle batteries to assist the main electric grid for the storage of energy provided by intermittent sources should become an essential tool to increase the penetration of green energies. However, this service induces additional usage on the cells and, therefore, could degrade them further. Since degradation is path-dependent, it is of paramount importance to test the impact of all the different grid applications on the batteries. In this work, we tested the additional usage induced by using electric vehicle batteries for frequency regulation at moderate rates during rest or charge and found no detrimental effect after around 2000 cycles on the cells. The main takeaway from our study was that some frequency regulation V2G usage at moderate rates (from C/5 to C/3 as a maximum current) did not accelerate the cell degradation despite close to 15% additional usage. In addition, there was no noticeable difference between performing this ancillary service during rest or charge or at maximum current fluctuations up to C/3 during rest and C/4 during charge. These results are extremely positive for the possible application of V2G/G2V strategies. However, it must be noted that our results hold for these specific cells and the duty cycle tested. More research is necessary to generalize the results. We already verified the results on another batch of commercial cells based on graphite//Nickel Cobalt Aluminum oxide. Due to the lack of differences in the degradation between duty cycles, the benefits of modulating the charge to eliminate the additional usage on the cells could not be verified. Experiments on other chemistries with more aggressive usage will be launched to test the hypothesis further. As for the diagnosis for the degradation, a new approach of coupling IC and DV analysis yielded four indicators for automatic diagnosis. This methodology will allow faster diagnosis and bolsters the value of the FOI approach for BMS implementation. However, our study also showed that under some conditions, in our case the apparent kinetic limitations, some of the FOI may become ineffective with time. Therefore, proper validation using full fits and sensibility analysis to check for the range of efficacy of chosen FOI is still essential. Finally, some cells showcased the 2nd stage of aging and we were not able to predict it from the voltage variations. This suggested that it was not induced by a widespread degradation of the electrodes but was more likely because of localized effects. The cells that did not perform any frequency regulation seemed to be the most affected, but those results need to be moderated by the fact that the differences were probably within what to expect between cells performing the same duty cycles.

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“…Thickness change measurement gives precise information about the SOC of the battery, conversely to voltage, which is flat for almost the whole SOC window, and it is highly affected by the applied current. Moreover, battery ageing can be estimated by thickness change measurements detecting the points of phase changes, which shift with ageing [41][42][43].…”

Section: Swelling Measurementsmentioning

confidence: 99%

Experimental Characterization of Lithium-Ion Cell Strain Using Laser Sensors

Clerici¹,

Mocera²,

Somà³

2021

Energies

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The characterization of thickness change during operation of LFP/Graphite prismatic batteries is presented in this work. In this regard, current rate dependence, hysteresis behaviour between charge and discharge and correlation with phase changes are deepened. Experimental tests are carried out with a battery testing equipment correlated with optical laser sensors to evaluate swelling. Furthermore, thickness change is computed analytically with a mathematical model based on lattice parameters of the crystal structures of active materials. The results of the model are validated with experimental data. Thickness change is able to capture variations of the internal structure of the battery, referred to as phase change, characteristic of a certain state of charge. Furthermore, phase change shift is a characteristic of battery ageing. Being able to capture these properties with sensors mounted on the external surface the cell is a key feature for improving state of charge and state of health estimation in battery management system.

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Quantification of aging mechanisms and inhomogeneity in cycled lithium-ion cells by differential voltage analysis

Cited by 55 publications

References 47 publications

Big data training data for artificial intelligence-based Li-ion diagnosis and prognosis

Big data training data for artificial intelligence-based Li-ion diagnosis and prognosis

Durability and Reliability of EV Batteries Under Electric Utility Grid Operations: Impact of Frequency Regulation Usage on Cell Degradation

Experimental Characterization of Lithium-Ion Cell Strain Using Laser Sensors

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