Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells

Fermín-Cueto, Paula; McTurk, Euan; Allerhand, Michael; Medina-López, Encarni; Anjos, Miguel F.; Sylvester, Joel; Reis, Gonçalo dos

doi:10.1016/j.egyai.2020.100006

Cited by 145 publications

(96 citation statements)

References 21 publications

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“…The model prediction results in 0.99 RMSE without considering the last data point, which can be regarded as unwanted electrochemical phenomena or an indication of battery failure. It clearly shows the dependency on the increased resistance of the battery termed as the capacity degradation knee point (Fermín-cueto et al, 2020). As only the cycling operating conditions are considered for the parameterization of the SeM, the model response could not capture the sudden degradation toward the EoL.…”

Section: Model Comparison For Dynamic Wltcmentioning

confidence: 99%

Battery lifetime prediction and performance assessment of different modeling approaches

et al. 2021

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Lithium-ion battery technologies have conquered the current energy storage market as the most preferred choice thanks to their development in a longer lifetime. However, choosing the most suitable battery aging modeling methodology based on investigated lifetime characterization is still a challenge. In this work, a comprehensive aging dataset of nickel-manganese-cobalt oxide (NMC) cell is used to develop and/or train different capacity fade models to compare output responses. The assessment is conducted for semi-empirical modeling (SeM) approach against a machine learning model and an artificial neural network model. Among all, the nonlinear autoregressive network (NARXnet) can predict the capacity degradation most precisely minimizing the computational effort as well. This research work signifies the importance of lifetime methodological choice and model performance in understanding the complex and nonlinear Liion battery aging behavior.

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Section: Model Comparison For Dynamic Wltcmentioning

confidence: 99%

Battery lifetime prediction and performance assessment of different modeling approaches

et al. 2021

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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

100

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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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“…For online applications, an early detection of an increasing capacity reduction and lithium plating is of great importance for the reliability and safety of LIBs. Many publications like [38] have therefore dealt with the prediction of the sudden reduction in capacity. As a further outcome of this study, the correlation found could be used to identify an advanced LAM An online and thus the risk of lithium plating and the resulting rapid loss of capacity at an early stage.…”

Section: Discussionmentioning

confidence: 99%

Determination of the Distribution of Relaxation Times by Means of Pulse Evaluation for Offline and Online Diagnosis of Lithium-Ion Batteries

Goldammer

Kowal

2021

Batteries

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The distribution of relaxation times (DRT) analysis of impedance spectra is a proven method to determine the number of occurring polarization processes in lithium-ion batteries (LIBs), their polarization contributions and characteristic time constants. Direct measurement of a spectrum by means of electrochemical impedance spectroscopy (EIS), however, suffers from a high expenditure of time for low-frequency impedances and a lack of general availability in most online applications. In this study, a method is presented to derive the DRT by evaluating the relaxation voltage after a current pulse. The method was experimentally validated using both EIS and the proposed pulse evaluation to determine the DRT of automotive pouch-cells and an aging study was carried out. The DRT derived from time domain data provided improved resolution of processes with large time constants and therefore enabled changes in low-frequency impedance and the correlated degradation mechanisms to be identified. One of the polarization contributions identified could be determined as an indicator for the potential risk of plating. The novel, general approach for batteries was tested with a sampling rate of 10 Hz and only requires relaxation periods. Therefore, the method is applicable in battery management systems and contributes to improving the reliability and safety of LIBs.

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Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells

Cited by 145 publications

References 21 publications

Battery lifetime prediction and performance assessment of different modeling approaches

Battery lifetime prediction and performance assessment of different modeling approaches

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

Determination of the Distribution of Relaxation Times by Means of Pulse Evaluation for Offline and Online Diagnosis of Lithium-Ion Batteries

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