Safety modeling and protection for lithium-ion batteries based on artificial neural networks method under mechanical abuse

Li, Yiding; Wang, Wenwei; Lin, Cheng; Fenghao, Zuo

doi:10.1007/s11431-021-1826-2

Cited by 6 publications

(2 citation statements)

References 40 publications

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“…In this work, the feature selection techniques include genetic algorithm (GA), [23] backward elimination (BE), [24] and forward selection (FS). [25] The modeling algorithms include support vector regression (SVR), [26][27] artificial neural network (ANN), [28][29] and partial least squares (PLS). [30] Therefore, GA, BE, and FS nested SVR, ANN, and PLS were adopted for feature selection, respectively.…”

Section: Feature Selectionmentioning

confidence: 99%

Accelerated Discovery of Ternary Gold Alloy Materials with Low Resistivity via an Interpretable Machine Learning Strategy

Wang

Zhou

et al. 2022

Chemistry An Asian Journal

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New ternary gold alloys with low resistivities (1) were screened out via an interpretable machine learning strategy by using the support vector regression (SVR) model integrated with SHAP analysis. The correlation coefficient (R) and the root mean square error (RMSE) of test set were 0.876 and 0.302, respectively, indicating the strong generalization ability of the model. The average 1 of top 10 candidates was 1.22 × 10 À 7 Ω m, which was 41% lower than the known minimum of 2.08 × 10 À 7 Ω m. The outputs of SVR model were analyzed with the critical SHAP values including first ionization energy of C-site (584 kJ • mol À 1 ), electronegativity of Csite (1.72) and the second ionization energy of B-site (1135 kJ • mol À 1 ), respectively. Moreover, an online web server was developed to share the model at http://materials-datamining.com/onlineservers/wxdaualloy.

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Section: Feature Selectionmentioning

confidence: 99%

Accelerated Discovery of Ternary Gold Alloy Materials with Low Resistivity via an Interpretable Machine Learning Strategy

Wang

Zhou

et al. 2022

Chemistry An Asian Journal

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“…The electrochemical model consists of a set of nonlinear differential equations with numerous parameters, and its use in LIBs products is constrained by the high cost of solving these equations and the difficulty of identifying their parameters. Contrarily, the empirical model requires no formal mechanism description and is obtained using methods for mining vast datasets, such as support vector machines and artificial neural networks [7][8][9][10]. The prediction accuracy of an empirical model is heavily dependent on the training algorithm and training data and it lacks interpretability and needs a lot of data training based on existing datasets before deployment.…”

Section: Introductionmentioning

confidence: 99%

Effect of Sample Interval on the Parameter Identification Results of RC Equivalent Circuit Models of Li-ion Battery: An Investigation Based on HPPC Test Data

Zhang

Deng²,

Zong³

et al. 2022

Batteries

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The validity of the equivalent circuit model (ECM), which is crucial for the development of lithium-ion batteries (LIBs) and state evaluation, is primarily dependent on the precision of the findings of parameter identification. In this study, the commonly used first-order RC (1-RC) circuit and second-order RC (2-RC) circuit models were selected for parameter identification. A time series of voltage with different sample intervals were used for function fitting based on the least square method, which were extracted from the hybrid pulse power characteristic (HPPC) test data of a commercial square punch LIB, and the sample intervals were set to be 0.1 s, 0.2 s, 0.5 s, and 1.0 s to evaluate the effect of sample interval on the parameter identification results. When the sample interval is more than 0.5 s, the results reveal that the 2-RC circuit model’s goodness of fit marginally declines, and for some data scenarios, the bias between the fitted terminal voltage curve and test curve increases obviously. With all of the sample intervals under consideration, the 1-RC circuit model’s imitative effect is satisfactory. This work demonstrates that the sample interval of data samples, in addition to the method itself, affects the accuracy and robustness of parameter identification, with the 1-RC circuit model showing larger advantages under low sample frequency compared to the 2-RC circuit model.

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Exposure Assessment Study on Lithium-Ion Battery Fire in Explosion Test Room in Battery Testing Facility

Jo,

Kim,

Kim

et al. 2024

Safety and Health at Work

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Safety modeling and protection for lithium-ion batteries based on artificial neural networks method under mechanical abuse

Cited by 6 publications

References 40 publications

Accelerated Discovery of Ternary Gold Alloy Materials with Low Resistivity via an Interpretable Machine Learning Strategy

Accelerated Discovery of Ternary Gold Alloy Materials with Low Resistivity via an Interpretable Machine Learning Strategy

Effect of Sample Interval on the Parameter Identification Results of RC Equivalent Circuit Models of Li-ion Battery: An Investigation Based on HPPC Test Data

Exposure Assessment Study on Lithium-Ion Battery Fire in Explosion Test Room in Battery Testing Facility

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