A rapid estimation and sensitivity analysis of parameters describing the behavior of commercial Li-ion batteries including thermal analysis

Vazquez‐Arenas, Jorge; Gimenez, Leonardo E.; Fowler, Michael; Han, Taeyoung; Chen, Shih-Ken

doi:10.1016/j.enconman.2014.06.076

Cited by 80 publications

(39 citation statements)

References 65 publications

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“…Therefore, we assign an average conductivity of the AM and carbon black to the solid phase. The resulting effective conductivity of the entire electrode was 0.0914 (±0.0025) S m −1 , which was in the range commonly reported in the modeling of Li‐Ion batteries . An extension of the model considering the contribution of the CBD separately is planned for future work.…”

Section: Methodsmentioning

confidence: 58%

Manufacturing Process for Improved Ultra‐Thick Cathodes in High‐Energy Lithium‐Ion Batteries

et al. 2019

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The effect of the mixing and drying process on the microstructure of ultra‐thick NCM 622 cathodes (50 mg cm−2, 8 mAh cm−2) and its implication for battery performance is investigated. It is observed that the shear force during the mixing process significantly influences the resulting microstructure with regard to binder migration during the drying process. Based on the information extracted from scanning electron microscopy–energy dispersive X‐ray spectroscopy (SEM–EDX) cross sections, the carbon binder domain (CBD) is distributed in the pore space of virtual electrodes generated by a stochastic 3D microstructure model. Simulations predict a CBD configuration that leads to optimal performance of the electrode. Furthermore, it is shown that a low drying rate has a beneficial influence toward the rate capability of the ultra‐thick cathodes. The specific energy of an ultra‐thick cathode is 18% higher compared with a cathode prepared according to the state of the art. With an improved process in a pilot scale, the advantage can be kept up to current densities of at least 3 mA cm−².

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Section: Methodsmentioning

confidence: 58%

Manufacturing Process for Improved Ultra‐Thick Cathodes in High‐Energy Lithium‐Ion Batteries

et al. 2019

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“…And all parameters change simultaneously. The global sensitivity analysis for the electrochemical battery model parameter is presented in Vazquez‐Arenas et al But it is only utilized to guide battery parameter identification. A detailed analytical method for local sensitivity and global sensitivity of the electrochemical model is proposed in Saltelli et al In practice, the ECM is more commonly utilized than the electrochemical battery model.…”

Section: Introductionmentioning

confidence: 99%

The equivalent circuit battery model parameter sensitivity analysis for lithium‐ion batteries by Monte Carlo simulation

Guo

Zhou

et al. 2019

Int J Energy Res

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To enhance the estimation accuracy of battery's state of charge, it is imperative to estimate the battery model parameter. To reduce the calculation efforts, the number of the battery model parameter to be estimated should be less while ensuring the state of charge estimation accuracy. Especially in engineering applications, the calculating ability is usually limited. So, it needs to choose the critical battery model parameter to be estimated. This paper's contributions are as follows: The global sensitivity analysis of the battery model parameter is achieved by the Monte Carlo simulation method. The results show that the open circuit voltage and the ohmic resistance are the high sensitivity parameters. Guided by the results of parameter sensitivity analysis, a dual extended Kalman filters method is utilized to achieve online battery model parameter estimation. The experiments prove that the state of charge estimation accuracy is improved by the online parameter estimation. Estimating high sensitivity parameters can reduce running time. And the SOC estimation accuracy can be guaranteed.

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“…In addition to the usage of UQ to investigate the UP between product parameters and cell performance, UQ also can provide a sensitivity analysis (SA) for parameter estimation and parameterization of physics‐based models . Especially for parameterization, the number of uncertain parameters is large compared with UP investigations.…”

Section: Introductionmentioning

confidence: 99%

Model‐Based Uncertainty Quantification for the Product Properties of Lithium‐Ion Batteries

et al. 2019

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A model‐based uncertainty quantification (UQ) approach is applied to the manufacturing process of lithium‐ion batteries (LIB). Cell‐to‐cell deviations and the influence of sub‐cell level variations in the material and electrode properties of the cell performance are investigated experimentally and via modeling. The electrochemical battery model of the Doyle–Newman type is extended to cover the effect of sub‐cell deviation of product properties of the LIB. The applied model is parameterized and validated using a stacked pouch cell containing Li(Ni1/3Co1/3Mn1/3)O2 (NMC) and graphite (LixC6). It is integrated into a sampling‐based UQ framework. A nested point estimate method (PEM) is applied to a large number of independent normal distributed parameters. The simulations follow two consecutive nonideal manufacturing process steps: coating and calendering. The nested PEM provides a global sensitivity analysis that shows a change in sensitivity of the investigated parameters depending on the applied C‐rate. Furthermore, the sub‐cell level deviation of parameters in heterogeneous electrodes provokes a nonuniform current distribution in the cell. This alters the variance of the discharge capacity distribution. Therefore, sub‐cell deviation has to be considered to quantify process uncertainties. The applied method is feasible and highly efficient for this purpose.

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A rapid estimation and sensitivity analysis of parameters describing the behavior of commercial Li-ion batteries including thermal analysis

Cited by 80 publications

References 65 publications

Manufacturing Process for Improved Ultra‐Thick Cathodes in High‐Energy Lithium‐Ion Batteries

Manufacturing Process for Improved Ultra‐Thick Cathodes in High‐Energy Lithium‐Ion Batteries

The equivalent circuit battery model parameter sensitivity analysis for lithium‐ion batteries by Monte Carlo simulation

Model‐Based Uncertainty Quantification for the Product Properties of Lithium‐Ion Batteries

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