This paper describes and analyzes the propagation of uncertainties from the lithium-ion battery electrode manufacturing process to the structural electrode parameters and the resulting varying electrochemical performance. It uses a multi-level model approach, consisting of a process chain simulation and a battery cell simulation. The approach enables to analyze the influence of tolerances in the manufacturing process on the process parameters and to study the process-structure-property relationship. The impact of uncertainties and their propagation and effect is illustrated by a case study with four plausible manufacturing scenarios. The results of the case study reveal that uncertainties in the coating process lead to high deviations in the thickness and mass loading from nominal values. In contrast, uncertainties in the calendering process lead to broad distributions of porosity. Deviations of the thickness and mass loading have the highest impact on the performance. The energy density is less sensitive against porosity and tortuosity as the performance is limited by theoretical capacity. The latter is impacted only by mass loading. Furthermore, it is shown that the shape of the distribution of the electrochemical performance due to parameter variation aids to identify, whether the mean manufacturing parameters are close to an overall performance optimum.
The application of batteries in electric vehicles and stationary energy-storage systems is widely seen as a promising enabler for a sustainable mobility and for the energy sector. Although significant improvements have been achieved in the last decade in terms of higher battery performance and lower production costs, there remains high potential to be tapped, especially along the battery production chain. However, the battery production process is highly complex due to numerous process–structure and structure–performance relationships along the process chain, many of which are not yet fully understood. In order to move away from expensive trial-and-error operations of production lines, a methodology is needed to provide knowledge-based decision support to improve the quality and throughput of battery production. In the present work, a framework is presented that combines a process chain model and a battery cell model to quantitatively predict the impact of processes on the final battery cell performance. The framework enables coupling of diverse mechanistic models for the individual processes and the battery cell in a generic container platform, ultimately providing a digital representation of a battery electrode and cell production line that allows optimal production settings to be identified in silico. The framework can be implemented as part of a cyber-physical production system to provide decision support and ultimately control of the production line, thus increasing the efficiency of the entire battery cell production process.
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.
Manufacturing high-quality lithium-ion batteries requires a detailed understanding of the complex correlations between the electrode microstructure and the kinetic processes. Sophisticated microstructure design is of interest to enable optimal ionic and electrical transport to minimize uncertainties and capacity fade. One important feature of the microstructure is the carbon black-binder matrix (CBM) forming a network-like structure. The CBM is often solely evaluated by its electrical bulk conductivity of the electrode. In this work, a hybrid model approach is established and applied to gain mechanistic understanding of the influence of different electrical network structures on uncertainty, degradation, and failure of battery cells. Artificial network structures are generated based on degree distributions, that is, normal distributions or power-law distributions. Degradation is modeled by edge removal, representing the mechanical decomposition of the CBM during battery utilization. Simulation results show that the electrical bulk conductivity is not sufficient to rate the quality of the electrical network, and also other properties such as the connectivity and the number of transit paths need to be taken into account. It can be seen that normally distributed connectivity in general yields low uncertainties and is robust against edge removal. In contrast, power-law distributions, that is, scale-free-like networks, possess few critical edges that yield significant uncertainty. The presented model approach allows us to study network structures in-depth and to identify beneficial structural network properties.
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