Measuring Instruments for Characterization of Intermediate Products in Electrode Manufacturing of Lithium‐Ion Batteries

Haghi, Sajedeh; Leeb, Matthias; Molzberger, Annika; Daub, Rüdiger

doi:10.1002/ente.202300364

Cited by 4 publications

(6 citation statements)

References 108 publications

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“…To ensure an exhaustive cross‐process analysis with independent input variables, the electrode porosity after calendering and the mass loading were chosen as input variables, eliminating the third dependent variable, the thickness. Due to the complexity involved in the digitalization of adhesion, with only offline characterization methods available, [13] and the difficulty in defining appropriate levels for the DoE, the temperature of the second dryer was chosen as an influential factor impacting adhesion [21,22] . Given the linear correlation between the wet film thickness and the mass loading, only the latter was considered a relevant product parameter representing the coating and drying process.…”

Section: Systematic Design Of Experimentsmentioning

confidence: 99%

“…From an operational perspective, various factors such as formulation, [53] mass loading, [54] drying conditions [21] and porosity [55] can impact the adhesion. According to the current state of the art, [13] adhesion can be only measured offline, hence it is also evaluated as highly complex concerning the digitalization (see Table 1). This limitation emphasizes the need for predictive solutions.…”

Section: Prediction Of Product Properties Using Data‐driven Modelsmentioning

confidence: 99%

“…The results were based on the assessment of two dimensions: the importance of the parameter, evaluated using the number of interdependencies and their impact on the intermediate or final product quality, and the degree of complexity associated with the digitalization of the parameter. In a subsequent work, [13] an overview of possible acquisition methods for the characterization of intermediate products in electrode manufacturing was presented. The intermediate products serve as quality indicators for both the set process parameters and the final product quality.…”

Section: Introductionmentioning

confidence: 99%

“…The intermediate products serve as quality indicators for both the set process parameters and the final product quality. Building upon the insights provided in the previous publications, [6,12,13] this article aims to proceed with the steps toward data generation and the development of data‐driven models.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Efficient Analysis of Interdependencies in Electrode Manufacturing Through Joint Application of Design of Experiments and Explainable Machine Learning

Haghi,

Keilhofer,

Schwarz

et al. 2023

Batteries & Supercaps

Self Cite

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Battery cell production is a key contributor to achieving a net‐zero future. A comprehensive understanding of the various process steps and their interdependencies is essential for speeding up the commercialization of newly developed materials and optimizing production processes. While several approaches have been employed to analyze and understand the complexity of the process chain and its interdependencies – ranging from expert‐ and simulation‐based to data‐driven – the latter holds significant potential for real‐time application. This is particularly relevant for inline process control and optimization. To streamline the development and implementation of data‐driven models, a holistic framework that encompasses all necessary steps – from identification of relevant parameters and generation of data to development of models – is imperative. This article aims to address this objective by presenting a comprehensive and systematic methodology, demonstrated for efficient cross‐process analysis in electrode manufacturing. Through the combined utilization of design of experiments methods, data‐driven models, and explainable machine learning methods, the interdependencies between production parameters and the physical, mechanical, and electrochemical characteristics of the electrodes were uncovered. These actionable insights are essential for enabling informed decision‐making, facilitating the selection of appropriate process parameters, and ultimately optimizing the production process.

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Section: Systematic Design Of Experimentsmentioning

confidence: 99%

Section: Prediction Of Product Properties Using Data‐driven Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Analysis of Interdependencies in Electrode Manufacturing Through Joint Application of Design of Experiments and Explainable Machine Learning

Haghi,

Keilhofer,

Schwarz

et al. 2023

Batteries & Supercaps

Self Cite

View full text Add to dashboard Cite

show abstract

“…The adhesion, as a crucial mechanical property affecting not only the electrode's processability during the production but also the cell's cyclability and durability, [36] was defined as a target variable. According to the current state of the art, [37] this quality-relevant parameter can only be assessed using offline methods. This limitation highlights the necessity of exploring the possibilities to model this target variable using parameters that can be seamlessly collected inline, ensuring efficient and timely quality control and optimization.…”

Section: Prediction Of Mechanical and Electrochemical Properties Usin...mentioning

confidence: 99%

Interdependencies in Electrode Manufacturing: A Comprehensive Study Based on Design Augmentation and Explainable Machine Learning

Haghi,

Chen,

Molzberger

et al. 2024

Batteries & Supercaps

Self Cite

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Electrode manufacturing, as the core of battery cell production, is a complex process chain with a large number of interrelated parameters. An in‐depth understanding of the processes, their relevant parameters, and the resulting effects on intermediate and final product properties can accelerate the transition toward quality‐oriented, efficient battery cell production. Given the complexity of the process chain, data‐driven models have emerged as promising solutions for analyzing the existing interdependencies. The accuracy and effectiveness of these models significantly depend on the quality and comprehensiveness of the underlying data. With a low‐quality dataset, there is an increased risk of drawing inaccurate conclusions or generating misleading results. This article aimed to demonstrate a use case for the evaluation and enhancement of historical datasets to provide a statistically robust foundation for the development of machine learning models. The study was based on pilot‐scale anode manufacturing and covered variations in the coating, drying, and calendering processes. The key intermediate product and process parameters were used to predict two primary target variables: adhesion strength and discharge capacity at different C‑rates. To gain a better understanding of the analyzed interdependencies, explainable machine learning methods were adopted.

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Integration of an Electrode‐Sheet‐Based Traceability System into the Manufacturing Process of Lithium‐Ion Battery Cells

Sommer,

Bazlen,

Tran

et al. 2024

Energy Tech

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The production of lithium‐ion batteries consists of a long and complex process chain. The individual process steps influence each other, resulting in unknown cause‐and‐effect interactions. As a result, production fluctuations cause variations in the final battery cell properties, which, coupled with high material costs, lead to costly scrap. Sensor and machine data provide important information during production about the manufacturing and condition of intermediate products. However, a challenge exists, in that these valuable production data cannot be precisely assigned to the corresponding intermediate products, and consequently, the cause of the production fluctuation cannot be explained. In this article, a method for tracking and tracing single‐electrode sections from coating to formation is described. Using the example of areal mass loading, it shows that all production data for the electrode production can be assigned automatically to the individual electrode sheets. In addition, all production data for cell assembly and cell finalization can be assigned to each cell stack and thus to the electrodes used. The monitored electrode areal mass loading of individual electrode sheets enables the identification of deviating electrode sections and a cell‐specific calculation of the final capacity from manufactured battery cells.

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Measuring Instruments for Characterization of Intermediate Products in Electrode Manufacturing of Lithium‐Ion Batteries

Cited by 4 publications

References 108 publications

Efficient Analysis of Interdependencies in Electrode Manufacturing Through Joint Application of Design of Experiments and Explainable Machine Learning

Efficient Analysis of Interdependencies in Electrode Manufacturing Through Joint Application of Design of Experiments and Explainable Machine Learning

Interdependencies in Electrode Manufacturing: A Comprehensive Study Based on Design Augmentation and Explainable Machine Learning

Integration of an Electrode‐Sheet‐Based Traceability System into the Manufacturing Process of Lithium‐Ion Battery Cells

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