A Conceptual Framework towards Data-Driven Models in Electrode Production of Lithium-Ion Battery Cells

Haghi, Sajedeh; Töpper, Hans-Christoph; Günter, Florian J.; Reinhart, Gunther

doi:10.1016/j.procir.2021.11.194

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Given the intricate nature of the process chain and its numerous interdependencies, the causality between manufacturing parameters and the resulting performance of both the electrode and the final cell remains largely unknown. A comprehensive understanding of process steps, the parameters, their interactions, and aggregated effects on the intermediate and final product quality provides the foundation for event‐driven decisions and pursuing holistic optimization strategies [3] . Within battery cell production, electrode manufacturing is of particular importance, as the majority of cell properties are established in this phase [4] …”

Section: Introductionmentioning

confidence: 99%

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

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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: Introductionmentioning

confidence: 99%

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

“…[ 10 ] This serves as the foundation for implementing smart production, enabling efficient and well‐informed decision‐making at every stage of the production process chain. [ 11 ] Digitalization is the prerequisite for exploiting the full potential of such approaches. Ayerbe et al reviewed the current status of digitalization in battery production and highlighted the opportunities and challenges that need to be addressed.…”

Section: Introductionmentioning

confidence: 99%

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

Haghi,

Leeb,

Molzberger

et al. 2023

Energy Tech

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Electrode manufacturing is considered the core of lithium‐ion battery cell production, with irreversible impacts on the electrochemical performance of the battery cell. The process chain is extensively complex, with a high number of interrelated parameters. The characterization of intermediate products in electrode manufacturing and the analysis of the correlations between process parameters and product properties can be considered a rigorous starting point to deepen process understanding and accelerate process optimization. Based on a holistic evaluation approach and a market analysis, this article provides a comprehensive overview of possible measuring instruments for intermediate products in electrode manufacturing, including the investment costs and inline/offline measurement strategy. The results can be used as a guideline for possible measuring instruments in electrode manufacturing, providing the foundation for in‐depth process analysis. The findings demonstrate the current possibilities and highlight the need for further technological advancement in the field of metrology and digitalization.

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“…The idea was introduced briefly as a tailored digitalization concept in the previous publication. [ 18 ] The tailored digitalization can be used as a stepping stone to implement data‐driven solutions and acquire a comprehensive understanding of the process chain. A two‐step, literature‐based, and expert‐based methodological approach is adopted to prioritize the parameters to be digitalized.…”

Section: Introductionmentioning

confidence: 99%

Tailored Digitalization in Electrode Manufacturing: The Backbone of Smart Lithium‐Ion Battery Cell Production

et al. 2022

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Following the introduction of Industry 4.0 and the development of information technologies, manufacturing companies have been undergoing a profound transformation. This transformation envisions the realization of the smart factory as a fully connected, flexible production system regulated by data. Digitalization and collection of the critical parameters are the vital prerequisites for this vision. Electrode manufacturing is regarded as the core phase in the battery cell production, having most of the properties determining the electrochemical performance of the battery cell established in this phase. There are a high number of parameters involved in electrode manufacturing. The digitalization of these parameters is associated with a considerable amount of effort and costs. Introducing a tailored digitalization concept provides the first step toward smart battery cell production. The tailored digitalization concept is based on the importance of the parameters from the quality management perspective and their complexity with regard to digitalization. The prioritization of parameters enables a successive quality‐oriented digitalization strategy. The concept is built on a two‐step literature‐based and expert‐based approach. The results include a comprehensive list of parameters and their prioritization for digitalization and integration in a tracking and tracing concept.

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A Conceptual Framework towards Data-Driven Models in Electrode Production of Lithium-Ion Battery Cells

Cited by 5 publications

References 3 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

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

Tailored Digitalization in Electrode Manufacturing: The Backbone of Smart Lithium‐Ion Battery Cell Production

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