Battery Cell Production Research Articles

Intelligente Anlaufsteuerung für die Batteriezellenproduktion

Abstract The ramp-up of production lines as a result of changing product variants or production processes puts battery cell manufacturers under enormous quality and cost pressure due to high reject rates. A control system based on artificial intelligence methods can help to speed up the start-up processes. The InTeAn research project developed a procedure for developing a process control system to reduce ramp-up times and costs in battery cell production.

Multiphysics Simulation of Battery Electrode Drying

Scaling Li-ion battery production to meet the demands of increasingly electrified grids and transportation systems will require higher throughput at all levels of the battery cell production process. One of the more energy intensive battery manufacturing steps is the necessary drying of solvents which persist in the electrodes after the initial slurry coating of their current collectors. It is critical to both optimize this drying process to minimize the time and resources required, as well as to understand the effect that the drying has on the electrode microstructure. As it is both expensive and time-intensive to iteratively test different temperatures on multiple initial electrode microstructures, it is necessary to have reliable, high-fidelity simulations which can model the evaporation process. Furthermore, it is easier to control microstructural features such as porosity, and particle size in a simulated environment, Indeed, by simulating evaporation, the design-of-experiments space can be minimized, and process conditions can be established to accelerate the production of high quality, lower cost electrodes. To that end, we develop a multiphysics model of solvent evaporation using Sandia’s in-house finite element software. By coupling the solvent equations of motion to species conservation equations for the carbon binder material and energy flux driven by evaporative cooling, we demonstrate good agreement between simulation and simple evaporation experiments. This model tackles several difficult aspects of evaporation, namely the evolution of free surfaces and gas-liquid phase-changes. We additionally extend our model to account for mesoscale features (i.e. particle size, porosity, binder distribution) and determine how different temperature profiles and microstructures affect the heating requirements for the electrode. These extensions present a key advantage over conventional 1D models, as these features can have a significant effect on the evaporation process and the final distribution of conductive additive and binder. We leverage this advantage to examine how several design parameters such as porosity, particle size distribution, and heating rate affect the resulting electrode structure and binder distribution. These results demonstrate a pathway to optimizing the electrode drying process and promise to inform the development of improved multiphysics models in the future.SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.

Determination of Electrode Balancing in Multilayer Pouch Cells Through Tracking and Tracing in Lithium‐Ion Battery Production

AbstractManufacturing lithium‐ion batteries is a complex procedure with interconnected process steps. Unknown interdependencies lead to production deviations, which, in combination with expensive materials, result in costly rejects. To gain better insight into the processes, tracking and tracing systems are increasingly being established in battery cell production. This improves the database and enhances transparency, as quality‐critical production data is already recorded during electrode production and specifically assigned to individual intermediate products. The data assignment aids in characterizing intermediate products properties to identify correlations and defects. An important parameter is the balancing from cathode to anode, which has a crucial influence on cell performance. Therefore, this paper demonstrates the determination of the balancing between all electrode layers in a lithium‐ion pouch cell using a traceability system. The traceable areal mass loading was determined for each electrode sheet. After the stacking process, the opposing electrode areal mass loadings of the cathodes and anodes were used to calculate the electrode balancing. Subsequently, their influence on the cycling performance of the cells was investigated. Battery cells with areal mass loadings within the permissible tolerances showed no abnormalities, whereas cells with production fluctuations exhibited rapid ageing.

Advancements in Battery Cell Finalization: Insights from an Expert Survey and Prospects for Process Optimization

Battery cell finalization is a crucial process chain in battery manufacturing, contributing to a significant share of CAPEX and OPEX. Thus, there is a high cost-saving potential by improving the process chain. This research paper investigates various crucial facets of the cell finalization process in battery cell production through an expert survey. These include investment cost allocation, potential cost savings in sub-processes, reject generation, early detection of faulty cells, quality measurement techniques, and the utilization of inline data for early quality determination and real-time process control during the formation process. A solution approach for the implementation of electrochemical impedance spectroscopy for inline early quality determination is given. The results yield valuable insights for optimizing the formation process and enhancing product quality.

Identification of Relevant Parameters for Traceability in the Continuous Mixing Process in Battery Cell Production

The growing demand for lithium‐ion batteries in electric vehicles pose challenges, particularly for the use of critical materials such as cobalt in electrode slurry mixing. Due to regulatory requirements such as the battery passport, an accurate traceability system is mandatory. However, there is no solution for the continuous mixing process, which is a major challenge in electrode production. This article describes an approach to solve this problem. Based on the results of a literature review, a design structure matrix is constructed to analyze parameter influences. The overall result is a list of relevant parameters that should be recorded in the continuous mixing process in order to characterize the mixing process.

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

AbstractElectrode 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.

Reducing Energy Consumption and Greenhouse Gas Emissions of Industrial Drying Processes in Lithium-Ion Battery Cell Production: A Qualitative Technology Benchmark

As the world’s automotive battery cell production capacity expands, so too does the demand for sustainable production. Much of the industry’s efforts are aimed at reducing the high energy consumption in battery cell production. A key driver is electrode drying, which is currently performed in long ovens using large volumes of hot air. Several drying technologies from other industries could reduce energy consumption and greenhouse gas emissions if successfully applied to battery cell production. High process and quality requirements must be met when adapting these technologies for battery cell production. Evaluating the technologies against these requirements is difficult due to the technological novelty of this industry and the associated lack of data. Furthermore, the significant differences in drying technologies render a comparison even more challenging. One objective of this study was to evaluate drying technologies and identify those that could be best adapted to lithium-ion battery cell production. Near-infrared and laser drying were found to be the best in terms of energy efficiency, cost savings and other parameters. Another aim was to analyse, in more detail, the technological challenges and the advantages and disadvantages of the top-ranked drying technologies. Finally, the saving potential for greenhouse gas emissions of near-infrared and laser drying was calculated for a global production scenario of LIB cells in 2030. The saving potential in this scenario would amount to 2.63 million metric tonnes (Mt) CO2eq per year if near-infrared drying was applied in all global LIB cell production facilities within the mentioned scenario and 1.47 million Mt CO2eq per year for laser drying.

Ontology‐Based Battery Production Dataspace and Its Interweaving with Artificial Intelligence‐Empowered Data Analytics

One of the key challenges of data management for smart manufacturing is dealing with data originating from both physical processes and virtual digital technologies. As image and sensor‐based production monitoring deliver a wealth of data along the process chain, artificial intelligence (AI) enables enhanced data analysis and new insight regarding relevance of observed process deviations. With constantly increased availability of data from manifold and specific sources, the complexity and heterogeneity of information structures are also growing rapidly. This is especially true for highly variable research, scale‐up, and pilot production, which poses new demands on data acquisition, data management, and data preprocessing. Herein, a unified framework for integrating an ontology and graph‐based data space with data acquisition and data analytics to improve data consistency, documentation of workflows, as well as the reproducibility of observations and results is presented. The framework consists of several open‐source web services that form an ontology‐based data space where physical and virtual process chains are represented by a semantic data fabric built from findable, accessible, interoperable, and reusable resource descriptions framework self‐descriptions. The feasibility of the proposed framework is demonstrated for a laboratory‐scale Li‐ion battery cell production facility with AI applied to two data analytics use cases.

Modeling machine-side influences on the Z-Folding process of battery cells

AbstractThe modeling of stacking machines for battery cell production offers potentials for quantifying interdependencies and thus optimizing development and commissioning processes against the background of a targeted efficient production. This paper presents a methodology to develop a model for quantifying machine-side influences using the example of a Z-Folding machine. The components and aspects of the machine to be modeled and their level of detail are systematically derived. Subsequently, it is shown how to parameterize the derived aspects. The components and aspects of the machine are modeled and connected through a multi-physics simulation. This makes it possible to predict the effects on the separator material to be processed depending on the selected setting parameters on the machine. This opens up potentials, for example, to identify optimal setting parameters in a risk-free and model-based manner, depending on the materials to be processed. As a result, material waste can be reduced by eliminating previous "trial and error" approaches.

Modeling Large-Scale Manufacturing of Lithium-Ion Battery Cells: Impact of New Technologies on Production Economics

The global demand for electric vehicles is increasing exponentially, as is the demand for lithium-ion battery cells. This has led to a strong ongoing competition among companies to achieve the lowest battery cell production cost. Herein, to provide guidance on the identification of the best starting points to reduce production costs, a bottom-up cost calculation technique, process-based cost modeling (PBCM), for battery cell production is reproduced and validated by drawing on a consistent dataset of a real battery cell production plant. The model is based on teardowns of a real battery cell factory and will prove useful for planning activities of today's, so-called, “giga factories.” The PBCM performed in the present study involves discussions on, e.g., production balancing, relocation of factories to low-wage countries, usage of new production and cell technologies, etc. The use of novel approaches, such as tabless cell design, dry coating, and NMC811 chemistry, is discussed. Finally, the ways in which battery cell production costs can be reduced further in the forthcoming years are shown, and implications for researchers, practitioners, and policy makers are provided.

“A milestone in battery research”

A unique competence centre for the research and production of battery cells is being built in Münster, Germany. The new facility will make a significant contribution to advancing the production of battery cells made in Germany and strengthening the country as a centre of technology.

High-frequency eddy current measurement for the characterization of graphite anode slurries in battery cell production

The global demand for lithium-ion batteries and their production in large quantities while maintaining high product quality and minimal reject rates poses new challenges for cell manufacturers. Quality control in slurry processing and electrode coating is becoming increasingly important due to high material costs. High-frequency eddy current measurement could enable characterization of the active material and reduce the reject rate by applying an inline quality measurement system. In this paper, the applicability of high-frequency eddy current measurement for characterization of graphite suspensions is investigated and its feasibility confirmed. Process parameters such as solids contents and type of graphite have been analyzed.

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

AbstractBattery 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.

Global warming potential of lithium-ion battery cell production: Determining influential primary and secondary raw material supply routes

This study assesses the global warming potential (GWP) impacts of lithium-ion battery (LIB) recycling and subsequent cell production from primary and secondary raw materials. Furthermore, the study proposes multiple allocation strategies for the GWP impacts of LIB recycling, which range from 18 to 22 kg CO2 equivalent per kilowatt-hour NMC111 and NMC811 battery packs to determine the environmental burdens of secondary raw materials. The results demonstrate that using secondary raw materials could ideally reduce NMC111 and NMC811 cell production GWP by 30 %. In practice, GWP impact reductions will vary depending on factors, such as the contributions of primary production and recycling to battery raw material supply, and the market share of the different technologies employed within both primary production and recycling.Based on estimates for the availability of recycled raw materials to the European battery value chain, the sensitivity analysis methods identified NMC hydroxide, nickel sulfate, and battery-grade graphite as the most influential recycled raw materials on NMC111 cell production GWP. These materials received importance scores of 59%, 8%, and 6% respectively. Primary supply routes for cobalt sulfate received the highest importance score, followed by the primary supply routes of lithium carbonate and nickel sulfate. For NMC811 cells, nickel sulfate (34%), NMC hydroxide (27%) and graphite (8%) were identified as the most influential recycled raw materials, with the primary supply routes of nickel sulfate, lithium hydroxide and battery-grade graphite being the most influential.

High speed laser cutting of ultrathin metal foils for battery cell production

Laser-based manufacturing has become a key enabling technology in the production of batteries and battery cells for the e-mobility field. Several applications, in fact, have already been industrialized, such as laser-based welding, cutting, stripping, and cleaning. Among all those technologies, laser cutting, in particular, has to deal with several very stringent constraints: the presence of highly reflective materials (aluminum and copper), very low thicknesses (6–12 μm), on-the-fly processing, and high quality of the cutting surface. According to those considerations, the present paper deals with the application of remote cutting of 12 μm thick aluminum and 6 μm thick copper foils by means of a galvo scanner and two different fiber laser sources: single mode constant wave and nanosecond pulsed wave ones. The experimental activity is devoted to understanding the feasibility of the process and to point out the pros and cons of the two different lasers involved. The cutting edges are analyzed by means of optical and SEM microscopy, in order to characterize cutting quality. The process is also characterized in terms of maximum achievable speed in order to understand the limits of both lasers and galvo scanning systems.

Energy consumption of current and future production of lithium-ion and post lithium-ion battery cells

Due to the rapidly increasing demand for electric vehicles, the need for battery cells is also increasing considerably. However, the production of battery cells requires enormous amounts of energy, which is expensive and produces greenhouse gas emissions. Here, by combining data from literature and from own research, we analyse how much energy lithium-ion battery (LIB) and post lithium-ion battery (PLIB) cell production requires on cell and macro-economic levels, currently and in the future (until 2040). On the cell level, we find that PLIB cells require less energy than LIB cells per produced cell energy. On the macro-economic level, we find that the energy consumption for the global production of LIB and PLIB cells will be 130,000 GWh if no measures are taken. Yet, it is possible to optimize future production and save up to 66% of this energy demand.

Data-Driven Approach to Analyze Interdependencies between Electrode Manufacturing Parameters and Electrochemical Performance of the Lithium-Ion Battery Cell

The lithium-ion battery cell production is a complex process chain with a high number of process steps and manifold interdependencies. The majority of the properties determining the electrochemical performance of the battery cell are established during electrode manufacturing. An in-depth understanding of electrode manufacturing and its influence on cell performance can pave the way toward high-quality, cost-efficient cell production. Considering the complexity of the process chain and the high share of material costs in battery production, the classic one-factor-at-a-time experimentation is deemed inadequate and expensive.Machine learning (ML) approaches have been proven suitable for analyzing complex process chains. Design of experiment (DoE) methods can be used to generate a sufficient informative database for ML techniques. This study was based on the joint application of DoE and ML methods in electrode manufacturing. For this purpose, in the first step, the response surface methodology (RSM) was used to conduct experiments and analyze the effect of mass loading, drying temperature, and porosity on cell performance. The study was focused on graphite anodes produced at a pilot scale. The electrochemical performance of electrodes was assessed on coin cells at discharge C-rates from C/10 to 5C. Additionally, symmetric coin cells were used to analyze the electrochemical impedance spectra. Supervised machine learning models were adopted to predict the cell characteristics based on the electrode properties. Additionally, a factor analysis reflecting the contribution and importance of each factor on the final cell properties was conducted. The efficient experiment-based data generation using RSM combined with the applied ML techniques can support practitioners towards smart quality-oriented battery cell production.

Dry Battery Electrode Manufacturing Enabled By Continuous Powder Mixing

Lithium-Ion Battery electrode manufacturing is a cost- and energy-intensive process that usually relies on the use of a hazardous and expensive solvent, N-methyl-2-pyrrolidone (NMP), for cathodes. After coating the battery-slurry to a current collector, the solvent needs to be evaporated to obtain a porous electrode suitable for the use in a Lithium-Ion Battery. The solvent is a processing material and unwanted in the final product. In fact, solvent residues can fuel parasitic side-reactions within the battery cell and hence deteriorate battery lifetime and safety. The utilization of NMP in battery production plants demands costly labor protection and explosion safety measurements during mixing and coating and necessitates a highly energy and floor-space demanding drying step using thermic drying ovens of up to 100 meters.[1] For ecological and economic reasons, NMP is not emitted in large-scale production facilities, but condensed and recycled by distillation. The NMP-recovery system adds additional floor space and energy demand to the production site. The drying and solvent recovery can account for up to 39% of the total energy demand of a Lithium-Ion Battery Cell production and therefore produce significant CO2 emissions.[2] A solvent-free, dry electrode manufacturing that eliminates the use of solvents hence reduces the floor space, energy demand and cost of a production plant and eases safety and environmental concerns. Different approaches have been reported in the literature to put dry coating into practice,[3],[4] yet most strategies, for example spray coating or brush coating, lack of a feasible implementation into large scale production. In contrast, a dry coating approach that is sometimes referred to as the Maxwell-Process is, according to media reports, currently installed at Tesla’s Gigafactories in Berlin and Austin.[5] In fact, a publicly available teardown of a Tesla 4680 battery cell hints that a dry coated anode could already be used in commercial vehicles.[6] Compared to the state-of-the-art electrode manufacturing process, dry coating requires different polymeric binder systems that form spiderweb-like structures of fine fibrils connecting the electrochemical active particles and the conductive additive particles. PTFE is highly suitable since it is stable towards typical electrolytes and cathode materials and is known to easily form fibrils. These properties are also utilized to produce expanded PTFE membranes like Gore-Tex. The fibril-network in the battery context is primarily formed by a high-shear dry mixing process. The powder mixture is then compressed to a free-standing electrode film (powder-to-film) by a heated rolling mill (calender) and the resulting film is ultimately laminated to a current collector foil (film-to-foil). We demonstrate that a twin screw-extruder can be used to tune the degree of binder-fibrillation during dry-mixing of NMC-based cathode mixtures. Extrusion based mixing allows to use powder mixtures with little amounts of PTFE binder (1 wt%) to produce self-supporting, free-standing cathode films and, ultimately, battery electrodes with high flexibility and sufficient mechanical stability. We show a superior rate-performance and cycling-stability of single-layer pouch cells with dry-coated cathodes compared to cells with wet coated reference electrodes of likewise composition and electrode design. Possible causes for the improved performance focusing on microstructural electrode properties are discussed.[1] Westphal, Bastian G.; Kwade, Arno (2018): Critical electrode properties and drying conditions causing component segregation in graphitic anodes for lithium-ion batteries. In: Journal of Energy Storage 18, S. 509–517.[2] Erik Emilsson, Lisbeth Dahllöf (2019): Lithium-Ion Vehicle Battery Production. IVL Swedish Environmental Research Institute.[3] Duffner, Fabian; Kronemeyer, Niklas; Tübke, Jens; Leker, Jens; Winter, Martin; Schmuch, Richard (2021): Post-lithium-ion battery cell production and its compatibility with lithium-ion cell production infrastructure. In: Nat Energy 6 (2), S. 123–134.[4] Verdier, Nina; Foran, Gabrielle; Lepage, David; Prébé, Arnaud; Aymé-Perrot, David; Dollé, Mickaël (2021): Challenges in Solvent-Free Methods for Manufacturing Electrodes and Electrolytes for Lithium-Based Batteries. In: Polymers 13 (3).[5] https://www.tagesspiegel.de/berlin/tesla-coup-mit-giga-berlin-neue-technologie-soll-wasserverbrauch-minimieren/27244032.html[6] https://www.youtube.com/watch?v=8WPPBhqeekw Figure 1

Challenges and Opportunities for Lithium Extraction from Geothermal Systems in Germany—Part 3: The Return of the Extraction Brine

Lithium (Li) is considered a crucial element for energy transition due to its current irreplaceability in Li-ion batteries, particularly in electric vehicles. Market analysis indicates that Germany’s future automotive sector and planned battery cell production will necessitate significant quantities of global lithium production. At the same time, only 1% of the world’s Li production is currently sourced from Europe. Recently, geothermal brines in Germany have gained attention as a potential local raw material source. These brines exhibit elevated Li concentrations and substantial flow rates in geothermal plants, suggesting the possibility of viable local production. However, a comprehensive full-scale Li extraction process from geothermal brines is yet to be established, and uncertainties persist regarding its long-term behavior. To address this, a generic model based on the geothermal settings of the Upper Rhine Graben was developed, simulating a 30-year operational period for Li extraction. The simulation revealed a 40% depletion of lithium during the observation period, while heat production remained constant. Nonetheless, the model also demonstrated a mean Li production of 231 t per year (equivalent to 1230 t per year of lithium carbonate equivalent), which could significantly enhance the economic prospects of a geothermal power plant and, if applied to multiple plants, reduce Germany’s dependence on global lithium imports. The primary factor influencing productivity is the achievable flow rate, as it directly impacts access to the raw material, hence, emphasizing the importance of detailed reservoir exploration and development in optimizing future lithium production from geothermal brines.

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

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.