Systematic analysis of the impact of slurry coating on manufacture of Li-ion battery electrodes via explainable machine learning

The manufacturing process strongly affects the electrochemical properties and performance of lithium-ion batteries. In particular, the flow of electrode slurry during the coating process is key to the final electrode properties and hence the characteristics of lithium-ion cells, however it is given little consideration. In this paper the effect of slurry structure is studied through the physical and rheological properties and their impact on the final electrode characteristics, for a graphite anode. As quantifying the impact of the large number of interconnected control variables on the electrode is a challenging task via traditional trial-and-error approaches, an explainable machine learning methodology as well as a systematic statistical analysis method is proposed for comprehensive assessments. The analysis is based upon an experimental dataset in lab-scale involving 9 main factors and 6 interest variables which cover practical range of variables through various combinations. While the predictability of response variables is evaluated via linear and nonlinear models, complementary techniques are utilised for variables importance, contribution, and first and second order effects to increase the model transparency. While coating gap is identified as the most influential factor for all considered responses, other subtle relationships are also extracted, highlighting that dimensionless numbers can serve as strong predictors for models. The impact of slurry viscosity and surface tension on electrode thickness, coat weight and porosity are also extracted, demonstrating their importance for electrode quality. These variables have been rarely considered in previous works, as the relationships are difficult to extract by trial and error due to interdependencies. Here we demonstrate how model-based analysis can overcome these difficulties and pave the way towards an optimised electrode manufacturing process of next generation Lithium-ion batteries

Experimental data of cathodes manufactured in a convective dryer at the pilot-plant scale, and charge and discharge capacities of half-coin lithium-ion cells.

Megtec Systems pilot-plant scale continuous convective coater. The data was generated as part of an experimental design involving the following coating-drying process variables and ranges: comma bar gap, 80-140 µm; web speed, 0.5-1.5 m/min; coating ratio, 110-150%; drying temperature, 85-110 °C and drying air speed, 5-15 m/s. The manufacturing data include pre-calendered coating thickness, mass loading dry and wet, pre-calendered porosity, spatial autocorrelation and join counting (SAJC) -score for carbon and for fluorine, cell thickness, coating weight and porosity of 15 different electrode coatings and 45 half-coin cells. The electrochemical data was obtained at 25 °C in a Maccor 4000 series battery cycler and consists of charge and discharge capacities at C/20, C/5, C/2, 1C, 2C, 5C and 10C C-rates. Discharge gravimetric and volumetric capacities, rate performance (at 5C:0.2C) and first cycle loss data is also reported. Details of the experimental design and a comprehensive analysis of the data can be found in the co-submitted manuscript (Román-Ramírez et al., 2021). Additional collected data not used in Román-Ramírez et al. (2021) is reported in the present manuscript and include visual observations of coating defects, rheological properties of the electrode slurries (solid content, viscosity, coating shear rate and viscosity at coating shear rate), room temperature and room humidity during the coatings and first cycle loss of the coin cells. Raw and analyzed data is made available. The reported data can be used to extend the analysis reported in Román-Ramírez et al. (2021), and for the comparison of relevant data obtained at different manufacturing scales. [Abstract copyright: © 2021 The Author(s). Published by Elsevier Inc.

Understanding the effect of coating-drying operating variables on electrode physical and electrochemical properties of lithium-ion batteries

The effect of coating and drying process variables (comma bar gap, web speed, coating ratio, drying temperature and drying air speed) on NMC622 cathode physical properties (thickness, mass loading and porosity) and electrochemical properties (gravimetric capacity, volumetric capacity and rate performance) is studied by a design of experiments approach. Electrochemical performance is assessed on half coin cells at C-rates from C/20 up to 10C. The statistical analysis of the data reveals that the cathode physical properties are mainly affected by comma bar gap and coating ratio. The electrochemical properties also show high correlations between comma bar gap and coating ratio for some C-rates. As a second evaluation, the relationship between the cathode half-cell physical characteristics with the electrochemical performance is studied through multiple linear regression analysis. A correlation mainly between coating weight and the electrochemical properties is found. Empirical linear models representing the relationship between the output and input variables are provided, showing correlation coefficients ( ) as high as 0.99

“Full factorial design of experiments dataset for parallel-connected lithium-ion cells imbalanced performance investigation”

This paper shares an experimental dataset of lithium-ion battery parallel-connected modules. The campaign, conducted at the Stanford Energy Control Laboratory, employs a comprehensive full factorial Design of Experiment methodology on ladder-configured parallel strings. A total of 54 test conditions were investigated under various operating temperatures, cell-to-cell interconnection resistance, cell chemistry, and aging levels. The module-level testing procedure involved Constant Current Constant Voltage (CC-CV) charging and Constant Current (CC) discharge. Beyond monitoring total module current and voltage, Hall sensors and thermocouples were employed to measure the signals from each individual cell to quantify both current and temperature distribution within each tested module configuration. Additionally, the dataset contains cell characterization data for every cell (i.e. NCA Samsung INR21700-50E and NMC LG-Chem INR21700-M50T) used in the module-level experiments. This dataset provides valuable resources for developing battery physics-based, empirical, and data-driven models at single cell and module level. Ultimately, it contributes to advance our understanding of how cell-to-cell heterogeneity propagates within the module and how that affects the overall system performance

Roadmap on Li-ion battery manufacturing research

Growth in the Li-ion battery market continues to accelerate, driven primarily by the increasing need for economic energy storage for electric vehicles. Electrode manufacture by slurry casting is the first main step in cell production but much of the manufacturing optimisation is based on trial and error, know-how and individual expertise. Advancing manufacturing science that underpins Li-ion battery electrode production is critical to adding to the electrode manufacturing value chain. Overcoming the current barriers in electrode manufacturing requires advances in materials, manufacturing technology, in-line process metrology and data analytics, and can enable improvements in cell performance, quality, safety and process sustainability. In this roadmap we explore the research opportunities to improve each stage of the electrode manufacturing process, from materials synthesis through to electrode calendering. We highlight the role of new process technology, such as dry processing, and advanced electrode design supported through electrode level, physics-based modelling. Progress in data driven models of electrode manufacturing processes is also considered. We conclude there is a growing need for innovations in process metrology to aid fundamental understanding and to enable feedback control, an opportunity for electrode design to reduce trial and error, and an urgent imperative to improve the sustainability of manufacture

Roadmap on Li-ion battery manufacturing research

Growth in the Li-ion battery market continues to accelerate, driven primarily by the increasing need for economic energy storage for electric vehicles. Electrode manufacture by slurry casting is the first main step in cell production but much of the manufacturing optimisation is based on trial and error, know-how and individual expertise. Advancing manufacturing science that underpins Li-ion battery electrode production is critical to adding to the electrode manufacturing value chain. Overcoming the current barriers in electrode manufacturing requires advances in materials, manufacturing technology, in-line process metrology and data analytics, and can enable improvements in cell performance, quality, safety and process sustainability. In this roadmap we explore the research opportunities to improve each stage of the electrode manufacturing process, from materials synthesis through to electrode calendering. We highlight the role of new process technology, such as dry processing, and advanced electrode design supported through electrode level, physics-based modelling. Progress in data driven models of electrode manufacturing processes is also considered. We conclude there is a growing need for innovations in process metrology to aid fundamental understanding and to enable feedback control, an opportunity for electrode design to reduce trial and error, and an urgent imperative to improve the sustainability of manufacture

Roadmap on Li-ion battery manufacturing research

Growth in the Li-ion battery market continues to accelerate, driven by increasing need for economic energy storage in the electric vehicle market. Electrode manufacture is the first main step in production and in an industry dominated by slurry casting, much of the manufacturing process is based on trial and error, know-how and individual expertise. Advancing manufacturing science that underpins Li-ion battery electrode production is critical to adding value to the electrode manufacturing value chain. Overcome the current barriers in the electrode manufacturing requires advances in material innovation, manufacturing technology, in-line process metrology and data analytics to improve cell performance, quality, safety and process sustainability. In this roadmap we present where fundamental research can impact advances in each stage of the electrode manufacturing process from materials synthesis to electrode calendering. We also highlight the role of new process technology such as dry processing and advanced electrode design supported through electrode level, physics-based modelling. To compliment this, the progresses in data driven models of full manufacturing processes is reviewed. For all the processes we describe, there is a growing need process metrology, not only to aid fundamental understanding but also to enable true feedback control of the manufacturing process. It is our hope this roadmap will contribute to this rapidly growing space and provide guidance and inspiration to academia and industry

Data of Physical and Electrochemical Characteristics of Calendered NMC622 Electrodes and Lithium-ion Cells at Pilot-Plant Scale Battery Manufacturing

Data of Physical and Electrochemical Characteristics of Calendered NMC622 Electrodes and Lithium-ion Cells at Pilot-Plant Scale Battery Manufacturing Co-Submission of same title Paper to Data in Brief Journal.Data For the research papers of:Hidalgo, M.F., Apachitei, G., Dogaru, D., Faraji Niri, M., Lain, M., Copley, M. and Marco, J., (2023) Design of Experiments for Optimizing the Calendering Process in Li-Ion Battery Manufacturing. Journal of Power Sources 573: 233091. https://doi.org/10.1016/j.jpowsour.2023.233091Faraji Niri, M., Apachitei, G., Lain, M., Copley, M. and Marco, J. (2022), The Impact of Calendering Process Variables on the Impedance and Capacity Fade of Lithium-Ion Cells: An Explainable Machine Learning Approach. Energy Technol., 10: 2200893. https://doi.org/10.1002/ente.202200893THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Effect of coating operating parameters on electrode physical characteristics and final electrochemical performance of lithium-ion batteries

The effect of coating parameters of NMC622 cathodes and graphite anodes on their physical structure and half-cell electrochemical performance is evaluated by design of experiments. Coating parameters include the coater comma bar gap, coating ratio and web speed. The electrochemical properties studied are gravimetric and volumetric capacity, rate performance, areal specific impedance (ASI) and C-rate. Differences in the manufacturing effects on the electrode physical structure and electrochemical performance are observed between the electrodes and are modelled by linear regression. The effect of cell coating weight and porosity on half-coin cell electrochemical performance is also evaluated by linear regression. The cathode performance at high gravimetric and volumetric C-rates is mainly influenced by coating weight, whereas porosity is the only explanatory variable for volumetric C-rates of 1C and below. For anode, correlations are only found for the C/20 and 5C gravimetric and volumetric capacities and are related to coating weight. An inverse relationship between ASI and coating weight is observed for cathode, but in general the cell physical characteristics cannot completely explain the observed ASI for both electrodes. The obtained models are useful for the design and robust manufacturing of electrodes since present a quantitative relationship between the coating parameters, cell characteristics and final cell electrochemical performance

Design of experiments for optimizing the calendering process in Li-ion battery manufacturing

Calendering is a key yet complex manufacturing process that has varied effects on the Li-ion battery cell performance. Finding the optimal compaction can require many experiments if using the traditional one-factor-at-a-time method, which would be both complex and resource intensive. Design of Experiments (DoE) coupled with modeling via multiple linear regression (MLR) are used in this study to better understand the complex process of electrode calendering. The factors studied in this report are rolling temperature, post-calendered porosity, and mass loading of NMC622 and their effect on the electrochemical performance. The exact combinations of these factors which will create an information-rich data set are prepared using DoE. MLR is then used to extract this information in the form of understanding how each factor (individually and in combination with other ones) affect the electrochemical performance of the cells. Via this approach, the physical and statistical significance of each factor is quantified. Furthermore, the models are used to determine which combinations of factors result in an optimal electrochemical performance. Overall, this study highlights the use of both DoE to highlight the most important regions in the design space to conduct experiments while MLR is used to model and understand the complex system (calendering)