The data reported here was prepared to study the effects of calendering process on NMC622 cathodes using a 3-3-2 full factorial design of experiments. The data set consists of 18 unique combinations of calender roll temperature (85 °C, 120 °C, or 145 °C), electrode porosity (30%, 35%, or 40%), and electrode mass loading (120 g/m² or 180 g/m²). The reported physical characteristics of the electrodes include thickness, coating weight, maximum tensile strength, and density. The electrochemical performances of the electrodes were obtained by testing coin cells. In this context, 54 half-cells were produced, 3 per each calendering experiment to ensure repeatability and reliability of the results. The responses of interest included, charge energy capacity at C/2, C/5, discharge energy capacity at C/20, C/5, C/2, C, 2C, 5C, 10C, gravimetric capacity (charge at C/2, C/5, discharge at C/20, C/5, C/2, C, 2C, 5C, 10C), volumetric capacity (charge at C/2, C/5, discharge at C/20, C/5, C/2, C, 2C, 5C, 10C), rate performance (5C:0.2C), area specific impedance (at 10% to 90% state of charge (SoC) in 10 breakpoints), long-term cycling capacity (charge at C/5 for 50 cycles, discharge at C/2 for 50 cycles), long-term cycling degradation (at C/2 during 50 cycles of charge and discharge), and cycling columbic efficiency (50 cycles of C/2 charge and discharge). The details of the experimental design that has led to this data as well as comprehensive statistical analysis, and machine learning-based models can be found in the recently published manuscripts by Hidalgo et al. and Faraji-Niri et al. [1,2].

Solid-state lithium metal batteries show substantial promise for overcoming theoretical limitations of Li-ion batteries to enable gravimetric and volumetric energy densities upwards of 500 Wh kg−1 and 1,000 Wh l−1, respectively. While zero-lithium-excess configurations are particularly attractive, inhomogeneous lithium plating on charge results in active lithium loss and a subsequent coulombic efficiency penalty. Excess lithium is therefore currently needed; however, this negatively impacts energy density and thus limiting its thickness is essential. Here we discuss the viability of various technologies for realizing thin lithium films that can be scaled up to the volumes required for gigafactory production. We identify thermal evaporation as a potentially cost-effective route to address these challenges and provide a techno-economic assessment of the projected costs associated with the fabrication of thin, dense lithium metal foils using this process. Finally, we estimate solid-state pack costs made using thermally evaporated lithium foils. Preparing suitable lithium anodes is crucial for high-performance solid-state batteries. This study evaluates methods for producing thin lithium films, emphasizing thermal evaporation as a cost-effective approach while estimating associated pack costs.

Solid state lithium-metal batteries show substantial promise for overcoming theoretical limitations of state-of-the-art Li-ion batteries to enable gravimetric and volumetric energy densities upwards of 500 Wh kg−1 and1000WhL−1, respectively. Whilezerolithiumexcessconfigurationsareparticularlyattractivefrom both an energy density and a manufacturing standpoint, inhomogeneous lithium plating on charge results in active lithium loss and a subsequent coulombic efficiency penalty accrued from Li anode irreversibility, which severely limits cycle life. Excess lithium in the form of a lithium foil is therefore currently needed to extend cycle life to practical values. On the other hand, excess lithium negatively impacts energy density and thus limiting its thickness is essential to derive any practical benefit from adoption of a lithium metal-based battery architecture. Currently, costs associated with the production of these foils at such thicknesses and scale are a major impediment to adoption by industry. Here we discuss the viability of various technologies in the realisation of thin lithium films that can be scaled up to surface areas required for gigafactory production. We identify thermal evaporation as a potentially cost-effective route to address these challenges and provide a technoeconomic assessment of the projected costs associated with the fabrication of thin, dense lithium metal foils using this process. Finally we estimate solid state pack costs made using thermally evaporated Li foils.

The size of a lithium iron phosphate (LFP) cathode mix was increased by a factor of thirty, and the capacity of the cells produced with it by a factor of three-hundred. As well as rate and cycling tests, the coatings were also characterised for adhesion and resistivity. The adhesion and total through-plane resistance were both dependent on the drying conditions during coating. The discharge capacities at high rates and the pulse resistances showed much less influence from the drying temperature. The mix formulation contained 97 wt% LFP, and was based on an earlier design of experiments (DoE) study, using relatively high active material contents. Overall, the mix exceeded the performance predicted by the modelling study.

The electrode formulation has a significant effect on the performance of lithium ion cells. The active material, binder, and conductive carbon all have different roles, and finding the optimum composition can be difficult using an iterative approach. In this study, a design of experiment (DoE) methodology is applied to the optimisation of a cathode based on lithium iron phosphate (LFP). The minimum LFP content in the electrodes is 94 wt%. Seventeen mixes are used to evaluate adhesion, resistivity, and electrochemical performance. The coating adhesion increases with binder content, and the coating conductivity increases with carbon nano-tube content. The best coatings achieve 5C:0.2C capacity ratios above 50%, despite the relatively high coat weight. Models based on just the component mixture do not replicate the discharge capacities at high rates. However, a combined mixture + process model can fit the data, and is used to predict an optimum formulation.

Measurements of the electronic conductivity of lithium ion coatings are an important part of electrode development, particularly for thicker electrodes and in high power applications. A resistance measurement system with 46 probes has been used to characterise lithium ion electrodes, with different formulations and coat weights. The results show that the total through plane resistance is dominated by the interface resistance between the coating and the metal foil, rather than the volumetric resistivity of the coating. For coatings containing carbon nano-tubes, the in plane resistivities in the coating and perpendicular directions are different. A finite volume model was developed to help analyse and interpret the resistivity data.

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 com-binations 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 un-derstanding 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 elec-trochemical 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).

Determining the calendering process variables during electrode manufacturing is critical to guarantee lithium‐ion battery cell's performance; however, it is challenging due to the strong and unknown interdependencies. Herein, explainable machine learning (ML) techniques are used to uncover the impact of calendering process variables on the cells’ performance in terms of impedance and capacity fade. The study is based on experimental data from pilot‐scale manufacturing line considering critical factors of calendering gap, calendering temperature, electrodes’ coating weight, and target porosity. It offers a hierarchical methodology based on designed experiment, data‐oriented modeling via ML techniques, and model explainability technologies. The study reveals the relative importance of calendering control variables for cell impedance and capacity fade and quantifies the contribution of factors and the predictability of the cell's characteristics. The results show that the calendering factors affect cell's performance differently and are dominated by electrode features.

This work studies the impact of the ratio between the areal capacity of Graphite anode to NMC622 cathode for Lithium-ion batteries compared to the electrode characteristics of thickness, mass loading and cathode areal capacity, on their electrochemical properties. The influence of factors on energy capacity and gravimetric capacity at various Crates starting from C/20 up to 10C is quantified by combining experiments obtained via design of experiment techniques, machine learning modelling and explanation techniques. The results highlight that the performance at all Crates is highly affected by all features however their relative importance, and the linearity and nonlinearity of the dependencies is quite unique for each Crate capacity. N:P ratio is showing a relatively smaller effect on electrochemical performance compared to thickness, mass loading of active material and cathode areal capacity. It is also concluded that while the impact of N:P ratio is almost linear at lower Crates, it is nonlinear with a local optimum at medium and high Crates. This study offers a methodology for smart selection of a ratio between anode and cathode aerial capacity for a balanced performance of cells at all Crates.

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.

Li-ion battery is one of the key players in energy storage technology empowering electrified and clean transportation systems. However, it is still associated with high costs due to the expensive material as well as high fluctuations of the manufacturing process. Complicated production processes involving mechanical, chemical, and electrical operations makes the predictability of the manufacturing process a challenge, hence the process is optimised through trial and error rather systematic simulation. To establish an in-depth understanding of the interconnected processes and manufacturing parameters, this paper combines data-mining techniques and real production to offer a method for the systematic analysis, understanding and improving the Li-ion battery electrode manufacturing chain. The novelty of this research is that unlike most of the existing research that are focused on cathode manufacturing only, it covers both of the cathode and anode case studies. Furthermore, it is based on real manufacturing data, proposes a systematic design of experiment method for generating high quality and representative data, and leverages the artificial intelligence techniques to identify the dependencies in between the manufacturing parameters and the key quality factors of the electrode. Through this study, machine learning models are developed to quantify the predictability of electrode and cell properties given the coating process control parameters. Moreover, the manufacturing parameters are ranked and their contribution to the electrode and cell characteristics are quantified by models. The systematic data acquisition approach as well as the quantified interdependencies are expected to assist the manufacturer when moving towards an improved battery production chain.

Join counting, a standard technique in spatial autocorrelation analysis, has been used to quantify the clustering of carbon, fluorine and sodium in cross-sectioned anode and cathode samples. The sample preparation and EDS mapping steps are sufficiently fast for every coating from two Design of Experiment (DoE) test matrices to be characterised. The results show two types of heterogeneity in material distribution; gradients across the coating from the current collector to the surface, and clustering. In the cathode samples, the carbon is more clustered than the fluorine, implying that the conductive carbon component is less well distributed than the binder. The results are correlated with input parameters systematically varied in the DoE e.g. coating blade gap, coating speed, and other output parameters e.g. coat weight, and electrochemical resistance.

Battery manufacturing plays a direct and pivotal role in determining battery performance, which, in turn, significantly affects the applications of battery-related energy storage systems. As a complicated process that involves chemical, mechanical and electrical operations, effective battery property predictions and reliable analysis of strongly-coupled battery manufacturing parameters or variables become the key but challenging issues for wider battery applications. In this paper, an interpretable machine learning framework that could effectively predict battery product properties and explain dynamic effects, as well as interactions of manufacturing parameters is proposed. Due to the data-driven nature, this framework can be easily adopted by engineers as no specific battery manufacturing mechanism knowledge is required. Reliable battery manufacturing dataset particularly for coating (one key stage) collected from a real battery manufacturing chain is adopted to evaluate the proposed framework. Illustrative results demonstrate that three types of battery capacities including cell capacity, gravimetric capacity, and volumetric capacity can be accurately predicted with R2 over 0.98 at the battery early-manufacturing stage. Besides, information regarding how the variations of coating mass, thickness, and porosity affect these battery capacities is effectively identified, while interactions of these coating parameters can be also quantified. The developed framework makes the data-driven model become more interpretable and opens a promising way to quantify the interactions of battery manufacturing parameters and explain how the variations of these parameters affect final battery properties. This could assist engineers to obtain critical insights to understand the underlying complicated battery material and manufacturing behavior, further benefiting smart control of battery manufacturing.

To investigate the influence of cell formats during a cell development programme, lithium-ion cells have been prepared in three different formats. Coin cells, single layer pouch cells, and stacked pouch cells gave a range of scales of almost three orders of magnitude. The cells used the same electrode coatings, electrolyte and separator. The performance of the different formats was compared in long term cycling tests and in measurements of resistance and discharge capacities at different rates. Some test results were common to all three formats. However, the stacked pouch cells had higher discharge capacities at higher rates. During cycling tests, there were indications of differences in the predominant degradation mechanism between the stacked cells and the other two cell formats. The stacked cells showed faster resistance increases, whereas the coin cells showed faster capacity loss. The difference in degradation mechanism can be linked to the different thermal and mechanical environments in the three cell formats. The correlation in the electrochemical performance between coin cells, single layer pouch cells, and stacked pouch cells shows that developments within a single cell format are likely to lead to improvements across all cell formats.

Commercial lithium ion cells with different power: energy ratios were disassembled, to allow the electrochemical performance of their electrodes to be evaluated. Tests on coin cell half cells included rate tests (continuous and pulsed), resistance measurements, and extended pulse tests. Pulse power tests at high rates typically showed three limiting processes within a 10 s pulse; an instantaneous resistance increase, a solid state diffusion limited stage, and then electrolyte depletion/saturation. On anodes, the third process can also be lithium plating. Most of the cells were rated for a 10 C continuous discharge, and the cathode charging voltage at 10 C was around 4.2 V. For anodes, the maximum charge current to avoid a negative voltage was 3?5 C. Negative anode voltages do not necessarily mean that lithium plating has occurred. However, lithium deposits were observed on all the anodes after 5000 pulse sequences with 10 s pulses at ? 20 C.

While silicon-based negative electrode materials have been extensively studied, to develop high capacity lithium-ion batteries (LIBs), implementing a large-scale production method that can be easily transferred to industry, has been a crucial challenge. Here, a scalable wet-jet milling method was developed to prepare a silicon-graphene hybrid material to be used as negative electrode in LIBs. This synthesized composite, when used as an anode in lithium cells, demonstrated high Li ion storage capacity, long cycling stability and high-rate capability. In particular, the electrode exhibited a reversible discharge capacity exceeding 1763 mAh g−1 after 450 cycles with a capacity retention of 98% and a coulombic efficiency of 99.85% (with a current density of 358 mA g−1). This significantly supersedes the performance of a Si-dominant electrode structures. The capacity fade rate after 450 cycles was only 0.005% per cycle in the 0.05–1 V range. This superior electrochemical performance is ascribed to the highly layered, silicon-graphene porous structure, as investigated via focused ion beam in conjunction with scanning electron microscopy tomography. The hybrid electrode could retain 89% of its porosity (under a current density of 358 mA g−1) after 200 cycles compared with only 35% in a Si-dominant electrode. Moreover, this morphology can not only accommodate the large volume strains from active silicon particles, but also maintains robust electrical connectivity. This confers faster transportation of electrons and ions with significant permeation of electrolyte within the electrode. Physicochemical characterisations were performed to further correlate the electrochemical performance with the microstructural dynamics. The excellent performance of the hybrid material along with the scalability of the synthesizing process is a step forward to realize high capacity/energy density LIBs for multiple device applications.

The large number of parameters involved in each step of Li-ion electrode manufacturing process as well as the complex electrochemical interactions in those affect the properties of the final product. Optimization of the manufacturing process, although very challenging, is critical for reducing the production time, cost, and carbon footprint. Data-driven models offer a solution for manufacturing optimization problems and underpin future aspirations for manufacturing volumes. This study combines machine-learning approaches with the experimental data to build data-driven models for predicting final battery performance. The models capture the interdependencies between the key parameters of electrode manufacturing, its structural features, and the electrical performance characteristics of the associated Li-ion cells. The methodology here is based on a set of designed experiments conducted in a controlled environment, altering electrode coating control parameters of comma bar gap, line speed and coating ratio, obtaining the electrode structural properties of active material mass loading, thickness, and porosity, extracting the manufactured half-cell characteristics at various cycling conditions, and finally building models for interconnectivity studies and predictions. Investigating and quantifying performance predictability through a systems' view of the manufacturing process is the main novelty of this paper. Comparisons between different machine-learning models, analysis of models' performance with a limited number of inputs, analysis of robustness to measurement noise and data-size are other contributions of this study. The results suggest that, given manufacturing parameters, the coated electrode properties and cell characteristics can be predicted with about 5% and 3% errors respectively. The presented concepts are believed to link the manufacturing at lab-scale to the pilot-line scale and support smart, optimised, and clean production of electrodes for high-quality Li-ion batteries.

Battery manufacturing is a highly complicated and multi-stage process with large number of parameters involved in each stage. Understanding the correlation of these factors and their impact on the performance of the cells is crucial and would provide opportunity to improve the quality of cells and reduce the production time and cost. While in traditional battery manufacturing, try and error approach is used to design experiments and reveal the correlations, a smart manufacturing requires advanced and systematic modelling techniques for representing the production line. Motivated by this urgent need, this study focuses on machine-learning models to correlate parameters of the manufacturing chain with the battery performance, i.e., to predict the cell areal capacity given electrode specifications. The study is dedicated to cathode manufacturing process, addresses capacities at various Crates and quantifies the predictability unlike the existing literature.

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 mu m; web speed, 0.5-1.5 m/min; coating ratio, 110-150%; drying temperature, 85-110 degrees 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) Z-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 degrees C in a Maccor 40 00 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 (Roman-et al., 2021). Additional collected data not used in Roman-Ramirez 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 Roman-Ramirez et al. (2021), and for the comparison of relevant data obtained at different manufacturing scales. (C) 2021 The Author(s). Published by Elsevier Inc.