Quantifying key factors for optimised manufacturing of Li-ion battery anode and cathode via artificial intelligence

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

Interpretable machine learning for battery capacities prediction and coating parameters analysis

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 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

Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays.

Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq). We applied Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA), which maps with single-cell resolution and high sensitivity the kinetics and directionality of transcriptional variation during mouse organogenesis. We used this information to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues such as the dorsal midbrain. Our panoramic atlas will facilitate in-depth investigation of longstanding questions concerning normal and abnormal mammalian development.This work is part of the ‘‘SpatioTemporal Omics Consortium’’ (STOC) paper package. A list of STOC members is available at: http://sto-consortium.org. We would like to thank the MOTIC China Group, Rongqin Ke (Huaqiao University, Xiamen, China), Jiazuan Ni (Shenzhen University, Shenzhen, China), Wei Huang (Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China), and Jonathan S. Weissman (Whitehead Institute, Boston, USA) for their help. This work was supported by the grant of Top Ten Foundamental Research Institutes of Shenzhen, the Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831), and the Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011); Longqi Liu was supported by the National Natural Science Foundation of China (31900466) and Miguel A. Esteban’s laboratory at the Guangzhou Institutes of Biomedicine and Health by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16030502), National Natural Science Foundation of China (92068106), and the Guangdong Basic and Applied Basic Research Foundation (2021B1515120075).S

Modeling of Organic Rankine Cycle for waste heat recovery using RBF neural networks

The Organic Rankine Cycle (ORC) process is promised to significantly recycle the waste heat from medium and low temperature heat sources, achieve better performance to recover low grade waste heat than traditional waste heat recovery processes used in the industrial applications. An accurate ORC model is indispensable for the optimization and control of ORC systems. A new Radial Basis Function (RBF) modelling approach, which combines the node selection based on Fast Recursive algorithm (FRA) and non-linear parameters optimization using the PSO algorithm is proposed to model the ORC system. The experimental results verify that the resultant models can achieve high training accuracy and desirable generalization performance

Machine-learning for Li-Ion battery capacity prediction in manufacturing process

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. The models here are supervised machine-learning models to classify the range of the cell capacity (mAh/cm2) given the characteristics of the cathode including thickness, and porosity. To prepare the data for training the models a set of experiments were run by altering the control parameters of cathode manufacturing, i.e., comma bar gap, coating ratio and coating line speed advised by an expert. Following the cathode coating process, cathode electrodes were obtained. The experiments led to cathode electrodes with various thickness and porosities all measured via high precision equipment. The cut electrodes were then used to build half coin-cells with lithium metal as the opposite electrode. To minimize the number of free factors, cathode formulation, calendaring control parameters, drying time and temperature were kept unchanged. Then the cells were cycled in controlled temperature of 25 C at various Crates, C/20, C/5, 1C and 5C. The classifier here is a support vector machine with radial basis function kernel. For training the model a set of 110 cell data are used after removing outliers and noisy measurements. The cell capacity data are then labelled by three labels of low, medium, and high for each Crate. The label ranges are selected to distinguish the cells with undesirably low, medium but acceptable, and high or desirable capacities. In order to validate the models single and double cross-validation (CV) approaches are utilised. In CV approach data are split in 5 folds, in each run 4 of those used for training and one for hyperparameter optimisation as well as testing the model, the process is repeated in a one or two loops for single and double validation. The accuracy of the models is considered to be the number of correctly classified items compared to all classified items. The modelling results and analysis reveal that the capacity range (A= low, B = medium and C = high) can be predicted with an accuracy of 96.68% for C/20 capacity, 97.3% for C/5 capacity, 96.67% for 1C capacity and 72.6% for 5C capacity in single CV. A summary of the results as well as the associated accuracies (confusion matrices) are given in Figure for C/20 and 5Crate capacities for single and double CV methods. The classification models confirm that capacities at all Crates are having stronger correlations with thickness compared to porosity (in range above 25%) therefore thickness is considered as a more fundamental feature to classify the batteries according to their performance index. For C/20, C/5 and 1Crate capacities, the values are directly related to cathode thickness, which means that thicker electrodes at almost all porosities are led to cells with higher capacities. However, for 5Crate, it’s the opposite and for thicker electrodes, the capacity drops off accordingly. This is matched with the fact that capacity at higher Crates is limited by electronic conduction in the solid components, ionic conduction in the pores of the electrode, or both. It is also evident that the classification accuracy is lower for higher Crate capacities and suggest that extra features of cathode electrodes should be taken into account for a more accurate classification. The model-based performance prediction discussed here quantifies the accuracy for the capacity prediction at various Crates given cathode characteristics. It is highly significant for performance prediction where physical cycling tests may take hours to be completed. Therefore, it can avoid waste of time and resource and reduce the production cost for the battery manufacturer

Machine learning for optimised and clean Li-ion battery manufacturing : revealing the dependency between electrode and cell characteristics

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

Effect of the Lignin Structure on the Physicochemical Properties of Lignin-Grafted-Poly(ε-caprolactone) and Its Application for Water/Oil Separation

Lignin-grafted poly(ε-caprolactone) copolymers (lignin-g-PCLs) have shown wide application potentials in coatings, biocomposites, and biomedical fields. However, the structural heterogeneity of lignin affecting the structures and properties of lignin-g-PCL has been scarcely investigated. Herein, kraft lignin is fractionated into four precursors, namely, Fins, F1, F2, and F3, with declining molecular weights and increased hydroxyl contents. Lignin-g-PCLs are synthesized via ring-opening polymerization of ε-caprolactone with lignin and characterized by GPC, FTIR, 1H and 31P NMR, DSC, TGA, and iGC. The mechanical properties, UV barrier, and enzymatic biodegradability of the lignin-g-PCLs are evaluated. Results show that lignin with a higher molecular weight and aliphatic OH favors the copolymerization, leading to lignin-g-PCLs with longer PCL arms. Moreover, lignin incorporation improves the thermal stability, hydrophobicity, and UV-blocking ability but reduces the lipase hydrolyzability of the copolymers. We also demonstrated that the lignin-g-PCL-coated filter paper could successfully separate chloroform–, petroleum ether–, and hexane–water mixtures with an efficiency up to 99.2%. The separation efficiency remains above 90% even after 15 cycles. The structural differences of copolymers derived from the fractionation showed minimal influence on the separation efficiency. This work provides new insights into lignin-based copolymerization and the versatility of lignin valorization

A Review of Lithium-Ion Battery Electrode Drying: Mechanisms and Metrology

Lithium-ion battery manufacturing chain is extremely complex with many controllable parameters especially for the drying process. These processes affect the porous structure and properties of these electrode films and influence the final cell performance properties. However, there is limited available drying information and the dynamics are poorly understood due to the limitation of the existing metrology. There is an emerging need to develop new methodologies to understand the drying dynamics to achieve improved quality control of the electrode coatings. A comprehensive summary of the parameters and variables relevant to the wet electrode film drying process is presented, and its consequences/effects on the finished electrode/final cell properties are mapped. The development of the drying mechanism is critically discussed according to existing modeling studies. Then, the existing and potential metrology techniques, either in situ or ex situ in the drying process are reviewed. This work is intended to develop new perspectives on the application of advanced techniques to enable a more predictive approach to identify optimum lithium-ion battery manufacturing conditions, with a focus upon the critical drying process