Automation, Instructional, and Statistical Tools for Correlative Multivariate Analysis in Battery Metrology
Authors: Lopez Carmen M , Vajaria Krisha , Sobotie Ona , Yao Xuhui , Samajdar Rudra Narayan , Zakaria Yahya , Jones Christopher , Robador Ana , Sánchez Samira , Kubiak Pierre 
Published: December 2024

The development of quality control methods that are suitable for academic laboratories and, at the same time, provide the kind of data needed to judge the commercialization potential of new technologies is the first step to bridge the gap between academia and industry. The second, but equally important step, is to obtain accurate interlaboratory comparisons to validate these methods. This can be achieved by creating data-rich environments in a precise and consistent way and based on systematic battery metrology principles and standards that are freely available to all.

In this presentation we demonstrate the use of our newly developed correlative multivariate analysis (CMA) to offer a robust and specific strategy that makes battery metrology accessible for different research laboratories, whether academic or industrial. We test commercial Nickel Manganese Cobalt Oxide (NMC 811) positive electrodes in half and full coin cells and use a variety of other experimental methods to harvest multivariate data; for example, electrode calendering and resistance measurements, effects of surface roughness and contaminants, quality of cell fabrication and post-mortem/post-test analysis, and chromatic confocal microscopy. A good practice guide developed at NPL serves as an instructional tool based on which we audit the electrode and cell fabrication process ensuring that it is carried out in a consistent manner; then we measure various characteristic parameters, use in-house developed Python code to support data structuring, and interrogate the data using the statistical analysis language R to extract quality control parameters that are then used to evaluate the cells.1, 2 We also discuss a mathematical treatment developed at NPL, based on CMA, which transforms the different responses and data points into a single data frame and allows us to interrogate it much more deeply than with independent variable analysis.3 This offers the huge advantage of using relatively big data sets to discover subtle relationships between process steps that affect cell quality without having to resort to long-term cycling or time-consuming data processing.

To the best of our knowledge this is the first time that a data-rich portfolio of methods has been developed for quality control of all the steps of cell fabrication and the ensuing curated data had been interrogated using this type of methodology. Our results indicate that this approach can also serve to improve parametrization of purely theoretical models, and that the data frames generated can be used for supervised training of artificial intelligence (AI) models.

References

C. M. López, et al., Assembling Coin Cells in Half Cell Format, NPL Good Practice Guide 153, DOI: https://doi.org/10.47120/npl.mgpg153, (2024).

O. Rodríguez, C. M. López, A Data-Driven Approach for Quality Control of Li-Ion Cells. Manuscript in preparation (2024).

G. Chatzopoulos, S. Marchesini, and C. M. López, Correlative Differential Analysis of Formation Cycles in Li-ion Cells Using in-Operando Chromatic Confocal Microscopy, Manuscript in Preparation (2024).