Automatic method for the estimation of li-ion degradation test sample sizes required to understand cell-to-cell variability

Energy and AI, vol. 9, pp. 100174

Abstract

The testing of battery cells is a long and expensive process, and hence understanding how large a test set needs to be is very useful. This work proposes an automated methodology to estimate the smallest sample size of cells required to capture the cell-to-cell variability seen in a larger population. We define cell-to-cell variation based on the slopes of a linear regression model applied to capacity fade curves. Our methodology determines a sample size which estimates this variability within user specified requirements on precision and confidence. The sample size is found using the distributional properties of the slopes under a normality assumption, and an implementation of the approach is available on GitHub. For the five datasets in the study, we find that a sample size of 8–10 cells (at a prespecified precision and confidence) captures the cell-to-cell variability of the larger datasets. We show that prior testing knowledge can be leveraged with machine learning models to operationally optimise the design of new cell-testing, leading up to a 75% reduction in experimental costs.

Authors 4

1. Calum Strange

   University of Edinburgh

   Affiliation as printed

   School of Mathematics, University of Edinburgh, The Kings buildings, Edinburgh, EH9 3JF, Scotland, United Kingdom

2. Michael Allerhand

   University of Edinburgh

   Affiliation as printed

   School of Mathematics, University of Edinburgh, The Kings buildings, Edinburgh, EH9 3JF, Scotland, United Kingdom

3. Philipp Dechent Aachen Institute for Power Electronics and Electrical Drives (ISEA)

   RWTH Aachen University

   Affiliation as printed

   Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Aachen, Germany

4. Gonçalo dos Reis corresponding

   University of Edinburgh

   Affiliation as printed

   Centro de Matematica e Aplicacoes (CMA), Faculdade de Ciencias e Tecnologia, Campus da Caparica, Caparica, 2829-516, Portugal

   School of Mathematics, University of Edinburgh, The Kings buildings, Edinburgh, EH9 3JF, Scotland, United Kingdom