Systematic Approach for the Test Data Generation and Validation of ISC/ESC Detection Methods
Jacob Klink,
Jens Grabow,
Nury Orazov,
Ralf Benger,
Ines Hauer,
Hans-Peter Beck
Affiliations
Jacob Klink
Research Center Energy Storage Technologies, Clausthal University of Technology, Am Stollen 19A, D-38640 Goslar, Germany
Jens Grabow
Research Center Energy Storage Technologies, Clausthal University of Technology, Am Stollen 19A, D-38640 Goslar, Germany
Nury Orazov
Research Center Energy Storage Technologies, Clausthal University of Technology, Am Stollen 19A, D-38640 Goslar, Germany
Ralf Benger
Research Center Energy Storage Technologies, Clausthal University of Technology, Am Stollen 19A, D-38640 Goslar, Germany
Ines Hauer
Institute of Electrical Power Engineering and Electrical Energy Engineering, Clausthal University of Technology, Leibnizstraße 28, D-38678 Clausthal-Zellerfeld, Germany
Hans-Peter Beck
Institute of Electrical Power Engineering and Electrical Energy Engineering, Clausthal University of Technology, Leibnizstraße 28, D-38678 Clausthal-Zellerfeld, Germany
Various methods published in recent years for reliable detection of battery faults (mainly internal short circuit (ISC)) raise the question of comparability and cross-method evaluation, which cannot yet be answered due to significant differences in training data and boundary conditions. This paper provides a Monte Carlo-like simulation approach to generate a reproducible, comprehensible and large dataset based on an extensive literature search on common assumptions and simulation parameters. In some cases, these assumptions are quite different from field data, as shown by comparison with experimentally determined values. Two relatively simple ISC detection methods are tested on the generated dataset and their performance is evaluated to illustrate the proposed approach. The evaluation of the detection performance by quantitative measures such as the Youden-index shows a high divergence with respect to internal and external parameters such as threshold level and cell-to-cell variations (CtCV), respectively. These results underline the importance of quantitative evaluations based on identical test data. The proposed approach is able to support this task by providing cost-effective test data generation with incorporation of known factors affecting detection quality.
