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In recent years, research on lithium–ion (Li-ion) battery safety and fault detection has become an important topic, providing a broad range of methods for evaluating the cell state based on voltage and temperature measurements. However, other measurement quantities and close-to-application test setups have only been sparsely considered, and there has been no comparison in between methods. In this work, the feasibility of a multi-sensor setup for the detection of Thermal Runaway failure of automotive-size Li-ion battery modules have been investigated in comparison to a model-based approach. For experimental validation, Thermal Runaway tests were conducted in a close-to-application configuration of module and battery case—triggered by external heating with two different heating rates. By two repetitions of each experiment, a high accordance of characteristics and results has been achieved and the signal feasibility for fault detection has been discussed. The model-based method, that had previously been published, recognised the thermal fault in the fastest way—significantly prior to the required 5 min pre-warning time. This requirement was also achieved with smoke and gas sensors in most test runs. Additional criteria for evaluating detection approaches besides detection time have been discussed to provide a good starting point for choosing a suitable approach that is dependent on application defined requirements, e.g., acceptable complexity.

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.

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