Canonical Variate Autoencoder-Based Interpretable Fault Detection for Lithium-Ion Battery Packs

With the large-scale application of electric vehicle and grid-scale energy storage systems, accurate and reliable fault detection (FD) of lithium-ion (Li-ion) battery packs is critical to the safe operation of such devices. As the key feature of battery faults, voltage correlation reflects the abnormal state of the battery by measuring the correlation change between data. Inspired by this, this brief proposes a canonical variate autoencoder for online FD in battery packs. First, the original voltages are reconstructed to obey the Gaussian distribution. Then, the canonical variable model is constructed to assess the synchronization between past and future voltages. Finally, a voltage correlation-based robust optimization objective is formulated to improve FD performance. Studies on a real lithium-ion battery experimental rig verify that the proposed method has reliable detection performance and clear physical interpretability.