-Equipping lithium-ion batteries with a reasonable thermal fault diagnosis can avoid thermal runaway and ensure the safe and reliable operation of the batteries. This research built a lithium-ion battery thermal fault diagnosis model that optimized the original mask region-based convolutional neural network based on the battery dataset in both parameters and structure. The model processes the thermal images of the battery surface, identifies problematic batteries, and locates the problematic regions. A backbone network is used to process the battery thermal images and extract feature information. Through the RPN network, the thermal feature is classified and regressed, and the Mask branch is used to ultimately determine the faulty battery's location. Additionally, we have optimized the original mask region-based convolutional neural network based on the battery dataset in both parameters and structure. The improved LBIP-V2 performs better than LBIP-V1 in most cases. We tested the performance of LBIP on the single-cell battery dataset, the 1P3S battery pack dataset, and the flattened 1P3S battery pack dataset. The results show that the recognition accuracy of LBIP exceeded 95 %. At the same time, we simulated the failure of the 1P3S battery pack within 0-15 min and tested the effectiveness of LBIP in real-time battery fault diagnosis. The results indicate that LBIP can effectively respond to online faults with a confidence level of over 98 %.
Keywords: Deep learning; Lithium-ion battery; Mask region-based convolutional neural network; Thermal diagnosis.
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