The power battery constitutes the fundamental component of new energy vehicles. Rapid and accurate fault diagnosis of power batteries can effectively improve the safety and power performance of the vehicle. In response to the issues of limited generalization ability and suboptimal diagnostic accuracy observed in traditional power battery fault diagnosis models, this study proposes a fault diagnosis method utilizing a Convolutional Block Attention Capsule Network (CBAM-CapsNet) based on a stacked sparse autoencoder (SSAE). The reconstructed dataset is initially input into the SSAE model. Layer-by-layer greedy learning using unsupervised learning is employed, combining unsupervised learning methods with parameter updating and local fine-tuning to enhance visualization capabilities. The CBAM is then integrated into the CapsNet, which not only mitigates the effect of noise on the SSAE but also improves the model’s ability to characterize power cell features, completing the fault diagnosis process. The experimental comparison results show that the proposed method can diagnose power battery failure modes with an accuracy of 96.86%, and various evaluation indexes are superior to CNN, CapsNet, CBAM-CapsNet, and other neural networks at accurately identifying fault types with higher diagnostic accuracy and robustness.

The widespread adoption and utilization of electric vehicles has been constrained by power battery performance. We proposed a fault diagnosis method for power batteries based on multiple-model fusion. The method effectively fused the advantages of various classification models and avoided the bias of a single model towards certain fault types. Firstly, we collected and sorted parameter information of the power battery during operation. Three common neural networks: back propagation (BP) neural network, convolution neural network (CNN), and long short-term memory (LSTM) neural network, were applied to battery fault diagnosis to output the fault types. Secondly, the fusion algorithm proposed in this paper determined the accurate fault type. Based on the improved voting method, the proposed fusion algorithm, named the multi-level decision algorithm, calculated the voting factors of the diagnostic results of each classification model. According to the set decision thresholds, multi-level decision voting was conducted to avoid neglecting effective classification information from minority models, which can occur with traditional voting methods. Finally, the accuracy and effectiveness of the proposed method were verified by comparing the accuracy of each classification model with the multiple model fusion algorithm.