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A Sampling Fault Diagnosis Method for Power Battery Data in Cloud Platform Based on ResNet-BiLSTM Neural Network
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DOI：10.1039/D5YA00093A.png)
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Abstract
En 中文
As the basis for many functions of the battery management system (BMS) such as state estimation and thermal runaway warning; stable sampling data is crucial for the safe operation of electric vehicles (EVs). In this paper; a sampling fault diagnosis method for power battery data in cloud platform is proposed based on residual network (ResNet) and bi-directional long short-term memory (BiLSTM) neural network; which can effectively identify the abnormalities of the battery sampling data and recognize the failure modes. Firstly; through the analysis of fault data and sampling circuits for real EVs; four typical failure modes are selected to complete the fault injection experiments. The physical simulation model of the fault circuit is established; and the corresponding mathematical empirical model is condensed. Then; based on the understanding of the abnormal data distribution pattern; the fault diagnosis algorithms based on threshold and ResNet-BiLSTM neural network are developed respectively. Finally; the algorithms are introduced into the simulation dataset and real-vehicle dataset for testing. The results show that both algorithms have high effectiveness and accuracy; and the latter has strong fault diagnosis capability. In summary; the proposed sampling fault diagnosis method is feasible and provides a theoretical basis for future multi-type fault diagnosis of BMSs.
Keywords:
battery management system
sampling fault diagnosis
ResNet-BiLSTM
electric vehicles
fault mode recognition
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