To address these challenges, this paper proposes a novel energy electrical equipment fault diagnosis method based on kernel Mel-scale frequency cepstral coefficients–Bayesian optimization algorithm–convolutional neural network–one dimensional (KMFCC-BOA-CNN-1D). . Rapid diagnosis of power battery faults in new energy vehicles based on improved boosting algorithm and big data Jiali Wang1*and Jia Chen2 Introduction With the intensification of the global greenhouse effect, reducing carbon emissions has become a consensus among all countries. New Energy Vehicles. . This work mainly discusses the establishment of the battery voltage fault diagnosis mechanism of new energy vehicles using electronic diagnosis technology. The Matlab/Simulink platform is used to simulate the open-circuit fault dataset, and the accuracy of the model is 97.
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