## 1 Introduction
The NasNetLarge deep learning method was employed to categorize the lung infection status of chest X-ray images and discriminate between the healthy state and virus-infected states [20]. A depth-separable Xception model included residual structure to accurately recognize and classify finger veins with fewer parameters [21]. EfficientNetB0 was utilized to monitor the structure of bridges and to repair faulty components [22]. GoogLeNet can learn information characteristics of known deposits and accurately classify the remote sensing images of minerals [23]. Improved ShuffleNet and Kalman filters were deployed on a UAV to monitor and calculate the distance between people in public areas, and also to effectively prevent the rampant spread of infectious diseases [24]. A multi-fault mode detection method of P2 diesel hybrid electric vehicle based on support vector machine was proposed for diagnosing the coupled faults of the generator, motor, and battery, which can classify the multi-faults more accurately, but the method can only categorize the faults into four fault categories: no fault, diesel engine fault, motor fault, and power battery fault; it cannot further categorize the fault types to the specific types of the subordinates of these four faults [25]. Deep learning and blockchain-based EV fault detection frameworks were proposed for detecting tire pressure, temperature, and battery faults, and the study utilized the CNN and LSTM deep learning models to predict the presence of faults with 70% prediction accuracy [26]. In recent years, attention mechanisms have been widely recognized for classification and prediction problems [27]. Multiple attention mechanisms have been proposed for image processing, natural language processing, and data prediction [28]. For example, attention mechanisms were combined with recurrent neural networks to process specific portions of an image at high resolution, extracting key features therein while omitting irrelevant features, and improving accuracy performances [29]. Moreover, based on the image characteristics, the channel attention mechanism was applied to improve the accuracy of image classification and prediction. For example, the squeeze-and-excitation networks (SENet) channel attention mechanism was added to the YOLOv5 model to predict the classification of crop disease categories by assigning higher weights to the key features [30]. Moreover, a two-stage spatial pooling method with the SE channel attention module was proposed to improve the classification effect on ImageNet significantly [31]. An integrated approach aims to combine multiple tools to improve prediction or classification accuracy. No method exists that can be trained quickly and with high accuracy for a wide range of datasets. For each different dataset, the network needs to be tested and matched with numerous specific iterations. In this study, inspired by ShuffleNet and DarkNet53, the results of both methods were synthesized by employing a deep fully connected layer to improve the accuracy of fault classification for EV electric system. In addition, inspired by channel attention, this study applied the channel attention mechanism to boost the training speed and prediction accuracy of the proposed ShuffleDarkNet37 network. Therefore, the key contributions of this study are summarized as follows:  
1. DarkNet53 network has few parameters and high efficiency, with 52 layers of convolution for multi-scale feature extraction, which speeds up the convergence of training and reduces the gradient loss by backpropagation, and greatly improves the accuracy and the generalization of the model. Inspired by the DarkNet53 network, the proposed Pearson-ShuffleDarkNet37-SE-Fully Connected-Net (PSDSEF) reduces 16 convolutional layers of the DarkNet53 network and improves the classification accuracy of the DarkNet53 network. The proposed PSDSEF is lighter in comparison to the DarkNet53 network and can reduce memory while maintaining accuracy. 2. The ShuffleNet network has a simple structure and few parameters with high accuracy.