Battery Life Prediction Model Based on Deep Learning

We propose a an innovative battery life prediction method, CBA, which is based on generative adversarial network (GAN) framework. First, the paper extracts key features from the battery data and uses them as input to the model. Then, an improved gated convolution layer is designed, which integrates causal convolution into traditional gated convolution to extract the implicit information of features more efficiently. Following feature extraction, LSTM was estimated the initial battery life. To further refine the prediction accuracy, CNN was introduced to serve as a discriminator, evaluating the prediction outcomes produced by the LSTM. According to the feedback of the discriminator, the training parameters of the combination of LSTM and improved gated convolutional layer are constantly updated to generate higher quality data to train the discriminator's recognition ability. Through comparative experiments, the accuracy of this method in battery life prediction is verified. Furthermore, we conducted ablation experiments to assess how the enhanced gated convolutional layers impact the performance of generative adversarial networks in predicting time series data. The experimental findings indicate that the enhanced gated convolutional layer can effectively augment the performance of GAN in time series prediction, ultimately leading to a higher accuracy in lifetime prediction.