Research on battery balance based on multi-modal information fusion and representation learning based on deep learning

This paper introduces a battery balance algorithm model based on multimodal information fusion and deep learning representation learning. The model integrates BP neural networks, transformers, and genetic algorithms for comprehensive analysis using various battery data types. Initially, BP neural networks capture the nonlinear dynamics of the battery data to establish a basic understanding of its behavior. These extracted features are then fed into Transformer, where a self-focused mechanism recognizes complex relationships between different data patterns for effective information fusion. This method ensures a comprehensive assessment of SOC influencing factors, thus improving the accuracy of prediction. In addition, to optimize model performance, genetic algorithms fine-tune hyperparameters such as learning rate and hidden layer counts, and use fitness functions to evaluate performance on validation sets. The reliability of the proposed model is verified by real world experiments.