Performance Analysis and Improvement of Lithium Battery Charging System Based on Deep Learning Algorithms

The research on traditional lithium battery charging systems has problems such as model simplification, insufficient data, insufficient accuracy, and poor real-time performance. Simplified electrochemical models cannot accurately reflect the complexity of actual systems, and limited experimental data limits the understanding of system behavior. Neglecting influencing factors leads to low prediction accuracy and a lack of real-time monitoring mechanisms to cope with dynamic changes in the system. These limitations affect the comprehensive analysis and optimization of system performance. This paper designed a Convolutional Neural Network - Gated Recurrent Unit (CNN-GRU) based on convolutional neural networks to extract local and global features from time series data, and combined it with time series prediction to predict charging temperature. In experimental verification, the quadratic mean error of the CNN-GRU algorithm model is 0.011, and the mean absolute deviation is 0.018. The error between the predicted electricity price and the actual value is within 2%.