A Fuzzy Neural Network-Based Intelligent Prediction Model for Useful Lifespan of Lithium-Ion Batteries

With the rapid development of the new energy industry, accurate prediction of the service life of lithium batteries as core energy storage components is of great significance for optimizing energy configuration and reducing operation and maintenance costs. Traditional prediction methods are difficult to accurately capture their dynamic characteristics. The research aims to utilize the powerful learning ability of hybrid neural networks to achieve accurate prediction of the service life of lithium batteries. The study first analyzed the application of existing neural network models in predicting the service life of lithium batteries. By comparing the predictive performance of these models, a new hybrid neural network model was proposed, which combines the advantages of convolutional neural networks (CNN) in feature extraction and long short-term memory networks (LSTM) in processing time-series data, while introducing the advantages of other neural network structures to improve the predictive ability of the model. The experimental results show that the hybrid neural network model proposed in this study performs well in predicting the inflection point of lithium battery remaining life. Compared with existing models, this model not only improves prediction accuracy, but also significantly enhances prediction stability.