Research on Multi-Scale Signal Processing and Efficient Model Construction Strategies in Lithium-Ion Battery State Prediction

Abstract To tackle the question of limited generalization and inefficiency in predicting state of health (SOH) and state of charge (SOC) in lithium-ion batteries across diverse sequence lengths, a novel hybrid model is developed. This model integrates multivariate variational mode decomposition (MVMD), informer, and long short-term Memory (LSTM) networks. Initially, battery health features are extracted from the charge and discharge curves, which are then validated for their relevance to SOH and SOC via correlation analysis and random forest algorithms. These features undergo multi-scale decomposition using MVMD, thereby encapsulating the intricate dynamics of battery state changes across various time scales. This decomposition enhances the model's adaptability to different sequence lengths, bolstering its generalization capability. Subsequently, the informer model is utilized to identify temporal patterns within the decomposed features. Finally, LSTM exploits its capacity to capture temporal dependencies for further refinement of the predictions. This hybrid strategy yields substantial enhancements in both efficiency and accuracy. Compared to the transformer model, the proposed hybrid model demonstrates a 30% reduction in SOH prediction error and a 22% decrease in SOC prediction error, concurrently slashing training time significantly. Spanning diverse sequence lengths and battery types, demonstrates the model's strong generalization capabilities.