Lithium battery SOH prediction based on modal decomposition and optimized transformer-bilstm framework with multi-strategy adaptive beaver behavior optimizer

Abstract

Accurate prediction of the State of Health (SOH) of lithium batteries is essential for ensuring safe operation, prolonging battery service life and optimizing energy management. Battery degradation is characterized by strong nonlinearity, multi-factor coupled interference and data redundancy. Furthermore, prevailing hybrid prediction models are constrained by several inherent limitations: insufficient feature extraction, limited optimization capacity of conventional algorithms, and oversimplified modular integration. To tackle these challenges, this paper proposes an SOH prediction method based on Variational Mode Decomposition (VMD) and the Transformer-Bidirectional Long Short-Term Memory (BiLSTM) framework optimized by the Multi-Strategy Adaptive Beaver Behavior Optimizer (MSA-BBO). First, multi-dimensional health features are extracted from raw data. Second, the VMD algorithm is employed to decompose and reconstruct multi-dimensional health features (such as charging voltage and current) in the training set, thereby suppressing noise and extracting critical aging characteristics. The obtained processing scheme is then directly applied to the test set. Subsequently, a Transformer-BiLSTM hybrid framework is established, where the Transformer captures long-range temporal dependencies and the BiLSTM extracts bidirectional correlation features of data, thereby synergistically overcoming the limitations of single-structure models. To address the deficiencies of the original Beaver Behavior Optimizer (BBO), including insufficient utilization of elite information, convergence oscillation, and non-targeted perturbation, the algorithm is improved in four aspects: population evolution, exploration-exploitation balance, local optimization, and global escape. The developed MSA-BBO is employed for adaptive hyperparameter optimization. In contrast to existing dual-module hybrid models, the proposed method establishes a three-stage collaborative framework that integrates feature processing, feature mining, and hyperparameter optimization. Finally, comparative experiments with five other models are conducted to objectively evaluate the prediction accuracy, stability, and generalization ability of the proposed model. The experimental results demonstrate that the proposed model achieves the optimal overall performance on five test sets, with all coefficients of determination (R2) above 0.94 and all Mean Absolute Percentage Error (MAPE) values below 0.04. This research can provide reliable technical support and theoretical reference for the full-life cycle health management of lithium batteries.