A Lithium-Ion Battery State-of-Health Prediction Model Combining Convolutional Neural Network and Masked Multi-Head Attention Mechanism

The existing data-driven methods for the state of health (SOH) prediction of lithium-ion battery are limited by the data quantity, resulting in insufficient generalization performance, prediction accuracy and prediction stability. To tackle this challenge, this paper proposes a novel model, namely the CNN-MMHA, for SOH prediction. The CNN-MMHA incorporates mask mechanisms and multi-head attention mechanisms (MHA) to effectively capture the interdependencies within time series data (i.e. MMHA). Simultaneously, the utilization of the data conversion module and convolutional neural network (CNN) enables the capture of temporal sequences and local characteristics of battery states, accordingly enhancing the predictive ability of MMHA. In this study, This paper execute a sequence of simulation experiments on NASA and CALCE data sets, where this paper compare the performance of a variety of models: Multilayer Perceptron (MLP), Long Short-Term Memory Network (LSTM), CNN, CNN-LSTM, MMHA, and CNN-MMHA respectively. The results demonstrate that the proposed model yields optimal convergence and generalization performance. Furthermore, it has the utmost prediction accuracy and stability. Ultimately, the exceptional performance and real-world applicability of the proposed model are corroborated through its experiments on the real battery.