A Model and Data Dual-Driven Framework for Lithium-Ion Battery Cycle Life Prediction Integrating Uncertainty Model

To address the shortcomings of single model-based or data-driven methods in battery life prediction, this paper proposes a model and data dual-driven framework for lithium-ion battery (LIB) cycle life prediction integrating uncertainty model. First, a cloud model is used to quantify the uncertainty inherent in the empirical model and construct a capacity degradation uncertainty model. Next, health features highly correlated with capacity are extracted from historical data to train a convolutional neural network, forming an online estimation model that guides subsequent parameter correction. Then, based on the prediction discrepancy between the two models, the error-feedback gate recurrent unit with improved attention model is developed for the online dynamic correction of empirical model parameters, realizing a closed-loop prediction framework. Finally, a rolling correction strategy based on statistical error is proposed to dynamically adjust the cloud model parameters, with the corrections fed back to the capacity degradation uncertainty model to further refine prediction uncertainty and accuracy. Experimental results indicate that the proposed method achieves high accuracy across various battery datasets, with relative error remaining below 2%, and provides uncertainty-quantified remaining useful life prediction, which effectively supports the online health monitoring and maintenance strategy formulation of LIBs in diverse scenarios.