Physics-Guided Gradient Bias in Deep Attention Networks with Bayesian Optimization for Lithium-Ion Battery SOH Prediction

The state of health (SOH) prediction of lithium-ion batteries plays a pivotal role in ensuring the safe and efficient operation of battery management systems. However, the inherent challenges arise from the nonlinear behavior and temporal dependencies within the battery-s degradation process, governed by fluctuating operating conditions and complex multivariable interactions. Conventional methods, including mechanism-based models and standalone data-driven approaches, often struggle to maintain high accuracy and generalization in such dynamic environments. To tackle these issues, this paper proposes a novel approach for predicting the SOH of lithium-ion batteries by integrating physical gradient bias into deep learning models. Specifically, the gradient bias of SOH, derived from ordinary differential equation network (ODENet), is incorporated into the attention mechanism of various architectures, such as long short-term memory attention mechanism (LSTM-AM), transformer, and triple parallel attention network (TPANet), to improve prediction accuracy and robustness. Additionally, Bayesian optimization (BO) is employed to tune hyperparameters and further augment model performance. Experimental results demonstrate that the proposed method outperforms conventional models in terms of prediction error, showcasing the potential of the method for practical applications in battery management systems. Future work will explore the integration of additional physical insights and the application of this framework to other domains.