Accurate estimation of lithium battery state-of-health(SOH)is essential for ensuring safe operation and efficient utilization.To address the challenges of complex degradation factors and unreliable feature extraction,...Accurate estimation of lithium battery state-of-health(SOH)is essential for ensuring safe operation and efficient utilization.To address the challenges of complex degradation factors and unreliable feature extraction,we develop a novel SOH prediction model integrating physical information constraints and multimodal feature fusion.Our approach employs a multi-channel encoder to process heterogeneous data modalities,including health indicators,raw charge/discharge sequences,and incremental capacity data,and uses multi-channel encoders to achieve structured input.A physics-informed loss function,derived from an empirical capacity decay equation,is incorporated to enforce interpretability,while a cross-layer attention mechanism dynamically weights features to handle missing modalities and random noise.Experimental validation on multiple battery types demonstrates that our model reduces mean absolute error(MAE)by at least 51.09%compared to unimodal baselines,maintains robustness under adverse conditions such as partial data loss,and achieves an average MAE of 0.0201 in real-world battery pack applications.This model significantly enhances the accuracy and universality of prediction,enabling accurate prediction of battery SOH under actual engineering conditions.展开更多
基金Project(2023YFB2303704-07)supported by the National Natural Science Foundation of China。
文摘Accurate estimation of lithium battery state-of-health(SOH)is essential for ensuring safe operation and efficient utilization.To address the challenges of complex degradation factors and unreliable feature extraction,we develop a novel SOH prediction model integrating physical information constraints and multimodal feature fusion.Our approach employs a multi-channel encoder to process heterogeneous data modalities,including health indicators,raw charge/discharge sequences,and incremental capacity data,and uses multi-channel encoders to achieve structured input.A physics-informed loss function,derived from an empirical capacity decay equation,is incorporated to enforce interpretability,while a cross-layer attention mechanism dynamically weights features to handle missing modalities and random noise.Experimental validation on multiple battery types demonstrates that our model reduces mean absolute error(MAE)by at least 51.09%compared to unimodal baselines,maintains robustness under adverse conditions such as partial data loss,and achieves an average MAE of 0.0201 in real-world battery pack applications.This model significantly enhances the accuracy and universality of prediction,enabling accurate prediction of battery SOH under actual engineering conditions.
