Novel Heterogeneous Dynamic Fusion Model Based on a Data-Mechanism Dual-Driven Framework for a State-of-Health Prediction of Lithium Batteries in Autonomous Underwater Vehicle Applications

Vollständiger Abstract

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Accurate state-of-health (SOH) prediction of lithium batteries is critical for guaranteeing the long endurance and system safety of autonomous underwater vehicles (AUVs) in marine operations. However, owing to complex underwater-operation conditions, data acquisition in AUVs is typically restricted to rest stages in the communication period, which would be characterized by incomplete data with high-frequency sensor noise. As a result, existing battery SOH prediction approaches would struggle to ensure estimation accuracy and model robustness for within-cell degradation trajectories in AUV applications. This paper proposes a novel heterogeneous dynamic fusion model based on a data-mechanism dual-driven (DMDD) framework for the SOH prediction of lithium batteries in AUV applications, innovatively utilizing features extracted from the rest stage after discharge. At first, a dual-filter strategy based on the interquartile range interception and the Savitzky–Golay algorithms is designed to effectively eliminate transient spikes and high-frequency artifacts of raw data. Furthermore, a two-stage feature-screening architecture is developed, which can not only filter out statistical redundancies but also elucidate the electrochemical mechanisms between extracted features and battery degradation. Moreover, a heterogeneous fusion model comprising random forest, support vector regression, and gated recurrent unit networks is constructed. On this basis, an adaptive dynamic fusion strategy based on the K-nearest neighbor and the minimum-variance unbiased estimation (MVUE) is proposed, which enables locally optimal credit assignments tailored to the specific characteristics of different aging stages. Experimental validations comprehensively demonstrate the superior performance of the heterogeneous dynamic fusion model based on the DMDD framework across the entire battery lifecycle. Specifically, the proposed heterogeneous dynamic fusion model can achieve a coefficient of determination (R2) over 0.99907, while the MAE and the RMSE can be maintained within 0.33958% and 0.56211%, respectively, showing satisfactory accuracy for battery SOH prediction in AUV applications.