Fault diagnosis of connection loosening in fast-swapping battery case for electric heavy trucks based on vibration-electric signal synergy

Vollständiger Abstract

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Abstract As the core energy storage unit of fast-swappable battery cases in electric heavy trucks, power batteries are particularly prone to connection loosening faults under complex operating conditions, such as frequent battery swapping, continuous vibration, and mechanical shocks. These faults are highly concealed and difficult to detect at an early stage, posing significant challenges for fault detection and diagnosis. To address this issue, this study proposes a vibration-electric synergy-based method for fault detection, localization, and diagnosis. In this framework, fault detection is used to determine whether an abnormal condition occurs, fault localization is employed to identify the faulty parallel branch, and fault diagnosis aims to recognize the specific fault type. First, the dynamic coupling relationship between vibration and current signals is accurately captured through linear correlation analysis, enabling efficient fault detection. Subsequently, by comparing the effective values of parallel branch currents, precise fault localization is achieved. Finally, leveraging multi-scale and multi-resolution analysis, three-dimensional signal feature point cloud construction, and a three-dimensional convolutional neural network (3DCNN), accurate fault diagnosis is realized, achieving an overall accuracy of 98.33%. Comparative and ablation experiments demonstrate that the proposed method significantly outperforms traditional machine learning approaches as well as certain deep learning methods, confirming its effectiveness and superiority. Furthermore, this study introduces a vibration-electric synergy-based framework for analyzing connection loosening faults, elucidating the causal relationship in which structural vibration induces electrical response evolution through variations in contact states, thereby providing a theoretical foundation for cross-domain studies.