Enhanced CNN-Based Failure Integrated Assessment Procedure for Energy Accumulator Packs

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This research presents a failure-integrated assessment procedure and structure for energy accumulator packs using an enhanced Convolutional Neural Network (CNN). The proposed approach involves wavelet packet decomposition processing of voltage change and State of Charge (SOC) signals from a lithium accumulator to extract energy values as input features. The assessment network performs a preliminary failure assessment on the energy accumulator pack, followed by evaluating whether the preliminary assessment result satisfies the assessment confirmation condition. If met, an assessment result for the energy accumulator pack is obtained. Otherwise, an auxiliary assessment using a CNN network is conducted for further analysis. The primary assessment result and auxiliary assessment result are then fused using the D-S evidence theory procedure to generate a comprehensive integrated assessment result. Finally, the integrated assessment result is evaluated, and the ultimate assessment result is determined. The proposed procedure improves the assessment accuracy of energy accumulator packs by enhancing the structure of the CNN network, determining the optimal size of the convolution kernel based on the Bayesian Information Criterion (BIC), and incorporating auxiliary assessment networks for enhanced accuracy and integrated assessment.