参数识别的电动汽车直流㎜墨水电容器基于IGWO〣P神经网络( Parameter Identification of DC㎜ink Capacitor for Electric Vehicle Based on IGWO〣P Neural Network )

F Yao C Dong S Tang W He DC‐link capacitor condition monitoring parameter identification BP neural network improved gray wolf optimization

DC﹍ink capacitor is one of the most vulnerable passive components in the drive system of electric vehicle, so the condition monitoring of DC﹍ink capacitors can significantly improve the reliability of drive system and even driving safety. The existing monitoring methods are subject to the low accuracy, added hardware and the irreversible impact on system. Therefore, based on Back Propagation (BP) neural network with Improved Gray Wolf Optimization (IGWO), a parameter identification method for the DC﹍ink capacitor in electric vehicle inverter is proposed. In this method, the capacitance (C) is taken as health parameter. The A﹑hase current and DC﹍ink capacitor voltage in electric drive system are taken as inputs, and the capacitance (C) is taken as output for condition monitoring. IGWO algorithm can be applied to obtain the optimal weights and thresholds. Ultimately, under four actual working conditions in electric drive system, the condition monitoring test is carried out. The results are compared and analyzed, which show that the monitoring using IGWO〣P neural network has better performance. 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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