Parameter Identification of DC‐Link Capacitor for Electric Vehicle Based on IGWO‐BP Neural Network

Abstract

DC‐link capacitor is one of the most vulnerable passive components in the drive system of electric vehicle, so the condition monitoring of DC‐link 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‐link capacitor in electric vehicle inverter is proposed. In this method, the capacitance (C) is taken as health parameter. The A‐phase current and DC‐link 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‐BP neural network has better performance. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

An online comprehensive health monitoring system for automotive auxiliary converter

“…A novel ripple measurement approach and a peak current approximation method are combined to monitor the ESR of output capacitors [8]. The back propagation neural network is combined with the improved grey wolf optimization algorithm to identify the parameters of the capacitor in an EV inverter [9]. The continuous wavelet transform, convolutional neural network, and hidden Markov model are combined to classify the health condition of capacitors in a 3-phase inverter [10].…”