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Parameter Identification of DC-Link Capacitor for Electric Vehicle Based on IGWO-BP Neural Network
IEEJ Transactions on Electrical and Electronic Engineering ( IF 1 ) Pub Date : 2021-04-26 , DOI: 10.1002/tee.23373
Fang Yao 1 , Chaoqun Dong 1 , Shengxue Tang 1 , Wenxuan He 1
Affiliation  

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.

中文翻译:

基于IGWO-BP神经网络的电动汽车直流环节电容器参数辨识。

直流母线电容器是电动汽车驱动系统中最易损坏的无源元件之一,因此对直流母线电容器的状态监测可以显着提高驱动系统的可靠性,甚至提高行驶安全性。现有的监控方法存在精度低,硬件增加以及对系统产生不可逆转的影响。因此,基于改进的灰狼优化算法(IGWO)的BP神经网络,提出了一种电动汽车逆变器直流环节电容器的参数辨识方法。在该方法中,将电容(C)作为健康参数。以电力驱动系统中的A相电流和直流母线电容器电压为输入,电容(C)作为状态监视的输出。可以将IGWO算法应用于获得最佳权重和阈值。最终,在电力驱动系统的四个实际工作条件下,进行了状态监视测试。对结果进行比较和分析,表明使用IGWO-BP神经网络进行监测具有较好的性能。©2021日本电气工程师学会。由Wiley Periodicals LLC发布。
更新日期:2021-05-25
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