Back Propagation Artificial Neural Network Based DC Bus Capacitance Identification Method in Three-Phase PWM Rectifier for Charging System of EVs

Abstract

In order to improve the performance and reliability of the three-phase PWM rectifier in electric vehicle (EV) charging system, it is important to focus on the identification of the DC bus capacitance. The existing methods have the disadvantages such as a low identification accuracy, a high hardware dependency, and high costs. Therefore, the development of new identification methodologies could be the way out of the aforementioned disadvantages, in terms of advanced algorithms. In this paper, a DC bus capacitance identification method based on back propagation (BP) artificial neural network (ANN) algorithm is proposed. The capacitance can be accurately identified in the diode rectification stage, by training the neural network to extract the potential laws in the data, which only includes the Phase-a voltage and current, as well as the DC bus voltage ripple. The regression response is 0.99991. Finally, the experimental results show the accuracy and effectiveness of the proposed method. A simple and effective BP ANN is designed, and it can identify the capacitance at different grid voltages and DC bus loads. It has a high identification accuracy that the maximum identification error is less than 4.5%.