Emerging Intelligent Bidirectional Charging Strategy Based on Recurrent Neural Network Accosting EMI and Temperature Effects for Electric Vehicle

Abstract

Transportation is currently advancing towards Electric Vehicle (EV) technology. This paper presents a brief and systematic analysis of the real-time issues obtained in Electric Vehicles (EVs) due to the various ranges of energy storage devices. In general, EV energy management system is integrated with power electronic circuits for effective power conversion and reliable operation. Some issues are addressed while using the batteries in EV systems such as charging time, efficiency of battery, and raw materials. Not only battery issues but also real-time non-technical issues and operational issues are also discussed in this paper. During energy conversion with power electronic circuits, the system attains extreme temperature levels which in turn reduces the performance of the system. To maintain an optimum temperature level, it is important to study the temperature effect of the system at the most prior levels. Due to the adaption of power electronic components, some extent of noise is generated, technically treated as Electromagnetic Interference (EMI), as system capacity increases the EMI content also improved proportionately. Therefore, to mitigate the EMI effect, the low pass filter-based EMI filter is introduced in the system such that the noise level is suppressed. Bidirectional Charging System (BCS) is one of the emerging technologies in EV to obtain autonomous power supply systems in the form of Vehicle to Grid (V2G), Grid to Vehicle (G2V), and Vehicle to Load (V2L). To know the behaviour of BCS the proposed RNN controller is employed and is compared with ANN bidirectional charging model. BCS charging system with RNN controller has better dynamic response to exchange the power via DC/DC converter and AC/DC converter as compared to ANN controller.