Abstract: Much of the work is done by fuel vehicles, but fuel costs are higher than that. The natural impact of this fuel combustion in the form of air pollution. Therefore, the company has determined that electric vehicles are the best choice for fuel vehicles and pollution reduction. Electric car needs to be charged. Therefore, a charging station is formed. However, there are no charging stations available when compared to gas stations. Some charging stations are nearby and others are far away. Therefore, I decided to build a charging station. From a hardware failure point of view, the weakest link in such a system is as a result, the focus of this study is on detecting and locating flaws [1].The aim of this work is to show how to detect defects in an electromechanical conversion chain for traditional or autonomous electric vehicles. EVs are feasible to operate the information and data collected by several sensors to recover a sequence of data such as currents, voltages, and speeds, and so on. Using the characteristics extraction technique, create an architecture for a fault detection model. The long short term memory (LSTM) technique for fault detection is displayed in this regard. This method has been used to build an electric vehicle prototype and has shown to be more accurate than other methods [2].This article describes a fault detection technique based on machine learning (ML) that can help maintenance assistant in discovering defects in induction machine power connections. The system has been built to handle not only single phasing failures but also opposing wiring connections.
Keywords: Electric vehicle, Battery Management, Monitoring, Safety.
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