With the continuous development of society and the economy and the popularization of the environmental protection concept, more and more people have begun to turn to electric vehicles. The application of electric vehicles can effectively avoid the damage caused by automobile fuel emissions to the surrounding environment and promote the development and utilization of new energy. However, the development of the electric vehicle industry has led to frequent accidents related to charging safety. In order to prevent accidents related to the charging safety of electric vehicles and ensure proper safety of passengers and people, the charging safety and charging safety protection methods of electric vehicles have become the research priorities for scholars. This paper reviewed the existing research results on the charging safety of electric vehicles, analyzed the influencing factors of the charging safety of electric vehicles, summarized the charging safety protection methods, and forecast the future research direction of charging safety, which has reference value and reference significance for the charging safety research of electric vehicles.
With the increase of fire problems of new energy vehicles (EVs), more and more attention has been paid to charging safety. Firstly, the charging safety problems and protection strategies in the power grid are summarized from the grid side, the charging equipment side, the vehicle side, and the operation platform side, and a solution for the vehicle side charging safety protection is proposed. Secondly, with regards to building a charging early warning protection system architecture, a real-time protection strategy for EV charging is proposed; a battery temperature difference, battery voltage ramp rate, and current ramp rate are proposed; and a double-layer protection model of an active protection layer and a big data protection layer is established based on the real-time monitoring of 27 parameters. Finally, by building a physical simulation platform of the early warning system, the simulation and verification are carried out based on the BYD Han model. The system was demonstrated in the State Grid Tianjin Electric Power Company of China. The results show that the system can realize the charging real-time early warning and deal with it in time when the battery charging is abnormal, which has practical application value for the popularization and development of EVs.
With the development of electric vehicles in China, the fault monitoring and warning systems for the charging process of electric vehicles have received the industry’s attention. A method for the monitoring and warning of electric vehicle charging faults based on a battery model is proposed in this paper. Through online estimation of the state of charge of the power battery model and battery electromotive force, parameters such as battery state of charge, voltage, and temperature can be adjusted in real time to simulate the charging response of the power battery, which can simulate power batteries of different types, specifications, and parameters. During the charging process, CAN (Controller Area Network) bus monitoring technology is used to receive and analyze the charging information of the charger, as well as the battery charging information and battery charging demand information. The charging response information simulated by the battery model is compared with the battery charging state information, and the charging state information of the charger is compared with the battery charging demand information to determine whether the charging process is normal. When it is judged that a charging fault occurs, a fault warning signal is sent. This method can identify more than 10 types of faults, including the failure of the BMS (Battery Management System) function. The comparison and analysis of actual charging accident data and power battery model data verifies the feasibility of the charging fault monitoring method proposed in this paper.
With the popularization and application of electric vehicles, the problem of charging failure is exposed frequently. In order to effectively and accurately determine the type of charging failure and find out the cause of the failure, a method for identifying the charging failure using neural network is proposed. By collecting charging data and using neural network for training, the failure results are obtained through continuous optimization, and compared with the results of preliminary judgment, the superiority and feasibility of BP neural network are verified.
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