Research on Electric Vehicle Charging Safety Early Warning Based on Outlier Detection Method
In the process of achieving the goal of carbon peak and carbon neutrality,in order to achieve environmentally friendly and energy-saving development,electric vehicles have begun to take the stage.With the rapid growth of electric vehicle ownership in the market,the safety problems caused by electric vehicles have gradually begun to emerge.Spontaneous combustion and fire accidents of electric vehicles are common.They have caused serious economic losses and even life hazards to automobile manufacturers,consumers,and operators of charging equipment.The charging safety of electric vehicles has begun to restrict the development of the electric vehicle industry.In this paper,starting from the constant voltage and constant current charging method,through the analysis of the causes of electric vehicle charging faults,a data mining early warning method based on discrete point detection of Gaussian distribution is proposed.The characteristic of outlier detection method is that it can effectively distinguish the data with significant difference from a set of data sets.A large amount of data will be generated during the charging process of electric vehicles.The Gaussian distribution model of normal charging state is obtained by obtaining the data under normal charging.The outlier detection method is used to judge whether the charging of electric vehicles is in a dangerous state,and the charging fault is warned in time.The real-time monitoring and early warning of electric vehicle charging process are realized.The experiment proves that the outlier detection method has good feasibility and accuracy for electric vehicle charging safety early warning.
electric vehiclecharging safetyoutlier detectionearly warning model
万方数据
维普
