Research On Fault Prediction Method Of Electric Vehicle Charging Pile Based On Data Mining

With the rapid development of the electric vehicle industry at home and abroad and the rapid increase in the number of electric vehicles,the demand for electric vehicle charging piles is increasing.Therefore,the management and maintenance of charging piles has become an important issue in the operation of charging piles.Due to the problems of insufficient information management,weak awareness of maintenance personnel,and lack of timely and effective emergency measures in the electric vehicle charging pile market,it is impossible to comprehensively and timely evaluate the quality of electric vehicle charging piles,which causes frequent failures of the charging pil es and causes a lot of losses.The reliability of electric vehicle charging piles cannot be ignored.Therefore,it is very important to find an effective method to predict the failure of electric vehicle charging piles.This article first introduces the research background of electric vehicle charging piles and the significance of fault prediction for cha rging piles.It also compares and investigates the development status of fault prediction and fault prediction methods for electric vehicle charging piles,and points out the existing fault prediction methods at home and abroad.Advantages and disadvantage s,and emphasized the high demand for fault prediction technology research in the field of electric vehicle charging pile fault prediction.Research and analysis of three commonly used fault prediction techniques,detailed comparison and analysis of several algorithms based on data-driven fault prediction techniques,detailed description of their advantages and disadvantages,and selection of decision tree algorithm as the fault prediction algorithm in this paper.Then,this paper uses the C4.5 decision tree algorithm to build a classification prediction model,and uses the standard UCI data set to train and predict the constructed model.However,due to the defect of the interdependence between condition attributes in the original C4.5 decision tree algorithm,The accuracy of the final prediction was affected,and the original algorithm was improved for this defect.The results of simulation and comparison experiments show that the model built by the improved algorithm improves the accuracy of prediction results and reduces the complexity of running time.Finally,in view of the problem of lack of effective preventive measures for electric vehicle charging pile failure,this paper uses C4.5 decision tree algorithm to build a charging pile failure prediction m odel,the collected K1K2 driving signal,electronic lock driving signal,emergency stop signal,The six parameters of access control signal,total harmonic distortion of voltage and total harmonic distortion of current are analyzed and processed,one part is used as training data,and the other part is used as test data to verify the prediction accuracy of the fault prediction model.And the improved C4.5 decision tree algorithm is used to establish the fault prediction model,and the final simulation exper iments are compared and verified to verify that the improved C4.5 decision tree algorithm model can more effectively predict the failure of the charging pile.