In recent years, with the development of new energy technology and the country’s strong support for electric vehicles, there is a lack of effective electric vehicle charging fault analysis and diagnosis methods at this stage. A comprehensive analysis of the working principle of the charging process of electric vehicles, based on the clarification of the failure mechanism of the power battery and charging equipment, analyzes the fault-related factors affecting the power battery and charging equipment from multiple angles, and summarizes the relationship between the power battery and charging equipment. The feature parameters related to equipment failure are discretized by the k-means clustering algorithm. Using the optimized FP-Growth algorithm based on weights, the association rules between the power battery and charging equipment failures and the characteristic parameters of the failure factors are mined, and the correlation of the failures is analyzed based on the association rules, and the correlation between the failure factors and the failures is obtained relevant level.

The multi-objective optimization method for distributed energy storage configuration has the problem of high network loss expectation. A multi-objective optimization method for distributed energy storage configuration under distribution network operation constraints is designed to solve the above problems. The intra-day charge/discharge balance is used as a criterion to identify the characteristics of distributed energy storage configuration, calculate the network loss sensitivity of nodes, construct a siting and capacity setting model, and integrate multiple power quality indicators to improve the multi-objective optimization model under the distribution network operation constraint. The experimental results show that the mean values of network loss expectations for the distributed energy storage configuration multi-objective optimization method in the paper and the other two distributed energy storage configuration multi-objective optimization methods are: 226.731 kW, 270.762 kW, and 276.728 kW, respectively, indicating that the designed distributed energy storage configuration multi-objective optimization method is more feasible after fully considering the distribution network operation constraints.

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