With the encouragement of national policies and the improvement of the public’s awareness of environmental protection, the number of electric vehicles in our country has increased significantly. It is necessary to reasonably add new charging stations. This paper uses machine learning methods to remove private charging pile data from the original vehicle driving data and combine the characteristics of the charging points to obtain the location of the charging demand point, and then use hierarchical clustering algorithm to select the location of the new charging station and use the machine learning model to provide a reference for the capacity selection of each new charging station. Using the data of 1,000 vehicles operating in Zhengzhou as a case analysis, the results show that the charging demand satisfaction rate has increased from 62.27% to 80.8%, and the average distance between the new charging stations and the poi is as low as 143m. The results show that the method proposed in this paper can meet the needs of as many electric vehicle owners as possible while having better charging convenience.
KEYWORDS: Failure analysis, Machine learning, Safety, Optimization (mathematics), Data modeling, Performance modeling, Lithium, Genetic algorithms, System on a chip, Resistance
In recent years, with the increase of the number of pure electric vehicles, the phenomenon of battery spontaneous combustion during charging is also emerging in an endless stream. For early fault early warning, we use a machine learning-based model to predict the probability of battery failure. We also use intelligent algorithm to optimize the hyperparameters of the model so that it can accurately predict the probability of battery failure after different time periods. Through our model, the driver or vehicle safety system can perceive the danger in advance and solve or avoid it in time.
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