This study evaluates the impact of technical and economic factors related to electric vehicles and the impact of socio-demographic factors related to electric vehicle owners on annual electric vehicle mileage from a statistical perspective. The data set was analyzed using regression and correlation analyses using Ms Excel and several Python libraries. The influence of the socio-demographic characteristics of the respondents was estimated as minimal and requiring reassessment. It was shown that among the socio-demographic factors considered, only the age of vehicle owners correlates with the annual mileage of electric vehicles. It is shown that technical and economic parameters are much more closely related to the annual mileage of electric cars than socio-demographic parameters. Significant factors among the technical and economic ones were battery capacity, power consumption of the electric car, and the size of the respondent's locality.
A new energy vehicle is a product that combines modern technology with traditional battery technology. It has the advantages of energy saving, environmental protection, and long service life. How to collect and feedback the utilization rate of new energy vehicles is a big problem. This paper designs the car's operation monitoring system by studying the convolutional neural network, the purpose is to extract useful information through the monitoring of the car, and promote the development of new energy vehicles. This paper mainly analyzes the application loss function and IOU of the monitoring system of new energy vehicles through experimental methods, and compares them to highlight the characteristics and functions of the convolutional neural network in the monitoring system. The experimental results show that the loss value is below 0.17 after a hundred trainings, and gradually decreases to 0.01. The application of convolutional neural network in the monitoring of new energy vehicles is feasible.
This study evaluates the impact of technical and economic factors related to electric vehicles and the impact of socio-demographic factors related to electric vehicle owners on annual electric vehicle mileage from a statistical perspective. The data set was analyzed using regression and correlation analyses using Ms Excel and several Python libraries. The influence of the socio-demographic characteristics of the respondents was estimated as minimal and requiring reassessment. It was shown that among the socio-demographic factors considered, only the age of vehicle owners correlates with the annual mileage of electric vehicles. It is shown that technical and economic parameters are much more closely related to the annual mileage of electric cars than socio-demographic parameters. Significant factors among the technical and economic ones were battery capacity, power consumption of the electric car, and the size of the respondent's locality.
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