Polycaprolactone (PCL)-based polymeric micelles are extensively used as drug delivery carriers to improve the bioavailability of poorly water soluble drugs due to their convenient tunability by varying functional groups on...
With the large-scale development of electric vehicles, in order to reduce the potential safety hazards in the charging process of electric vehicles, in this paper, through analyzing the characteristics of the faults in the charging process of electric vehicles and the charging and discharging fault location of electric vehicles based on artificial intelligence, a fault location method based on theoretical information fusion is proposed. The fault location method mainly includes three key modules which is fault data acquisition, address label analysis and information reverse traceability. Through the analysis of the hidden dangers of the electric vehicle charging process, the corresponding early warning process is analyzed by the established integrated safety protection model for electric vehicle charging. In this way, the structured design of the early warning system and the construction of the integrated charging and discharging safety early warning process for electric vehicles can effectively improve the charging safety of electric vehicles and promote the healthy development of electric vehicles.
In order to play the important role of electric vehicles to promote the realization of the 3060 double carbon target, electric vehicles have seen explosive growth. However, due to the tight symmetry between the number and distribution of electric vehicles and their corresponding charging facilities, the layout of charging facilities has higher requirements. This paper collects travel data in the form of a traffic travel questionnaire for electric vehicle users. Based on the vehicle parking demand model of the queuing theory and Monte Carlo simulation, the paper gives the number of stopping vehicles and the time of vehicles stopping in different places such as residential areas, workplaces, supermarket parking and roadside. In addition, based on the Bass prediction model, the main parameters are modeled in the model, and the price correction coefficient is introduced. The improved Bass model is used to predict the growth trend of electric vehicles in different regions in different years and in different incentive sites. By predicting the ownership of urban electric vehicles and accurately grasping the distribution and operation of electric vehicles, this paper can provide guidance and suggestions for the planning and construction of charging facilities in different regions, effectively reduce the investment cost of charging facilities and guide local governments to formulate reasonable planning schemes.
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