Driving Conditions of New Energy Logistics Vehicles Using Big Data Technology

Abstract

With the development of e-commerce and trade, China's logistics transportation demand has increased significantly. To improve the operation efficiency of new energy trucks, logistics transportation companies need scientific management methods. They need to analyze a large number of real driving conditions for new energy trucks. Additionally, to reduce new energy trucks' energy consumption and pollutant emissions, automobile manufacturers have increased the research and development of new energy trucks, and the analysis of new energy truck driving conditions is the basis for the technical development and evaluation of new models. The research in this article is based on an actual project of an automobile manufacturing company, consulting a large amount of relevant domestic and foreign literature, summarizing the current status of driving conditions at home and abroad, explaining the principle of data collection for the driving conditions of new energy trucks, and developing in-vehicle data for driving conditions based on the principle transmission method and remote transmission method. Using the membership function and K-means clustering algorithm to determine the attribute characteristics of the new energy truck driving condition analysis, a truck driving condition analysis model is built, the software function of the model is designed, and a small amount of sample data is used to import the model instance to verify the model effectiveness. Finally, based on the constructed new energy truck driving condition analysis model, big data technology was used to perform a big data analysis experiment on the actual operation data of 200 trucks of an automobile manufacturing enterprise. The Spark big data calculation framework was used to perform stream calculation and offline analysis calculations on a large amount of data from the new energy trucks. The results show that the operating efficiency of the new energy truck driving condition analysis method using big data technology is significantly higher than that of traditional technology. This study provides a theoretical basis for controlling the energy consumption and pollutant emissions of new energy trucks in logistics transportation, and provides management and logistics support for transportation logistics companies. The technical development and evaluation of the company's new models provided data references.

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