Limited driving range is one of the major obstacles to the widespread application of electric vehicles (EVs). Accurately predicting the remaining driving range can effectively reduce the range anxiety of drivers. In this paper, a blended machine learning model was proposed to predict the remaining driving range of EVs based on real-world historical driving data. The blended model fuses two advanced machine learning algorithms of Extreme Gradient Boosting Regression Tree (XGBoost) and Light Gradient Boosting Regression Tree (LightGBM). The proposed model was trained to "learn" the relationship between the driving distance and the proposed features such as cumulative output energy of the motor and the battery, different driving patterns, and temperature of the battery). In addition, an "anchor (baseline) based" strategy was proposed and was seen to be able to effectively eliminate the unbalance distribution of dataset. The results of experiments suggest that our proposed anchor-based blended model has better performances with a smaller prediction error range of [-0.8, 0.8] as compared with previous methods. INDEX TERMS electric vehicle, remaining driving range estimation, machine learning, data mining.

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With the continuous accumulation of application data of various business information systems of network and informatization, a campus big data environment with distinctive characteristics of colleges and universities has been formed. Through the in-depth mining and analysis of the existing smart campus data using big data technology, based on the support of intelligent decision-making, personalized adaptive learning analysis and campus security warning of big data, and using the results to feed back the improvement of information literacy of university teachers and students in the era of big data, the construction of ubiquitous learning environment is carried out, which points out the direction for the construction of ubiquitous learning environment to explore the role of big data in improving the quality of education, promoting educational equity and optimizing educational philosophy, so as to improve the ubiquitous education environment.

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