The remaining driving range of an electric vehicle (EV) is a complex function of various types of data with different formats that need to be extracted and collected from different sources using big data analytics. In our previous project, we had designed a big data framework to extract, classify, and pre-analyze the data regarding the route, terrain, driving behavior, weather, etc. from different resources, and find the correlations between the data and range estimation.
In this project, we Incorporate and analyze the ancillary power consumed mainly by the heating, ventilating, and air conditioner (HVAC) system in calculating the driving range of the vehicle using big data analysis techniques. Moreover, the current version of the algorithm estimates the range based on certain assumptions about the driving conditions of each trip such as weather, driving behavior, road condition, etc. However, the driving conditions are subject to change due to unpredicted events along the trip (such as accidents, sudden climate changes, etc.) which can make the predicted range inaccurate. Therefore, we design an adaptive structure to update the predicted power consumption using weather forecast and traffic report information in real-time, to compensate for the changes in environmental and driving conditions.
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