Influence Analysis of Driving Style on the Energy Consumption of an Electric Vehicle Through PID Signals Study

This research analyzes how driving style affects the energy consumption of a Kia Soul electric vehicle, studying the signals of the Identification Parameters (PID) in the city of Cuenca, Ecuador. A Real Driving Emissions (RDE) cycle including urban, rural, and highway segments is described, and it is observed that acceleration is a variable directly related to the energy consumption of the electric vehicle. This is more evident in highway areas where speed limits are higher than in urban areas, which makes the vehicle require higher energy consumption. As a result, a 31.14% increase in road consumption can be verified compared to the urban area. The unit density identifies the type of driving (conservative, normal, and aggressive) on the road by means of acceleration profiles and their distribution range. With the implementation of Machine Learning architecture, it is possible to estimate the most important variables, such as accelerator pedal open position (APS), vehicle speed sensor (VSS), and longitudinal acceleration (Ax), in relation to the state of charge (SOC), after applying an ANN to the model. This achieved a prediction with a determination factor of 0.9866 compared to the actual vehicle range.