Fuzzy Classification Based Driving Distance Estimation for Electric Vehicles

Abstract

Electric vehicle technology is an essential research field for improving full-electric vehicle (FEVs) capabilities. Different subsystem parameters in the FEVs should be monitored on a regular basis. The better these subsystems are used, the better the FEVs' performance, life, and range become. Nowadays, estimation of the state of charge (SoC) of the batteries and the driving distance is the area not been standardized sufficiently. In this study, a novel fuzzy classification method (FCM) is proposed to make the exact driving distance estimation of FEVs. The proposed FCM considers the consumed power and parameters of the battery under dynamic conditions. A test location was selected for the proposed FCM and tested under 3 different test conditions, namely, no-load, half-load and full-load conditions. Also, the performance of FCM is studied under several slope conditions, and the result shows that if the battery voltage decreases then the power consumed by the vehicle is improved in the uphill travel and the battery voltage is normal and the power consumption of the vehicle is decreased in the downhill drive. Finally, the drive distance of the proposed FCM is determined. HIGHLIGHTS Fuzzy classification based driving distance estimation for full-electric vehicle is proposed Parameters of battery and power consumption has been considered under dynamic condition CAN communication is established between different subsystems of electric vehicle Three test conditions (no-load, half load, and full load) have been considered