Adaptive Multiresolution Energy Consumption Prediction for Electric Vehicles

This paper introduces an adaptive multiresolution framework for electric vehicle (EV) energy consumption estimation with real-time capability. Three key parameters, namely powertrain efficiency, wind speed, and rolling resistance, are adaptively estimated using a two-step nonlinear iterative algorithm. Based on this algorithm, a multichannel framework for high-resolution powertrain efficiency estimation is introduced. Employing the “connected vehicles” concept, more reliable trip level energy estimates are achieved by sharing sensed environmental information. In addition, state-of-charge aware energy cost prediction methods of different accuracy and complexity are introduced to combat range anxiety and reduce computational complexity during times of high energy reserves. A variety of detailed simulations illustrate the introduced concept and its benefits for future EV systems.