Remaining Driving Range Estimation Framework for Electric Vehicles in Platooning Applications

Abstract

Range anxiety has been one of the largest issues for battery electric vehicle (BEV) adoption. Accurate estimation of EV remaining driving range (RDR) can alleviate this issue by informing drivers of appropriate timings and energy needed to propel their EVs to meet their needs, particularly during a long trip. Vehicle platooning with a short inter-vehicle distance has demonstrated significant energy saving benefits on high-way operation by reducing aerodynamic drag force. On the other hands, vehicle platooning also introduces uncertainties to aerodynamic drag coefficient estimation and model-based RDR estimation algorithms. Therefore, it is a challenging task to precisely estimate the RDR for EVs in vehicle platoon. This work proposes a novel EV RDR estimation method that integrates the real-time estimation of aerodynamic drag coefficient and the operation mode from advanced driver-assistance systems (ADAS) or platooning into a physics-based EV model to improve RDR estimation accuracy in highway operation. Simulation results demonstrate that the proposed estimation method can potentially reduce EV RDR estimation errors when compared to the baseline EV RDR estimation method which is based on historical data.