Accurate Remaining Range Estimation for Electric Vehicles

Abstract

EVs (Electric vehicle) generally have only around 22% driving ranges compared with ICEVs (Internal combustion engine vehicle) with a similar price range. Running out of the EV battery SoC (State of charge) while driving gives the same inconvenience as a vehicle breakdown. In this paper, we emphasize that an accurate remaining range estimation can efficiently mitigate the range anxiety of EV drivers. Most EV drivers reserve 30% of the on-dash estimated remaining range gauge of their EV because they do not trust the current remaining range estimation accuracy of production EVs. In other words, an accurate remaining range estimation is equivalent to increasing the EV battery capacity up to 30%.

Just like the analogous concepts used in the power estimation of digital circuits, a model-based remaining range estimation consists of the two consecutive steps, a driving profile estimation and a power consumption estimation using the power model. In this paper, we focus on increasing the accuracy of the power model. We come up with a hybrid modeling methodology combining a physics equation based model with empirical data. We validate the accuracy of the hybrid model in the remaining range estimation with the target EV. We collect the power consumption, velocity, road inclination, etc. of the EV in every half second with an onboard monitoring system, a perform multivariable linear regression and create an accurate EV power model. The proposed remaining range estimation yields only 2.52% error while the state-of-the-art model-based EV remaining range estimation shows 9.33% error when the same future route and speed estimation are given.