Customized Uncertainty Quantification of Parking Duration Predictions for EV Smart Charging

As electric vehicle (EV) demand increases, so does the demand for efficient smart charging (SC) applications. However, SC is only acceptable if the EV user's mobility requirements and risk preferences are fulfilled, i.e., their respective EV has enough charge to make their planned journey. To fulfill these requirements and risk preferences, the SC application must consider the predicted parking duration at a given location and the uncertainty associated with this prediction. However, certain regions of uncertainty are more critical than others for user-centric SC applications, and therefore, such uncertainty must be explicitly quantified. Therefore, this article presents multiple approaches to customize the uncertainty quantification of parking duration predictions specifically for EV user-centric SC applications. We decompose parking duration prediction errors into a critical component which results in undercharging, and a noncritical component. Furthermore, we derive quantile-based security levels that can minimize the probability of a critical error given a user's risk preferences. We evaluate our customized uncertainty quantification with four different probabilistic prediction models on an openly available semi-synthetic mobility data set and a data set consisting of real EV trips. We show that our customized uncertainty quantification can regulate critical errors, even in challenging real-world data with high fluctuation and uncertainty.