The increasing adoption of electric vehicles (EVs) demands accurate methodologies for predicting their maximum driving range (MDR) under dynamic and uncertain operating conditions. Monte Carlo simulations (MCs) serve as a fundamental tool for MDR prognostics. However, their enormous computational cost makes real-time implementation impractical. In order to address this challenge, we propose a novel framework that replaces MC-based methods with Uncertain Event Likelihood Functions (UELFs) for real-time driving range prognostics, significantly reducing computational overhead while maintaining predictive accuracy. The UELF framework leverages probabilistic modeling and efficient numerical solutions to predict the likelihood of end-of-power availability events, complemented by machine learning (ML) models such as a stochastic dropout-based Gated Recurrent Unit for vehicle speed and a Light Gradient Boosting Machine for energy consumption prediction. These models are trained on data from various geographic and environmental conditions, ensuring generalization and robustness. Test results confirm that the UELF-based approach achieves accuracy comparable to MC simulations while reducing computational time by over 99 %, enabling seamless integration into online applications. Combining state-of-the-art ML methodologies with advanced uncertainty quantification strategies successfully bridges the gap between abstract mathematical models and practical engineering solutions, offering a scalable and efficient tool for advancing electromobility and supporting real-time decision-making algorithms in EV energy management and route planning.
Bustos et al. (Tue,) studied this question.