Enabling online maximum driving range prognostics in electric vehicles via uncertain event likelihood functions

Abstract

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.