Evidential Deep Learning for Quantification of Uncertainty in Lithium-Ion Batteries Remaining Useful Life Estimation

Abstract

Lithium-ion batteries are widely used across diverse applications due to their high energy density, long cycle life, and fast charging capabilities. As battery-powered systems become increasingly critical, accurate estimation of the Remaining Useful Life (RUL) is essential for ensuring reliability, safety, and effective maintenance planning. This work investigates Evidential Deep Learning (EDL) for data-driven RUL estimation and introduces a novel risk-aware loss function designed to enhance both predictive accuracy and uncertainty quantification in the End-of-Life (EoL) region, where precise and trustworthy predictions are most needed. Using a publicly available dataset of lithium iron phosphate (LFP) cells, we benchmark the proposed approach against a baseline Conv–LSTM model, Monte Carlo (MC) Dropout, and Deep Ensembles. The results show that integrating the risk-aware loss into the EDL framework substantially improves the calibration of predictive uncertainty while achieving state-of-the-art accuracy near EoL. Unlike MC Dropout and Deep Ensembles, which exhibit increasing or unstable uncertainty as degradation accelerates, the proposed EDL model demonstrates a consistent reduction in uncertainty and significantly higher reliability in late-stage predictions. The findings indicate that the risk-aware evidential framework offers a reliable and computationally efficient solution for battery RUL estimation, enabling more informed decision-making in both safety-critical and consumer-oriented applications.