Enabling Delayed-Full Charging Through Transformer-Based Real-Time-to-Departure Modeling for EV Battery Longevity

TL;DR

This paper introduces a Transformer-based model that predicts EV departure times accurately using real-time streaming data, enabling delayed full charging to extend battery life and promote sustainable transportation.

Contribution

The paper presents a novel Transformer-based real-time-to-event model that leverages streaming contextual data for precise EV departure prediction, improving over previous methods.

Findings

Outperforms baseline models in real-world EV departure prediction

Effectively captures irregular departure patterns within individual routines

Demonstrates potential for practical deployment in sustainable transportation

Abstract

Electric vehicles (EVs) are key to sustainable mobility, yet their lithium-ion batteries (LIBs) degrade more rapidly under prolonged high states of charge (SOC). This can be mitigated by delaying full charging \ours until just before departure, which requires accurate prediction of user departure times. In this work, we propose Transformer-based real-time-to-event (TTE) model for accurate EV departure prediction. Our approach represents each day as a TTE sequence by discretizing time into grid-based tokens. Unlike previous methods primarily dependent on temporal dependency from historical patterns, our method leverages streaming contextual information to predict departures. Evaluation on a real-world study involving 93 users and passive smartphone data demonstrates that our method effectively captures irregular departure patterns within individual routines, outperforming baseline models. These results highlight the potential for practical deployment of the \ours algorithm and its contribution to sustainable transportation systems.