Electric vehicles (EVs) are essential for sustainable mobility and combating climate change. EV performance heavily relies on lithium-ion batteries (LIBs), which degrade over time, reducing driving range and increasing maintenance costs. Prolonged exposure to high states of charge (SOC) accelerates battery degradation, which can be mitigated by delaying full charging (\ours). However, successful implementation of \ours requires accurate predictions of user departure times to ensure vehicles reach full charge precisely before use. In this work, we propose Transformer-based real-time-to-event (TTE) model for accurate EV departure prediction. Our approach models each day as a TTE sequence by discretizing the timeline into grids, which are represented as tokens. Unlike previous methods primarily dependent on temporal dependency from historical patterns, our method leverages streaming contextual behavioral and environmental 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, significantly outperforming baseline models. Personalized fine-tuning further improves prediction accuracy, highlighting our approach’s potential for practical deployment of the \ours algorithm and its contribution to sustainable transportation systems.