Enabling Delayed-Full Charging Through Transformer-Based Real-Time-to-Departure Modeling for EV Battery Longevity
This work addresses battery longevity for EV users, offering a practical solution to reduce degradation, but it is incremental as it builds on existing prediction methods with a novel model adaptation.
The paper tackles the problem of lithium-ion battery degradation in electric vehicles by delaying full charging until just before departure, requiring accurate departure time prediction. They propose a Transformer-based real-time-to-event model that outperforms baseline models on a real-world dataset of 93 users, effectively capturing irregular departure patterns.
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.