用Transformer预测电动车出发时间，延后充满电以延长电池寿命
Enabling Delayed-Full Charging Through Transformer-Based Real-Time-to-Departure Modeling for EV Battery Longevity
- 将每日时间划分为网格令牌，用Transformer建模出发时间序列
- 在93人真实数据上，准确捕捉不规律出行模式，优于基线模型
- 适合关注电池健康与可持续出行的电动汽车用户
电动汽车是可持续出行的关键，但其锂离子电池在长时间高充电状态（SOC）下会加速老化。通过将充满电延迟至出发前一刻可缓解此问题，这需要精准预测用户出发时间。本文提出基于Transformer的实时到出发时间（TTE）模型，将每一天表示为基于网格的时间序列令牌。不同于依赖历史时序模式的方法，本方法利用流式上下文信息进行预测。在包含93名用户的实地研究及被动手机数据评估中，该方法有效捕捉了个体内不规则的出行模式，显著优于基准模型。结果表明， ours算法具备实际部署潜力，对可持续交通系统具有重要贡献。
原文摘要 · Abstract (English)
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.
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