Profit-aware battery swapping station energy scheduling via hybrid hierarchical deep reinforcement learning
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影响因子：7.7
DOI码：10.1016/j.trd.2026.105236
教研室：Q. Zhou, M. Huang, H. Tang, J. Cheng, J. Wu
发表刊物：Transportation Research Part D: Transport and Environment
关键字：Battery swapping stations; Intelligent energy scheduling; Electric vehicles; Deep reinforcement learning; Hybrid hierarchical control framework
摘要：As urban transportation electrification advances, range anxiety has become a key concern for electric vehicles (EVs). Battery swapping stations (BSS) offer a fast and efficient solution, yet traditional scheduling methods struggle to balance profitability with operational risk in dynamic environments. To address this, we propose a data-driven optimization method. First, historical data is used to derive optimal operational decisions, and an EV battery swapping demand forecasting model is built. Based on the forecasted demand and historical strategies, a long short-term memory network predicts the BSS's overall charging and discharging power. A double deep Q-network is then employed to allocate this power to individual batteries, ensuring timely swaps. Validation using real operational data from Chengdu, China, shows the proposed method effectively meets battery swapping demand, enhances scheduling efficiency and station profitability, and reduces peak loads and power fluctuations, demonstrating the potential for practical application in managing smart EV infrastructure.
论文类型：期刊论文
学科门类：工学
文献类型：J
卷号：153
页面范围：105236
是否译文：否
发表时间：2026-01-24
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/abs/pii/S1361920926000295