<em>dRefine</em> : A Data-Driven Deep Reinforcement Learning Model for Battery Swapping Station Network Refinement

In recent years, battery swapping services have experienced rapid growth, positioning themselves as a significant alternative to traditional charging stations. One determinant of their success lies in the ability to continuously refine the battery swapping station network, <em>i.e</em>., redistributing batteries across stations, to meet the fluctuating demands of electric scooters. However, achieving such network refining is far from straightforward and presents two major challenges. First, for regions where stations have been recently deployed, the lack of historical data makes it difficult to accurately predict user demand. Secondly, effective battery scheduling is inherently complex, as it requires meticulous coordination across all stations to dynamically balance users’ fluctuating long-term demand. To tackle these challenges, we propose <em>dRefine</em>, a novel data-driven battery swapping station network refinement system. Specifically, to address the first challenge, we develop a station-level spatiotemporal representation-guided conditional diffusion model, which leverages data from regions with established networks to predict demand in regions lacking historical data. For the second challenge, we develop a demand-oriented deep reinforcement learning model that dynamically optimizes battery scheduling strategies. By continuously learning from real-time demand patterns and operational feedback, it ensures efficient and adaptive battery redistribution across the entire network. We evaluate dRefine using a real-world dataset encompassing 388 stations, 41,358 batteries, and 108,574 electric scooter users. Extensive experimental results demonstrate that our method consistently outperforms state-of-the-art approaches by an average of 29.28%.