促进货运卡车电池换电服务的时空需求预测
Facilitating Battery Swapping Services for Freight Trucks with Spatial-Temporal Demand Prediction
AI总结：
本文利用时空需求预测模型预测重型卡车交通模式，并指导电池换电服务的优化分配，通过分析2500英里高速数据，发现初期宜用移动换电站，成熟后宜用固定站点。
摘要：
重型卡车电气化为减少碳排放、迈向碳中和未来提供了重大机遇。然而，电池能量有限和重型卡车自重带来的固有挑战导致续航里程缩短和充电时间延长。因此，电池换电服务成为这些卡车的理想解决方案。本文采用双管齐下的方法来探究此类服务的潜力并提升其效率。首先，采用时空需求预测模型来预测未来数小时的交通模式。随后，预测结果指导优化模块进行高效的电池分配和部署。通过分析覆盖超过2500英里高速公路网络上的重型卡车数据，我们的模型和分析强调了预测/机器学习在促进未来决策中的价值。特别是，我们发现实施电池换电服务的初始阶段倾向于移动电池换电站，但随着系统成熟，固定站点更为可取。
英文摘要：
Electrifying heavy-duty trucks offers a substantial opportunity to curtail carbon emissions, advancing toward a carbon-neutral future. However, the inherent challenges of limited battery energy and the sheer weight of heavy-duty trucks lead to reduced mileage and prolonged charging durations. Consequently, battery-swapping services emerge as an attractive solution for these trucks. This paper employs a two-fold approach to investigate the potential and enhance the efficacy of such services. Firstly, spatial-temporal demand prediction models are adopted to predict the traffic patterns for the upcoming hours. Subsequently, the prediction guides an optimization module for efficient battery allocation and deployment. Analyzing the heavy-duty truck data on a highway network spanning over 2,500 miles, our model and analysis underscore the value of prediction/machine learning in facilitating future decision-makings. In particular, we find that the initial phase of implementing battery-swapping services favors mobile battery-swapping stations, but as the system matures, fixed-location stations are preferred.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
