基于物理模型指导的远程电动运载车能量管理在线贝叶斯框架
A Physics Model-Guided Online Bayesian Framework for Energy Management of Extended Range Electric Delivery Vehicles
摘要：
通过基于优化的能量管理策略（EMS）来提高混合动力电动车辆（HEV）和增程式电动车辆（EREV）的燃料经济性一直是交通领域的活跃研究领域。然而，这是很难应用基于优化的EMS目前正在使用的EREV，因为不充分的知识是已知的未来的行程，因为这样的方法是计算昂贵的大规模部署。因此，过去的大多数研究都是在标准驾驶循环或记录的来自过去真实驾驶循环的高分辨率数据上进行验证的。本文改进了一种在使用中的基于规则的EMS，该EMS用于配备双向车辆到云连接的交付车队。描述了一种物理模型引导的在线贝叶斯框架，并在大量用于最后一英里包裹递送的EREV的使用中驾驶样本上进行了验证。该框架包括：数据库、预处理模块、车辆模型和在线贝叶斯算法模块。它使用历史0.2 Hz分辨率行程数据作为输入，并将更新的参数输出到车辆上的发动机控制逻辑，以减少下一次行程的燃油消耗。这项工作的主要贡献是一个框架，提供了一个立即解决方案，减少燃料使用的在用EREV。该框架还在实际路线上运行的真实世界EREV交付车辆上进行了演示。结果显示，在155次实际交付行程中，测试车辆的燃料消耗平均减少12.8%。所提出的框架可扩展到其他EREV应用，包括乘用车，公交车，和其他职业车辆的行程是类似的日常。
英文摘要：
Increasing the fuel economy of hybrid electric vehicles (HEVs) and extended range electric vehicles (EREVs) through optimization-based energy management strategies (EMS) has been an active research area in transportation. However, it is difficult to apply optimization-based EMS to current in-use EREVs because insufficient knowledge is known about future trips, and because such methods are computationally expensive for large-scale deployment. As a result, most past research has been validated on standard driving cycles or on recorded high-resolution data from past real driving cycles. This paper improves an in-use rule-based EMS that is used in a delivery vehicle fleet equipped with two-way vehicle-to-cloud connectivity. A physics model-guided online Bayesian framework is described and validated on large number of in-use driving samples of EREVs used for last-mile package delivery. The framework includes: a database, a preprocessing module, a vehicle model and an online Bayesian algorithm module. It uses historical 0.2 Hz resolution trip data as input and outputs an updated parameter to the engine control logic on the vehicle to reduce fuel consumption on the next trip. The key contribution of this work is a framework that provides an immediate solution for fuel use reduction of in-use EREVs. The framework was also demonstrated on real-world EREVs delivery vehicles operating on actual routes. The results show an average of 12.8% fuel use reduction among tested vehicles for 155 real delivery trips. The presented framework is extendable to other EREV applications including passenger vehicles, transit buses, and other vocational vehicles whose trips are similar day-to-day.
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