论文标题
带有现实世界旅行信息的插电式混合动力电动汽车的数据驱动能源管理策略
Data-driven Energy Management Strategy for Plug-in Hybrid Electric Vehicles with Real-World Trip Information
论文作者
论文摘要
本文介绍了针对插电式混合动力汽车的数据驱动的监督能源管理策略（EMS），该策略通过学习控制政策从完成的旅行中学习控制政策，从而利用车辆对云连接来提高能源效率。提出的EMS由两个层，一个云层和一个板载层组成。云层有两个主要任务：第一个任务是从历史旅行数据中学习EMS策略参数，第二个任务是沿着车辆请求的特定路线提供策略参数。板载层从云层接收到学习的策略参数，并使用模型预测控制方案计算动力总成能量管理问题的实时解决方案。拟议的EMS在3000多英里（48个独立驾驶周期）的现实旅行数据中进行了评估，该数据沿加利福尼亚州的三个通勤路线收集。对于这些路线，与基线EMS相比，拟议的算法在平均MPGE中显示3.3％，7.3％和6.5％。
This paper presents a data-driven supervisory energy management strategy (EMS) for plug-in hybrid electric vehicles which leverages Vehicle-to-Cloud connectivity to increase energy efficiency by learning control policies from completed trips. The proposed EMS consists of two layers, a cloud layer and an on-board layer. The cloud layer has two main tasks: the first task is to learn EMS policy parameters from historical trip data, and the second task is to provide the policy parameters along a certain route requested from the vehicle. The on-board layer receives the learned policy parameters from the cloud layer and computes a real-time solution to the powertrain energy management problem, using a model predictive control scheme. The proposed EMS is evaluated on more than 3000 miles (48 independent driving cycles) of real-world trip data, collected along three commuting routes in California. For the routes, the proposed algorithm shows 3.3%, 7.3%, and 6.5% improvement in average MPGe when compared to a baseline EMS.
