Intelligent Control of Connected and Automated Vehicles and Powertrains for Cold Climates

适用于寒冷气候的互联自动化车辆和动力系统的智能控制

• 批准号: RGPIN-2020-04403
• 负责人: Shahbakhti, Mahdi
• 金额: $ 2.84万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=739947
• 关键词: Intelligent; Control; Connected; Automated; Vehicles

Connected and automated vehicles (CAVs) will become major parts of the Canadian transportation industry. By 2022, the majority of vehicles sold in the US and Canada will have embedded connectivity. In addition, major automotive companies have launched large-scale programs for deploying CAVs on the road over the next 10 years. CAVs reduce traffic congestion, improve mobility, and decrease vehicular energy consumption and greenhouse gas emissions. Currently in Canada, end users of the transportation industry consume about 30% of Canada's total energy, causing 24% of Canada's greenhouse gas emissions. The availability of V2X (vehicle to vehicle, infrastructure) data, along with vehicle automation, provides a great opportunity for Canadians to save energy and reduce their GHG emissions. The long-term vision of the proposed research is to enable energy-optimal CAVs for cold climate operations via advanced control methods and understanding major dynamics that affect vehicle energy consumption and emissions. The short-term objectives of this program include utilizing V2X data to design optimal and integrated control strategies that includes vehicle powertrain, heating, ventilation, air conditioning (HVAC), vehicle dynamics, and powertrain thermal management. The goal of the proposed research is to provide fundamental contributions towards reducing vehicle fuel consumption up to 34% and substantially reducing greenhouse gas emissions for operation in cold climates. This program centers on CAVs ranging from partial automation (L2) to high automation (L4). The long-term objectives include i) developing cold climate CAV control strategies by leveraging vehicle full autonomy and complete penetration of connectivity data, and ii) developing Artificial Intelligence methods based on peer-learning for integrated powertrain and thermal energy management. Despite the fact that over 85% of Canada's population live in regions with long cold winters, and energy consumption and emissions from vehicles increase drastically in cold climates, very little research has been done in the area of vehicle powertrain and HVAC controls for CAV operation in cold climates. This Discovery program will address the challenges of CAV powertrain and HVAC control, with both conventional and electrified powertrains. The research methodology will include experimental powertrain and vehicle studies, dynamical analysis, advanced predictive control techniques, and methods of machine learning for robust vehicle controls. The program will provide training to 14 highly qualified personnel and include outreach to high school students. The experimental work will be conducted in Edmonton (>1.3M metropolitan population) with average 180 days with the minimum temperature of the day = 0oC. The results will lead to a unique data bank of powertrain, HVAC, and emissions information for CAVs operating in cold climates. This unique data bank will be shared with the public and the research community.

联网和自动驾驶汽车（cav）将成为加拿大交通运输业的主要组成部分。到2022年，在美国和加拿大销售的大多数汽车将具有嵌入式连接。此外，各大汽车公司已经启动了在未来10年内大规模部署自动驾驶汽车的计划。自动驾驶汽车减少了交通拥堵，提高了机动性，减少了汽车的能源消耗和温室气体排放。目前在加拿大，运输行业的最终用户消耗了加拿大总能源的30%左右，造成了加拿大24%的温室气体排放。V2X（车对车，基础设施）数据的可用性，以及车辆自动化，为加拿大人节省能源和减少温室气体排放提供了一个很好的机会。这项研究的长期目标是通过先进的控制方法和了解影响车辆能耗和排放的主要动力学因素，使自动驾驶汽车能够在寒冷气候下运行，从而实现能源优化。该项目的短期目标包括利用V2X数据设计优化的综合控制策略，包括车辆动力系统、供暖、通风、空调（HVAC）、车辆动力学和动力系统热管理。拟议研究的目标是为在寒冷气候下运行的车辆减少高达34%的燃料消耗和大幅减少温室气体排放提供基础贡献。该项目以自动驾驶汽车为中心，范围从部分自动化（L2）到高度自动化（L4）。长期目标包括i)通过利用车辆完全自主和完全渗透连接数据来开发寒冷气候的自动驾驶汽车控制策略，以及ii)开发基于同行学习的集成动力总成和热能管理的人工智能方法。尽管事实上，超过85%的加拿大人口生活在冬季寒冷的地区，在寒冷的气候下，汽车的能源消耗和排放急剧增加，但在寒冷气候下，汽车动力总成和暖通空调控制方面的研究却很少。Discovery项目将解决CAV动力系统和HVAC控制的挑战，包括传统动力系统和电动动力系统。研究方法将包括实验动力总成和车辆研究，动态分析，先进的预测控制技术，以及鲁棒车辆控制的机器学习方法。该项目将为14名高素质人才提供培训，并包括向高中生提供服务。实验将在埃德蒙顿（拥有130万大都会人口）进行，平均气温为180天，最低气温为0℃。研究结果将为在寒冷气候下运行的自动驾驶汽车提供一个独特的动力系统、暖通空调和排放信息数据库。这个独特的数据库将与公众和研究界共享。