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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=760281
• 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.

连接和自动化的车辆（CAV）将成为加拿大运输行业的主要部分。到2022年，在美国和加拿大出售的大多数车辆都将嵌入连接性。此外，在未来10年内，主要的汽车公司已经启动了大型计划，用于在路上部署骑士。骑士减少交通拥堵，改善活动能力并减少车辆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％，造成加拿大温室气体排放的24％。 V2X（车辆，基础设施）数据以及车辆自动化的可用性为加拿大人提供了节省能源并减少温室气体排放的绝佳机会。拟议的研究的长期视野是通过高级控制方法和理解影响车辆能耗和排放的主要动态来使能量最佳的骑士进行寒冷的气候操作。该计划的短期目标包括利用V2X数据设计最佳和集成的控制策略，包括车辆动力总成，供暖，通风，空调（HVAC），车辆动力学和动力总成热管理。拟议的研究的目的是为在寒冷的气候下的运营中减少最高34％的车辆燃料消耗提供基本贡献。该程序以骑士为中心，从部分自动化（L2）到高自动化（L4）。长期目标包括i）通过利用车辆充分的自主权和完整的连接数据渗透来制定寒冷的气候CAV控制策略，ii）开发基于同伴学习的人工智能方法，用于集成动力总成和热能管理。尽管有超过85％的加拿大人口居住在寒冷冬季长的地区，而在寒冷的气候下，能源消耗和车辆的排放却大大增加了，但在汽车动力总成和HVAC控制方面的研究很少，用于在寒冷气候下进行CAV操作。该发现计划将通过常规和电动动力总成解决CAV动力总成和HVAC控制的挑战。研究方法将包括实验性动力总成和车辆研究，动态分析，高级预测控制技术以及用于稳健车辆控制的机器学习方法。该计划将为14名高素质的人员提供培训，并包括向高中生的宣传。实验工作将在埃德蒙顿（> 130万个大都市人口）中进行，平均为180天，而当天的最低温度= 0oc。结果将导致在寒冷气候下运行的骑士的动力总成，HVAC和排放信息的独特数据库。这个独特的数据库将与公众和研究界共享。