Smart powertrains for optimized fuel economy and reduced emissions

智能动力系统可优化燃油经济性并减少排放

• 批准号: 402057-2011
• 负责人: LashgarianAzad, Nasser
• 金额: $ 1.68万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=550760
• 关键词: Smart; powertrains; optimized; fuel; economy

The past decades have witnessed tremendous advances in automotive systems and technologies. However, further enhancements to achieve better performance are still highly desirable. The proposed research contains the development of predictive control strategies which utilize real-time traffic data, GPS measurements, 3-D road maps and information provided by modern vehicle on-board probes to enhance the efficiency of existing internal combustion engine powertrains. These predictive strategies can be implemented using engine and transmission control systems, and also, available driver assistance technologies, to achieve a fuel-efficient, safe and comfortable driving with lower emissions, without considerable effects on the travel time. The findings are critical to improve the performance of more complex powertrain architectures with extra components, such as micro-hybrid, mild-hybrid, full-hybrid and plug-in hybrid drive systems, which are now becoming widely available. This research program paves the way for the evolution of next-generation smart hybrid powertrains which use intelligent transportation systems, global positioning systems, geographic information systems and advanced driver assistance systems in an optimal manner. The automotive industry is very important for the Canadian economy. Competitive pressures and globalization of markets are introducing new challenges to Canada's automotive industry. It is expected that the predictive control strategies developed in this research program will cause approximate fuel savings up to 15% and pollutant emissions reductions of about 5-10% in practice, without affecting overall vehicle travel times. The local companies can easily implement the devised control algorithms to create new opportunities to export their products into the foreign markets. The training of highly qualified personnel is also important to the Canadian economy. The requested funding from NSERC will support several graduate students to carry out the proposed research goals. It is expected that these students will have employment opportunities within the automotive and transportation industries.

在过去的几十年里，汽车系统和技术取得了巨大的进步。然而，仍然非常期望进一步增强以实现更好的性能。拟议的研究包括预测控制策略的开发，该策略利用实时交通数据，GPS测量，3D道路地图和现代车辆车载探头提供的信息，以提高现有内燃机动力系统的效率。这些预测策略可以使用发动机和变速器控制系统以及可用的驾驶员辅助技术来实现，以实现燃油效率，安全和舒适的驾驶，同时降低排放，而不会对行驶时间产生显著影响。这些发现对于提高具有额外组件的更复杂动力系统架构的性能至关重要，例如微混合动力系统，轻度混合动力系统，全混合动力系统和插电式混合动力驱动系统，这些系统现已广泛使用。该研究计划为下一代智能混合动力系统的发展铺平了道路，这些系统以最佳方式使用智能交通系统，全球定位系统，地理信息系统和先进的驾驶员辅助系统。汽车工业对加拿大经济非常重要。竞争压力和市场全球化给加拿大汽车工业带来了新的挑战。预计本研究计划中开发的预测控制策略将在实践中节省约15%的燃料，减少约5-10%的污染物排放，而不会影响整体车辆行驶时间。当地公司可以轻松地实施设计的控制算法，创造新的机会，将其产品出口到国外市场。高素质人才的培养对加拿大经济也很重要。NSERC要求的资金将支持几名研究生开展拟议的研究目标。预计这些学生将在汽车和运输行业中获得就业机会。