New High Power Density Hybrid Drive Package Family for the Rail and the Automotive Applications with Energy Storage Capabilities
New High Power Density Hybrid Drive Package Family for the Rail and the Automotive Applications with Energy Storage Capabilities
批准号:
DDG-2015-00033
负责人:
Youssef, Mohamed
金额:
$0.73万
依托单位国家:
加拿大
项目类别:
Discovery Development Grant
财政年份:
2016
资助国家:
加拿大
项目状态:
已结题
起止时间:
2016-01-01 至 2017-12-31
中文摘要
受当地和全球变暖问题以及汽油和柴油价格飙升的推动,对绿色和高效汽车、地铁以及铁路机车的需求不断增加。该研究计划将推出创新的绿色和高效的推进驱动包,并将服务于汽车和铁路的地铁和机车部门。该计划还将整合无线电力传输技术,为铁路和汽车汽车供电。在铁路行业,这将是革命性的,因为它将消除路边电力系统变电站和轨道的需要。这意味着在运输系统的运营中节省了破坏性的成本。将开发新的软件包,以确保根据车辆重量和路线负载循环成功设计驱动包。该团队将以多学科的眼光研究电机、逆变器、电池和接口算法。这将使电机设计师,逆变器开发人员和机电集成商与软件编程能力的培训。在研究计划中,将开发新型驱动包,在电动机运行期间,功率流向逆变器/电机,制动能量通过双向逆变器反馈到电池,直到零速度,从而延长摩擦制动器的寿命。能量存储介质可以是电池、超级电容器(SC)、飞轮或它们的组合。解决方案的选择取决于驾驶模式、路线和重量。该计划的目标是:(1)为混合燃料电池(FC)/蓄电池车辆设计一个动力传动系统的实例,以提高燃料效率并减少排放到环境中的主要污染物;(2)构建代表任何城市(如多伦多和温哥华)的驾驶模式或铁路线形的软件包,并将其用于设计和验证车辆和路线模型;以及(3)建立测试台架,该测试台架按比例缩小并将任何内燃机(ICE)车辆转换为混合燃料电池/电池/SC车辆。该计划将在以下方面对加拿大产生重大影响:(1)在当地建立研究专门知识和基础设施;(2)通过创造就业机会和将开发的技术作为非利基产品使用,对UOIT社区产生影响;(3)减少污染。本研究项目基于申请人强大的高素质人才培养历史,将培养3名博士,8名硕士和4名本科生设计电机,构建逆变器和编程接口算法,以将组件集成在一起;所有这些都着眼于性能改进。该研究计划不仅关注吸引了巨大研究关注的动力传动系统的更好选择,而且还关注电源管理,更好的封装和控制算法的智能技术,申请人拥有丰富的工业/学术经验。
英文摘要
Driven by the local and global warming problem, and the soaring prices of gas and diesel; there is relentless need for green and efficient cars, metros as well as rail locomotives. The research program will introduce innovative green and efficient propulsion drive packages and will serve the automotive and the rail's metro and locomotive sectors. This program will also integrate the wireless power transfer technology in powering rail and automotive cars. In the rail industry, this will be revolutionary as it will eliminate the need of the wayside power system substations and rails. This means a disruptive cost savings in the operation of transit systems. New software packages will be developed to guarantee successful design of drive packages based on vehicle weight and route load cycles. The team will work on motors, inverters, batteries, and interface algorithms with a multidisciplinary vision. This will allow the training of motor designers, inverter developers, and electromechanical integrators with software programming capabilities. In the research program, novel drive packages will be developed, where during motoring, the power flows to the inverter/motor with the braking energy fed back to the battery through the bidirectional inverter until zero speed, resulting in a longer friction brake life span. The energy storage media can be a battery, super capacitors (SC), a flywheel or a combination of them. The selection of the solution relies on the driving pattern, route and weight. The objectives of the program are to (1) design an example of a power train for a hybrid fuel cell (FC)/battery vehicle that improves fuel efficiency and reduces the main pollutants emitted to the environment; (2) build software packages that represent the driving pattern or rail alignment in any city, such as Toronto and Vancouver, and use it in designing and validating vehicle and route models; and (3) build test bench that scales down and transform any internal combustion engine (ICE) vehicle to hybrid FC/battery/SC vehicle. This program will have a major impact on Canada, in terms of (1) the establishment of research expertise and infrastructure in the local area; (2) the impact on the UOIT community through creating jobs and the use of the developed technologies as non-niche products; (3) and pollution reduction. This research program, based on the applicant's strong high qualified personnel training history, will train 3 Ph.D, 8 M.Sc, and 4 undergraduate students to design a motor, build an inverter, and program interface algorithms for integrating the components together; all with wide eyes on performance improvement. This research program will not only focus on better options for the power train, which has attracted huge research attention, but also intelligent techniques for power management, better packaging, and control algorithms, in which the applicant possesses extensive industrial/academic experience.
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