Exergy-Wise Predictive Control of Building and Automotive Energy Systems

建筑和汽车能源系统的火用预测控制

基本信息

  • 批准号:
    RGPIN-2019-04601
  • 负责人:
  • 金额:
    $ 1.97万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Discovery Grants Program - Individual
  • 财政年份:
    2019
  • 资助国家:
    加拿大
  • 起止时间:
    2019-01-01 至 2020-12-31
  • 项目状态:
    已结题

项目摘要

Building and automotive energy systems account for 57% of total energy use by consumers in Canada, while causing 36% of Greenhouse Gas (GHG) Emissions in Canada. The goal of this program is to develop new control methods to minimize energy consumption and GHG emissions from building and automotive energy systems. The short term objective of this program is to create a unified modeling and control theory for energy systems based on an ordered metric, “exergy,” i.e., the maximum theoretically available energy that can do work. This program will create a new paradigm for exergy-wise control through a novel theory based on the Second Law of Thermodynamics, Exergy Flow Controls, and Feedback Control Stability. The PI's recent results show exergy-wise control of Mechanical-Thermal-Chemical (MTC) energy systems provides opportunity for significant (7-36%) energy savings. Building upon the PI's results, the proposed research approach will include fundamental multi-physics exergy destruction modeling, exergy surface shaping, and thermodynamic flow control. The research program will center on MTC energy systems; applications of the theory will be demonstrated for internal combustion engines (ICEs) and building heating, ventilation, and air conditioning (HVAC) systems that cause over 46% of total energy use by consumers in Canada.***The long term goal is to extend the unified modeling and control theory framework from this program to include broad Mechanical-Thermal-Chemical & Electrical (MTCE) energy systems (e.g., hybrid electric vehicles) and connected MTCE energy systems (e.g., buildings-to-power grid systems). ******This program will train 11 Doctoral, Master of Science (MSc), and Bachelor of Science (BSc) students for developing unique skills to minimize energy consumption of MTC systems by using novel and powerful techniques of exergy-wise controls. The resulting knowledge from this program will be transferred to the relevant industry through planned industry short courses. In addition, new courses will be developed that will become available on-line for public use and outreach to the global research community.******Intellectual Merit: There is a significant knowledge gap between the controls research community and the thermodynamic research community that must be filled to enable exergy-wise control methods for energy systems. This research program aims to fill this knowledge gap.******Broader Impacts: The new exergy-wise control theory will be applicable to all mechanical-thermal-chemical systems. In particular, the application of the theory to ICEs in this program is anticipated to make a major contribution to the development of fuel-efficient ICEs that are widely used in automotive, power generation, oil & gas, marine, and rail industries. The second direct impact area includes energy saving for commercial & residential buildings HVAC systems that cause over 16% of the total energy use by consumers in Canada. **
建筑和汽车能源系统占加拿大消费者总能源使用量的57%,同时造成加拿大36%的温室气体(GHG)排放。该计划的目标是开发新的控制方法,以最大限度地减少建筑和汽车能源系统的能源消耗和温室气体排放。该计划的短期目标是建立一个统一的建模和控制理论的能源系统的基础上有序的度量,“火用”,即,理论上可以做功的最大能量。该计划将通过基于热力学第二定律、火用流量控制和反馈控制稳定性的新理论创建火用控制的新范式。PI最近的结果表明,机械-热-化学(MTC)能源系统的有效能控制提供了显着(7-36%)节能的机会。基于PI的结果,建议的研究方法将包括基本的多物理场(火用)破坏建模,(火用)表面成形和热力学流量控制。该研究计划将以MTC能源系统为中心;该理论的应用将被证明用于内燃机(ICE)和建筑供暖,通风和空调(HVAC)系统,这些系统占加拿大消费者总能源使用量的46%以上。长期目标是将该计划的统一建模和控制理论框架扩展到包括广泛的机械-热-化学和电气(MTCE)能源系统(例如,混合电动车辆)和连接的MTCE能量系统(例如,建筑物到电网系统)。****** 该计划将培训11名博士,理学硕士(MSC)和理学学士(BSC)学生,以开发独特的技能,通过使用新颖而强大的控制技术来最大限度地减少MTC系统的能耗。由此产生的知识,从这个程序将通过计划的行业短期课程转移到相关行业。此外,还将编制新的课程,供公众在线使用,并与全球研究界联系。智力优势:控制研究界和热力学研究界之间存在着巨大的知识差距,必须填补这一差距,才能实现能源系统的有效能控制方法。本研究旨在填补这一知识空白。*更广泛的影响:新的有效能控制理论将适用于所有的机械-热-化学系统。特别是,该理论在该计划中的ICE的应用预计将对广泛应用于汽车,发电,石油和天然气,海洋和铁路行业的节能ICE的发展做出重大贡献。第二个直接影响领域包括商业和住宅建筑暖通空调系统的节能,这些系统占加拿大消费者总能源使用量的16%以上。**

项目成果

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Shahbakhti, Mahdi其他文献

Data-Driven Model Learning and Control of RCCI Engines based on Heat Release Rate
基于热释放率的 RCCI 发动机数据驱动模型学习和控制
  • DOI:
    10.1016/j.ifacol.2022.11.249
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Sitaraman, Radhika;Batool, Sadaf;Borhan, Hoseinali;Velni, Javad Mohammadpour;Naber, Jeffrey D.;Shahbakhti, Mahdi
  • 通讯作者:
    Shahbakhti, Mahdi
Real-time modeling of ringing in HCCI engines using artificial neural networks
  • DOI:
    10.1016/j.energy.2017.02.137
  • 发表时间:
    2017-04-15
  • 期刊:
  • 影响因子:
    9
  • 作者:
    Bahri, Bahram;Shahbakhti, Mahdi;Aziz, Azhar Abdul
  • 通讯作者:
    Aziz, Azhar Abdul
Input-output Data-driven Modeling and MIMO Predictive Control of an RCCI Engine Combustion
RCCI 发动机燃烧的输入输出数据驱动建模和 MIMO 预测控制
  • DOI:
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Khoshbakht Irdmousa, Behrouz;Naber, Jeffrey Donald;Mohammadpour Velni, Javad;Borhan, Hoseinali;Shahbakhti, Mahdi
  • 通讯作者:
    Shahbakhti, Mahdi
Identification of State-space Linear Parameter-varying Models Using Artificial Neural Networks
使用人工神经网络识别状态空间线性参数变化模型
  • DOI:
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Bao, Yajie;Mohammadpour Velni, Javad;Basina, Aditya;Shahbakhti, Mahdi
  • 通讯作者:
    Shahbakhti, Mahdi
Closed-Loop Predictive Control of a Multi-mode Engine Including Homogeneous Charge Compression Ignition, Partially Premixed Charge Compression Ignition, and Reactivity Controlled Compression Ignition Modes
多模式发动机的闭环预测控制,包括均质充气压缩点火、部分预混合充气压缩点火和反应性控制压缩点火模式

Shahbakhti, Mahdi的其他文献

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{{ truncateString('Shahbakhti, Mahdi', 18)}}的其他基金

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
  • 财政年份:
    2022
  • 资助金额:
    $ 1.97万
  • 项目类别:
    Discovery Grants Program - Individual
Optimum high-efficient hybrid electric natural gas powertrain designs towards economically viable low emission trucks
优化高效混合电动天然气动力系统设计,打造经济可行的低排放卡车
  • 批准号:
    551990-2020
  • 财政年份:
    2021
  • 资助金额:
    $ 1.97万
  • 项目类别:
    Alliance Grants
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
  • 财政年份:
    2021
  • 资助金额:
    $ 1.97万
  • 项目类别:
    Discovery Grants Program - Individual
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
  • 财政年份:
    2020
  • 资助金额:
    $ 1.97万
  • 项目类别:
    Discovery Grants Program - Individual
Optimum high-efficient hybrid electric natural gas powertrain designs towards economically viable low emission trucks
优化高效混合电动天然气动力系统设计,打造经济可行的低排放卡车
  • 批准号:
    551990-2020
  • 财政年份:
    2020
  • 资助金额:
    $ 1.97万
  • 项目类别:
    Alliance Grants
Engine air fuel ratio control during cold phase to lower air pollution and reduce fuel consumption
冷态发动机空燃比控制,降低空气污染,降低油耗
  • 批准号:
    388139-2010
  • 财政年份:
    2012
  • 资助金额:
    $ 1.97万
  • 项目类别:
    Postdoctoral Fellowships
Engine air fuel ratio control during cold phase to lower air pollution and reduce fuel consumption
冷态发动机空燃比控制,降低空气污染,降低油耗
  • 批准号:
    388139-2010
  • 财政年份:
    2011
  • 资助金额:
    $ 1.97万
  • 项目类别:
    Postdoctoral Fellowships
Engine air fuel ratio control during cold phase to lower air pollution and reduce fuel consumption
冷态发动机空燃比控制,降低空气污染,降低油耗
  • 批准号:
    388139-2010
  • 财政年份:
    2010
  • 资助金额:
    $ 1.97万
  • 项目类别:
    Postdoctoral Fellowships

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