CAREER: Integrated Modeling and Control of Aftertreatment Systems for Clean, Efficient and High-Performing Gasoline Direct Injection Engines

职业:清洁、高效和高性能汽油直喷发动机后处理系统的集成建模和控制

基本信息

  • 批准号:
    1839050
  • 负责人:
  • 金额:
    $ 50万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2017
  • 资助国家:
    美国
  • 起止时间:
    2017-09-27 至 2024-06-30
  • 项目状态:
    已结题

项目摘要

This Faculty Early Career Development (CAREER) project will enable future vehicles to benefit from the improved efficiency and performance of gasoline direct injection (GDI) engines, without suffering from increased soot emissions. An integrated approach is necessary because engine operating conditions determine oxygen and fuel content and temperature of the exhaust gas, which influence the output of the catalytic converter, which in turn governs soot accumulation and oxidation in particulate filters. While the dynamics of diesel engine particulate filters are well understood, particulates produced in GDI engines have substantially different characteristics. Particulate emissions are associated with many adverse health effects, including decreased lung function in both children and adults. As the number of vehicles using GDI engines increases, the need to safeguard public health by mitigating particulate emissions becomes an urgent social concern. Thus this project addresses urgent technological and societal needs. Integration of research and education will be pursued through development of a new graduate course on advanced aftertreatment systems modeling and control, a social media-based discussion group, and a new course for the Clemson Creative Inquiry program. The relationships established in the course of this research will support development of an international research and education program on fundamental modelling and control in exhaust gas aftertreatment systems.This project will enable new exhaust gas aftertreatment technologies for GDI engines, based on a transformative modeling framework at the intersection of macroscale modeling, numerical simulations and optimization theory. System-level models of the engine, catalytic converter, and gasoline particulate filter will be integrated across length scales, incorporating effects ranging from clogging and regeneration of the filter pores, to continuum gas flow in the exhaust manifold. The framework will enable formulation of low-order models of aftertreament systems suitable for real-time optimization-based control, based on systematic and rigorous reduction of continuum models while maintaining accuracy and fidelity. This project will substantially improve macro-scale representations of soot layer physical properties and pore-scale loading and regeneration phenomena in particulate filters. The results will be used to design physics-based estimators for robust control of advanced aftertreament systems.
这个教师早期职业发展(CAREER)项目将使未来的车辆受益于汽油直喷(GDI)发动机的效率和性能的提高,而不会增加碳烟排放。集成方法是必要的,因为发动机操作条件决定了废气的氧和燃料含量以及温度,这影响催化转化器的输出,这又控制了颗粒过滤器中的烟灰积聚和氧化。虽然柴油发动机颗粒过滤器的动力学是很好理解的,但GDI发动机中产生的颗粒具有显著不同的特性。颗粒物排放与许多不利的健康影响有关,包括儿童和成人的肺功能下降。随着使用GDI发动机的车辆数量的增加,通过减少颗粒物排放来保障公众健康的需求成为迫切的社会问题。因此,该项目解决了迫切的技术和社会需求。研究和教育的整合将通过开发先进的后处理系统建模和控制的新研究生课程,基于社交媒体的讨论组,以及克莱姆森创意调查计划的新课程来实现。在此研究过程中建立的关系将支持一个国际研究和教育计划的发展,在排气后处理系统的基础建模和控制。该项目将使新的排气后处理技术的GDI发动机,基于一个变革性的建模框架,在宏观建模,数值模拟和优化理论的交叉点。发动机、催化转化器和汽油颗粒过滤器的系统级模型将在整个长度范围内集成,包括从过滤器孔隙的堵塞和再生到排气歧管中的连续气流的影响。该框架将使制定低阶模型的后处理系统适合于实时优化为基础的控制,基于系统和严格的减少连续模型,同时保持精度和保真度。该项目将大大改善烟尘层的物理特性和颗粒过滤器中的孔隙尺度负载和再生现象的宏观尺度表示。结果将被用来设计先进的后处理系统的鲁棒控制的物理为基础的估计。

项目成果

期刊论文数量(12)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Modeling the thermal and soot oxidation dynamics inside a ceria-coated gasoline particulate filter
  • DOI:
    10.1016/j.conengprac.2019.104199
  • 发表时间:
    2020-01-01
  • 期刊:
  • 影响因子:
    4.9
  • 作者:
    Arunachalam, Harikesh;Pozzato, Gabriele;Onori, Simona
  • 通讯作者:
    Onori, Simona
Modeling the Flow and Transport Dynamics in Gasoline Particulate Filters to Improve Filtration Efficiency
Design and Experimental Validation of a Spatially Discretized, Control-Oriented Temperature Model for a Ceria-Washcoated Gasoline Particulate Filter
二氧化铈涂层汽油颗粒过滤器的空间离散、面向控制的温度模型的设计和实验验证
What if the Doyle-Fuller-Newman Model Fails? A New Macroscale Modeling Framework
如果道尔-富勒-纽曼模型失败怎么办?
  • DOI:
    10.1109/cdc.2018.8619793
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Arunachalam, Harikesh;Onori, Simona
  • 通讯作者:
    Onori, Simona
Sensitivity Study on Thermal and Soot Oxidation Dynamics of Gasoline Particulate Filters
  • DOI:
    10.4271/2019-01-0990
  • 发表时间:
    2019-04
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Aki Takahashi;S. Korneev;S. Onori
  • 通讯作者:
    Aki Takahashi;S. Korneev;S. Onori
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Simona Onori其他文献

“Full factorial design of experiments dataset for parallel-connected lithium-ion cells imbalanced performance investigation”
  • DOI:
    10.1016/j.dib.2024.110227
  • 发表时间:
    2024-04-01
  • 期刊:
  • 影响因子:
  • 作者:
    Gabriele Piombo;Simone Fasolato;Robert Heymer;Marc F. Hidalgo;Mona Faraji Niri;Davide M. Raimondo;James Marco;Simona Onori
  • 通讯作者:
    Simona Onori
Exergy-based modeling framework for hybrid and electric ground vehicles
  • DOI:
    10.1016/j.apenergy.2021.117320
  • 发表时间:
    2021-10-15
  • 期刊:
  • 影响因子:
  • 作者:
    Federico Dettù;Gabriele Pozzato;Denise M. Rizzo;Simona Onori
  • 通讯作者:
    Simona Onori
COBRAPRO: A MATLAB toolbox for Physics-based Battery Modeling and Co-simulation Parameter Optimization
COBRAPRO:用于基于物理的电池建模和联合仿真参数优化的 MATLAB 工具箱
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Sara Ha;Simona Onori
  • 通讯作者:
    Simona Onori
Coordinated Energy Dispatch of Autonomous Microgrids With Distributed MPC Optimization
分布式 MPC 优化的自治微电网协调能源调度
Multistage Time-Optimal Control for Synchronization Process in Electric-Driven Mechanical Transmission With Angle Alignment Considering Torque Response Process
考虑扭矩响应过程的角度对准电驱动机械变速器同步过程多级时间最优控制

Simona Onori的其他文献

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{{ truncateString('Simona Onori', 18)}}的其他基金

CAREER: Integrated Modeling and Control of Aftertreatment Systems for Clean, Efficient and High-Performing Gasoline Direct Injection Engines
职业:清洁、高效和高性能汽油直喷发动机后处理系统的集成建模和控制
  • 批准号:
    1653836
  • 财政年份:
    2017
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant

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