Sensors: Multi-Sensor Information Processing with Automotive Applications

传感器：汽车应用中的多传感器信息处理

• 批准号: 0329597
• 负责人: Le Yi Wang
• 金额: --
• 依托单位: Wayne State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-15 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0329597&HistoricalAwards=false
• 关键词: Sensors; Multi; Sensor; Information; Processing

Technology advancement in automotive engine, transmission, and emission aftertreatment systems hasushered in new challenges in developing sophisticated information processing and control strategies tooptimize vehicle performance at reduced costs. Sensor information processing is of vital importance inthis pursuit. Optimal utility of low-cost sensors, information coordination of multiple sensors, andestimation of internal states and system parameters using sensors of limited capability and accuracy havebecome one of the key design considerations.Using automotive systems as a key platform, and gasoline direct injection engine and its aftertreatmentsystem for methodology development and implementation, this project will investigate the followingfundamental issues on sensor information processing: (1) How can one identify system parameters orestimate internal states by using low-cost sensors that provide only limited information and accuracy? (2)How can one maximize the information utility of multiple sensor systems? (3) What is the impact ofsensor location configuration and characteristics on performance benefits and costs? Due to highnonlinearity, large uncertainty, and sensor limitations, these issues are extremely challenging, anddemand new methodologies and implementation technologies that will push forward the frontiers ofsystem identification and practical sensor information processing.Intellectual Merit of the Project: With over 220 million registered vehicles and 2.8 trillion miles ofannually traveled distances, the US automotive industry bears an enormous impact on the national andglobal economy, safety, and environment. This project will develop an innovative methodology of multi-sensoridentification and estimation, on the basis of extensive past research effort from the PI and hiscollaborators. In collaboration with researchers from the automotive industry, findings from thisinvestigation will be employed and implemented to design better control and adaptation strategies inautomotive systems for improved performance.Successful completion of this project will also introduce new identification and sensor informationmethods that go much beyond what is known in these fields. In particular, it will lead to new multi-sensoridentification methods that use binary-valued and other nonlinear sensors, and new understanding ofimplementation issues on practical sensor information processing. In light of tremendous sensordevelopment effort in many application areas, such as gas content sensors, biosensors, wireless medicalsensors, nanosensors, etc., which will all demand advanced and new information processing techniques tomaximize their capabilities and utilities, this project anticipates such emerging requirements for sensorinformation processing methodologies, and develops generic methods that will see increased utility whennew sensors are developed.Broad Impact: Beyond the automotive powertrain platforms, the findings from this project will havedirect utility in a wide array of applications, including vehicle rollover prediction, fault diagnosis of fuelcell systems and vehicles, computer network traffic control, medical sensor information processing, andprocess control problems. The PI and his industry collaborators are currently pursuing methodologyenhancement, technology transfer, and device development in these areas.This research project encompasses fundamental research and technology development across a widerange of disciplines. It targets directly at a broad and important automotive application; involvesmathematics modeling, sensor signal processing, and system identification; and utilizes the mostadvanced facility at Ford Motor Company. As such it provides participating undergraduate and graduatestudents an excellent opportunity to be exposed to a large spectrum of scientific and technology frontierswith fundamental methodology development, hand-on design skills, and industry experience. Theresearch findings and course material from this project will be widely disseminated in professionalconferences and journals. Software packages resulted from this project will be released through theproject homepage for the public use free of charge.

汽车发动机、变速器和排放后处理系统的技术进步为开发复杂的信息处理和控制策略带来了新的挑战，以降低成本优化车辆性能。在这种追求中，传感器信息处理是至关重要的。低成本传感器的优化利用，多个传感器的信息协调，以及利用能力和精度有限的传感器估计内部状态和系统参数已成为设计的关键考虑因素之一。本项目以汽车系统为关键平台，以汽油直喷式发动机及其后处理系统为方法开发和实施，研究传感器信息处理的以下基本问题：(1)如何使用仅提供有限信息和精度的低成本传感器来识别系统参数或真实的内部状态？(2)如何最大化多个传感器系统的信息效用？(3)传感器的位置配置和特性对性能效益和成本的影响是什么？由于高度的非线性、巨大的不确定性和传感器的局限性，这些问题极具挑战性，需要新的方法和实现技术来推动系统识别和实用传感器信息处理的前沿。该项目的智力价值：美国汽车工业拥有超过2.2亿辆注册车辆和2.8万亿英里的年行驶里程，对国家和全球经济、安全和环境产生巨大影响。这个项目将在PI和他的合作者过去广泛研究工作的基础上，开发一种创新的多传感器识别和估计方法。与汽车行业的研究人员合作，这项调查的结果将被用于汽车系统中设计更好的控制和适应策略，以提高性能。该项目的成功完成还将引入新的识别和传感器信息方法，远远超出这些领域的已知。特别是，它将导致使用二值化和其他非线性传感器的新的多传感器识别方法，以及对实际传感器信息处理的实现问题的新的理解。鉴于传感器在许多应用领域的巨大开发工作，如气体含量传感器、生物传感器、无线医学传感器、纳米传感器等，这些都需要先进和新的信息处理技术来最大化其能力和用途，该项目预见到对传感器信息处理方法的新需求，并开发通用方法，随着新传感器的开发将增加实用性。广泛影响：除了汽车动力总成平台，本项目的发现将在广泛的应用中产生直接作用，包括车辆侧翻预测、燃料电池系统和车辆的故障诊断、计算机网络交通控制、医疗传感器信息处理和过程控制问题。PI和他的行业合作伙伴目前正在这些领域追求方法论的增强、技术转让和设备开发。这一研究项目涵盖了跨多个学科的基础研究和技术开发。它直接针对广泛而重要的汽车应用；涉及数学建模、传感器信号处理和系统识别；并利用福特汽车公司最先进的设施。因此，它为参与的本科生和研究生提供了一个极好的机会，让他们接触到广泛的科学和技术前沿，包括基本的方法开发、动手设计技能和行业经验。该项目的研究结果和课程材料将在专业会议和期刊上广泛传播。本项目产生的软件包将通过项目主页免费发布给公众使用。