Sensors: Multi-Sensor Information Processing with Automotive Applications

点击翻译按钮获取中文摘要

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.