Sensors: Multi-Sensor Information Processing with Automotive Applications

Wayne State University, Detroit MI

Investigators

Abstract

Technology advancement in automotive engine, transmission, and emission aftertreatment systems has ushered in new challenges in developing sophisticated information processing and control strategies to optimize vehicle performance at reduced costs. Sensor information processing is of vital importance in this pursuit. Optimal utility of low-cost sensors, information coordination of multiple sensors, and estimation of internal states and system parameters using sensors of limited capability and accuracy have become one of the key design considerations. Using automotive systems as a key platform, and gasoline direct injection engine and its aftertreatment system for methodology development and implementation, this project will investigate the following fundamental issues on sensor information processing: (1) How can one identify system parameters or estimate 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 of sensor location configuration and characteristics on performance benefits and costs? Due to high nonlinearity, large uncertainty, and sensor limitations, these issues are extremely challenging, and demand new methodologies and implementation technologies that will push forward the frontiers of system identification and practical sensor information processing. Intellectual Merit of the Project: With over 220 million registered vehicles and 2.8 trillion miles of annually traveled distances, the US automotive industry bears an enormous impact on the national and global economy, safety, and environment. This project will develop an innovative methodology of multi-sensor identification and estimation, on the basis of extensive past research effort from the PI and his collaborators. In collaboration with researchers from the automotive industry, findings from this investigation will be employed and implemented to design better control and adaptation strategies in automotive systems for improved performance. Successful completion of this project will also introduce new identification and sensor information methods that go much beyond what is known in these fields. In particular, it will lead to new multi-sensor identification methods that use binary-valued and other nonlinear sensors, and new understanding of implementation issues on practical sensor information processing. In light of tremendous sensor development effort in many application areas, such as gas content sensors, biosensors, wireless medical sensors, nanosensors, etc., which will all demand advanced and new information processing techniques to maximize their capabilities and utilities, this project anticipates such emerging requirements for sensor information processing methodologies, and develops generic methods that will see increased utility when new sensors are developed. Broad Impact: Beyond the automotive powertrain platforms, the findings from this project will have direct utility in a wide array of applications, including vehicle rollover prediction, fault diagnosis of fuel cell systems and vehicles, computer network traffic control, medical sensor information processing, and process control problems. The PI and his industry collaborators are currently pursuing methodology enhancement, technology transfer, and device development in these areas. This research project encompasses fundamental research and technology development across a wide range of disciplines. It targets directly at a broad and important automotive application; involves mathematics modeling, sensor signal processing, and system identification; and utilizes the most advanced facility at Ford Motor Company. As such it provides participating undergraduate and graduate students an excellent opportunity to be exposed to a large spectrum of scientific and technology frontiers with fundamental methodology development, hand-on design skills, and industry experience. The research findings and course material from this project will be widely disseminated in professional conferences and journals. Software packages resulted from this project will be released through the project homepage for the public use free of charge.