Andrew Hsu (Other), Marian Kazimierczuk (Committee Member), Kuldip Rattan (Committee Member), Kefu Xue (Committee Co-chair), Xiaodong Zhang (Committee Chair)
Master of Science in Engineering (MSEgr)
Automobiles depend more and more on electric power. Analysis of warranty data by automotive OEMs shows that faults in the automotive electrical power generation and storage (EPGS) system are often misdiagnosed. Therefore, monitoring of the state of health (SOH) of the automotive EPGS system is vital for early and correct diagnosis of faults in it, ensuring a reliable supply of electric power to the vehicle and reducing maintenance costs. In this research project, a model-based SOH monitoring method for the EPGS system is developed without the requirement of an alternator current sensor. A model representing the dynamic relationship between the battery current and the alternator filed duty voltage cycle is presented. An important model parameter that characterizes the current generation efficiency of the alternator system is adaptively estimated by using a recursive least square algorithm. Based on fault modes and effect analysis, a model-based fault detection and isolation decision scheme is developed for the EPGS system faults under consideration. The SOH monitoring method has been implemented using an EPGS system experimental test bench at GM R and D Center. Real-time evaluation results have shown its effectiveness and robustness.
Department or Program
Department of Electrical Engineering
Year Degree Awarded
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