MS, University of Cincinnati, 2010, Engineering : Mechanical Engineering

The functionality and reliability of Li-ion battery as major energy storage device has received more and more attentions from a wide spectrum of stakeholders including federal/state policymakers, business leaders, technical researchers, environmental groups and general public. Failures of Li-ion battery not only result in serious inconvenience and enormous costs, but also increase the risk of inducing catastrophic consequences. In order to prevent severe failure from happening and optimize the Li-ion battery maintenance schedules, breakthroughs in prognostics and health monitoring of Li-ion battery with emphasis on fault detection and correction, remaining-useful-life prediction and assist maintenance scheduling must be achieved. This paper firstly reviews recent research and development of health monitoring and prognostics for a variety of battery chemistries and summarizes the techniques, algorithms and models used for state-of-charge (SOC) estimation, current/voltage estimation, capacity estimation and remaining-useful-life (RUL) prediction. Based on the understanding of these researches, two data-driven approaches have been proposed to evaluate and predict the health status of Li-ion battery. The first approach, Gaussian mixture modeling, is an unsupervised learning method which doesn't require capacity as target. This is surely a pleasant feature since the capacity, though a good indicator of battery health, is neither easy nor cheap to acquire. Instead, the underlying pattern of input space is studied and the useful information (CV) extracted from it discloses the health status of Li-ion battery. The other approach, adaptive regressions, is a supervised learning method. Though targets are required this time, capacity is still not used. Instead, an important feature is found to have close relationship with capacity while easy to get in real world application. Based on this relevant feature, an adaptive framework for health assessment and prognostics is introdu (open full item for complete abstract)

Committee: Jay Lee PhD (Committee Chair); Samuel Huang PhD (Committee Member); Manish Kumar PhD (Committee Member) Subjects: Mechanical Engineering