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http://dx.doi.org/10.3795/KSME-B.2013.37.4.313

Remaining Useful Life Prediction of Li-Ion Battery Based on Charge Voltage Characteristics  

Sim, Seong Heum (Dept. of Aerospace and Mechanical Engineering, Korea Aerospace Univ.)
Gang, Jin Hyuk (Dept. of Aerospace and Mechanical Engineering, Korea Aerospace Univ.)
An, Dawn (Dept. of Aerospace and Mechanical Engineering, Korea Aerospace Univ.)
Kim, Sun Il (Dept. of Aerospace and Mechanical Engineering, Korea Aerospace Univ.)
Kim, Jin Young (Dept. of Aerospace and Mechanical Engineering, Korea Aerospace Univ.)
Choi, Joo Ho (Dept. of Aerospace and Mechanical Engineering, Korea Aerospace Univ.)
Publication Information
Transactions of the Korean Society of Mechanical Engineers B / v.37, no.4, 2013 , pp. 313-322 More about this Journal
Abstract
Batteries, which are being used as energy sources in various applications, tend to degrade, and their capacity declines with repeated charging and discharging cycles. A battery is considered to fail when it reaches 80% of its initial capacity. To predict this, prognosis techniques are attracting attention in recent years in the battery community. In this study, a method is proposed for estimating the battery health and predicting its remaining useful life (RUL) based on the slope of the charge voltage curve. During this process, a Bayesian framework is employed to manage various uncertainties, and a Particle Filter (PF) algorithm is applied to estimate the degradation of the model parameters and to predict the RUL in the form of a probability distribution. Two sets of test data-one from the NASA Ames Research Center and another from our own experiment-for an Li-ion battery are used for illustrating this technique. As a result of the study, it is concluded that the slope can be a good indicator of the battery health and PF is a useful tool for the reliable prediction of RUL.
Keywords
Prognostics and Health Management; Particle Filter; Li-Ion Battery; Remaining Useful Life;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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