Title :
A New Algorithm of Online Monitoring and Fault Prediction for the Battery Set State
Author :
Chunjie, Yin ; Jiejun, Sun ; Chenghui, Zhang
Author_Institution :
Shandong Univ., Jinan
Abstract :
Based on the traditional floating voltage examination method of the VRLA battery, this paper proposed a new online examination method of the VRLA battery interface resistance, and established GM(1,1) forecast model of the battery interface resistance to examine the batteries´ condition and predict the fault. Because many uncertain factors impact the internal resistance, this paper give a real time method and establish a dynamic innovation forecast model of GM(1,1). The forecast precision is improved greatly through experiment, and the feature of internal resistance of battery can be forecast exactly.
Keywords :
fault diagnosis; forecasting theory; lead acid batteries; statistical analysis; GM(1,1) forecast model; VRLA battery interface resistance; battery set state; dynamic innovation forecast model; fault prediction; floating voltage examination method; online monitoring; Batteries; Condition monitoring; Pareto analysis; Predictive models; Sun; Technological innovation; Testing; Voltage control; Grey model; Internal resistance; VRLA battery;
Conference_Titel :
Control Conference, 2007. CCC 2007. Chinese
Conference_Location :
Hunan
Print_ISBN :
978-7-81124-055-9
Electronic_ISBN :
978-7-900719-22-5
DOI :
10.1109/CHICC.2006.4347167
