Title :
Application of statistical parameter identification in battery management system
Author :
Zhang, Jinlong ; Xia, Chaoying
Author_Institution :
Sch. of Electr. Eng. & Autom., Tianjin Univ., Tianjin, China
Abstract :
As a vital part of the battery employed systems, battery management system (BMS) must correctly estimate values descriptive of the battery´s present operating condition. Without a doubt, state of charge (SOC) is a key battery state for BMS to estimate. This paper carefully analyzes the operating performance of VRLA battery through a large number of experimental tests; a statistical parameter identification strategy is employed in electromotive force (EMF) SOC estimation method, then this EMF method is combined with ampere-hour (Ah) counting through a parallel feedback weighting structure, finally an improved SOC estimator is built up. A BMS platform based on TI DSP2407 is established; besides, a BMS monitoring system is built in VC++ as an auxiliary tool. The results tend to be satisfactory.
Keywords :
battery management systems; electric potential; feedback; power system parameter estimation; statistical analysis; BMS; EMF method; SOC estimation method; battery management system; electromotive force; parallel feedback weighting structure; state of charge; statistical parameter identification; Batteries; Discharges; Estimation; Monitoring; Parameter estimation; Resistance; System-on-a-chip; Battery management system; electromotive force; state of charge; statistical parameter identification;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2010 8th World Congress on
Conference_Location :
Jinan
Print_ISBN :
978-1-4244-6712-9
DOI :
10.1109/WCICA.2010.5554485
