Title :
Online SOC Estimation of High-power Lithium-ion Batteries Used on HEVs
Author :
Dai, Haifeng ; Wei, Xuezhe ; Sun, Zechang
Author_Institution :
Tongji Univ., Shanghai
Abstract :
Battery management systems (BMS) in hybrid electric vehicles (HEVs) should be able to online estimate the present state of charge (SOC) of the battery pack accurately. In this paper, we proposed a SOC estimating method for battery packs based on the well-known extended Kalman filter (EKF). The underlying dynamic behavior of the battey pack was described by a model which was based on an equivalent circuit comprising of two capacitors and three resistors. Measurements of current and voltage in two different tests were applied to validate the proposed method. By comparing the SOC estimated by model based EKF to the SOC estimated by coulomb counting, we got the results showing that the methodologies we proposed were able to perform a good estimation of SOC of the battery pack. Moreover, a corresponding BMS including hardware and software based on our estimating method was designed.
Keywords :
Kalman filters; battery management systems; electrical engineering computing; hybrid electric vehicles; lithium; secondary cells; BMS; HEV; Li; battery management systems; coulomb counting; current measurement; equivalent circuit; extended Kalman filter; high-power lithium-ion batteries; hybrid electric vehicles; online state-of-charge estimation; voltage measurement; Battery charge measurement; Battery management systems; Capacitors; Current measurement; Equivalent circuits; Hybrid electric vehicles; Resistors; State estimation; Vehicle dynamics; Voltage; Extended Kalman filter; HEVs; Lithium-ion batteries; Model; SOC estimation;
Conference_Titel :
Vehicular Electronics and Safety, 2006. ICVES 2006. IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
1-4244-0759-1
Electronic_ISBN :
1-4244-0759-1
DOI :
10.1109/ICVES.2006.371612