Title :
Improved EKF for SOC of the storage battery
Author :
Dan Xu ; Xiaoning Huo ; Xin Bao ; Changguang Yang ; Hui Chen ; Binggang Cao
Author_Institution :
Dept. of Nucl. Power Eng., Xi´an Jiaotong Univ., Xianning, China
Abstract :
Aiming at the electric automobile in the running state of the complicated working condition, an innovative battery SOC estimation method is presented. Based on a new type of on-line measurement in storage battery parameters, improved EKF algorithm is used to estimate the remaining battery capacity. By isolating single cells and acquainting parameters, the unit cell´s SOC is estimated through the Kalman algorithm, and we can calculate assembled battery SOC by integrating unit cell´s SOC. This algorithm overcomes the changes of electric vehicle battery parameters which are complicated and the traditional estimation algorithm has defects of low accuracy of SOC. The technology put forward in this paper overcomes the flaw. And the internal resistance of the battery can be estimated. The research has an important significance on SOH. Analysis of the test shows that, using this method for on-line estimation of battery SOC, the estimation accuracy is relatively high can reflect the real residual capacity of battery better.
Keywords :
Kalman filters; automobiles; battery powered vehicles; energy storage; nonlinear filters; secondary cells; SOH; battery SOC estimation method; battery capacity estimation; electric automobile; electric vehicle; improved EKF algorithm; internal resistance; single cell isolation; storage battery parameter; Batteries; Battery charge measurement; Estimation; Kalman filters; Mathematical model; Resistance; System-on-chip; State of charge; extended Kalman filter; innovative topology; storage battery;
Conference_Titel :
Mechatronics and Automation (ICMA), 2013 IEEE International Conference on
Conference_Location :
Takamatsu
Print_ISBN :
978-1-4673-5557-5
DOI :
10.1109/ICMA.2013.6618135