TY - GEN
T1 - Performance assessment of two adaptive Kalman filters for battery state-of-charge estimation
AU - Cheng, Ximing
AU - Yao, Liguang
N1 - Publisher Copyright:
© 2015 Technical Committee on Control Theory, Chinese Association of Automation.
PY - 2015/9/11
Y1 - 2015/9/11
N2 - An accurate state of charge (SOC) is required to improve the reliability, cycle life, safety, and economics of the batteries used in power applications such as electric vehicles and smart grids. The adaptive extended Kalman filter (AEKF) is an advanced technique used to determine the SOC. The first task in estimating the SOC is to choose the initial state covariance (P0) when the process noise covariance (Qk) and the measurement noise covariance (Rk) are simultaneously estimated in the AEKF. The performance of the adaptive methods is also determined by the initial states. This study evaluates the performances of two AEKF approaches, including the Bayesian adaptive estimator (BAE) and the innovation-based adaptive estimator (IAE), which are applied to simultaneously estimate Qk and Rk. These two adaptive filtering methods are implemented on the experimental data of a real lithium-ion battery pack. Their performances, including filtering stability and convergence speed, are compared, and their impact factors are discussed.
AB - An accurate state of charge (SOC) is required to improve the reliability, cycle life, safety, and economics of the batteries used in power applications such as electric vehicles and smart grids. The adaptive extended Kalman filter (AEKF) is an advanced technique used to determine the SOC. The first task in estimating the SOC is to choose the initial state covariance (P0) when the process noise covariance (Qk) and the measurement noise covariance (Rk) are simultaneously estimated in the AEKF. The performance of the adaptive methods is also determined by the initial states. This study evaluates the performances of two AEKF approaches, including the Bayesian adaptive estimator (BAE) and the innovation-based adaptive estimator (IAE), which are applied to simultaneously estimate Qk and Rk. These two adaptive filtering methods are implemented on the experimental data of a real lithium-ion battery pack. Their performances, including filtering stability and convergence speed, are compared, and their impact factors are discussed.
KW - Adaptive extended Kalman filter
KW - Equivalent circuit model
KW - Lithium-ion battery
KW - State of charge
UR - http://www.scopus.com/inward/record.url?scp=84946615486&partnerID=8YFLogxK
U2 - 10.1109/ChiCC.2015.7260886
DO - 10.1109/ChiCC.2015.7260886
M3 - Conference contribution
AN - SCOPUS:84946615486
T3 - Chinese Control Conference, CCC
SP - 7843
EP - 7848
BT - Proceedings of the 34th Chinese Control Conference, CCC 2015
A2 - Zhao, Qianchuan
A2 - Liu, Shirong
PB - IEEE Computer Society
T2 - 34th Chinese Control Conference, CCC 2015
Y2 - 28 July 2015 through 30 July 2015
ER -
