TY - GEN
T1 - A discrete-Time nonlinear observer for state of charge estimation of lithium-ion batteries
AU - Liang, Liliuyuan
AU - Li, Weilin
AU - Liu, Wenjie
AU - Wu, Xiaohua
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/17
Y1 - 2016/11/17
N2 - This paper presents a novel state of charge (SOC) estimation algorithm for lithium-ion batteries (LIBs) using a discrete-Time nonlinear observer (DNLO) and a second-order resistor-capacitor (2RC) equivalent circuit model. Considering the hysteresis characteristic of battery, the parameters of the 2RC equivalent circuit model depend on the SOC and the direction of battery current simultaneously. Then the exponential-function fitting method identifies the offline parameters of the battery model. The convergence and stability of the proposed observer is proved by the Lyapunov inequality equation. The performance of the proposed method is also verified by the experiments based on the hybrid pulse power characteristic (HPPC) test. The experiment results show that the proposed observer has better performance in reducing the computation cost, improving the estimation accuracy and enhancing the convergence capability, than the EKF algorithm and the discrete-Time sliding mode observer (DSMO) algorithm.
AB - This paper presents a novel state of charge (SOC) estimation algorithm for lithium-ion batteries (LIBs) using a discrete-Time nonlinear observer (DNLO) and a second-order resistor-capacitor (2RC) equivalent circuit model. Considering the hysteresis characteristic of battery, the parameters of the 2RC equivalent circuit model depend on the SOC and the direction of battery current simultaneously. Then the exponential-function fitting method identifies the offline parameters of the battery model. The convergence and stability of the proposed observer is proved by the Lyapunov inequality equation. The performance of the proposed method is also verified by the experiments based on the hybrid pulse power characteristic (HPPC) test. The experiment results show that the proposed observer has better performance in reducing the computation cost, improving the estimation accuracy and enhancing the convergence capability, than the EKF algorithm and the discrete-Time sliding mode observer (DSMO) algorithm.
UR - http://www.scopus.com/inward/record.url?scp=85006698986&partnerID=8YFLogxK
U2 - 10.1109/AUS.2016.7748066
DO - 10.1109/AUS.2016.7748066
M3 - 会议稿件
AN - SCOPUS:85006698986
T3 - AUS 2016 - 2016 IEEE/CSAA International Conference on Aircraft Utility Systems
SP - 313
EP - 319
BT - AUS 2016 - 2016 IEEE/CSAA International Conference on Aircraft Utility Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE/CSAA International Conference on Aircraft Utility Systems, AUS 2016
Y2 - 10 October 2016 through 12 October 2016
ER -
