TY - GEN
T1 - Online parameter estimation/tracking for Lithium-ion battery RC model
AU - Cen, Zhaohui
AU - Kubiak, Pierre
AU - Belharouak, Ilias
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/7/18
Y1 - 2017/7/18
N2 - as a basis for Battery SOC estimation and grid integration numerical analysis, Equivalent Circuit Model (ECM) based on RC circuit topology is one of the most widely-used battery models. In the ECM model, model parameters such as internal resistance, RC circuit capacitance, and resistance are physically time-variant and depend on the battery SOC and temperature. However, as a trade-off on the gap between model complexities computation simplification, the ECM model parameters are usually considered as constant or be piece-wisely constant. In this paper, we proposed an Adaptive Thau Observer(ATO) based online parameter estimation/tracking method, which can estimate the time-variant ECM model parameter such as RC circuit capacitance in real-time. The metrics of this method only depends on measurements of battery voltage and current, which is more feasible for real-world battery SOC/SOH estimation applications comparing with existing offline battery parameter identification methods. Finally, simulation results demonstrate the effectiveness of the proposed method.
AB - as a basis for Battery SOC estimation and grid integration numerical analysis, Equivalent Circuit Model (ECM) based on RC circuit topology is one of the most widely-used battery models. In the ECM model, model parameters such as internal resistance, RC circuit capacitance, and resistance are physically time-variant and depend on the battery SOC and temperature. However, as a trade-off on the gap between model complexities computation simplification, the ECM model parameters are usually considered as constant or be piece-wisely constant. In this paper, we proposed an Adaptive Thau Observer(ATO) based online parameter estimation/tracking method, which can estimate the time-variant ECM model parameter such as RC circuit capacitance in real-time. The metrics of this method only depends on measurements of battery voltage and current, which is more feasible for real-world battery SOC/SOH estimation applications comparing with existing offline battery parameter identification methods. Finally, simulation results demonstrate the effectiveness of the proposed method.
KW - Adaptive Thau Observer
KW - Battery RC Model
KW - OCV Estimation
KW - Online Parameter Estimation
UR - http://www.scopus.com/inward/record.url?scp=85027862514&partnerID=8YFLogxK
U2 - 10.1109/IRSEC.2016.7983979
DO - 10.1109/IRSEC.2016.7983979
M3 - Conference contribution
AN - SCOPUS:85027862514
T3 - Proceedings of 2016 International Renewable and Sustainable Energy Conference, IRSEC 2016
SP - 936
EP - 940
BT - Proceedings of 2016 International Renewable and Sustainable Energy Conference, IRSEC 2016
A2 - Zaz, Youssef
A2 - Essaaidi, Mohamed
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 International Renewable and Sustainable Energy Conference, IRSEC 2016
Y2 - 14 November 2016 through 17 November 2016
ER -