TY - JOUR
T1 - Lithium-ion battery SOC estimation study based on Cubature Kalman filter
AU - Luo, Jiayi
AU - Peng, Jiankun
AU - He, Hongwen
N1 - Publisher Copyright:
© 2019 The Authors. Published by Elsevier Ltd.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)Peer-review under responsibility of the scientific committee of ICAE2018 The 10th International Conference on Applied Energy.
PY - 2019
Y1 - 2019
N2 - In this paper, a state of charge (SOC) estimation method of Lithium-Ion battery is developed based on a cubature Kalman filter (CKF) supported by experimental data. Firstly, an equivalent circuit model and a fractional order model are established to evaluate the estimation accuracy of different models. Secondly, model parameters are identified through HPPC (Hybrid Pulse Power Characteristic) experiments based on the Sequential Quadratic Programming (SQR) method. Then, a CKF algorithm is used to eliminate the battery SOC under different battery models with no prior knowledge of initial SOC. The experimental results show that the proposed method can estimate the battery SOC with high accuracy and the fractional order model can achieve higher accuracy while it consumes more computing resources compared with EKF (Extended Kalman filter) algorithm.
AB - In this paper, a state of charge (SOC) estimation method of Lithium-Ion battery is developed based on a cubature Kalman filter (CKF) supported by experimental data. Firstly, an equivalent circuit model and a fractional order model are established to evaluate the estimation accuracy of different models. Secondly, model parameters are identified through HPPC (Hybrid Pulse Power Characteristic) experiments based on the Sequential Quadratic Programming (SQR) method. Then, a CKF algorithm is used to eliminate the battery SOC under different battery models with no prior knowledge of initial SOC. The experimental results show that the proposed method can estimate the battery SOC with high accuracy and the fractional order model can achieve higher accuracy while it consumes more computing resources compared with EKF (Extended Kalman filter) algorithm.
KW - Cubature Kalman Filter (CKF)
KW - Equivalent Circuit Model
KW - Fractional Order Model
KW - Lithium-ion battery
KW - State of Charge
UR - http://www.scopus.com/inward/record.url?scp=85063871449&partnerID=8YFLogxK
U2 - 10.1016/j.egypro.2019.01.933
DO - 10.1016/j.egypro.2019.01.933
M3 - Conference article
AN - SCOPUS:85063871449
SN - 1876-6102
VL - 158
SP - 3421
EP - 3426
JO - Energy Procedia
JF - Energy Procedia
T2 - 10th International Conference on Applied Energy, ICAE 2018
Y2 - 22 August 2018 through 25 August 2018
ER -
