TY - JOUR
T1 - An Enhanced Equivalent Circuit Model with Real-Time Parameter Identification for Battery State-of-Charge Estimation
AU - Naseri, Farshid
AU - Schaltz, Erik
AU - Stroe, Daniel-Ioan
AU - Gismero, Alejandro
AU - Farjah, Ebrahim
PY - 2022/4
Y1 - 2022/4
N2 - This article introduces an efficient modeling approach based on the Wiener structure to reinforce the capacity of classical equivalent circuit models (ECMs) in capturing the nonlinearities of lithium-ion (Li-ion) batteries. The proposed block-oriented modeling architecture is composed of a simple linear ECM followed by a static output nonlinearity block, which helps achieving a superior nonlinear mapping property while maintaining the real-time efficiency. The observability of the established battery model is analytically proven. This article also introduces an efficient parameter estimator based on extended-kernel iterative recursive least squares algorithm for real-time estimation of the parameters of the proposed Wiener model. The proposed approach is applied for state-of-charge (SoC) estimation of 3.4-Ah 3.6-V nickel-manganese-cobalt-based Li-ion cells using the extended Kalman filter (EKF). The results show about 1.5% improvement in SoC estimation accuracy compared with the EKF algorithm based on the second-order ECM. A series of real-time tests are also carried out to demonstrate the computational efficiency of the proposed method.
AB - This article introduces an efficient modeling approach based on the Wiener structure to reinforce the capacity of classical equivalent circuit models (ECMs) in capturing the nonlinearities of lithium-ion (Li-ion) batteries. The proposed block-oriented modeling architecture is composed of a simple linear ECM followed by a static output nonlinearity block, which helps achieving a superior nonlinear mapping property while maintaining the real-time efficiency. The observability of the established battery model is analytically proven. This article also introduces an efficient parameter estimator based on extended-kernel iterative recursive least squares algorithm for real-time estimation of the parameters of the proposed Wiener model. The proposed approach is applied for state-of-charge (SoC) estimation of 3.4-Ah 3.6-V nickel-manganese-cobalt-based Li-ion cells using the extended Kalman filter (EKF). The results show about 1.5% improvement in SoC estimation accuracy compared with the EKF algorithm based on the second-order ECM. A series of real-time tests are also carried out to demonstrate the computational efficiency of the proposed method.
KW - Equivalent circuit model (ECM)
KW - Wiener model
KW - extended Kalman filter (EKF)
KW - least squares
KW - lithium-ion (Li-ion) battery
KW - state of charge (SoC)
UR - http://www.scopus.com/inward/record.url?scp=85104242805&partnerID=8YFLogxK
U2 - 10.1109/TIE.2021.3071679
DO - 10.1109/TIE.2021.3071679
M3 - Journal article
SN - 0278-0046
VL - 69
SP - 3743
EP - 3751
JO - I E E E Transactions on Industrial Electronics
JF - I E E E Transactions on Industrial Electronics
IS - 4
ER -