TY - JOUR
T1 - A Simplified Model based State-of-Charge Estimation Approach for Lithium-ion Battery with Dynamic Linear Model
AU - Jinhao, Meng
AU - Stroe, Daniel-Ioan
AU - Ricco, Mattia
AU - Guangzhao, Luo
AU - Teodorescu, Remus
PY - 2019/10
Y1 - 2019/10
N2 - The performance of model based State-of-Charge (SOC) estimation method relies on an accurate battery model. Nonlinear models are thus proposed to accurately describe the external characteristics of the Lithium-ion (Li-ion) battery. The nonlinear estimation algorithms and online parameter identification methods are needed to guarantee the accuracy of the model based SOC estimation with nonlinear battery models. A new approach forming a dynamic linear battery model is proposed in this paper, which enables the application of the linear Kalman filter for SOC estimation and also avoids the usage of online parameter identification methods. With a moving window technology, Partial Least Squares (PLS) regression is able to establish a series of piecewise linear battery models automatically. One element state space equation is then obtained to estimate the SOC from the linear Kalman filter. The experiments on a LiFePO4 battery prove the effectiveness of the proposed method compared with the Extended Kalman Filter (EKF) with two Resistance and Capacitance (RC) Equivalent Circuit Model (ECM) and the Adaptive Unscented Kalman Filter (AUKF) with Least Squares Support Vector Machines (LSSVM).
AB - The performance of model based State-of-Charge (SOC) estimation method relies on an accurate battery model. Nonlinear models are thus proposed to accurately describe the external characteristics of the Lithium-ion (Li-ion) battery. The nonlinear estimation algorithms and online parameter identification methods are needed to guarantee the accuracy of the model based SOC estimation with nonlinear battery models. A new approach forming a dynamic linear battery model is proposed in this paper, which enables the application of the linear Kalman filter for SOC estimation and also avoids the usage of online parameter identification methods. With a moving window technology, Partial Least Squares (PLS) regression is able to establish a series of piecewise linear battery models automatically. One element state space equation is then obtained to estimate the SOC from the linear Kalman filter. The experiments on a LiFePO4 battery prove the effectiveness of the proposed method compared with the Extended Kalman Filter (EKF) with two Resistance and Capacitance (RC) Equivalent Circuit Model (ECM) and the Adaptive Unscented Kalman Filter (AUKF) with Least Squares Support Vector Machines (LSSVM).
KW - State-of-charge estimation
KW - Partial least squares regression
KW - Kalman filter
KW - Lithium-ion battery
UR - http://www.scopus.com/inward/record.url?scp=85056744366&partnerID=8YFLogxK
U2 - 10.1109/TIE.2018.2880668
DO - 10.1109/TIE.2018.2880668
M3 - Journal article
SN - 0278-0046
VL - 66
SP - 7717
EP - 7727
JO - I E E E Transactions on Industrial Electronics
JF - I E E E Transactions on Industrial Electronics
IS - 10
M1 - 8536907
ER -