Enhanced online model identification and state of charge estimation for lithium-ion battery under noise corrupted measurements by bias compensation recursive least squares

2020 ◽  
Vol 456 ◽  
pp. 227984 ◽  
Author(s):  
Yigang Li ◽  
Jiqing Chen ◽  
Fengchong Lan
Keyword(s):  
Least Squares ◽  
Lithium Ion Battery ◽  
Model Identification ◽  
Lithium Ion ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
State Of Charge Estimation ◽  
Bias Compensation

Noise-Immune Model Identification and State-of-Charge Estimation for Lithium-Ion Battery Using Bilinear Parameterization

2021 ◽  
Vol 68 (1) ◽  
pp. 312-323 ◽  
Author(s):  
Zhongbao Wei ◽  
Guangzhong Dong ◽  
Xinan Zhang ◽  
Josep Pou ◽  
Zhongyi Quan ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Model Identification ◽  
Lithium Ion ◽  
State Of Charge ◽  
State Of Charge Estimation

Enhanced online model identification and state of charge estimation for lithium-ion battery with a FBCRLS based observer

2016 ◽  
Vol 181 ◽  
pp. 332-341 ◽  
Author(s):  
Zhongbao Wei ◽  
Shujuan Meng ◽  
Binyu Xiong ◽  
Dongxu Ji ◽  
King Jet Tseng
Keyword(s):  
Lithium Ion Battery ◽  
Model Identification ◽  
Lithium Ion ◽  
State Of Charge ◽  
State Of Charge Estimation

scholarly journals Online Parameters Identification and State of Charge Estimation for Lithium-ion Battery Using Adaptive Cubature Kalman Filter

2021 ◽  
Vol 12 (3) ◽  
pp. 123
Author(s):  
Wei Li ◽  
Maji Luo ◽  
Yaqian Tan ◽  
Xiangyu Cui
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Stress Test ◽  
Model Identification ◽  
Lithium Ion ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Battery Management System ◽  
Soc Estimation ◽  
Cubature Kalman Filter

The state of charge (SOC) of a lithium-ion battery plays a key role in ensuring the charge and discharge energy control strategy, and SOC estimation is the core part of the battery management system for safe and efficient driving of electric vehicles. In this paper, a model-based SOC estimation strategy based on the Adaptive Cubature Kalman filter (ACKF) is studied for lithium-ion batteries. In the present study, the dual polarization (DP) model is employed for SOC estimation and the vector forgetting factor recursive least squares (VRLS) method is utilized for model parameter online identification. The ACKF is then designed to estimate the battery’s SOC. Finally, the Urban Dynamometer Driving Schedule and Dynamic Stress Test are utilized to evaluate the performance of the proposed method by comparing with results obtained using the extended Kalman filter (EKF) and the cubature Kalman filter (CKF) algorithms. The simulation and experimental results show that the proposed ACKF algorithm combined with VRLS-based model identification is a promising SOC estimation approach. The proposed algorithm is found to provide more accurate SOC estimation with satisfying stability than the extended EKF and CKF algorithms.

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