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

• Published in: IEEE transactions on industrial electronics (1982) Vol. 68; no. 1; pp. 312 - 323
• Main Authors: Wei, Zhongbao, Dong, Guangzhong, Zhang, Xinan, Pou, Josep, Quan, Zhongyi, He, Hongwen
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Algorithms; Batteries; Battery management; bias compensation; Current measurement; Lithium-ion batteries; Mathematical models; model identification; Noise; Noise measurement; Observers; Parameter estimation; Parameter identification; Parameterization; Rechargeable batteries; State of charge; state of charge (SOC)
• ISSN: 0278-0046; 1557-9948; 1557-9948
• DOI: 10.1109/TIE.2019.2962429

Accurate estimation of state of charge (SOC) is critical to the safe and efficient utilization of a battery system. Model-based SOC observers have been widely used due to their high accuracy and robustness, but they rely on a well-parameterized battery model. This article scrutinizes the effect of measurement noises on model parameter identification and SOC estimation. A novel parameterization method combining instrumental variable (IV) estimation and bilinear principle is proposed to compensate for the noise-induced biases of model identification. Specifically, the IV estimator is used to reformulate an overdetermined system so as to allow coestimating the model parameters and noise variances. The coestimation problem is then decoupled into two linear subproblems which are solved efficiently by a two-stage least squares algorithm in a recursive manner. The parameterization method is further combined with a Luenberger observer to estimate the SOC in real time. Simulations and experiments are performed to validate the proposed method. Results reveal that the proposed method is superior to existing method in terms of the immunity to noise corruption.