Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 68, Issue 1, Pages 312-323Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2019.2962429
Keywords
Battery management; bias compensation; model identification; noise; state of charge (SOC)
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Funding
- National Natural Science Foundation of China [U1864202]
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Accurate estimation of state of charge (SOC) in battery systems is crucial for safe and efficient utilization. This article examines the impact of measurement noises on model parameter identification and SOC estimation, proposing a novel parameterization method to address noise-induced biases and improving immunity to noise corruption.
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.
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