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

Zhongbao Wei, Shujuan Meng, Binyu Xiong, Dongxu Ji, King Jet Tseng*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

160 Citations (Scopus)

Abstract

State of charge (SOC) estimators with online identified battery model have proven to have high accuracy and better robustness due to the timely adaption of time varying model parameters. In this paper, we show that the common methods for model identification are intrinsically biased if both the current and voltage sensors are corrupted with noises. The uncertainties in battery model further degrade the accuracy and robustness of SOC estimate. To address this problem, this paper proposes a novel technique which integrates the Frisch scheme based bias compensating recursive least squares (FBCRLS) with a SOC observer for enhanced model identification and SOC estimate. The proposed method online estimates the noise statistics and compensates the noise effect so that the model parameters can be extracted without bias. The SOC is further estimated in real time with the online updated and unbiased battery model. Simulation and experimental studies show that the proposed FBCRLS based observer effectively attenuates the bias on model identification caused by noise contamination and as a consequence provides more reliable estimate on SOC. The proposed method is also compared with other existing methods to highlight its superiority in terms of accuracy and convergence speed.

Original languageEnglish
Pages (from-to)332-341
Number of pages10
JournalApplied Energy
Volume181
DOIs
Publication statusPublished - 1 Nov 2016
Externally publishedYes

Keywords

  • Bias compensation
  • Lithium-ion battery
  • Model identification
  • Noise variances estimate
  • Online estimation
  • State of charge

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