Journal
JOURNAL OF ENERGY STORAGE
Volume 41, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.est.2021.102843
Keywords
State of charge (SOC) estimation; Lagrange multiplier; Sigma point Kalman filter (SPKF); Open-circuit voltage (OCV); Model identification
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This paper proposes a fused online approach consisting of the Lagrange multiplier technique and sigma point Kalman filter for lithium-ion battery model identification and state of charge estimation. Experimental results show that the proposed method outperforms other techniques in terms of accuracy.
In this paper, a fused online approach consisting of the Lagrange multiplier technique and sigma point Kalman filter (SPKF) is proposed for the lithium-ion battery model identification and state of charge (SOC) estimation, respectively. The Lagrange multiplier technique minimized the error between the reference and estimated SOC by estimating the accurate battery parameters, whereas SPKF helps to calculate non-linear system dynamics more precisely. The effectiveness of the proposed technique is evaluated using different publicly available experimental profiles such as the Beijing dynamic stress test, dynamic stress test, and hybrid pulse power characteristics. The effect of sensor accuracy on the SOC estimation is also analyzed. The comparative analysis reveals that the proposed methodology yields better performance than recursive least squares (RLS)-SPKF and forgetting factor RLS-SPKF. The maximum noted errors for the proposed technique were 0.2%, 0.4%, and 0.9% for hybrid pulse power characteristics, dynamic stress test, and Beijing dynamic stress test, respectively. The improvement in SOC estimation accuracy shows the effectiveness, superiority, and distinctiveness of the proposed approach.
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