A Lagrange multiplier and sigma point Kalman filter based fused methodology for online state of charge estimation of lithium-ion batteries

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.

Automated summary

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.