State-of-charge Estimation for Lithium-ion Battery using a Combined Method

Guidan Li; Kai Peng; Bin Li

State-of-charge Estimation for Lithium-ion Battery using a Combined Method

Guidan Li

Kai Peng

Bin Li

Vol. 18, No. 1, pp. 129-136, Jan. 2018

10.6113/JPE.2018.18.1.129

Forgetting factor least squares

Lithium-ion battery

Particle Filter

State-of-charge

Unscented Kalman filter

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Abstract

An accurate state-of-charge (SOC) estimation ensures the reliable and efficient operation of a lithium-ion battery management system. On the basis of a combined electrochemical model, this study adopts the forgetting factor least squares algorithm to identify battery parameters and eliminate the influence of test conditions. Then, it implements online SOC estimation with high accuracy and low run time by utilizing the low computational complexity of the unscented Kalman filter (UKF) and the rapid convergence of a particle filter (PF). The PF algorithm is adopted to decrease convergence time when the initial error is large; otherwise, the UKF algorithm is used to approximate the actual SOC with low computational complexity. The effect of the number of sampling particles in the PF is also evaluated. Finally, experimental results are used to verify the superiority of the combined method over other individual algorithms.

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Cite this article

[IEEE Style]

G. Li, K. Peng, B. Li, "State-of-charge Estimation for Lithium-ion Battery using a Combined Method," Journal of Power Electronics, vol. 18, no. 1, pp. 129-136, 2018. DOI: 10.6113/JPE.2018.18.1.129.

[ACM Style]

Guidan Li, Kai Peng, and Bin Li. 2018. State-of-charge Estimation for Lithium-ion Battery using a Combined Method. Journal of Power Electronics, 18, 1, (2018), 129-136. DOI: 10.6113/JPE.2018.18.1.129.