Adaptive State-of-Charge Estimation Method for an Aeronautical Lithium-ion Battery Pack Based on a Reduced Particle-unscented Kalman Filter

A reduced particle-unscented Kalman filter estimation method, along with a splice-equivalent circuit model, is proposed for the state-of-charge estimation of an aeronautical lithium-ion battery pack. The linearization treatment is not required in this method and only a few sigma data points are used, which reduce the computational requirement of state-of-charge estimation. This method also improves the estimation covariance properties by introducing the equilibrium parameter state of balance for the aeronautical lithium-ion battery pack. In addition, the estimation performance is validated by the experimental results. The proposed state-of-charge estimation method exhibits a root-mean-square error value of 1.42% and a mean error value of 4.96%. This method is insensitive to the parameter variation of the splice-equivalent circuit model, and thus, it plays an important role in the popularization and application of the aeronautical lithium-ion battery pack.

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Cite this article

[IEEE Style]

S. Wang, C. Yu, C. Fernandez, M. Chen, G. Li, X. Liu, "Adaptive State-of-Charge Estimation Method for an Aeronautical Lithium-ion Battery Pack Based on a Reduced Particle-unscented Kalman Filter," Journal of Power Electronics, vol. 18, no. 4, pp. 1127-1139, 2018. DOI: 10.6113/JPE.2018.18.4.1127.

[ACM Style]

Shun-Li Wang, Chun-Mei Yu, Carlos Fernandez, Ming-Jie Chen, Gui-Lin Li, and Xiao-Han Liu. 2018. Adaptive State-of-Charge Estimation Method for an Aeronautical Lithium-ion Battery Pack Based on a Reduced Particle-unscented Kalman Filter. Journal of Power Electronics, 18, 4, (2018), 1127-1139. DOI: 10.6113/JPE.2018.18.4.1127.