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Open AccessArticle

Research on Parameter Self-Learning Unscented Kalman Filtering Algorithm and Its Application in Battery Charge of State Estimation

by Fang Liu 1,*, Jie Ma 1, Weixing Su 1,2,3, Hanning Chen 1 and Maowei He 1
1
School of Computer Science & Technology, Tiangong University, Tianjin 300387, China
2
State Key Laboratory of Process Automation in Mining & Metallurgy, Beijing 100160, China
3
Beijing Key Laboratory of Process Automation in Mining & Metallurgy, Beijing 100160, China
*
Author to whom correspondence should be addressed.
Energies 2020, 13(7), 1679; https://doi.org/10.3390/en13071679
Received: 7 March 2020 / Revised: 25 March 2020 / Accepted: 27 March 2020 / Published: 3 April 2020
(This article belongs to the Special Issue Battery Management for Electric Vehicles)
A novel state estimation algorithm based on the parameters of a self-learning unscented Kalman filter (UKF) with a model parameter identification method based on a collaborative optimization mechanism is proposed in this paper. This algorithm can realize the dynamic self-learning and self-adjustment of the parameters in the UKF algorithm and the automatic optimization setting Sigma points without human participation. In addition, the multi-algorithm collaborative optimization mechanism unifies a variety of algorithms, so that the identification method has the advantages of member algorithms while avoiding the disadvantages of them. We apply the combination algorithm proposed in this paper for state of charge (SoC) estimation of power batteries and compare it with other model parameter identification algorithms and SoC estimation methods. The results showed that the proposed algorithm outperformed the other model parameter identification algorithms in terms of estimation accuracy and robustness. View Full-Text
Keywords: unscented Kalman filter; parameter identification; battery management system; state of charge unscented Kalman filter; parameter identification; battery management system; state of charge
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MDPI and ACS Style

Liu, F.; Ma, J.; Su, W.; Chen, H.; He, M. Research on Parameter Self-Learning Unscented Kalman Filtering Algorithm and Its Application in Battery Charge of State Estimation. Energies 2020, 13, 1679.

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