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RISS 인기검색어
- 검색키워드
- 저자 : Jingbiao Liu (검색결과 2 건)
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Constrained State Estimation via Projection based Optimized Parameters UKF
Yuanyuan Liu,Jingbiao Liu,Zhiwei He 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.11
The unscented Kalman filter (UKF) has become a popular method for nonlinear state estimation during the last decade. However, the conventional UKF may not be suitable for real-world applications with state constrains that stem from physical definitions, physical laws or model restrictions. A UKF based method with optimized parameters was proposed in this paper to handle state constraints via the projection of sigma points. In the proposed method, the generated sigma points that violate the state constraints were projected onto the constraint boundary first. The three free parameters of the UKF, i.e., α ,β ,κ , were then optimized using a Gaussian process optimization (GPO) method. Simulations indicate that the proposed optimized UKF algorithm with the projection of sigma points can handle constrained state estimation problem effectively and efficiently.
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State of Charge Estimation for Li-Ion Batteries Based on an Unscented H-Infi nity Filter
Yuanyuan Liu,Tiantian Cai,Jingbiao Liu,Mingyu Gao,Zhiwei He 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.6
The state of charge (SOC) of lithium-ion batteries refl ects their remaining capacity. Accurate estimation of SOC helps battery safety and is benefi cial to the effi cient management of batteries. The charging and discharging processes of lithium-Ion batteries are very complicated, and it is diffi cult to obtain accurate SOC estimation results. Therefore, it is important to study improved algorithms for SOC estimation for this nonlinear non-Gaussian battery system. In this paper, we propose an unscented H-infi nity fi lter (UHF) based SOC estimation method, which combines the advantages of both the unscented Kalman fi lter (UKF) and the H-infi nity fi lter (HF). The UKF propagates the sigma points through the nonlinear system and does not need the fi rst-order linear approximation of the system equation, while the HF can suppress the non-Gaussian noise in the system to the greatest extent. The proposed UHF based SOC estimation algorithm is verifi ed and evaluated in the battery management system, and further optimized in practical problems. Experimental results show that the proposed UHF based algorithm can perform accurate SOC estimation for lithium-ion batteries, and is superior to the UKF based SOC estimation.
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