GNSS attitude estimation based on adaptive Kalman filtering using phase measurement

Zhang, Siyu; Chang, Guobin; Chen, Chao; Zhang, Laihong; Zhu, Ting

doi:10.1049/iet-rsn.2019.0439

Cited by 6 publications

(1 citation statement)

References 29 publications

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“…Where W is the sliding data window length with a great likelihood criterion, an optimal unbiased estimation [31].…”

Section: Fig 2 Battery Model Parameter Identification Processmentioning

confidence: 99%

An Improved Multi innovation Adaptive Robust Dual Kalman Filter Algorithm for Estimating Battery State

guan,

Yang,

et al. 2023

Preprint

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In response to the inaccurate estimation of State of Charge (SOC) in current power battery management systems, and considering that SOC may be subject to offset constraints from State of Health (SOH) when estimated separately. A method combining adaptive robust Kalman filtering with multiple innovation theories and online identification of dual Kalman filtering parameters is proposed. This control method is based on adaptive robust Kalman control. It corrects the estimated values using multiple innovation values and Kalman gains at different times. Dual Kalman filtering is used for online parameter identification and joint estimation of battery health status, which increases the amount of error information and provides an optimized method for accurate estimation of SOC and SOH. To verify the rationality of the algorithm, a second-order RC equivalent circuit model is used to characterize the dynamic characteristics of the battery, and experimental verification is carried out under different operating conditions. The experimental results show that the average error of SOC under three operating conditions: UDDS, FUDS, and US06 is 0.56\%, 0.31\%, and 1.23\%, respectively. The estimated error of SOH after stabilization is less than 1.73\%. The estimation error is the lowest among the five estimation algorithms. The proposed algorithm has been verified to have good accuracy and convergence. The multi-innovation adaptive robust dual Kalman filtering algorithm provides a theoretical basis for accurate state estimation and widespread application of lithium batteries.

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“…Where W is the sliding data window length with a great likelihood criterion, an optimal unbiased estimation [31].…”

Section: Fig 2 Battery Model Parameter Identification Processmentioning

confidence: 99%

An Improved Multi innovation Adaptive Robust Dual Kalman Filter Algorithm for Estimating Battery State

guan,

Yang,

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

An improved multi innovation adaptive robust dual kalman filter algorithm for estimating battery state

Guan,

Yang,

et al. 2023

Ionics

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An effective LS-SVM/AKF aided SINS/DVL integrated navigation system for underwater vehicles

Sun

Wang²

2022

Peer-to-Peer Netw. Appl.

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GNSS attitude estimation based on adaptive Kalman filtering using phase measurement

Cited by 6 publications

References 29 publications

An Improved Multi innovation Adaptive Robust Dual Kalman Filter Algorithm for Estimating Battery State

An Improved Multi innovation Adaptive Robust Dual Kalman Filter Algorithm for Estimating Battery State

An improved multi innovation adaptive robust dual kalman filter algorithm for estimating battery state

An effective LS-SVM/AKF aided SINS/DVL integrated navigation system for underwater vehicles

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