Distributed incremental bias-compensated RLS estimation over multi-agent networks

Lou, Jian; Jia, Lijuan; Tao, Ran; Wang, Yue

doi:10.1007/s11432-016-0284-2

Cited by 16 publications

(2 citation statements)

References 19 publications

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“…However, the RLS method cannot compensate for the error caused by noise. To overcome this shortcoming, Lou et al [23] proposed a bias compensation recursive least squares (BCRLS) algorithm, which introduced a correction term on the iterative estimation parameters to compensate for the error caused by noise in the acquisition process. However, with the increase of iteration times, the algorithm will produce the phenomenon of data saturation, which makes the gain of the data collected in the next sampling time smaller and reduces the accuracy of the parameters.…”

Section: Battery Modelling and Parameter Identificationmentioning

confidence: 99%

SOC estimation for lithium‐ion batteries based on a novel model

Gao

et al. 2021

IET Power Electronics

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Lithium‐ion batteries (LIBs) are widely used in electric vehicles because of their high energy density and less pollution. As an important parameter of the battery management system, accurate estimation of the state of charge (SOC) of the battery can ensure the energy distribution and safe use of the battery. This paper obtains better estimation accuracy from four aspects. First, the battery model is established via Thevenin equivalent circuit model, and the parameters are identified by the forgetting factor recursive least squares. Second, the influence of dual extended Kalman filter on SOC estimation is analysed, a novel algorithm‐based improved dual Kalman filter is proposed. Besides, to reduce the influence of the system noise on the estimation results, an adaptive intelligent algorithm is applied to promote the accuracy of SOC estimation. Finally, compared with the estimated SOC results of the traditional algorithm, the experimental results show the effectiveness of the algorithm.

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Section: Battery Modelling and Parameter Identificationmentioning

confidence: 99%

SOC estimation for lithium‐ion batteries based on a novel model

Gao

et al. 2021

IET Power Electronics

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“…Unlike the centralized method with a fusion center, the distributed scheme has the advantages of flexibility, robustness to node or link failures as well as reducing communication load and calculation pressure. Consequently, the theoretical analysis of distributed estimation or filtering algorithms based on several typical distributed strategies such as the incremental, the diffusion and the consensus strategies have been provided (Abdolee and Champagne, 2016;Lou et al, 2017;Battilotti et al, 2020;Liu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Distributed Sparse Identification for Stochastic Dynamic Systems under Cooperative Non-Persistent Excitation Condition

Gan¹,

Liu²

2022

Preprint

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This paper considers the distributed sparse identification problem over wireless sensor networks such that all sensors cooperatively estimate the unknown sparse parameter vector of stochastic dynamic systems by using the local information from neighbors. A distributed sparse least squares algorithm is proposed by minimizing a local information criterion formulated as a linear combination of accumulative local estimation error and L1-regularization term. The upper bounds of the estimation error and the regret of the adaptive predictor of the proposed algorithm are presented. Furthermore, by designing a suitable adaptive weighting coefficient based on the local observation data, the set convergence of zero elements with a finite number of observations is obtained under a cooperative non-persistent excitation condition. It is shown that the proposed distributed algorithm can work well in a cooperative way even though none of the individual sensors can fulfill the estimation task. Our theoretical results are obtained without relying on the independency assumptions of regression signals that have been commonly used in the existing literature. Thus, our results are expected to be applied to stochastic feedback systems. Finally, the numerical simulations are provided to demonstrate the effectiveness of our theoretical results.

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