How to predict capacity for lithium-ion battery is one of the most important problems in the field of battery health *** make the newest data more efficiently,this paper proposes recursiveleastsquares with forgettin...
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ISBN:
(纸本)9781510806528
How to predict capacity for lithium-ion battery is one of the most important problems in the field of battery health *** make the newest data more efficiently,this paper proposes recursive least squares with forgetting factor to estimate the coefficients of the linear capacity degradation model,and presents the adaptive capacity prediction based on the estimation *** experiment example demonstrates the effectiveness of the proposed approach
Lithium-ion batteries are usually connected in series in large-scale battery energy storage systems (BESSs), and the safety operation of battery packs has become a hotspot. The accurate estimation of the State of Char...
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ISBN:
(纸本)9798350344455
Lithium-ion batteries are usually connected in series in large-scale battery energy storage systems (BESSs), and the safety operation of battery packs has become a hotspot. The accurate estimation of the State of Charge (SOC) is a key factor in ensuring the safe and reliable operation of battery packs. The SOC of the battery is important for the diagnosis of internal short circuit (ISC) fault, while the external characteristics of the ISC fault are not obvious, and the continuous micro short circuit discharge of the battery brings difficulties to the accurate estimation of the SOC. This paper proposes a SOC estimation method for ISC battery, which combines Extended Kalman Filtering (EKF) and recursive least squares with forgetting factor (FFRLS). An equivalent circuit model of the ISC battery is established first and the FFRLS algorithm is used to identify model parameters. Then, the EKF algorithm and the model parameters obtained through identification are employed to estimate SOC. The experimental results demonstrate that the proposed method can effectively estimate the battery SOC under the FUDS discharging condition and the maximum estimation error of SOC is 0.9%.
Lithium-ion batteries are used more and more extensively, and the state-of-charge estimation of lithium-ion batteries is essential for their efficient and reliable operation. In order to improve the accuracy and relia...
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Lithium-ion batteries are used more and more extensively, and the state-of-charge estimation of lithium-ion batteries is essential for their efficient and reliable operation. In order to improve the accuracy and reliability of battery state-of-charge estimation, the Thevenin model was established and the parameters of the least square method model with forgettingfactor were used for online identification estimation. To reduce the impact of noise, an adaptive extended Kalman algorithm is developed by combining Sage-Husa adaptive filter with extend Kalman filter algorithm for SOC estimation. The experimental results compared with ampere-time integral method and standard extend Kalman filter method, the improved adaptive extend Kalman filter algorithm has good convergence speed, higher estimation accuracy and stability. The initial SOC error is 5%, and the root mean square error of extend Kalman filter SOC estimation algorithm is 0.0124. In contrast, the root mean square error of the proposed adaptive extend Kalman filter SOC estimation algorithm is 0.0109.
The main limitation of perturbation based extremum seeking methods is the requirement of a multiple time-scale separation between the system dynamics, the perturbation frequency, and the adaptation rate so as to avoid...
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The main limitation of perturbation based extremum seeking methods is the requirement of a multiple time-scale separation between the system dynamics, the perturbation frequency, and the adaptation rate so as to avoid interactions and possible instabilities. This causes the convergence to he extremely slow. In the present work, we propose a simple modification to the perturbation-based extremum seeking control method that can be used when the system cannot be accurately approximated by a Wiener-Hammerstein model for which convergence rate acceleration schemes are available. The linear filtering used in the perturbation based extremum seeking control for estimating the objective function gradient is replaced by a recursiveleast square with forgettingfactor estimation algorithm. It is shown that this simple modification can accelerate convergence to the optimum by removing one time scale separation. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Modeling and analyzing the human tremor signal is necessary to avoid its negative effect for the fine operation. However, there are some defects in the traditional method for tremor signal analysis, which cannot resol...
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ISBN:
(纸本)9781479983537
Modeling and analyzing the human tremor signal is necessary to avoid its negative effect for the fine operation. However, there are some defects in the traditional method for tremor signal analysis, which cannot resolve the localization contradictions in time domain and frequency domain. This paper proposes the statistical learning modeling method for tremor signal, which decomposes the tremor signal based on the empirical mode decomposition method, and constructs a composite two-order linear model for tremor signal based on the recursiveleastsquares method with forgettingfactor. Simulation results showed the high accuracy of the tremor model, which will be used to filter out the tremor signal during fine operation and improve the precision and stability of the operation.
Modeling and analyzing the human tremor signal is necessary to avoid its negative effect for the fine operation. However, there are some defects in the traditional method for tremor signal analysis, which cannot resol...
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Modeling and analyzing the human tremor signal is necessary to avoid its negative effect for the fine operation. However, there are some defects in the traditional method for tremor signal analysis, which cannot resolve the localization contradictions in time domain and frequency domain. This paper proposes the statistical learning modeling method for tremor signal, which decomposes the tremor signal based on the empirical mode decomposition method, and constructs a composite two-order linear model for tremor signal based on the recursiveleastsquares method with forgettingfactor. Simulation results showed the high accuracy of the tremor model, which will be used to filter out the tremor signal during fine operation and improve the precision and stability of the operation.
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