In this paper, an accelerated proximal gradient based forgettingfactorrecursiveleastsquares (APG-FFRLS) algorithm is proposed for state of charge (SOC) estimation with output outliers. First, a second-order resist...
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In this paper, an accelerated proximal gradient based forgettingfactorrecursiveleastsquares (APG-FFRLS) algorithm is proposed for state of charge (SOC) estimation with output outliers. First, a second-order resistance-capacitance (RC) equivalent circuit model is built to reflect the operating characteristics of the battery. Then, the APG method is applied to correct the output outliers. The FFRLS and extended Kalman filtering (EKF) are used to estimate the battery model parameters and SOC interactively. In order to verify the effectiveness of the proposed algorithm, this paper models the Samsung lithium battery and compares the effectiveness of different algorithms in estimating SOC. The experimental results show that the proposed APG-FFRLS-EKF algorithm has higher accuracy.
The least mean square methods include two typical parameter estimation algorithms, which are the projection algorithm and the stochastic gradient algorithm, the former is sensitive to noise and the latter is not capab...
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The least mean square methods include two typical parameter estimation algorithms, which are the projection algorithm and the stochastic gradient algorithm, the former is sensitive to noise and the latter is not capable of tracking the time-varying parameters. On the basis of these two typical algorithms, this study presents a generalised projection identification algorithm (or a finite data window stochastic gradient identification algorithm) for time-varying systems and studies its convergence by using the stochastic process theory. The analysis indicates that the generalised projection algorithm can track the time-varying parameters and requires less computational effort compared with the forgetting factor recursive least squares algorithm. The way of choosing the data window length is stated so that the minimum parameter estimation error upper bound can be obtained. The numerical examples are provided.
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