Topic
Recursive least squares filter
About: Recursive least squares filter is a research topic. Over the lifetime, 8907 publications have been published within this topic receiving 191933 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: In this article, an online adaptive battery model is proposed to reproduce the vanadium redox battery dynamics accurately, and the model parameters are online identified with both the recursive least squares (RLS) and the extended Kalman filter (EKF).
76 citations
TL;DR: In this paper, a model-based condition monitoring strategy is developed for lithium-ion batteries on the basis of an electrical circuit model incorporating hysteresis effect, which systematically integrates 1) a fast upper-triangular and diagonal recursive least squares algorithm for parameter identification of the battery model, 2) a smooth variable structure filter for the SOC estimation, and 3) a recursive total least square algorithm for estimating the maximum capacity, which indicates the SOH.
76 citations
TL;DR: The recursive implementation of the novel recursive predictor-based subspace identification method is not only able to identify linear time-invariant models from measured data, but can also be used to track slowly time-varying dynamics if adaptive filters are used.
Abstract: A novel recursive predictor-based subspace identification method is presented to identify linear time-invariant systems with multi inputs and multi outputs. The method is implemented in real-time and is able to operate in open loop or closed loop. The recursive identification is performed via the subsequent solution of only three linear problems, which are solved using recursive least squares. The recursive implementation of the method is not only able to identify linear time-invariant models from measured data, but can also be used to track slowly time-varying dynamics if adaptive filters are used. The computational complexity is reduced by exploiting the structure in the data equations and by using array algorithms to solve the main linear problem. This results in a fast recursive predictor-based subspace identification method suited for real-time implementation. The real-time implementation and the ability to work with multi-input and multi-output systems operating in closed loop makes this approach suitable for online estimation of unstable dynamics. The ability to do so is demonstrated by the detection of flutter on an experimental 2-D-airfoil system.
76 citations
18 Aug 2011
TL;DR: In this article, a battery model that is suitable for real-time state-of-charge (SOC) estimation of a Lithium-Ion battery is presented, where the battery open circuit voltage (OCV) as a function of SOC is described by an adaptation of the Nernst equation.
Abstract: A battery model that is suitable for real-time State-of-Charge (SOC) estimation of a Lithium-Ion battery is presented in this paper. The battery open circuit voltage (OCV) as a function of SOC is described by an adaptation of the Nernst equation. The analytical representation can facilitate Kalman filtering or observer-based SOC estimation methods. A zero-state hysteresis correction term is used to depict the hysteresis effect of the battery. A parallel resistance-capacitance (RC) network is used to depict the relaxation effect of the battery. A linear discrete-time formulation of the battery model is derived. A recursive least squares algorithm with forgetting is applied to implement the online parameter calibration. Validation results show that the calibrated model can accurately simulate the dynamic voltage behavior of the Lithium-Ion battery for two different experimental data sets.
76 citations
20 Jul 2001
TL;DR: In this paper, the intricate relationship between adaptive filtering and signal analysis is discussed, highlighting stochastic processes, signal representations and properties, analytical tools, and implementation methods, as well as practical applications in information, estimation, and circuit theories.
Abstract: This text emphasizes the intricate relationship between adaptive filtering and signal analysis - highlighting stochastic processes, signal representations and properties, analytical tools, and implementation methods. This second edition includes new chapters on adaptive techniques in communications and rotation-based algorithms. It provides practical applications in information, estimation, and circuit theories.
75 citations