The Experts below are selected from a list of 18711 Experts worldwide ranked by ideXlab platform
Xiaolin Tang - One of the best experts on this subject based on the ideXlab platform.
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Nonlinear fractional order Estimator with guaranteed robustness and stability for lithium ion batteries
IEEE Transactions on Industrial Electronics, 2018Co-Authors: Changfu Zou, Satadru Dey, Lei Zhang, Xiaolin TangAbstract:This paper proposes a new Estimator design algorithm for state-of-charge (SoC) indication of lithium-ion batteries. A fractional-order model-based Nonlinear Estimator is first framed including a Luenberger term and a sliding mode term. The Estimator gains are designed by Lyapunov's direct method, providing a guarantee for stability and robustness of the error system under certain assumptions. This generic estimation algorithm is then applied to lithium-ion batteries. A fractional-order circuit model is adopted to predict battery dynamic behaviours. Assumptions based on which the estimation algorithm is developed are justified and remarked. Experiments corresponding to electric vehicle applications are conducted to parameterize the battery model and demonstrate the estimation performance. It shows that the proposed approach is able to estimate SoC with errors less than 0.03 in the presence of initial deviation and persistent noise. Furthermore, the benefits of using the proposed Estimator relative to other Estimators are calculated over different cycles and conditions.
John Folkesson - One of the best experts on this subject based on the ideXlab platform.
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the antiparticle filter an adaptive Nonlinear Estimator
9 December 2011 through 12 December 2011, 2017Co-Authors: John FolkessonAbstract:We introduce the antiparticle filter, AF, a new type of recursive Bayesian Estimator that is unlike either the extended Kalman Filter, EKF, unscented Kalman Filter, UKF or the particle filter PF. We show that for a classic problem of robot localization the AF can substantially outperform these other filters in some situations. The AF estimates the posterior distribution as an auxiliary variable Gaussian which gives an analytic formula using no random samples. It adaptively changes the complexity of the posterior distribution as the uncertainty changes. It is equivalent to the EKF when the uncertainty is low while being able to represent non-Gaussian distributions as the uncertainty increases. The computation time can be much faster than a particle filter for the same accuracy. We have simulated comparisons of two types of AF to the EKF, the iterative EKF, the UKF, an iterative UKF, and the PF demonstrating that AF can reduce the error to a consistent accurate value.
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the antiparticle filter an adaptive Nonlinear Estimator
International Symposium on Robotics, 2011Co-Authors: John FolkessonAbstract:We introduce the antiparticle filter, AF, a new type of recursive Bayesian Estimator that is unlike either the extended Kalman Filter, EKF, unscented Kalman Filter, UKF or the particle filter PF. W ...
Mario Terzo - One of the best experts on this subject based on the ideXlab platform.
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on the real time estimation of the wheel rail contact force by means of a new Nonlinear Estimator design model
Mechanical Systems and Signal Processing, 2018Co-Authors: Salvatore Strano, Mario TerzoAbstract:Abstract The dynamics of the railway vehicles is strongly influenced by the interaction between the wheel and the rail. This kind of contact is affected by several conditioning factors such as vehicle speed, wear, adhesion level and, moreover, it is Nonlinear. As a consequence, the modelling and the observation of this kind of phenomenon are complex tasks but, at the same time, they constitute a fundamental step for the estimation of the adhesion level or for the vehicle condition monitoring. This paper presents a novel technique for the real time estimation of the wheel-rail contact forces based on an Estimator design model that takes into account the Nonlinearities of the interaction by means of a fitting model functional to reproduce the contact mechanics in a wide range of slip and to be easily integrated in a complete model based Estimator for railway vehicle.
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Actuator dynamics compensation for real-time hybrid simulation: an adaptive approach by means of a Nonlinear Estimator
Nonlinear Dynamics, 2016Co-Authors: Salvatore Strano, Mario TerzoAbstract:In real-time hybrid simulation, hydraulic actuators, equipped with suitable controllers, are typically used to impose displacements to experimental substructures. Interaction between actuators and physical substructures can result in a Nonlinear behaviour of the overall experimental testing system (ETS), making the controller design very challenging. The accuracy of the hydraulic actuation system (HAS) is very crucial because actuator displacement errors lead to incorrect simulation results. For this purpose, several methods have been developed by researchers in order to compensate tracking error of HASs. This paper presents a novel adaptive compensator that takes into account the actual ETS dynamics by adopting an extend Kalman filter for the real-time estimation of the ETS model parameters. The adaptive approach improves the actuator control accuracy and avoids ad hoc system identification procedures. The novel compensator has been verified experimentally on a test rig for seismic isolator shear tests. The feasibility of the proposed compensation method has been also demonstrated through real-time hybrid simulation of a building with a base isolation system. Both numerical and experimental results confirmed that the proposed compensation strategy provides good results even in the case of inevitable Nonlinearities of the ETS. Furthermore, the method has also demonstrated good performance in terms of stability and robustness with respect to variations of the operating conditions.
Mahinda Vilathgamuwa - One of the best experts on this subject based on the ideXlab platform.
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Constrained Ensemble Kalman Filter for Distributed Electrochemical State Estimation of Lithium-Ion Batteries
IEEE Transactions on Industrial Informatics, 2024Co-Authors: Yang Li, Binyu Xiong, Mahinda VilathgamuwaAbstract:This paper proposes a novel model-based Estimator for distributed electrochemical states of lithium-ion batteries. Through systematic simplifications of a high-order electrochemical-thermal coupled model consisting of partial differential-algebraic equations, a reduced-order battery model is obtained that features an equivalent circuit form and captures local state dynamics of interest inside the battery. Based on the physics-based equivalent circuit model, a constrained ensemble Kalman filter (EnKF) is pertinently designed to detect internal variables such as the local concentrations, overpotential, and molar flux. To address slow convergence issues due to weak observability of the battery model, the lithium ions mass conservation is judiciously considered as constraints in the estimation algorithm. The estimation performance is comprehensively examined under a wide operating range. It demonstrates that the proposed EnKF-based Nonlinear Estimator is able to accurately reproduce the physically-meaningful state variables at a low computational cost and is significantly superior to its prevalent benchmarks for online applications.
Changfu Zou - One of the best experts on this subject based on the ideXlab platform.
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Nonlinear fractional order Estimator with guaranteed robustness and stability for lithium ion batteries
IEEE Transactions on Industrial Electronics, 2018Co-Authors: Changfu Zou, Satadru Dey, Lei Zhang, Xiaolin TangAbstract:This paper proposes a new Estimator design algorithm for state-of-charge (SoC) indication of lithium-ion batteries. A fractional-order model-based Nonlinear Estimator is first framed including a Luenberger term and a sliding mode term. The Estimator gains are designed by Lyapunov's direct method, providing a guarantee for stability and robustness of the error system under certain assumptions. This generic estimation algorithm is then applied to lithium-ion batteries. A fractional-order circuit model is adopted to predict battery dynamic behaviours. Assumptions based on which the estimation algorithm is developed are justified and remarked. Experiments corresponding to electric vehicle applications are conducted to parameterize the battery model and demonstrate the estimation performance. It shows that the proposed approach is able to estimate SoC with errors less than 0.03 in the presence of initial deviation and persistent noise. Furthermore, the benefits of using the proposed Estimator relative to other Estimators are calculated over different cycles and conditions.
