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State of Charge Estimation for Li-ion Battery Based on extended kalman filter

Energy Procedia 2017年 105卷 3515-3520页

作者： Li Zhi Zhang Peng Wang Zhifu Song Qiang Rong Yinan Collaborative Innovation Center of Electric Vehicles in Beijing Beijing 100081 China College of Vehicle Engineering Huanghe Jiaotong University Wuzhi Henan Province 454950 China

It is difficult to estimate Lithium-ion battery state of charge (SOC) accurately. By using extended kalman filter (EKF).the interference of system noise can be effectively reduced, which improved the estimation accuracy. First, the battery model was studied and a Thevenin model was established. Then the appropriate battery charge-and-discharge experiments were performed to identify the parameters of the model. Finally EKF applied to the model experiments show that EKF has high precision.

关键词： Lithium-ion battery Thevenin model extended kalman filter algorithm Battery state of charge

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Tire-Ground Forces Estimation in a 4-Wheel Vehicle Using a Delayed Interconnected Cascade-Observer Structure

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IFAC-PapersOnLine 2016年第15期49卷 139-144页

作者： Cordeiro, Rafael A. Victorino, Alessandro C. Ferreira, Paulo A.V. de Paiva, Ely C. Bueno, Samuel S. School of Electrical and Computer Engineering University of Campinas Campinas Brazil Heudiasyc Lab UMR CNRS 7253 Université de Technologie de Compiègne Compiègne France School of Mechanical Engineering University of Campinas Campinas Brazil Division of Robotics and Computer Vision Center for Information Technology Renato Archer Campinas Brazil

The knowledge of tire-ground interaction forces is interesting for intelligent vehicles. However, tire forces transducers are expensive and not suitable for ordinary passengers cars. An alternative is to estimate these forces using common sensors. This paper presents an estimator structure capable of reconstructing tire-ground interaction forces in all directions. A delayed interconnected cascade-observer structure is proposed to eliminate mutual dependence between estimators. Observers are developed based on nonlinear vehicle dynamic models with the extended kalman filter algorithm. The estimator is validated with experimental results. The results are also compared with an existent estimator of the literature. © 2016

关键词： kalman filters Intelligent vehicle highway systems Tires Between estimators extended kalman filter algorithm Ground forces Interaction forces Mutual dependences State observer Vehicle dynamic model Vehicle dynamics

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kalman filter Carrier Synchronization algorithm

Kalman Filter Carrier Synchronization Algorithm

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10th International Symposium on Electronics and Telecommunications (ISETC)

作者： Campeanu, Andrei Gal, Janos Fac Elect & Telecommun Dept Commun Timisoara Romania

ISBN: (纸本)9781467311762

A new kalman carrier synchronization algorithm was developed for high-order QAM modulated signals which showed fast acquisition times and good frequency tracking performances. The proposed solution relies on a Decision Directed kalman filter combined with a lock detector which determines the status of the synchronization process, modifying consequently the filter parameters. Simulation results show that, compared to the performance of a basic kalman synchronization system, a tenfold improvement or better is achieved in terms of acquisition speed, while keeping a low residual phase noise.

关键词： component Acquisition Mode and Tracking Mode Decision-Directed Carrier Synchronization extended kalman filter algorithm Quadrature Amplitude Modulation Random Walk

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Identification of Hammerstein-Wiener ARMAX Systems Using extended kalman filter

Identification of Hammerstein-Wiener ARMAX Systems Using Ext...

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23rd Chinese Control and Decision Conference

作者： Mansouri, M. Tolouei, H. Shoorehdeli, M. Aliyari KN Toosi Univ Technol Control Dept Fac Elect Engn Intelligent Syst Lab ISLAB Tehran Iran KN Toosi Univ Technol Control Dept Fac Elect Engn Adv Proc Automat & Control Lab APAC Tehran Iran KN Toosi Univ Technol Mechatron Dept Fac Elect Engn Adv Proc Automat & Control Lab APAC Tehran Iran

ISBN: (纸本)9781424487363

In this study, extended kalman filter (EKF) algorithm is developed to estimate the parameters of Hammerstein-Wiener (H-W) ARMAX models. The basic idea is to estimate the original parameters of the identification model, which are appeared in the form of product terms, directly. While, other algorithms like extended Forgetting Factor Stochastic Gradient (EFG), extended Stochastic Gradient (ESG), Forgetting Factor Recursive Least Square (FFRLS) and kalman filter (KF), estimate parameters in the product form and they need another algorithms such averaging method (AVE method), singular value decomposition method (SVD method) to separate the parameters. So, the computational complexity of the proposed approach decreases. To show the efficiency of this method the results are compared with EFG and ESG method.

关键词： Hammerstein-Wiener model extended kalman filter algorithm extended Stochastic Gradient algorithm extended Forgetting Factor Stochastic Gradient algorithm

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Discrete-Time Neural Block Control for a Doubly Fed Induction Generator

Discrete-Time Neural Block Control for a Doubly Fed Inductio...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ruiz, R. Sanchez, E. N. Loukianov, A. G. CINVESTAV IPN Zapopan 45015 Jalisco Mexico

ISBN: (纸本)9781424496365

This paper proposes a control scheme based on the discrete-time block control technique using sliding modes, for a doubly fed induction generator connected to an infinity bus. In order to obtain the generator mathematical model, it is proposed to use a recurrent high order neural network (RHONN) identifier, which is trained with an extended kalman filter (EFK) algorithm. Parameter changes are applied to test the scheme robustness. Its performance is illustrated via simulations.

关键词： Induction generators kalman filters Reactive power Resistance Rotors Stator windings Torque asynchronous generators discrete time systems discrete-time block control discrete-time neural block control doubly fed induction generator extended kalman filter algorithm generator mathematical model infinity bus machine control neurocontrollers nonlinear filters recurrent high order neural network identifier recurrent neural nets sliding modes variable structure systems

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High-Order QAM Fast Carrier Synchronization by an Adaptive Decision-Directed EKF algorithm

High-Order QAM Fast Carrier Synchronization by an Adaptive D...

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34th International Conference on Telecommunications and Signal Processing (TSP)

作者： Campeanu, A. Gal, J. Politehn Univ Dept Commun Timisoara 300223 Romania

ISBN: (纸本)9781457714115

A new kalman carrier synchronization algorithm is developed for high-order QAM modulated signals which shows fast acquisition times and good frequency tracking performances. The proposed solution relies on a decision directed extended kalman filter combined with a lock detector which determines the status of the synchronization process, modifying consequently the filter parameters. Simulation results show that, compared to the performance of a basic kalman synchronization system, a tenfold improvement or better is achieved in terms of acquisition speed, while keeping a low residual phase noise.

关键词： Acquisition Mode and Tracking Mode Decision-Directed Carrier Synchronization extended kalman filter algorithm Quadrature Amplitude Modulation Random Walk

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Neural Network Identification for Biomass Gasification Kinetic Model

Neural Network Identification for Biomass Gasification Kinet...

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International Joint Conference on Neural Networks (IJCNN)

作者： Carrasco, R. Sanchez, E. N. Carlos-Hernandez, S. CINVESTAV Unidad Guadalajara Apartado Postal 31-438Plaza La Luna Guadalajara 45091 Jalisco Mexico Carr Monterrey-Saltillo Km 13 Ramos Arizpe 25900 Coahuila Mexico

ISBN: (纸本)9781424496365

This paper presents a neural network application to identify a kinetic model for the char reduction zone of a solid fuel gasification process. The considered model consists of six differential equations which represent the production of six components (carbon, hydrogen, carbon monoxide, water, carbon dioxide and methane) and are obtained from reaction rate equations of the four main reactions in the char reduction zone of a fluidized bed gasifier. On the other hand, the identification presented in this work is based on a discrete-time high order neural network (RHONN), which is trained with an extended kalman filter (EKF) algorithm. The objective is to reproduce with the neural network the different components production under various operating conditions. The neural identifier performance is illustrated via simulation.

关键词： Biological neural networks Biological system modeling Biomass Carbon Equations kalman filters Mathematical model biomass gasification kinetic model carbon carbon compounds carbon dioxide carbon monoxide char reduction zone differential equation differential equations discrete-time high order neural network extended kalman filter algorithm fluidised beds fluidized bed gasifier fuel gasification hydrogen hydrogen compounds methane neural nets neural network identification production engineering computing reaction rate equation solid fuel gasification process water

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An adaptive fuzzy neural network for MIMO system model approximation in high-dimensional spaces

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 1998年第3期28卷 436-446页

作者： Chak, CK Feng, G Ma, J Univ New S Wales Sch Elect Engn Dept Syst & Control Sydney NSW 2052 Australia City Univ Hong Kong Dept Informat Syst Kowloon Hong Kong

An adaptive fuzzy system implemented within the framework of neural network is proposed. The integration of the fuzzy system into a neural network enables the new fuzzy system to have learning and adaptive capabilities. The proposed fuzzy neural network can locate its rules and optimize its membership functions by competitive learning, kalman filter algorithm and extended kalman filter algorithms. A key feature of the new architecture is that a high-dimensional fuzzy system can be implemented with fewer number of rules than the Takagi-Sugeno fuzzy systems. A number of simulations are presented to demonstrate the performance of the proposed system including modeling nonlinear function, operator's control of chemical plant, stock prices and bioreactor (multioutput dynamical system).

关键词： competitive learning extended kalman filter algorithm fuzzy systems kalman filter algorithm neural networks supervised learning

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Learning techniques to train neural networks as a state selector for inverter-fed induction machines using direct torque control

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IEEE TRANSACTIONS ON POWER ELECTRONICS 1997年第5期12卷 788-799页

作者： Cabrera, LA Elbuluk, ME Zinger, DS NO ILLINOIS UNIV DEPT ELECT ENGNDE KALBIL 60115

Neural networks are receiving attention as controllers for many industrial applications. Although these networks eliminate the need for mathematical models, they require a lot of training to understand the model of a plant or a process. Issues such as learning speed, stability, and weight convergence remain as areas of research and comparison of many training algorithms. This paper discusses the application of neural networks to control induction machines using direct torque control (DTC), A neural network is used to emulate the state selector of the DTC, The training algorithms used in this paper are the backpropagation, adaptive neuron model, extended kalman filter, and the parallel recursive prediction error, Computer simulations of the motor and neural-network system using the four approaches are presented and compared, Discussions about the parallel recursive prediction error and the extended kalman filter algorithms as the most promising training techniques is presented, giving their advantages and disadvantages.

关键词： adaptive neuron algorithm backpropagation algorithm direct torque control extended kalman filter algorithm gradient descent algorithms induction machines neural-networks structure and training Newton algorithms parallel recursive prediction error algorithm

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