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Identification of Time-Varying Hammerstein-Wiener Systems

IEEE ACCESS 2020年 8卷 136906-136916页

作者： Yu, Feng Mao, Zhizhong He, Dakuo Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Integrated Automat Proc Ind Shenyang 110819 Peoples R China

This paper focuses on identifying time-varying Hammerstein-Wiener systems. A modified algorithm based on extended kalman filter is proposed. The algorithm is derived from the comparison between the first and the second order extended kalman filter algorithms. The modification is based on the first order algorithm by adding a multiplier to its recursive step. It greatly enhances the convergence of the algorithm. The convergence performance of the algorithm is also studied and the conditions for the boundedness of the parameter estimation error are obtained. Simulation experiments of the proposed method and the traditional methods are carried out. Simulation results show the superior performance of the proposed method.

关键词： Hammerstein-Wiener system time-varying parameter estimation extended kalman filter algorithm

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Model Predictive Torque Control for PMSM Driven by Three-Level Inverter Based on extended kalman filter Speed Observer

Model Predictive Torque Control for PMSM Driven by Three-Lev...

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IEEE International Conference of Intelligent Robotics and Control Engineering (IRCE)

作者： Han, Xiao Teng, Qingfang Luo, Weiduo Lanzhou Jiaotong Univ Dept Automat & Elect Engn Lanzhou 730070 Gansu Peoples R China

ISBN: (纸本)9781538674161

Based on the extended kalman filter algorithm, a non sensor model predictive torque control strategy is proposed for the permanent magnet synchronous motor (PMSM) driven by three-level inverter. The method of MPTC is proposed in order to reduce the torque and flux ripple and improve the control performance of the system, the speed observer based on the extended kalman filter algorithm can realize the online observation of motor speed and enhance the robustness of the system. Compared with the traditional sliding mode speed observer, the observer is more simple and easier to implement. Also, the observer reduces the ripple of the motor speed in high frequency range in an efficient way. The control strategy in this paper can make PMSM MPTC system have better control performance, stronger robustness and good dynamic *** simulation results validate the feasibility and effectiveness of the proposed scheme.

关键词： permanent magnet synchronous motor (PMSM) Three-level inverter extended kalman filter algorithm model predictive torque control (MPTC)

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Germinal Center Optimization Applied to Recurrent High Order Neural Network Observer

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IFAC-PapersOnLine 2018年第13期51卷 332-337页

作者： Rios, Jorge D. Villaseñor, Carlos Alanis, Alma Y. Arana-Daniel, Nancy Lopez-Franco, Carlos Centro Universitario de Ciencias Exactas e Ingenierías Universidad de Guadalajara Blvd. Marcelino García Barragán 1421 Guadalajara44430 Mexico

In this work, a germinal center optimization (GCO) algorithm which implements temporal leadership through modeling a non-uniform competitive-based distribution for particle selection is used to find an optimal set of parameters for a recurrent high order neural network observer (RHONNO). The RHONNO is trained with an extended kalman filter algorithm and it is capable of giving a model of the system besides of just giving state estimation. Furthermore, the RHONNO does not need previous knowledge of the system model, nor measurements, estimation or bounds of delays and disturbances. Applicability of the proposed methodology is presented using simulation results. © 2018

关键词： Parameter estimation Adaptive algorithms extended kalman filters Models Neural networks State estimation Time delay extended kalman filter algorithm Germinal centers Non uniform Optimal sets Parameter optimization Particle selection Recurrent high order neural networks System modeling

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TDOA-based adaptive observer trajectory optimisation algorithm for tracking in passive coherent location system

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JOURNAL OF ENGINEERING-JOE 2019年第20期2019卷 6863-6867页

作者： Jing, Tong Tian, Wei Huang, Gaoming Naval Univ Engn Sch Elect Engn Wuhan Hubei Peoples R China

In the passive coherent location system with fixed observers, the target tracking performance is limited by the inappropriate localisation geometry. This study proposes an adaptive observer trajectory optimisation (AOTO) algorithm in terms of minimising the geometric dilution of precision (GDOP) based on the time difference of arrival (TDOA) measurements gained from multiple emitters. The target states are estimated by the extended kalman filter (EKF) algorithm. Simulation results demonstrate that the AOTO algorithm can effectively improve the tracking accuracy compared with the corresponding tracking approaches with fixed observers.

关键词： kalman filters target tracking nonlinear filters time-of-arrival estimation observers direction-of-arrival estimation minimisation TDOA-based adaptive observer trajectory optimisation algorithm passive coherent location system fixed observers target tracking performance extended kalman filter algorithm AOTO algorithm tracking accuracy tracking approaches localisation geometry geometric dilution minimization target state estimation time difference of arrival measurements multiple emitters

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Research on Modeling and SOC Estimation of Lithium Iron Phosphate Battery at Low Temperature

Research on Modeling and SOC Estimation of Lithium Iron Phos...

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Applied Energy Symposium and Forum - Low-Carbon Cities and Urban Energy Systems (CUE)

作者： Wu, Jian Li, Tong Zhang, Hao Lei, Yanxiang Zhou, Guangquan Beijing Jiaotong Univ Natl Act Distribut Network Technol Res Ctr Beijing 100044 Peoples R China CRRC QINGDAO SIFANG CO LTD Qingdao 266111 Shandong Peoples R China

The battery model is the basis for battery status estimation, and its accuracy will have a direct impact on accuracy of status estimation. In the field of rail transit, the reasonable allocation of battery capacity in conjunction with the actual operating conditions of the train also requires to establish an accurate battery model. Current battery models rarely consider the effect of temperature on model parameters. However, in some areas, it is very likely to encounter extremely cold conditions while the train is in motion. Firstly, taking into account the effects of temperature on available battery capacity, open-circuit voltage, ohm resistance, and polarization parameters, this article constructed a new battery model suitable for low temperature and small rate discharge conditions based on the lithium iron phosphate battery that used in the project. Then, this paper built a battery model in Matlab/Simulink and verified the accuracy of it through simulation. After that, this paper used extended kalman filter (EKF) algorithm to estimate the state of change (SOC) at different temperatures and carried out simulation verification. Simulation results showed that the battery model and the SOC estimation method established in this paper had higher estimation accuracy in low-temperature environment. Copyright (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Low temperature Lithium iron phosphate battery SOC estimation extended kalman filter algorithm

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State of charge estimation for Li-ion battery based on extended kalman filter 8th

State of charge estimation for Li-ion battery based on exten...

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8th International Conference on Applied Energy (ICAE)

作者： Li Zhi Zhang Peng Wang Zhifu Song Qiang Rong Yinan Collaborat Innovat Ctr Elect Vehicles Beijing Beijing 100081 Peoples R China Huanghe Jiaotong Univ Coll Vehicle Engn Wuzhi 454950 Henan Province Peoples R 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. (C) 2017 Published by Elsevier Ltd.

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

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Lithium Battery SOC Estimation Based on extended kalman filtering algorithm

Lithium Battery SOC Estimation Based on Extended Kalman Filt...

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IEEE 4th International Conference on Control Science and Systems Engineering (ICCSSE)

作者： Li Haoran Li Liangdong Zheng Xiaoyin Sun Mingxuan Wuhan Univ Technol Sch Int Educ Wuhan Hubei Peoples R China Wuhan Univ Technol Hubei Key Lab Adv Technol Automot Components Wuhan Hubei Peoples R China Wuhan Univ Technol Hubei Collaborat Innovat Ctr Automot Components T Wuhan Hubei Peoples R China Wuhan Univ Technol Sch Automot Engn Wuhan Hubei Peoples R China

ISBN: (纸本)9781538678879

Battery power detection has always been the core of the battery management system of electric vehicles, and the fast and accurate estimation of charged state can guarantee the safe operation of electric vehicles. This article is based on the electric car with iron phosphate lithium-ion battery PNGV model to complete the estimate of the lithium ion battery SOC, using Matlab Simulink module and simulation of the model. And it proves that PNGV model had higher precision. This system uses the extended kalman filter algorithm to estimate the charge state (SOC) of lithium battery and give the concrete implementation steps. Experimental simulation show that using the extended kalman filtering algorithm can rapidly estimate the charged state of lithium-ion batteries, the maximum error is about 4%, the average error is around 2%, can satisfy the accuracy of the battery management system needs.

关键词： PNGV model extended kalman filter algorithm SOC estimation battery management system Lithium battery

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Recursive parameter estimation for Hammerstein-Wiener systems using modified EKF algorithm

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ISA TRANSACTIONS 2017年 70卷 104-115页

作者： Yu, Feng Mao, Zhizhong Yuan, Ping He, Dakuo Jia, Mingxing Northeastern Univ Dept Control Theory & Control Engn 11Lane 3WenHua Rd Shenyang Liaoning Peoples R China

This paper focuses on the recursive parameter estimation for the single input single output Hammerstein-Wiener system model, and the study is then extended to a rarely mentioned multiple input single output Hammerstein-Wiener system. Inspired by the extended kalman filter algorithm, two basic recursive algorithms are derived from the first and the second order Taylor approximation. Based on the form of the first order approximation algorithm, a modified algorithm with larger parameter convergence domain is proposed to cope with the problem of small parameter convergence domain of the first order one and the application limit of the second order one. The validity of the modification on the expansion of convergence domain is shown from the convergence analysis and is demonstrated with two simulation cases. (C) 2017 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Hammerstein-Wiener system Recursive parameter estimation extended kalman filter algorithm Multiple input single output Uniform convergence

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Smartphone-based crowdsourcing for position estimation of public transport vehicles

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IET INTELLIGENT TRANSPORT SYSTEMS 2017年第9期11卷 588-595页

作者： Mukheja, Pankaj Kiran, Mukesh K. Velaga, Nagendra R. Sharmila, R. B. Indian Inst Technol Dept Civil Engn Bombay 400076 Maharashtra India

In this research, a real-time positioning method, which utilises crowdsourced positioning data obtained from smartphone GPS is developed. Such vehicle location information obtained from crowdsourcing and smartphones in public transport could replace traditional automatic vehicle location systems. However, the location information from smartphone GPS is more erroneous. The proposed methodology serves as an alternative to existing positioning methods to improve the vehicle positioning accuracy. The developed enhanced particle filter algorithm takes smartphone GPS positioning data [from multiple passengers in a single transit vehicle (e.g. bus)] as input data. This crowdsourced' data can then be utilised to calculate the vehicles' positioning information with better accuracy using the developed enhanced particle filter algorithm. The developed algorithm was tested using data collected on 14 different bus routes in urban and suburban areas of Mumbai, India, and it was identified that the algorithm is effective in reducing the average error up to 21.3% from a regular smartphone GPS and 10% from extended kalman filter algorithm and was able to curtail positioning error within 8.672m (average over 14 routes).

关键词： smart phones crowdsourcing public transport Global Positioning System traffic information systems particle filtering (numerical methods) mobile computing intelligent transportation systems smartphone-based crowdsourcing position estimation public transport vehicles real-time positioning method vehicle location information automatic vehicle location systems vehicle positioning accuracy enhanced particle filter algorithm smartphone GPS positioning data crowdsourced data data collection bus routes extended kalman filter algorithm

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Research on Modeling and SOC Estimation of Lithium Iron Phosphate Battery at Low Temperature

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Energy Procedia 2018年 152卷 556-561页

作者： Jian Wu Tong Li Hao Zhang Yanxiang Lei Guangquan Zhou National Active Distribution Network Technology Research Center Beijing Jiaotong University Beijing 100044 China CRRC QINGDAO SIFANG CO. LTD Qingdao 266111 China

关键词： Low temperature Lithium iron phosphate battery SOC estimation extended kalman filter algorithm

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