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6th International Conference on Smart Grid and Electrical Automation (ICSGEA)

作者： Wu, Youyu Zhou, Dingyinan Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Peoples R China

ISBN: (纸本)9781665432634

State of charge (SOC) estimation is one of the key technologies in battery management system of electric vehicle. In order to improve the estimation accuracy of SOC, this paper improves the extended kalman filter (EKF) algorithm;in the identification of model parameters, this paper uses the least square method based on the forgetting factor to construct a lithium battery time-varying parameter model;and then uses EKF algorithm to realize SOC online estimation. Matlab simulation results show that the improved algorithm is more accurate than the traditional EKF algorithm, and the estimation error is less than 2%. Finally, the algorithm is implemented on the vehicle embedded system by using the automatic code generation technology.

关键词： SOC estimation Time-varying battery model extended kalman filter algorithm Automatic code generation

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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 improved second-order electrical equivalent modeling method for the online high power Li-ion battery state of charge estimation 12

An improved second-order electrical equivalent modeling meth...

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12th IEEE Energy Conversion Congress and Exposition-Asia (ECCE-Asia)

作者： Deng, Dan Liu, Sheng-Yong Wang, Shun-Li Xia, Li-li Chen, Lei Guangxi Univ Sci & Technol Sch Elect & Informat Engn Liuzhou Peoples R China Guangxi Key Lab Auto Parts & Vehicle Technol Liuzhou Peoples R China Southwest Univ Sci & Technol Sch Informat Engn Mianyang Sichuan Peoples R China

ISBN: (纸本)9781728163444

The estimation of power battery state of charge and the selection of an effective equivalent model have always been the fi)cus and difficulty of current research. This article has improved and optimized the traditional 'rhevenin model, and the performance of the Li-ion-ion battery is tested through hybrid Pulse Power Characterization (HPPC) experiments. 'the off-line curve fitting identification method is used to identify the relevant parameters in the model to obtain the intrinsic relationship with the state of charge (SOC). An equivalent circuit model is established on the simulation platform to compare and verify actual data. The error of the verification result is controlled within 3.05%, which proves the effectiveness of the improved 'Ilievenin model. On the basis that the relevant parameters of the equivalent model have been identified, the recursive principle of the extended kalman filter algorithm (EKY) is studied, and the algorithm is applied to the Li-ion battery SOC estimation to obtain an accurate state estimation of the 1,i-ion battery. The algorithm is simulated and verified under working conditions, and the verification showed that its accuracy reached more than 98.376%, which ensured the reliability of the battery during use.

关键词： Li-ion battery improved Thevenin model parameter identification HPPC experiment extended kalman filter algorithm state of charge

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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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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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Identification of Time-Varying Hammerstein-Wiener Systems

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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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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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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： localisation geometry minimisation Optimisation techniques passive coherent location system nonlinear filters time-of-arrival estimation geometric dilution minimization AOTO algorithm fixed observers tracking accuracy observers filtering methods in signal processing target tracking performance tracking approaches kalman filters time difference of arrival measurements extended kalman filter algorithm Signal processing theory target state estimation target tracking TDOA-based adaptive observer trajectory optimisation algorithm direction-of-arrival estimation multiple emitters

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