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第35届中国控制会议

作者： Min LI CHEN Zhimei SHAO Xuejuan School of Electronic and Engineering Taiyuan University of Science & Technology

ISBN: (纸本)9781509009107

Considering the problem of declining in accuracy caused by particles impoverishment in the Iterated Unscented Kalman particle filter algorithm,self-adaptive artificial fish swarm algorithm with changing step optimized Iterated Unscented Kalman particle filter algorithm was presented in this *** uses the alternation of behaviors of preying and swarming in the self-adaptive artificial fish swarm algorithm with changing step to optimize the re-sampling process firstly,which makes prior particles move towards the high likelihood region,the problem of sample impoverishment was *** a result,the estimation accuracy of the system state was *** results proves the effectiveness of the proposed algorithm.

关键词： particle filter algorithm Iterated Unscented Kalman particle filter algorithm a new self-adaptive artificial fish swarm algorithm with changing step

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Lithium battery state-of-health estimation and remaining useful lifetime prediction based on non-parametric aging model and particle filter algorithm

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ETRANSPORTATION 2022年 11卷

作者： Li, Xiaoyu Yuan, Changgui Wang, Zhenpo He, Jiangtao Yu, Shike Hebei Univ Technol Sch Mech Engn Tianjin 300130 Peoples R China China State Shipbldg Corp Ltd Res Inst 705 Kunming Branch Kunming 650106 Yunnan Peoples R China Beijing Inst Technol Natl Engn Lab Elect Vehicles Beijing 100081 Peoples R China FinDreams Battery Corp Ltd Shenzhen 518122 Peoples R China

State of health estimation (SOH) and remaining useful lifetime (RUL) prediction are significant health indicators for improving the safety and reliability of battery systems. Herein, a data-fusion method is developed to establish a non-parametric degradation model and a particle filter algorithm for forecasting battery health conditions. Firstly, a dynamic battery aging state-space system is developed, in which Gaussian process regression is applied to establish state equation using historical capacity series and current capacity as input and output variables, respectively. Meanwhile, multi-output Gaussian process regression maps the relationship between capacity degradation and battery health indicators to construct an observation equation. Second, two filter methods are unitized to obtain the smooth differential thermal voltammetry curves and the significant health indicators are extracted from partial differential thermal voltammetry curves. Third, the short-term SOH estimation and long-term RUL prediction are carried out using a particle filter algorithm. Moreover, two types of five batteries with various designed cases are conducted to verify and analyze the proposed method. The results show that the estimation errors of short-term SOH are within 4% and prediction errors of long-term RUL are around 7% (relative error/EOL, 12/159), which indicate the proposed method has an excellent capability for accurate and robust forecasting battery health conditions. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Lithium battery State of health Remaining useful life Multi-output Gaussian process regression particle filter algorithm

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Moving Trajectory Tracking Method of Multi Degree of Freedom Manipulator Based on particle filter algorithm

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JORDAN JOURNAL OF MECHANICAL AND INDUSTRIAL ENGINEERING 2022年第4期16卷 591-600页

作者： Zhao, Pengzhan Chongqing City Vocat Coll Sch Informat & Intelligent Mfg Chongqing 402160 Peoples R China

In this study, a moving track tracking method of multi degree of freedom manipulator based on particle filter algorithm is proposed. By constructing the transformation matrix and the Jacobian matrix, the kinematics of the multi degree of freedom manipulator is analyzed. According to the three coordinates of the end of the multi degree of freedom manipulator, the forward kinematics model is solved and the inverse kinematics model is obtained. particle filter algorithm is adopted to track the moving trajectory of multi degree of freedom manipulator, and the trajectory tracking error is corrected. Experiments show that this method has the advantages of small difference between the trajectory of the multi degree of freedom manipulator and the actual target trajectory, such as high trajectory tracking accuracy and short tracking time, and can meet the requirements of people for the trajectory control accuracy of the joint manipulator. It is expected that this paper can provide valuable references and help for the application field of robots as well as the actual life and production activities. (c) 2022 Jordan Journal of Mechanical and Industrial Engineering. All rights reserved

关键词： particle filter algorithm Multiple degrees of freedom Mechanical arm Moving track Kinematic model

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State of Charge Estimation for Electric Vehicle Batteries Based on a particle filter algorithm

State of Charge Estimation for Electric Vehicle Batteries Ba...

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2017 2nd International Conference on Software, Multimedia and Communication Engineering（SMCE 2017)

作者： Jun BI Yong-xing WANG Xiao-mei ZHAO MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology Beijing Jiaotong University Beijing Jiaotong University

Accurate State of Charge(SOC) estimation is critical for improving the battery *** order to realize the accurate estimation of SOC for electric vehicle(EV) batteries,considering the complex operating mode and nonlinear characteristics of EV batteries,this paper proposes a particle filter algorithm for estimating SOC based on the battery data from EVs operating in *** determine the state-space model for EV batteries,the data are used to estimate the parameters of the ***,based on the actual collected data,the experiments are designed to demonstrate the particle filter *** results indicate that the estimation values of particle filter algorithm are close to the true values and the error is comparatively ***,the particle filter algorithm has high accuracy in the SOC estimation for EV batteries.

关键词： Electric vehicles Battery data State of Charge estimation particle filter algorithm

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SOC estimation optimization method based on parameter modified particle Kalman filter algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第3-Sup期22卷 S6009-S6018页

作者： Zhang, Shouzhen Xie, Changjun Zeng, Chunnian Quan, Shuhai Wuhan Univ Technol Sch Automot Engn Wuhan 430070 Hubei Peoples R China Wuhan Univ Technol Sch Automat Wuhan 430070 Hubei Peoples R China

Traditional Kalman filter algorithm requires the system noise to be Gaussian distribution, but the power battery operating condition generally can not meet the requirement due to complexity and disturbance by the environment. However, the particle filter algorithm can adapt to various forms of system noise. In this work, the calculation process of the standard particle filter algorithm is improved based on the engineering characteristics of SOC estimation. In the calculation process, the key parameters including the total number of particles and the effective particle threshold are optimized and verified under FTP75 and NEDC conditions. The systematic error under different conditions is evaluated, based on the vehicle platform computing capacity, the proposed total number of particles is 1000, the effective particle threshold is 0.01. In this case, the SOC estimation accuracy can reach 1-2%, meeting the practical requirements.

关键词： SOC estimation particle filter algorithm Optimization modified parameters Recommended parameters

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The particle filter based on random number searching algorithm for parameter estimation

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2017年第2期46卷 1401-1413页

作者： Zheng, Wei Han, Juan Kong, Weijie Ren, Dewang Beijing Jiaotong Univ Natl Engn Res Ctr Rail Transportat Operat & Contr Beijing 100044 Peoples R China

This article addresses the issue of parameter estimation in linear system in the presence of Gaussian noises, under which the random number searching algorithm (LJ (Luus and Jaakola) algorithm) is combined with the Rao-Blackwellised particle filter (RBPF) algorithm. This yields the so-called RBPF algorithm based on LJ (RBPF-LJ). Unlike the mature alternatives of generic particle filter, the parameter particles of RBPF-LJ are set as random numbers that search in the parameter value scope, which is regulated based on the estimation result to track the changes of the unknown parameter. The contrasting simulations show that the proposed RBPF-LJ outperform the RBPF as well as the particle filter based on kernel smoothing contraction algorithm on the estimation of the dynamically linear or nonlinear parameter and it can obtain the similar estimation results on the static parameter if some coefficients are regulated.

关键词： Linear Gaussian system Parameter estimation particle filter algorithm Random number searching algorithm

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An adaptive hybrid approach for online battery state of charge estimation

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JOURNAL OF ENERGY STORAGE 2025年 115卷

作者： Lin, Qiongbin Hong, Huiyang Huang, Ruochen Fan, Yuhang Chen, Jia Wang, Yaxiong Dan, Zhimin Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350108 Peoples R China Fujian Engn Res Ctr High Energy Biatteries & New E Fuzhou 350108 Fujian Peoples R China Fuzhou Univ Coll Mech Engn & Automat Fuzhou 350108 Fujian Peoples R China Contemporary Amperex Technol Co Ltd Ningde 352100 Fujian Peoples R China

With the widespread adoption of electric vehicles (EVs) and energy storage in renewable energy systems, the use of lithium-ion batteries has increased significantly, making the battery safety performance a primary concern. The accurate state of charge (SOC) estimation can help mitigate the safety risks for the utilisation of EVs and renewable energy systems. Due to the dynamic and non-linear properties of batteries, an adaptive online SOC estimation is proposed in this paper by combining the online parameters estimation using equivalent circuit model (ECM) and the improved particle filter (PF) algorithm. It firstly deduces ECM parameters equations using bilinear transformation with the elimination of the variation caused by the ambient temperature. Then, the seeker optimization algorithm (SOA)-based fixed-length weighted least square (LS) algorithm is introduced to online estimate the battery parameters accurately. With the established ECM, the battery SOC can be estimated by the improved genetic algorithm (IGA) resampling-based PF algorithm, which effectively alleviates the particle degeneracy problem during the estimation, consequently, offering a better performance in SOC estimation. Both simulations and experiments have been conducted to validate the effectiveness of the proposed method. Compared with other existing algorithms, it shows that the proposed algorithm can accurately model the battery with the root mean squared error (RMSE) <0.1 % and achieve the real-time SOC estimation with less computation burden and high accuracy.

关键词： State of charge Equivalent circuit model Parameter estimation particle filter algorithm Genetic algorithm Batteries

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Online identification of battery model parameters and joint state of charge and state of health estimation using dual particle filter algorithms

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2022年第14期46卷 19615-19652页

作者： Xu, Yonghong Chen, Xia Zhang, Hongguang Yang, Fubin Tong, Liang Yang, Yifan Yan, Dong Yang, Anren Yu, Mingzhe Liu, Zhuxian Wang, Yan Beijing Univ Technol Fac Environm & Life Key Lab Enhanced Teat Transfer & Energy Conservat Beijing Key Lab Heat Transfer & Energy Convers Pingleyuan 100 Beijing 100124 Peoples R China Beijing Informat Sci & Technol Univ Sch Elect & Mech Engn Beijing Peoples R China

Aiming at the problems of time-varying battery parameters and inaccurate estimations of state of charge (SOC) and state of health (SOH), a joint estimation algorithm of SOC and SOH is proposed. A particle filter algorithm is used to identify the parameters online on the basis of a second-order equivalent circuit model. The algorithm feasibility is verified through the terminal voltage estimation accuracy. Considering that an accurate SOH is one of the foundations to achieve an accurate SOC estimation, a dual particle filter is used to jointly estimate SOC and SOH. Under different test conditions, the effect of different initial values (initial SOC and capacity), temperatures, operation conditions, particle number, and model parameters on the estimation accuracy and robustness is compared and analyzed. The effectiveness of the proposed algorithm is validated by experimental data under different operation conditions. Experimental results show that the online particle filter algorithm can well predict the dynamic battery model parameters. The proposed algorithm has high robustness and a good tracking effect when estimating SOC with a mean absolute error of less than 1.3%, a root mean square error of less than 1%, and a tracking terminal voltage.

关键词： online joint estimation online parameter identification particle filter algorithm state of charge state of health

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State estimation for a penicillin fed-batch process combining particle filtering methods with online and time delayed offline measurements

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CHEMICAL ENGINEERING SCIENCE 2018年 177卷 234-244页

作者： Kager, Julian Herwig, Christoph Stelzer, Ines Viktoria TU Wien Inst Chem Environm & Biol Engn Gumpendorfer Str 1a A-1060 Vienna Austria TU Wien Christian Doppler Lab Mech & Physiol Methods Impr Gumpendorfer Str 1a A-1060 Vienna Austria Med Univ Vienna Ctr Publ Hlth Dept Hlth Econ Kinderspitalgasse 15 A-1090 Vienna Austria Gen Hosp Vienna Ludwig Boltzmann Inst Appl Diagnost Wahringer Gurtel 18-20 A-1090 Vienna Austria

Real time monitoring of physiological characteristics during a cultivation process is of great importance in the pharmaceutical industry. Measuring biomass, product, substrate and precursor concentrations continuously however is limited due to time-consuming laboratory analysis or expensive and hard-to-handle devices. In this work, a particle filter algorithm for estimating these difficult-to-measure process states in a Penicillium chrysogenum fed-batch cultivation is presented. The implemented particle filter represents a new algorithmic framework, combining several already existing methods and techniques for state estimation. It is based on nonlinear process and measurement models and takes into account both online measurements for state estimation and time delayed offline measurements, ensuring the observability of the considered system and being essential for the adaptation of dynamic model parameters. The application on real experimental data showed the convincing performance of the algorithm, estimating biomass, precursor and product concentration, as well as the specific growth rate, requiring standard measurements only. Furthermore, the positive effect of parameter estimation with respect to estimation quality was analyzed and the effect of the time delay was highlighted exemplarily. Despite of being computationally expensive due to time delayed data, the algorithm can be considered as an alternative monitoring strategy for industrial applications. Thus, it can be used further for process understanding and control. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： particle filter algorithm Online measurements Time delayed offline measurements State estimation Parameter estimation Monitoring of fed-batch processes

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Improving physically based snow simulations by assimilating snow depths using the particle filter

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WATER RESOURCES RESEARCH 2017年第2期53卷 1125-1143页

作者： Magnusson, Jan Winstral, Adam Stordal, Andreas S. Essery, Richard Jonas, Tobias Norwegian Water Resources & Energy Directorate Oslo Norway WSL Inst Snow & Avalanche Res SLF Davos Switzerland IRIS Stavanger Norway Univ Edinburgh Sch Geosci Edinburgh Midlothian Scotland

Data assimilation can help to ensure that model results remain close to observations despite potential errors in the model, parameters, and inputs. In this study, we test whether assimilation of snow depth observations using the particle filter, a generic data assimilation method, improves the results of a multilayer energy-balance snow model, and compare the results against a direct insertion method. At the field site Col de Porte in France, the particle filter reduces errors in SWE, snowpack runoff, and soil temperature when forcing the model with coarse resolution reanalysis data, which is a typical input scenario for operational simulations. For those variables, the model performance after assimilation of snow depths is similar to model performance when forcing with high-quality, locally observed input data. Using the particle filter, we could also estimate a snowfall correction factor accurately at Col de Porte. The assimilation of snow depths also improves forecasts with lead-times of, at least, 7 days. At further 40 sites in Switzerland, the assimilation of snow depths in a model forced with numerical weather prediction data reduces the root-mean-squared-error for SWE by 64% compared to the model without assimilation. The direct insertion method shows similar performance as the particle filter, but is likely to produce inconsistencies between modeled variables. The particle filter, on the other hand, avoids such limitations without loss of performance. The methods proposed in this study efficiently reduces errors in snow simulations, seems applicable for different climatic and geographic regions, and are easy to deploy.

关键词： energy-balance snowmelt model particle filter algorithm state and parameter updating snow depth

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