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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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Day-ahead PV Power Forecast by Hybrid ANN Compared to the Five Parameters Model Estimated by particle filter algorithm 25th

Day-ahead PV Power Forecast by Hybrid ANN Compared to the Fi...

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25th International Conference on Artificial Neural Networks (ICANN)

作者： Ogliari, Emanuele Bolzoni, Alberto Leva, Sonia Mussetta, Marco Politecn Milan Dept Energy Via Lambruschini 4 I-20156 Milan Italy

ISBN: (纸本)9783319447810;9783319447803

A comparison between the hybrid method (PHANN - Physical Hybrid Artificial Neural Network) and the 5 parameter Physical model, which have been determined by the particle filter algorithm, is presented here. These methods have been employed to perform the day-ahead forecast of the output power of a photovoltaic plant. The aim of this work is to assess the forecast accuracy of the two methods.

关键词： Day-ahead energy forecast Artificial neural networks particle filter algorithm

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Iterated Unscented Kalman particle filter algorithm based on a variable-step adaptive artificial fish swarm optimization 35

Iterated Unscented Kalman Particle Filter algorithm based on...

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35th Chinese Control Conference, CCC 2016

作者： Li, Min Chen, Zhimei Shao, Xuejuan School of Electronic and Engineering Taiyuan University of Science and Technology Taiyuan030024 China

ISBN: (纸本)9789881563910

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 paper. It 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 resolved. As a result, the estimation accuracy of the system state was improved. Simulation results proves the effectiveness of the proposed algorithm. © 2016 TCCT.

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

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Iterated Unscented Kalman particle filter algorithm based on a variable-step adaptive Artificial Fish Swarm optimization

Iterated Unscented Kalman Particle Filter algorithm based on...

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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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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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An improved adaptive weights correction-particle swarm optimization-unscented particle filter method for high-precision online state of charge estimation of lithium-ion batteries

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IONICS 2024年第1期30卷 311-334页

作者： Li, Zehao Wang, Shunli Yu, Chunmei Qi, Chuangshi Shen, Xianfeng Fernandez, Carlos Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China Sichuan Univ Sch Elect Engn Chengdu 610065 Peoples R China Robert Gordon Univ Sch Pharm & Life Sci Aberdeen AB10 7GJ Scotland

In the battery management system (BMS), the state of charge (SOC) of lithium-ion batteries is an indispensable part, and the accuracy of SOC estimation has attracted wide attention. Accurate SOC estimation can improve the efficiency of battery use while ensuring battery safety and improving battery life. Taking ternary lithium battery as the research object, this paper proposes a parameter identification method using adaptive forgetting factor recursive least squares and an improved joint unscented particle filter algorithm to estimate SOC. Firstly, an adaptive method is used to select the appropriate forgetting factor value to improve the accuracy of the forgetting factor recursive least squares (FFRLS) method. Meanwhile, the improved particle swarm (IPSO) optimization algorithm that incorporates variable weights and shrinkage factors is utilized to make the best choice of the noise for the unscented Kalman filter (UKF) algorithm in order to improve the estimation accuracy of the UKF algorithm. At the same time, the UKF algorithm is used as the suggestion density function of the particle filter (PF) algorithm to form the unscented particle filter (UPF) algorithm. In this paper, the AFFRLS algorithm and IPSO-SDUPF algorithm are combined to estimate the SOC of Li-ion batteries in real time. Experimental results under different working conditions show that the proposed algorithm has good convergence and high stability for SOC estimation of lithium-ion batteries. The maximum estimation errors of this algorithm are 1.137% and 0.797% for BBDST and DST conditions at 25 degrees C, and 1.015% and 1.029% for BBDST and DST conditions at 35 degrees C, which are lower than those of the commonly used algorithms of EKF, SDUKF, IPSO-SDUKF, and SDUPF, and provide a reference for future. The maximum estimation errors are lower than those of the commonly used EKF, SDUKF, IPSO-SDUKF, and SDUPF algorithms, which provide a reference for the future high-precision SOC estimation of Li-ion

关键词： Lithium-ion battery Charge state Adaptive unscented Kalman filter algorithm Adaptive forgetting factor recursive least squares method particle filter algorithm

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An improved forgetting factor recursive least square and extended particle filtering algorithm for accurate lithium-ion battery state of energy estimation

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IONICS 2024年第10期30卷 6179-6195页

作者： Shen, Xianfeng Wang, Shunli Yu, Chunmei Li, Zehao Fernandez, Carlos Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China Sichuan Univ Sch Elect Engn Chengdu 610065 Peoples R China Robert Gordon Univ Sch Pharm & Life Sci Aberdeen Scotland

State of energy (SOE) estimation of lithium-ion batteries is the basis for electric vehicle range prediction. To improve the estimation accuracy of SOE under complex dynamic operating conditions. In this paper, ternary lithium-ion batteries are used as the object of study and propose a hybrid approach that combines a particle swarm optimization-based forgetting factor recursive least squares method with an improved curve-increasing particle swarm optimization-extended particle filter algorithm for accurate estimation of the state of energy of lithium-ion batteries. Firstly, for the accuracy defects of the FFRLS method, the particle swarm optimization algorithm is used to optimize the initial value of the optimal parameters and the value of the forgetting factor. Secondly, the curve-increasing strategy is introduced into particle swarm optimization to solve the sub-poor problem of extended particle filtering. Experimental validation through different working conditions at multiple temperatures. The results show that the maximum error of parameter identification using the PSO-FFRLS algorithm is stabilized within 1.5%, and the SOE estimation error is within 1.5% for both BBDST and DST conditions at both temperatures. Therefore, the algorithm has high accuracy and robustness under different complex working conditions. The estimation results prove the effectiveness of the energy state estimation.

关键词： Lithium-ion batteries Second-order RC-PNGV model Curve-increasing strategy particle filter algorithm State of energy

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A scale adaptive generative target tracking method based on modified particle filter

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第20期82卷 31329-31349页

作者： Xiao, Yuqi Wu, Yongjun Yang, Fan West Anhui Univ Sch Mech & Vehicle Engn Luan City 237012 Anhui Peoples R China Chongqing Jiaotong Univ Sch Traff & Transportat Chongqing 400074 Peoples R China Agr Bank China Zhuzhou Branch Zhuzhou 412000 Hunan Peoples R China

This paper proposes an advanced particle filter (PF) algorithm based on the quantum particle swarm optimization method (QPSO) and adaptive genetic algorithm (QAPF). After resampling of the PF, the position updating equation of the QPSO is applied to improve the particle distribution. Then replace the individuals with lower fitness with those with higher fitness. The genetic operation from the adaptive genetic algorithm (AGA) is then applied to increase the accuracy and sample diversity. An frame size adaptive adjustment model is proposed to reduce the number of useless features and improve the accuracy of target positioning. Multiple simulations of the nonlinear target tracking model are carried out, and the results demonstrate that the numerical stability, efficiency and accuracy of our QAPF algorithm are significantly better than those of other similar algorithms. QAPF is also compared with similar tracking algorithms via a set of tracking experiments. Our experiments on the OTB-100 dataset prove that the QAPF algorithm is much better than the PF, PF improved by particle swarm optimization (PSO-PF) and PF advanced by genetic algorithm (GAPF) tracking algorithms and other typical generative trackers in terms of the tracking precision, success rate, efficiency and robustness.

关键词： Visual tracking Generative tracker particle filter algorithm Scale adaptive tracking frame

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Structural response reconstruction based on the information fusion of multi-source particle filters

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2023年第2期37卷 631-641页

作者： Shi, Yonghe Yin, Hong Peng, Zhenrui Wang, Zenghui Bai, Yu Lanzhou Jiaotong Univ Sch Mech Engn Lanzhou 730070 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China

Aiming at the problems that the lack of theoretical basis for the selection of particle set sampling variance and the resampling methods in traditional particle filter algorithms, and sampling process is easily disturbed by noise, an uncertainty structural response reconstruction method based on the information fusion of multi-source particle filters is proposed. Firstly, the sampling variance of particle set is analogous to the accuracy index of sensors, and a number of independent particle filtering samples from different sources are performed to ensure the independence of particles. Then, abnormal filters are screened and eliminated according to relative percentage error (RPE) threshold of preliminary reconstruction, and the state estimation results of remained particle filters are fused by the multi-source sensors information fusion technique to approximate to the real state values with high accuracy. Finally, the fused state values and the state space models are employed to reconstruct the responses of key positions, and the effectiveness of the proposed method is verified by numerical example of the space truss structure and the cantilever beam test. The results show that the proposed method can reduce the influence of the above uncertainties on reconstruction results, effectively improve the particle impoverishment problem, the filtering stability is good and the reconstruction accuracy is high.

关键词： Information fusion particle filter algorithm Structural response reconstruction State space model Uncertainty

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A novel hybrid information-based PF-WOA algorithm for gas source localization in 3D space

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ROBOTICA 2024年第11期42卷 3748-3767页

作者： Wang, Li Ren, Ziyu Fan, Shurui Zhang, Yong Hebei Univ Technol Sch Elect Informat Engn Tianjin Peoples R China Tianjin Univ Commerce Sch Informat Engn Tianjin Peoples R China

In recent years, dangerous gas leakage events occur frequently. Rapid and accurate location of gas leakage sources by mobile robots is the key to avoid the expansion of disasters. In order to solve the problem of discontinuous gas concentration gradient and sparse gas environment in three-dimensional space, particle filter, and whale swarm optimization algorithm are integrated to locate gas source. Firstly, the Z-shape search and comb search are used to locate the plume, and then, the particle filter algorithm is combined with the whale optimization method to guide the particle movement, and the random inertial disturbance term is designed to improve the convergence speed and search accuracy of the algorithm. Experimental results in three-dimensional environment show that the proposed information-driven particle filter whale optimization hybrid algorithm effectively guides the robot in localizing gas source within a certain range, significantly enhancing both the efficiency and accuracy of localization compared to other algorithms.

关键词： gas source localization whale optimization algorithm information entropy particle filter algorithm

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