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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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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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A Novel Shilling Attack Detection Model Based on particle filter and Gravitation

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China Communications 2019年第10期16卷 112-132页

作者： Lingtao Qi Haiping Huang Feng Li Reza Malekian Ruchuan Wang College of Computer Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing 210003China “Broadband Wireless Communication and Sensor Network Technology”Key Lab of Ministry of Education NUPTNanjing 210003China College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 210016China Department of Computer Science and Media Technology Malmo UniversityMalmo 20506Sweden

With the rapid development of e-commerce, the security issues of collaborative filtering recommender systems have been widely investigated. Malicious users can benefit from injecting a great quantities of fake profiles into recommender systems to manipulate recommendation results. As one of the most important attack methods in recommender systems, the shilling attack has been paid considerable attention, especially to its model and the way to detect it. Among them, the loose version of Group Shilling Attack Generation algorithm (GSAGenl) has outstanding performance. It can be immune to some PCC (Pearson Correlation Coefficient)-based detectors due to the nature of anti-Pearson correlation. In order to overcome the vulnerabilities caused by GSAGenl, a gravitation-based detection model (GBDM) is presented, integrated with a sophisticated gravitational detector and a decider. And meanwhile two new basic attributes and a particle filter algorithm are used for tracking prediction. And then, whether an attack occurs can be judged according to the law of universal gravitation in decision-making. The detection performances of GBDM, HHT-SVM, UnRAP, AP-UnRAP Semi-SAD,SVM-TIA and PCA-P are compared and evaluated. And simulation results show the effectiveness and availability of GBDM.

关键词： shilling attack detection model collaborative filtering recommender systems gravitation-based detection model particle filter algorithm

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State-of-health estimation of lithium-ion battery packs in electric vehicles based on genetic resampling particle filter

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APPLIED ENERGY 2016年第0期182卷 558-568页

作者： Bi, Jun Zhang, Ting Yu, Haiyang Kang, Yanqiong Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Beijing Jiaotong Univ MOE Key Lab Urban Transportat Complex Syst Theory Beijing 100044 Peoples R China Beihang Univ Beijing Key Lab Cooperat Vehicle Infrastruct Syst Sch Transportat Sci & Engn Beijing 100191 Peoples R China

Power battery packs are the energy source of battery electric vehicles (BEVs). A precise state-of-health (SOH) estimation for batteries is crucial to ensure the operational security and stability of BEVs. This paper employs an equivalent circuit model of battery pack in SOH estimation. Since a battery pack is a complex and nonlinear system, the equivalent circuit model of battery pack is always complicated. To balance estimation accuracy and computational complexity, the equivalent circuit model of battery pack should be simplified. However, much noise is produced in the simplified model. In addition, the errors during SOH estimation are from various sources so that SOH estimation is a non-Gaussian problem. Given the genetic resampling particle filter (GPF) performs efficiently in solving non-Gaussian problems, this paper proposes a new GPF-based method for battery SOH dynamic estimation when accuracy of the equivalent circuit model is not high. First, a second-order equivalent circuit model of Resistance Capacitance (RC) circuit for the battery pack is developed. The unknown parameters are identified using the recursive least-squares method with forgetting factor. Second, a state-space model of the GPF is developed based on the equivalent circuit model. Finally, a case study is conducted using real data collected from operating electric taxis in Beijing to investigate the estimation performance of the proposed model. Estimation results show that the proposed GPF model outperforms the particle filter method in the SOH estimation problem. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： State of health Battery pack model Least-squares estimation particle filter algorithm Genetic 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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Night vehicle target recognition based on fuzzy particle filter

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第4期38卷 3707-3716页

作者： Wang, Pin Fan, En Wang, Peng Shenzhen Polytech Sch Mech & Elect Engn Shenzhen Guangdong Peoples R China Shenzhen Polytech Sch Artificial Intelligence Shenzhen Guangdong Peoples R China Chinese Acad Sci Garden Ctr South China Bot Garden Guangzhou Guangdong Peoples R China

All along, the identification of night-driving safety car features is a major research direction in the field of intelligent traffic management, with a wide range of applications and development space, and these identification technologies include theoretical knowledge and important theoretical research in many fields. Due to the interference of lights and other light sources, the gray value of the background area also changes frequently. A common method during the day is to detect these background areas as moving vehicles, which greatly reduces the detection accuracy. The most reliable information at night is the headlights. If the light can be accurately detected and other sources are excluded, accurate detection can be performed and tracking accuracy can be guaranteed. Vehicle safety assisted driving technology is one of the main research directions of intelligent transportation systems. It mainly uses computer technology, sensor technology and communication technology to collect and analyze the state information of roads, other vehicles and drivers. Provide advice and warnings to the driver before reaching the danger, determine current traffic conditions and avoid traffic accidents in advance. This paper studies some problems of night vehicle target recognition and detection, mainly the division of target and background, and the classification and recognition of target extraction. To solve these problems, a particle filter algorithm is introduced to introduce nonlinear statistics. The fuzzy theory is introduced to classify the video processed by the particle filter algorithm. The target recognition is realized by the method, and the purpose of identifying the night vehicle target is achieved. Comparative experimental analysis shows that this method is more accurate and powerful than the common target recognition algorithm and can be applied to real scenes.

关键词： Night vehicle recognition particle filter algorithm nonlinear statistics fuzzy clustering

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GNSS multipath estimation and mitigation based on particle filter

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IET RADAR SONAR AND NAVIGATION 2019年第9期13卷 1588-1596页

作者： Qin, Honglei Xue, Xia Yang, Qian Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China

Multipath interference is the main error source for high-precision positioning applications, and multipath suppression and mitigation is particularly important. particle filters are widely used to solve non-linear filtering problems without limitation of Gaussian distribution, and Global navigation satellite system (GNSS) multipath estimation and mitigation based on particle filter are proposed in this study. This approach has four innovations: Firstly, the Kalman-based multipath signal model is improved to obtain a particle filter multipath signal model, and I and Q two channel signals in the algorithm model solve the coherence correlation phase estimation problem. Secondly, the particle filter algorithm constructs the correlation function using the current particle amplitude and delay. Thirdly, taking full advantage of every particle information, the weighted average is used to calculate state quantity of multipath. Fourthly, three resampling algorithms, including simple random, pseudo-parallel genetic algorithm and niche genetic algorithm, are taken to resample. particle filter algorithm for multipath estimation and mitigation based on simulated data and actual navigation satellite signal data is verified. The simulation results show the proposed algorithm has high accuracy in multipath estimation than MEDLL and TK-MEDLL, especially in multichannel multipath estimation. The actual experimental results show particle filter algorithm is effective for improving the positioning accuracy in complex environments.

关键词： phase estimation Kalman filters interference suppression satellite navigation filtering theory radio receivers genetic algorithms multipath channels Gaussian distribution particle filtering (numerical methods) time-of-arrival estimation high-precision positioning applications multipath suppression nonlinear filtering problems Global navigation satellite system multipath estimation Kalman-based multipath signal model coherence correlation phase estimation problem particle filter algorithm current particle amplitude particle information actual navigation satellite signal data multichannel multipath estimation GNSS multipath estimation multipath interference

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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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Fuzzy energy management strategy for hybrid electric vehicles on battery state-of-charge estimation by particle filter

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SN APPLIED SCIENCES 2022年第10期4卷 256页

作者： Feng, Na Ma, Tiehua Chen, Changxin North Univ China State Key Lab Dynam Testing Technol Taiyuan 030051 Peoples R China North Univ China Sch Elect & Control Engn Taiyuan 030051 Peoples R China

The battery/ultracapacitor hybrid power supply system can solve the problems of high cost and short life of a single power system, and the energy management of hybrid power system has become a vital issue in the field of electric vehicles. In this paper, a fuzzy energy management strategy on the state-of-charge (SOC) estimation of power battery is proposed. particle filter (PF) algorithm is used to estimate SOC of power battery, then estimated result is regarded as the input variable of fuzzy energy management controller, and the energy distribution result is obtained after fuzzy logic operation. The simulation results show that the SOC estimation result of the PF algorithm is closer to the actual value of power battery SOC. When the SOC estimation result of PF is embedded into the fuzzy controller for joint simulation, it is found that the charge and discharge current, and SOC consumption of the power battery are reduced, which shows the algorithm's effectiveness. It also provides a specific reference value for the further study of the power supply control strategy of hybrid electric vehicles.

关键词： Hybrid electric vehicle (HEV) SOC estimation Energy management strategy Fuzzy controller particle filter algorithm

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Positioning optimisation based on particle quality prediction in wireless sensor networks

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IET NETWORKS 2019年第2期8卷 107-113页

作者： Zhang, Chunjiong Xie, Tao Yang, Kai Ma, Hui Xie, Yuxia Xu, Yueyao Luo, Pan Tongji Univ Coll Elect & Informat Engn 4800 Caoan Highway Shanghai 4800 Peoples R China Southwest Univ Coll Comp & Informat Sci 2 Tiansheng Rd Chongqing Peoples R China

The particle degradation problem of particle filter (PF) algorithm caused by reduction of particle weights significantly influences the positioning accuracy of target nodes in wireless sensor networks. This study presents a predictor to obtain the particle swarm of high quality by calculating non-linear variations of ranging between particles and flags and modifying the reference distribution function. To this end, probability variations of distances between particles and star flags are calculated and the maximum inclusive distance using the maximum probability of high-quality particle swarm is obtained. The quality of particles is valued by the Euclidean distance between the predicted and real observations, and hereafter particles of high quality are contained in spherical coordinate system using the distance as diameter. The simulation results show that the proposed algorithm is robust and the computational complexity is low. The method can effectively improve the positioning accuracy and reduce the positioning error of target nodes.

关键词： particle swarm optimisation particle filtering (numerical methods) wireless sensor networks probability nonlinear variations reference distribution function star flags maximum inclusive distance maximum probability high-quality particle swarm Euclidean distance positioning accuracy target nodes positioning optimisation particle quality prediction wireless sensor networks particle degradation problem particle filter algorithm positioning error reduction particle weights reduction computational complexity

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