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Remaining Useful Life Prediction of Lithium-Ion Batteries Using particle filter algorithm in Communication Power Supply

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Russian Electrical Engineering 2024年第10期95卷 870-879页

作者： Zichen Wu Wang, Yicheng Zhang, Yunxiang Gu, Bin Xu, Mi State Grid Jiangsu Electric Power Co. LTD. Information and Communication Branch Jiangsu Province Nanjing City China

Abstract: With the development of communication technologies, the technology of power support, particularly the technology of lithium-ion batteries plays an important role in the communication area. The evaluation for the remaining useful life of lithium-ion batteries is a hot issue, to provide the maintenance plans and ensure the reliability and safety of system. Besides, existing methods lack to analyze the influence factors of prediction of the particle filter algorithm. In this paper, a method using particle filter algorithm for the remaining useful life prediction of lithium-ion batteries in communication power supply is carried out, and the comparisons of prediction effect with the least square method are investigated. From the results, it is clearly seen that the proposed particle filter algorithm presents a better prediction accuracy and stability than the least square method. In addition, the proposed particle filter algorithm presents a weak data dependence, and some cases with high prediction error are because of the starting points of prediction belong to the outlier of the fitting line. For a better prediction result, the number of data to be predicted by the proposed particle filter algorithm accounts about 55% of the total number of the cycle time, which is smaller than that accounts 70% by the least square method. This paper provides a technology support for the remaining useful life prediction of lithium-ion batteries. © Allerton Press, Inc. 2024.

关键词： communication power supply lithium-ion batteries particle filter algorithm remaining useful life prediction

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Remaining Useful Life Prediction for Rolling Bearings With a Novel Entropy-Based Health Indicator and Improved particle filter algorithm

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IEEE ACCESS 2023年 11卷 3062-3079页

作者： Zhang, Tianyu Wang, Qingfeng Shu, Yue Xiao, Wang Ma, Wensheng Beijing Univ Chem Technol Beijing Key Lab Hlth Monitoring & Selfrecovery Hi Beijing 100029 Peoples R China Beijing Univ Chem Technol Sch Mech & Elect Engn Beijing 100029 Peoples R China Hefei Gen Machinery Res Inst Co Ltd Hefei 230031 Anhui Peoples R China Western Branch Natl Pipe Network Grp United Pipel Urumqi Xinjiang Peoples R China

Due to the importance of bearings in modern machinery, the prediction of the remaining useful life (RUL) of rolling bearings has been widely studied. When predicting the RUL of rolling bearings in engineering practice, the RUL is usually predicted based on historical data, and as the historical data increases, the prediction results should be more accurate. However, the existing methods usually have the shortcomings of low prediction accuracy, large cumulative error and failure to dynamically give prediction results with the increase of historical data, which are not suitable for engineering *** address the above problems, a novel RUL prediction method is proposed. The proposed method consists of 3 parts: First, the multi-scale entropy-based feature - namely "average multi-scale morphological gradient power spectral information entropy (AMMGPSIE) " - from the rolling bearings as the Health indicator (HI) is extracted to ensure all the fault-related information is well-included;Then, the HI is processed with the enhanced Hodrick Prescott trend-filtering with boundary lines (HPTF-BL) to ensure good performance and small fluctuation on the HI;Finally, the deterioration curve is predicted using an LSTM neural network and the improved particle filter algorithm that we proposed. The proposed method is validated using the experimental bearing degradation dataset and the casing data of a centrifugal pump bearing from an actual industrial site. Comparing the results with other recent RUL prediction methods, the proposed method achieved state-of-the-art feasibility and effectiveness, conform to the needs of practical application of the project.

关键词： Entropy-based features particle filter algorithm rolling bearing RUL prediction

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particle filter algorithm optimized by genetic algorithm combined with particle swarm optimization 9

Particle filter algorithm optimized by genetic algorithm com...

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International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)

作者： Yang, Jin Cui, Xuerong Li, Juan Li, Shibao Liu, Jianhang Chen, Haihua China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China

The standard particle filter (PF) algorithm has the issue of particle diversity loss caused by particle degradation and resampling, which makes it impossible for particle samples to accurately represent the true distribution of state probability density function. particle swarm optimization (PSO) algorithm can effectively improve the particle degradation problem of particle filter namely, PSO-PF, but its fitness function is greatly affected by the variance of measurement noise, and is easy to fall into local optimal, which greatly limits the filtering accuracy. Therefore, this paper proposes an algorithm that combines genetic algorithm (GA) and PSO algorithm to improve particle filtering, namely, GA-PSO-PF. This algorithm combines the fast convergence speed of particle swarm optimization with the strong global searching ability of genetic algorithm to increase the diversity of particles while ensuring the effectiveness of superior particles, and improve the speed and accuracy of finding the optimal solution. Experimental results show that the filtering performance of the proposed algorithm is better than PF and PSO-PF, and the positioning and tracking accuracy is improved by 54.44% compared with PF and 27.20% compared with PSO-PF. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the International Conference on Identification, Information and Knowledge in the internet of Things, 2020.

关键词： particle filter algorithm particle swarm optimization genetic algorithm target tracking and location

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Research on Fusion Monitoring Method of Turning Cutting Tool Wear Based on particle filter algorithm

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IEEE ACCESS 2021年 9卷 85903-85917页

作者： Wang, Jun Zhou, Tingting Qilu Univ Technol Sch Mech & Automot Engn Shandong Acad Sci Jinan 250353 Peoples R China Shandong Inst Mech Design & Res Jinan 250031 Peoples R China

The monitoring of cutting tool wear has a great significance for the processing quality and stability. To overcome the difficulty to reflect all the wear mechanisms for the monitoring method based on finite element model or the excessive dependence on data extraction quality for the monitoring method based on sensor data, this paper proposes a model and data fusion method based on particle filter algorithm. Two different kinds of materials AISI 1045 and AISI 4340 are chosen to carry out the turning experiments. The mean absolute percentage error (MAPE) of the fusion method is 0.6%similar to 4.1% lower and the coefficient of determination (R-2) is more close to 1 compared with the finite element model method or sensor data method individually. Experimental results verify the feasibility and the superiority of the fusion method.

关键词： Tool wear monitoring particle filter algorithm model and data fusion finite element model sensor data

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particle filter algorithm optimized by genetic algorithm combined with particle swarm optimization

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Procedia Computer Science 2021年 187卷 206-211页

作者： Jin Yang Xuerong Cui Juan Li Shibao Li Jianhang Liu Haihua Chen College of computer science and technology China University of Petroleum(East China) Qingdao 266580 China College of Oceanography and Space Informatics China University of Petroleum(East China) Qingdao 266580 China

关键词： particle filter algorithm particle swarm optimization genetic algorithm target tracking location

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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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Speeding-Up the particle filter algorithm for Tracking Multiple Targets Using CUDA Programming

Speeding-Up the Particle Filter Algorithm for Tracking Multi...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Zhang, Jinhua Guan, Wenkai Ababei, Cristinel Medeiros, Henry Povinelli, Richard J. Marquette Univ Dept Elect & Comp Engn Milwaukee WI 53233 USA

ISBN: (纸本)9781665458412

Object detection and tracking are essential tasks in many computer vision applications. One of the most popular tracking algorithms is the particle filter, which is widely used for real-time object tracking in live video streams. While very popular, the particle filter algorithm suffers from increased computational runtimes for high-resolution frames and large numbers of particles. In this paper, we investigate the use of CUDA programming as a method to parallelize portions of the particle filter algorithm in order to speed-up its execution time on compute systems that are equipped with NVIDIA GPUs. Experiments that compare a CPU sequential version, as the base case, with the CUDA parallelized version demonstrate an achievable speed-up of up to 7.5x for a 3840x2160 video resolution, and 9216 particles on a computer equipped with an NVIDIA Tesla K40c GPU.

关键词： object tracking particle filter algorithm GPU CUDA programming

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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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Influence of Reference Map Resolution on Indoor Terminal Positioning with the use of RSSI Measurements and particle filter algorithm 13

Influence of Reference Map Resolution on Indoor Terminal Pos...

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13th European Conference on Antennas and Propagation (EuCAP)

作者： Kawecki, Robert Korbel, Piotr Hausman, Slawomir Lodz Univ Technol Inst Elect Lodz Poland

ISBN: (纸本)9788890701887

The article discusses the factors affecting the performance of indoor positioning system employing three main components: Bluetooth Low Energy (BLE) transmitters, received signal strength maps obtained by measurements, and a particle filter algorithm. The results of experimental analysis of the impact of the resolution of the received power distribution reference maps on the positioning accuracy has been shown. The relation between the resolution of the maps and processing speed and accuracy of the positioning algorithms has been presented, showing that careful selection of the map resolution can result in significant reduction of the computation time while maintaining the accuracy of the positioning system.

关键词： indoor positioning particle filter algorithm received signal strength

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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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