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Optimal Sizing of Battery and Super-Capacitor Based on the MOPSO Technique via a New FC-HEV Application

ENERGIES 2023年第9期16卷 3902页

作者： Djouahi, Abdeldjalil Negrou, Belkhir Rouabah, Boubakeur Mahboub, Abdelbasset Samy, Mohamed Mahmoud Univ Kasdi Merbah Ouargla Lab Promot & Valorisat Ressources Sahariennes VPRS BP 511 Ouargla 30000 Algeria Univ Kasdi Merbah Ouargla Elect Engn Dept BP 511 Ouargla 30000 Algeria Beni Suef Univ Fac Engn Elect Engn Dept Bani Suwayf 2722165 Egypt

In light of the energy and environment issues, fuel cell vehicles have many advantages, including high efficiency, low-temperature operation, and zero greenhouse gas emissions, making them an excellent choice for urban environments where air pollution is a significant problem. The dynamics of fuel cells, on the other hand, are relatively slow, owing principally to the dynamics of the air compressor and the dynamics of manifold filling. Because these dynamics can limit the overall performance of fuel cell vehicles, two key technologies that have emerged as critical components of electric vehicle powertrains are batteries and supercapacitors. However, choosing the best hybrid energy storage system that combines a battery and a supercapacitor is a critical task nowadays. An electric vehicle simulated application by MATLAB Code is modeled in this article using the multi-objective particle swarm optimization technique (MOPSO) to determine the appropriate type of batteries and supercapacitors in the SFTP-SC03 drive cycle. This application optimized both component sizing and power management at the same time. Batteries of five distinct types (Lithium, Li-ion, Li-S, Ni-Nicl2, and Ni-MH) and supercapacitors of two different types (Maxwell BCAP0003 and ESHSR-3000CO) were used. Each storage component is distinguished by its weight, capacity, and cost. As a consequence, using a Li-ion battery with the Maxwell BCAP0003 represented the optimal form of hybrid storage in our driving conditions, reducing fuel consumption by approximately 0.43% when compared to the ESHSR-3000CO.

关键词： fuel-cell hybrid electric vehicle particle swarm optimization algorithm hydrogen consumption multi-objective function problem energy management strategy

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Improved Whale optimization algorithm for VSG Control

Improved Whale Optimization Algorithm for VSG Control

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2025 International Conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025

作者： Xia, Mingliang Wang, Xihuai Shanghai Maritime University School of Logistics Engineering Shanghai China

ISBN: (纸本)9798331506797

With the increasing seriousness of energy problems and the extensive use of new energy sources, how to ensure that the new energy power generation system has good grid-connected stability has become a new research hotspot. Virtual synchronous generator (VSG) technology introduces the operating characteristics of synchronous generator (SG) on the basis of grid-connected inverter, so that it has the same rotor inertia and damping. However, the design of VSG system parameters is complicated, and the traditional whale optimization (WOA) algorithm often fails to achieve the best optimization effect. To address this problem, a mathematical model of the VSG system is established, and based on the WOA algorithm, a mixed-strategy improved whale optimization (IWOA) algorithm is proposed. By comparing the simulation results under the environment of grid voltage and frequency fluctuation, it is proved that the IWOA algorithm is more suitable for VSG grid stabilization. © 2025 IEEE.

关键词： grid particle swarm optimization algorithm power oscillation stability virtual synchronous generator

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optimization of hydropneumatic suspension for articulated wheel loader based on kriging model and particle swarm algorithm

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ADVANCES IN MECHANICAL ENGINEERING 2018年第11期10卷

作者： Zhao, Huanyu Wang, Guoqiang Lv, Weidong Cao, Yue Li, Xuefei Jilin Univ Sch Mech Sci & Engn Changchun 130022 Jilin Peoples R China

Articulated wheel loaders that travel on unstructured roads experience severe vibration and poor stability. Introducing suspended axles on wheel loaders, which are traditionally constructed without wheel suspension, is desirable for ride comfort. This study mainly focuses on the parameter optimization of the hydropneumatic suspension to obtain the minimum root mean square of vertical accelerations under different driving conditions, thereby improving the ride comfort of the wheel loader. The multibody model of the wheel loader with hydropneumatic suspension was developed by RecurDyn in co-simulation with MATLAB/Simulink. The vertical acceleration root mean square at the seat position was analyzed when the wheel loader was traveling on class C, D, and E roads with different travel speeds. The surrogate model of the vertical acceleration root mean square with respect to the suspension parameters was established based on kriging method. The established surrogate model was then optimized using particle swarm optimization algorithm. The optimization results of the hydropneumatic suspension parameters of the wheel loader under different road excitations and driving speeds were obtained. Simulation and optimization results show that a well-designed hydropneumatic suspension system can significantly improve wheel loader performance in reducing the vertical acceleration at the seat position compared to a suspension system without optimization.

关键词： Wheel loader vehicle suspension systems optimization of hydropneumatic suspension kriging-based surrogate model particle swarm optimization algorithm

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Dynamic scheduling of workshop resource in cloud manufacturing environment

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 138卷

作者： Hu, Yanjuan Pan, Leiting Pan, Xueqiao Changchun Univ Technol Sch Mechatron Engn Changchun 130012 Peoples R China 2055 Yan An St Changchun Jilin Peoples R China

In the cloud manufacturing environment, workshop resource scheduling serves as a pivotal component, characterized by increased dynamics and complexities. Nevertheless, existing dynamic scheduling methods are often limited to solving specific dynamic events. Thus, considering the actual workshop resource scheduling in a cloud manufacturing environment, this article examines the methods to address unexpected events including randomly arriving tasks, resource breakdown, as well as resource maintenance. Besides, a dynamic scheduling method based on the Game Theory, considering workshop capacity in cloud manufacturing, was developed. In the first place, the priority of workshop tasks was evaluated by Game Theory, and the optimal task processing sequence in the workshop was determined to maximize benefits. Secondly, to verify the dynamic regulation performance of the method, it was combined with the particle swarm optimization (PSO) algorithm considering multi-objective factors to obtain an ameliorated PSO algorithm addressing the challenge of resource optimization scheduling in a genuinely dynamic workshop environment. Finally, this method was tested through a case study, and the results demonstrate that it can achieve superior dynamic and static performance compared to alternative algorithms.

关键词： Workshop resource Dynamic scheduling Game theory particle swarm optimization algorithm Cloud manufacturing

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Graph convolution network-based fault diagnosis method for the rectifier of the high-speed train 4

Graph convolution network-based fault diagnosis method for t...

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4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS)

作者： Chen, Zhiwen Xu, Jiamin Ke, Haobin Fan, Xinyu Peng, Tao Cent South Univ Sch Automat Changsha Peoples R China Guangdong Univ Technol Sch Automat Guangzhou Peoples R China

ISBN: (纸本)9781728162072

With its powerful data processing ability and convenient end-to-end characteristics, fault diagnosis method based on deep neural network (DNN) has been widely used in many professional fields. However, the current network models mostly are trained based on the measurement information, and few make use of the prior knowledge of the system of interest. Therefore, a new fault diagnosis method based on an improved graph convolution network (GCN) is proposed. Specifically, this method uses the prior knowledge to construct the structural analysis (SA) method, then the SA is used to pre-diagnose the faults and construct the association graph. Next, the association graph and measurements are sent into the improved GCN model to train the network model iteratively, in which a weight coefficient (theta) is proposed to adjust the influence of measurements and the prior knowledge. A particle swarm optimization algorithm (PSO) is used to find the optimal theta. Finally the fault diagnosis is realized by trained GCN model. This method makes comprehensive use of measurement information and prior knowledge, and achieves better results than other existing fault diagnosis methods in the experiment. Especially, to achieve the same level of diagnosis performance, much less samples are needed in the proposed method when comparing with the state-of-the-art DNN methods.

关键词： Graph convolutional network structural analysis prior knowledge particle swarm optimization algorithm fault diagnosis

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An integrative analytical framework and evaluation system of water environment security in the context of agricultural non-point source perspective

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ENVIRONMENTAL RESEARCH COMMUNICATIONS 2023年第1期5卷 015009页

作者： Chen, Shouji Wang, Jiayang Chengdu Univ Informat Technol Coll Resources & Environm Chengdu 610066 Peoples R China

Agricultural non-point source pollution (ANPSP) caused a contradiction between economic growth and water environmental security protection. In order to understand the trade-off between social-economic development and water environmental security in the context of agricultural non-point source pollution, a Driving force-Agricultural non-point source pollution-Pressure-State-Response (DAPSR) model framework was proposed, and 23 indicators were selected to construct the evaluation system of water environment security in this study. And we take Ya'an City, China as case study from 2017 to 2019, the characteristics of water pollution was analyzed, and the water environment security was evaluated by method of particle swarm projection pursuit. The results show that: (1) Agricultural non-point source pollutant discharge in Ya'an generally shows a decreasing trend. (2) The agricultural non-point source pollution subsystem and the response subsystem have a great impact on water environment security. (3) According to the values of water environment security, Yucheng, Hanyuan, Tianquan and Lushan are basically safe in level III, Mingshan is unsafe in level IV, Yingjing is safe in level II, Shimian and Baoxing are safe in level II. (4) The degree of agricultural non-point sources is highly correlated with the water environment security. This study shows that the DAPSR model is feasible and practical, and can provide a scientific basis for the decision-making of regional agricultural non-point source pollution prevention and water environmental security protection.

关键词： water environment security agricultural non-point source pollution projection pursuit particle swarm optimization algorithm

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Fuzzy Identification of Nonlinear Dynamic System Based on Input Variable Selection and particle swarm optimization Parameter optimization

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IEEE ACCESS 2020年 8卷 220557-220569页

作者： Jinfeng, Lv Fucai, Liu Yaxue, Ren Yanshan Univ Minist Educ Intelligent Control Syst & Intelligen Engn Res Ctr Qinhuangdao 066004 Hebei Peoples R China Hebei Normal Univ Sci & Technol Qinhuangdao 066004 Hebei Peoples R China

Input variable selection is an essential step in the development of data-driven models. In order to establish a fuzzy model with high identification accuracy for complex nonlinear systems (such as variable load pneumatic loading system) in engineering, a novel fuzzy identification method is proposed, which is based on the selection of important input variables. Firstly, the simplified Two Stage Fuzzy Curves and Surfaces method is used to rank the original input variables according to their significance, and the variables which are most relevant to the output are selected as the input of the T-S fuzzy model. Then, the Fuzzy c-Means clustering algorithm and particle swarm optimization algorithm are used to identify the antecedent parameters, and the Recursive Least Square method is used to identify the consequent parameters. The validity of the proposed fuzzy identification method is verified by two benchmark problems, and the results show that the accuracies of identified models have been improved significantly compared with the other existing models. Finally, the proposed approach is implemented to the practical data of an actual variable load pneumatic loading system, and preponderant trajectory matching performance is achieved.

关键词： Input variables Adaptation models optimization Clustering algorithms Heuristic algorithms Computational modeling Approximation algorithms Fuzzy identification fuzzy c-means Gaussian function input variable selection particle swarm optimization algorithm T-S fuzzy modeling

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SCP model based on the new energy vehicle collaborative detection configuration method research

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第1期9卷

作者： Zhou, Mingyang Qiu, Chengcheng Chongqing Jiaotong Univ Coll Aviat Chongqing 400000 Peoples R China Nanjing Audit Univ Sch Accounting Nanjing 210000 Jiangsu Peoples R China

To address the problem of inaccurate positioning of new energy vehicles during cooperative detection, this paper investigates the positioning accuracy of different combinations of radar and infrared sensors. In order to meet the control requirements of the vehicle co-detection formation, a distributed control method considering the vehicle dynamics characteristics is adopted for this purpose. And considering the cost of sensors, the optimal parameters are calculated, and an optimal aircraft co-detection configuration design scheme based on the SCP model (structure-behavior-performance model) is proposed. At the working level of multi-craft cooperative detection guidance, a two-layer cooperative guidance structure is designed based on the basic framework. Research on vehicle configuration design, vehicle data transmission, processing technology, and cooperative detection and guidance is carried out. The basic principles of the particle swarm optimization algorithm and the algorithmic flow of the discrete particle swarm algorithm are applied to calculate the assignment of targets by the particle swarm optimization algorithm. The GDOP values are analyzed according to the simulation results, and it can be concluded that: the change rate of 2 similar to 5 sensors accuracy can reach 71.1%, 71.6%, and 71.5%, respectively, when the infrared sensor spacing is 1km similar to 3km. 5 similar to 10 sensors accuracy change rate is only 53.3%, 52.2%, and 49.7%, respectively, which shows that the value is improved by 20%, indicating that the collaborative sensing accuracy of the aircraft has been effectively improved, which is of great help to the collaborative detection of new energy vehicles based on SCP model.

关键词： SCP model particle swarm optimization algorithm Radar and infrared sensor Cooperative vehicle detection configuration Two-layer cooperative guidance

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An improved variational mode decomposition method based on particle swarm optimization for leak detection of liquid pipelines

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2020年 143卷 106787-106787页

作者： Diao, Xu Jiang, Juncheng Shen, Guodong Chi, Zhaozhao Wang, Zhirong Ni, Lei Mebarki, Ahmed Bian, Haitao Hao, Yongmei Nanjing Tech Univ Jiangsu Key Lab Hazardous Chem Safety & Control Nanjing 211816 Jiangsu Peoples R China Changzhou Univ Sch Environm & Safety Engn Changzhou 213164 Jiangsu Peoples R China Univ Gustave Eiffel Lab Multi Scale & Simulat MSME UPECCNRSUMR 8208 5 Blvd Descartes Marne La Vallee 77454 France Nanjing Tech Univ Nanjing Peoples R China

Leak detection is critical for the safety management of pipelines since leakages may cause serious accidents. The present paper aims to develop an efficient method able to detect the presence and importance of leaks in pipelines. This method relies on adequate signal processing of acoustic emission (AE) signals, and improves the variational mode decomposition (VMD) for signal de-noising. In order to optimize the governing parameters, i.e. the penalty term and the mode number of VMD, the particle swarm optimization (PSO) algorithm is coupled to a fitness function based on maximum entropy (ME). After the signal reconstruction, based on the energy ratio of each VMD sub-mode, the waveform feature vectors for leak detection are extracted. Finally, the support vector machine (SVM) is employed for leak pattern recognition. For calibration purposes, artificial input signal is simulated. The results show that the proposed PSO-VMD method is capable of de-noising background noise. For validation purposes, experiments have been conducted on metal pipelines, with water flow. For sensitivity analysis, a set of five different leak apertures are adopted: aperture diameters as {10;12;15;20;27} mm, whereas the pipeline diameter is 108 mm. A database of AE signals is collected for each leak aperture, and different time sequences. The proposed method appears to be efficient since the classification accuracy of the SVM method reaches up to 100% in identifying the size of the leak on the basis of the AE signals collected in the database for the same leak size, and 89.3% on the basis of the whole database. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Variational mode decomposition particle swarm optimization algorithm Maximum entropy Waveform features Support vector machine Pipeline leak detection

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Optimum Design of PI Controller for Brushless DC Motor Based on PSO-CS algorithm 22

Optimum Design of PI Controller for Brushless DC Motor Based...

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Proceedings of the 2022 International Conference on Big Data, IoT, and Cloud Computing

作者： Silin Cui Xing Liu Shoushan Jiang Wei Wu School of Mechanical Engineering Northwestern Polytechnical University China School of Electronic Information Engineering Xi'an Technological University China

ISBN: (纸本)9781450399548

Aiming at the problems of slow response and low accuracy of traditional brushless DC motor (BLDCM) speed control system, the control strategy for the BLDCM based on PSO-CS fusion optimization algorithm to optimize the parameters of motor PI controller is proposed. Firstly, the BLDCM's double closed-loop control system mathematical model is established. Secondly, based on PSO-CS fusion optimization algorithm, a PI controller parameter optimization method is designed to determine the optimal parameters for motor speed control. Finally, the BLDCM control system model is built in MATLAB / Simulink. The current loop adopts traditional PI control, and the speed loop is controlled by traditional PI, PSO PI and PSO-CS PI respectively. The operation of the motor under different working conditions is simulated. The research shows that compared with the basic PSO algorithm, the PSO-CS fusion optimization algorithm has higher computational accuracy, and the parameter values obtained by using PSO-CS algorithm to optimize PI controller are better. At the same time, the PSO-CS PI controller has a better effect on BLDCM speed control. Also, it has low overshoot and short adjusting time. It shows that the proposed control strategy can make the BLDCM system has good robustness and stability.

关键词： BLDCM Cuckoo search algorithm PI control particle swarm optimization algorithm

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