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State of health estimation for lithium-ion battery based on particle swarm optimization algorithm and extreme learning machine

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Green Energy and Intelligent Transportation 2024年第1期3卷 46-54页

作者： Kui Chen Jiali Li Kai Liu Changshan Bai Jiamin Zhu Guoqiang Gao Guangning Wu Salah Laghrouche School of Electrical Engineering Southwest Jiaotong UniversityChengdu611756China FEMTO-ST UMR CNRS 6174and FCLABFR CNRS 3539UniversitéBourgogne Franche-ComtéBelfortUTBM90000France

Lithium-ion battery State of Health(SOH)estimation is an essential issue in battery management *** order to better estimate battery SOH,Extreme Learning Machine(ELM)is used to establish a model to estimate lithium-ion battery *** swarm optimization algorithm(PSO)is used to automatically adjust and optimize the parameters of ELM to improve estimation ***,collect cyclic aging data of the battery and extract five characteristic quantities related to battery capacity from the battery charging curve and increment capacity *** Grey Relation Analysis(GRA)method to analyze the correlation between battery capacity and five characteristic ***,an ELM is used to build the capacity estimation model of the lithium-ion battery based on five characteristics,and a PSO is introduced to optimize the parameters of the capacity estimation *** proposed method is validated by the degradation experiment of the lithium-ion battery under different *** results show that the battery capacity estimation model based on ELM and PSO has better accuracy and stability in capacity estimation,and the average absolute percentage error is less than 1%.

关键词： Lithium-ion battery State of health estimation Grey relation analysis method particle swarm optimization algorithm Extreme learning machine

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Retrieval of particle size distribution from aerosol optical thickness using an improved particle swarm optimization algorithm

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OPTICAL REVIEW 2015年第5期22卷 809-818页

作者： Mao, Jiandong Li, Jinxuan Beifang Univ Nationalities Sch Elect & Informat Engn Yinchuan 750021 Peoples R China

particle size distribution is essential for describing direct and indirect radiation of aerosols. Because the relationship between the aerosol size distribution and optical thickness (AOT) is an ill-posed Fredholm integral equation of the first type, the traditional techniques for determining such size distributions, such as the Phillips-Twomey regularization method, are often ambiguous. Here, we use an approach based on an improved particle swarm optimization algorithm (IPSO) to retrieve aerosol size distribution. Using AOT data measured by a CE318 sun photometer in Yinchuan, we compared the aerosol size distributions retrieved using a simple genetic algorithm, a basic particle swarm optimization algorithm and the IPSO. Aerosol size distributions for different weather conditions were analyzed, including sunny, dusty and hazy conditions. Our results show that the IPSO-based inversion method retrieved aerosol size distributions under all weather conditions, showing great potential for similar size distribution inversions.

关键词： Aerosol particles particle swarm optimization algorithm particle size distribution Simple genetic algorithm Aerosol optical thickness

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Path Planning of Continuum Robot Based on a New Improved particle swarm optimization algorithm

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Journal of Harbin Institute of Technology(New Series) 2013年第4期20卷 78-84页

作者： Fang Gao Qiang Zhao Gui-Xian Li School of Mechanical Electrical Engineering Harbin Institute of Technology Traffic College Northeast Forestry University

Continuum robot is a new type of biomimetic robot,which realizes the motion by bending some parts of its *** its path planning becomes more difficult even compared with hyper-redundant *** this paper a circular arc spline interpolating method is proposed for the robot shape description,and a new two-stage position-selectable-updating particle swarm optimization(TPPSO)algorithm is put forward to solve this path planning *** algorithm decomposes the standard PSO velocity’s single-step updating formula into twostage multi-point updating,specifically adopting three points as candidates and selecting the best one as the updated position in the first half stage,and similarly taking seven points as candidates and selecting the best one as the final position in the last half *** scheme refines and widens each particle’s searching trajectory,increases the updating speed of the individual best,and improves the converging speed and *** at the optimization objective to minimize the sum of all the motion displacements of every segmental points and all the axial stretching or contracting displacements of every segment,the TPPSO algorithm is used to solve the path planning *** detailed solution procedure is *** examples of five path planning cases show that the proposed algorithm is simple,robust,and efficient.

关键词： continuum robot path planning particle swarm optimization algorithm

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An improved particle swarm optimization algorithm for frequency-selective surface design

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INDIAN JOURNAL OF PHYSICS 2020年第12期94卷 1909-1915页

作者： Sheng, Xianjun Gao, Xiang Liu, Ning Dalian Univ Technol Sch Elect Engn Dalian 116024 Peoples R China

In this paper, an improved particle swarm optimization (PSO) algorithm, which can perform a global search over the search space with a faster convergence speed, is proposed for frequency-selective surface (FSS) design. Four improvement strategies including better particle initialization, acceleration coefficient dynamic adjustment, search space reset, and particle density control are applied to PSO algorithm aiming to accelerate convergence and, at the same time, improve its search capability. For verification, the improved PSO algorithm has been successfully implemented to the square loop FSS design. Then, the optimized results are compared to those obtained by the standard PSO algorithm as well as those existing in the literature, respectively, proving that the improved PSO algorithm has the capacity for accelerating convergence on the premise of ensuring the optimization accuracy.

关键词： Frequency-selective surface particle swarm optimization algorithm Equivalent circuit model 41 20 Jb 52 25 Os 02 20 -a

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An adaptive particle swarm optimization algorithm for reservoir operation optimization

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APPLIED SOFT COMPUTING 2014年 18卷 167-177页

作者： Zhang, Zhongbo Jiang, Yunzhong Zhang, Shuanghu Geng, Simin Wang, Hao Sang, Guoqing Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin 300072 Peoples R China China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River Beijing 100038 Peoples R China

Reservoir operation optimization (ROO) is a complicated dynamically constrained nonlinear problem that is important in the context of reservoir system operation. In this study, improved adaptive particle swarm optimization (IAPSO) is proposed to solve the problem, which involves many conflicting objectives and constraints. The proposed algorithm takes particle swarm optimization (PSO) as the main evolution method. To overcome the premature convergence of PSO, adjusting dynamically the two sensitive parameters of PSO guides the evolution direction of each particle in the evolution process. In the IAPSO method, an adaptive dynamic parameter control mechanism is applied to determine parameter settings. Moreover, a new strategy is proposed to handle the reservoir output constraint of ROO problem. Finally, the feasibility and effectiveness of the proposed IAPSO algorithm are validated by the Three Gorges Project (TGP) with 42.23 bkW power generation and XiLuoDo Project (XLDP) with 30.10 bkW. Compared with other methods, the IAPSO provides a better operational result with greater effectiveness and robustness, and appears to be better in terms of power generation benefit and convergence performance. Meanwhile, the optimal results could meet output constraint at each interval. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Reservoir operation optimization Adaptive particle swarm optimization algorithm Constraints

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Smart prediction for tunnel fire state evolution based on an improved fire simulation curve through particle swarm optimization algorithm

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FIRE SAFETY JOURNAL 2023年 136卷

作者： Liu, Xiaojiang Xu, Zhao-Dong Sun, Bin Liu, Xuanya Xu, Dajun Southeast Univ China Pakistan Belt & Rd Joint Lab Smart Disaster Nanjing 210096 Peoples R China Tianjin Fire Res Inst Minist Emergency Management Tianjin 300381 Peoples R China

Tunnel fires have received significant attention because they can cause large economic losses every year. To estimate the fire development and realize a prompt response to the fire disaster are thus crucial to tackle the above issue. Here, a smart prediction method for fire state evolution is developed based on an improved fire simulation curve through the particle swarm optimization algorithm. The improved fire simulation curve is established by supplementing the stable stage during the tunnel fire based on a fire curve proposed in 2002. Experimental results from a prototypical tunnel fire test indicate that the proposed method can effectively portray the behavior of a typical tunnel fire development. Moreover, it is accurate over a wide range of conditions, including different heat release rates, different measurement noises, different sampling intervals, and different prior durations. Particularly, the fluctuation range of estimation results is becoming larger with the increasing noise level. The influence of the sampling interval is mainly reflected in the calculation time. Prior duration is the most important factor of the prediction accuracy, the longer prior duration corresponds to the more precise results. 200s and 300s can be regarded as the critical duration by considering the estimation error of the whole-time domain and the preliminary stage, respectively. The proposed method has a broad application prospect, it can avoid complicated temperature spreading mechanisms, the additional workload for prior information, and the limitation of the specific fire scene.

关键词： Tunnel fire Fire state evolution particle swarm optimization algorithm Fire simulation curve Full-scale tests

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An integrated energy system optimization strategy based on particle swarm optimization algorithm

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ENERGY REPORTS 2022年 8卷 679-691页

作者： Wu, Min Du, Pengcheng Jiang, Meihui Goh, Hui Hwang Zhu, Hongyu Zhang, Dongdong Wu, Thomas Guangxi Univ Coll Elect Engn Nanning 530004 Peoples R China Elect Power Res Inst Guangxi Power Grid Co Ltd Nanning 530000 Peoples R China

For the sake of increasing the energy efficiency of the city's energy supply, it is an effective approach to adopt the joint planning of the integrated energy system employing a group of cooling heat and power. However, the coupling matrix in the integrated energy system is quite difficult to formulate. In this paper, a standardized construction method for modeling Energy Hub is proposed. The construction of the model is divided into several steps. In addition, the difficult of solving the coupling matrix of integrated energy system can be availably reduced through this method. Considering the nonlinear problem caused by dispatch factors, this paper proposes a comprehensive energy system optimization strategy by using improved particle swarm optimization algorithm. Finally, using an example of a residential area with cooling, heating and electricity system to verify the effectiveness of the proposed modeling and calculation method . At the same time, the influence of demand response and energy storage on economy during operation is analyzed (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Integrated energy system Energy hub Coupling matrix particle swarm optimization algorithm

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The Method to Integrate Species Explode and Deracinate algorithm With particle swarm optimization algorithm

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IEEE ACCESS 2024年 12卷 52439-52451页

作者： Yang, Yongjian Deng, Youwei Xiao, Bingsong Zhao, Xiaohong Air Force Engn Univ Aviat Engn Sch Xian 710038 Peoples R China

Effectively combining various evolutionary computing algorithms and leveraging the advantages of each can significantly enhance the convergence speed and solution quality of the algorithm. However, a mere combination of evolutionary computing algorithms may not comprehensively improve optimization performance and may even lead to poorer performance in certain optimization problems. The aim of the paper is to provide a fundamental integrating platform and method based on species explode and deracinate algorithm. Utilizing the species explode and deracinate algorithm as a foundation, this study presents a hybrid algorithm named SED-PSO algorithm by utilizing the particle swarm optimization algorithm as an exemplar. The outcomes of the simulations conducted on 27 benchmark functions published by the Competition on Evolutionary Constrained demonstrate that the SED-PSO algorithm exhibits exceptional convergence accuracy, robust stability, and rapid convergence speed. The simulation results comprehensively illustrate that the species explode and deracinate algorithm serves as a fundamental integrating platform for diverse evolutionary computing algorithms, while also incorporating the strengths of each algorithm. Additionally, the outcomes of the optimization of sensor network coverage reveal that the SED-PSO algorithm exhibits superior solution quality, minimal occurrence of local extremum, and enhanced stability and efficacy.

关键词： Evolutionary computing algorithm species explode and deracinate algorithm particle swarm optimization algorithm competition on evolutionary constrained optimization of sensor network coverage

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Hybrid of human learning optimization algorithm and particle swarm optimization algorithm with scheduling strategies for the flexible job-shop scheduling problem

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NEUROCOMPUTING 2020年 414卷 313-332页

作者： Ding, Haojie Gu, Xingsheng East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

The flexible job-shop scheduling problem (FJSP) is a well-known combinational optimization problem. Studying FJSP is essential for promoting production efficiency and effectiveness. Different kinds of improved particle swarm optimization (PSO) algorithms have produced superior results for FJSP in the last few decades. Meanwhile, the human learning optimization (HLO) algorithm, a simple and adaptive learning algorithm for learning system, has helped improve algorithm performance by imitating human learning behavior in recent research. The study proposes a hybrid HLO-PSO algorithm, which utilizes various combinations of the proposed improved PSO and proposed scheduling strategies to solve FJSP under the algorithm architecture of HLO. With the guidance of HLO, the individual learning ability of every particle is further promoted based on the existed advantage of collective action decision of PSO;and with the help of rule-based scheduling strategies, the search capacity of the proposed improved PSO is also further enhanced. By the detailed exposition and analysis, the proposed HLO-PSO is easily implemented and embedded in other production system software or learning system software. Meanwhile, by using it to solve several groups of FJSP instances, the result comparisons with other related algorithms reveal that HLO-PSO can efficiently solve most of single-objective FJSP. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Human learning algorithm Adaptive learning system particle swarm optimization algorithm Flexible job-shop scheduling problem Scheduling strategy Operations research

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An extension theory-based maximum power tracker using a particle swarm optimization algorithm

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ENERGY CONVERSION AND MANAGEMENT 2014年 86卷 435-442页

作者： Chao, Kuei-Hsiang Natl Chin Yi Univ Technol Dept Elect Engn Taichung 41170 Taiwan

The aim of this work is to present an adaptive maximum power point tracking (MPPT) approach for photovoltaic (PV) power generation system. Integrating the extension theory as well as the conventional perturb and observe method, an maximum power point (MPP) tracker is made able to automatically tune tracking step size by way of the category recognition along a P-V characteristic curve. Accordingly, the transient and steady state performances in tracking process are improved. Furthermore, an optimization approach is proposed on the basis of a particle swarm optimization (PSO) algorithm for the complexity reduction in the determination of weighting values. At the end of this work, a simulated improvement in the tracking performance is experimentally validated by an MPP tracker with a programmable system-on-chip (PSoC) based controller. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Maximum power point tracking Extension theory particle swarm optimization algorithm Programmable system-on-chip

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