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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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Optimal sensor placement for long-span cable-stayed bridge using a novel particle swarm optimization algorithm

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JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING 2015年第5期5卷 677-685页

作者： Li, Juelong Zhang, Xun Xing, Jianchun Wang, Ping Yang, Qiliang He, Can PLA Univ Sci & Technol Coll Def Engn Nanjing Peoples R China Res Ctr Coastal Def Engn Beijing Peoples R China

In health monitoring of long-span structures, proper arrangement of sensors is a key point because of the need to acquire effective structural health information with limited testing resources. This study proposes a novel approach called dual-structure coding and mutation particle swarm optimization (DSC-MPSO) algorithm for the sensor placement. The cumulative effective modal mass participation factor is firstly derived to select the main contributions modes. A novel method combining dual-structure coding with the mutation operator is then utilized to determine the optimal sensors configurations. Finally, the feasibility of the DSC-MPSO algorithm is verified by optimizing the sensors locations for a long-span cablestayed bridge. The effective independence method, genetic algorithm and standard particle swarm optimization algorithm are taken as contrast experiments. The simulation results show that the proposed algorithm in this paper could improve the convergence speed and precision. Accordingly, the method is effective in solving optimal sensor placement problems.

关键词： Cable-stayed bridge Structural health monitoring Cumulative effective modal mass particle swarm optimization algorithm Optimal sensor placement

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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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Blasting vibration velocity prediction based on least squares support vector machine with particle swarm optimization algorithm

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GEOSYSTEM ENGINEERING 2019年第5期22卷 279-288页

作者： Yuan, Qing Zhai, Shihong Wu, Li Chen, Peishuai Zhou, Yuchun Zuo, Qingjun CCCC Second Harbour Engn Co Ltd Postdoctoral Programme Wuhan Hubei Peoples R China China Univ Geosci Wuhan Fac Engn 388 Lumo Rd Wuhan 430074 Hubei Peoples R China China Three Gorges Univ Minist Educ Key Lab Geol Hazards Three Gorges Reservoir Area Yichang Peoples R China

In blasting excavation engineering of super-large section underground caverns, blasting vibration velocity prediction accuracy is affected by many factors. In order to improve its accuracy, the key problem is to obtain these affect factors comprehensively. In this paper, we innovatively put forward eight independent factors in the aspect of explosion source conditions, engineering conditions and propagation medium conditions. These factors have complex non-linear relationship with blasting vibration velocity. We consider particle swarm optimization (PSO) algorithm and least squares support vector machine (LS-SVM) method for prediction (PSO-LSSVM). In this way, how to determine the characteristic parameters and calculation rules of PSO-LSSVM method is another key problem, which has been innovatively solved in this paper. Then it is used to predict the blasting vibration velocity of underground water-sealed LPG caverns in Yantai, China, and compared with Sadov's formula (SA), fuzzy neural network (FNN) and LS-SVM methods. The results indicate that relative errors of PSO-LSSVM are significantly less than LS-SVM, FNN and SA. Whether global root mean square relative error for prediction accuracy, or group number meeting requirement of error threshold value for generalization performance, the PSO-LSSVM method is superior to LS-SVM, FNN and SA with best availability and superiority.

关键词： particle swarm optimization algorithm blasting vibration velocity prediction least squares support vector machine fuzzy neural network characteristic parameter optimization

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New method for prediction and solving the problem of drilling fluid loss using modular neural network and particle swarm optimization algorithm

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2014年第4期4卷 371-379页

作者： Toreifi, Hojjat Rostami, Habib Manshad, Abbas Khaksar Persian Gulf Univ Sch Engn Oil Engn Dept Bushehr Iran Persian Gulf Univ Sch Engn Comp Engn Dept Bushehr Iran Petr Univ Technol Abadan Fac Petr Engn Dept Petr Engn Abadan Iran

Loss circulation is a common problem in drilling industry that causes high expenditure on drilling companies. Nowadays minimizing of loss circulation is a main goal and preference for drilling engineers. Artificial intelligence (Al) is a new method of solving engineering problems that has the ability to consider all effective parameters simultaneously. Moreover, it has generalization and the ability to learn directly from field data. In this paper, two models were designed using Al and data of 38 wells located in Maroun oil field. Both models were developed by modular neural network, to predict loss circulation in quality and quantity. Then, the particle swarm optimization algorithm was used to minimize loss circulation. The accuracy of two models in predicting loss circulation quantitatively and qualitatively is 0.94 and 0.98 %, respectively.

关键词： Loss circulation Modular neural network Loss circulation reduction particle swarm optimization algorithm

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An adaptive particle swarm optimization algorithm for reservoir operation optimization

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APPLIED SOFT COMPUTING 2014年第0期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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A combined model based on seasonal autoregressive integrated moving average and modified particle swarm optimization algorithm for electrical load forecasting

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017年第5期32卷 3447-3459页

作者： Ma, Tao Wang, Fen Wang, Jianzhou Yao, Yukai Chen, Xiaoyun Lanzhou Univ Sch Informat Sci & Engn 222 Tianshui South Rd Lanzhou 730000 Gansu Peoples R China Dongbei Univ Finance & Econ Sch Stat Dalian Peoples R China Ningxia Normal Univ Sch Math & Comp Sci Ningxia Peoples R China Lanzhou Univ Technol Coll Comp & Commun Lanzhou Peoples R China

Electric load prediction is an important decision tool in area of electricity economy. Recently researchers have presented innovative models to improve the forecasting accuracy of short-term electricity series, which is valuable in allowing both consumers and electric power sector to make effective planning. This study proposed novel combing optimization model to improve the precision of electric load forecasting and called SSPM. First, taken the advantage of linear prediction for the seasonal autoregressive integrated moving average (SARIMA) model and non-linear prediction for the support vector machines (SVM) model to combine a new model. Next, the produce results by SARIMA model is regarded as linear component and used SVM model for correcting the residual from SARIMA as non-linear component of forecasting results. Third, in order to show the dynamic relationship of linear and non-linear components, the weight variable of alpha(1) and alpha(2) are proposed that optimized by particle swarm optimization (PSO) algorithm with lower error of fitness function, the combining model is applied in the daily electric load data at New South Wales (NSW) in Australia. The experimental results indicate that the proposed optimization model obtains better performance of precise and stability than models of SARIMA and SVM respectively, outperform than conventional artificial neural network (ANN). Although the novel model is applied to electric load forecasting in this paper, it has more scopes for application in a number of areas to gain improvement of forecast accuracy in complex time series.

关键词： Support vector machines seasonal autoregressive integrated moving average combining optimization model electric load forecasting particle swarm optimization algorithm

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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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Automatic boiling water reactor control rod pattern design using particle swarm optimization algorithm and local search

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NUCLEAR ENGINEERING AND DESIGN 2013年 255卷 273-279页

作者： Wang, Cheng-Der Lin, Chaung Inst Nucl Energy Res Nucl Engn Div Longtan Township 32546 Taoyuan County Taiwan Natl Tsing Hua Univ Dept Engn & Syst Sci Hsinchu 30013 Taiwan

This study developed a method for the automatic design of a boiling water reactor (BWR) control rod pattern (CRP) using the particle swarm optimization (PSO) algorithm. The PSO algorithm is more random compared to the rank-based ant system (RAS) that was used to solve the same BWR CRP design problem in the previous work. In addition, the local search procedure was used to make improvements after PSO, by adding the single control rod (CR) effect. The design goal was to obtain the CRP so that the thermal limits and shutdown margin would satisfy the design requirement and the cycle length, which is implicitly controlled by the axial power distribution, would be acceptable. The results showed that the same acceptable CRP found in the previous work could be obtained. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Boiling water reactor control rod pattern particle swarm optimization algorithm Local search

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Bus-Priority Intersection Signal Control System Based on Wireless Sensor Network and Improved particle swarm optimization algorithm

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SENSOR LETTERS 2012年第8期10卷 1823-1829页

作者： Ma, Chang Xi Li, Yin Zhen He, Rui Chun Qi, Bo Wu, Fang Sun, Li Diao, Ai Xia Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Peoples R China

In order to make buses have the priority to bus and ease the intersection congestion, this paper designs bus-priority intersection signal control system based on wireless sensor network and improved layered parallel particle swarm optimization (ILPPSO) algorithm. Dynamic network mode of wireless sensor network is used to collect the information of bus flow by adding nodes to it. The channelization method of intersections is improved by setting up bus lane. We build signal timing optimization model based on the traffic data collected by wireless sensor network and use improved particle swarm optimization algorithm to solve it. Finally, based on VISSIM soft, the signal control system is tested by programming. The results show that the signal control system can ease bus flow of intersections quickly and reduce vehicle's delay efficiently.

关键词： Bus-Priority Intersection Signal Control System Wireless Sensor Network particle swarm optimization algorithm

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