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Sensitivity analysis and identification of battery physicochemical model parameters under different temperature impedances

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JOURNAL OF ENERGY STORAGE 2024年 101卷

作者： Shen, Xianhao Li, Xuewen Niu, Shaohua Du, Liuyuan Guilin Univ Technol Coll Phys & Elect Informat Engn Guilin 541006 Peoples R China Guilin Univ Technol Guangxi Lab Embedded Technol & Intelligent Syst Guilin 541006 Peoples R China Guilin Univ Technol Coll Comp Sci & Engn Guilin 541006 Peoples R China Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China

Lithium-ion batteries are widely used vehicle energy storage batteries globally, and their reaction mechanism directly influences the safety and performance of energy storage systems. Simulating the actual internal state of the battery through a simulation model has become a crucial approach. Despite the high level of physical interpretation provided by the physicochemical models of the battery, the parameterization process poses significant challenges. Impedance spectroscopy at various temperatures is analyzed in this study to assess the sensitivity of battery model parameters. The goal is to investigate the changes in model parameter sensitivity at different temperatures and identify model parameters under various states of charge. Conducting a sensitivity analysis of model parameters allows for the selection of highly sensitive and appropriate parameters, which enhances the accuracy of the model output. In addition, the gray wolf optimization algorithm is utilized to identify the parameters of the battery model. The identified parameter set is then applied to the battery P2D model to simulate the impedance data. The root mean square error between the simulation and experimental data of the model is less than 0.241 mS2, and the average absolute error is less than 0.185 mS2. This method can accurately identify model parameters, which results in high model simulation accuracy. Findings from the parameter sensitivity analysis provide valuable references for parameter identification and state estimation considering temperature effects.

关键词： Lithium-ion Physicochemical model Impedance spectroscopy gray wolf optimization algorithm Parameter sensitivity analysis Parameter identification

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Program source code comprehension by module clustering using combination of discretized gray wolf and genetic algorithms

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ADVANCES IN ENGINEERING SOFTWARE 2022年第0期173卷

作者： Arasteh, Bahman Abdi, Mohammad Bouyer, Asgarali Istinye Univ Fac Engn & Nat Sci Dept Software Engn Istanbul Turkey Islamic Azad Univ Dept Comp Engn Tabriz Branch Tabriz Iran Azarbaijan Shahid Madani Univ Dept Comp Engn Tabriz Iran Istinye Univ Istanbul Turkey

Maintenance is a critical and costly phase of software lifecycle. Understanding the structure of software will make it much easier to maintain the software. Clustering the modules of software is regarded as a useful reverse engineering technique for constructing software structural models from source code. Minimizing the connections between produced clusters, maximizing the internal connections within the clusters, and maximizing the clustering quality are the most important objectives in software module clustering. Finding the optimal software clustering model is regarded as an NP-complete problem. The low success rate, limited stability, and poor modularization quality are the main drawbacks of the previous methods. In this paper, a combination of gray wolf optimization algorithm and genetic algorithms is suggested for efficient clustering of software modules. An extensive series of experiments on 14 standard benchmarks have been conducted to evaluated the proposed method. The results illustrate that using the combination of gray wolf and genetic algorithms to the softwaremodule clustering problem increases the quality of clustering. In terms of modularization quality and convergence speed, proposed hybrid method outperforms the other heuristic approaches.

关键词： Source code comprehension Software modules clustering Modularization quality gray wolf optimization algorithm Cohesion Coupling

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Temperature Compensation of Wind Tunnel Balance Signal Detection System Based on IGWO-ELM

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SENSORS 2023年第16期23卷 7224-7224页

作者： Dong, Xiang Xu, Hu Cao, Huibin Cui, Tao Sun, Yuxiang Anhui Univ Sch Elect Engn & Automat Hefei 230601 Peoples R China Chinese Acad Sci Hefei Inst Phys Sci Hefei 230031 Peoples R China Univ Sci & Technol China Dept Sci Isl Hefei 230026 Peoples R China Chengdu Sci & Technol Dev Ctr CAEP Chengdu 610200 Peoples R China

The wind tunnel balance signal detection system is widely employed in aerospace applica-tions for the accurate and automated measurement of aerodynamic forces and moments. However, measurement errors arise under different environmental temperature. This paper addresses the issue of measurement accuracy under different temperature conditions by proposing a temperature com-pensation method based on an improved gray wolf optimization (IGWO) algorithm and optimized extreme learning machine (ELM). The IGWO algorithm is enhanced by improving the initial popu-lation position, convergence factor, and iteration weights of the gray wolf optimization algorithm. Subsequently, the IGWO algorithm is employed to determine the optimal network parameters for the ELM. The calibration decoupling experiment and high-low temperature experiment are designed and carried out. On this basis, ELM, GWO-ELM, PSO-ELM, GWO-RBFNN and IGWO-ELM are used for temperature compensation experiments. The experimental results show that IGWO-ELM has a good temperature compensation effect, reducing the measurement error from 20%FS to within 0.04%FS. Consequently, the accuracy and stability of the wind tunnel balance signal detection system under different temperature environments are enhanced.

关键词： wind tunnel balance signal detection system temperature compensation gray wolf optimization algorithm extreme learning machine

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A comprehensive compensation method for piezoresistive pressure sensor based on surface fitting and improved grey wolf algorithm

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MEASUREMENT 2023年第1期207卷

作者： Zhao, Xueliang Chen, Ying Wei, Guanghua Pang, LiLi Xu, Chongxuan Yanshan Univ Sch Elect Engn Hebei Prov Key Lab Test Measurement Technol & Inst Qinhuangdao 066004 Hebei Peoples R China Minist Nat Resources Geol Environm Monitoring Engn Technol Innovat Ctr Ctr Hydrogeol & Environm Geol China Geol Survey Baoding 071051 Peoples R China

The optimized surface fitting method base on improved gray wolf algorithm (IGWO) is proposed to overcome the nonlinearity and temperature drift of piezoresistive pressure sensor. To improve the convergence rate and search ability of gray wolf optimization algorithm (GWO), the adjustable nonlinearity convergence factor is established. The weight vector and weight adjustment coefficient are integrated into the residual function of the least square method. Employing the maximum full range error as the fitness function, the least square method is continuously optimized by IGWO to establish the high-performance comprehensive compensation mathematical formula. The simulation and practical test results indicate that the maximum full-scale error in the full range is better than 0.03 %, and the maximum full-scale error in the local range is less than 0.02 % especially. The proposed method proposed method is very suitable for the application and production of industrial pressure sensor with many advantages.

关键词： Silicon piezoresistive pressure sensor Nonlinearity and temperature drift Surface fitting gray wolf optimization algorithm Application in sensors

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Short-term photovoltaic power prediction on modal reconstruction: A novel hybrid model approach

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2021年 45卷 101048-101048页

作者： Liu, Zhi-Feng Luo, Shi-Fan Tseng, Ming-Lang Liu, Han-Min Li, Lingling Mashud, Abu Hashan Md Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300130 Peoples R China Hebei Univ Technol Key Lab Electromagnet Field & Elect Apparat Relia Tianjin 300130 Peoples R China Northeastern Univ Coll Engn Boston MA 02115 USA Asia Univ Inst Innovat & Circular Econ Taichung Taiwan China Med Univ China Med Univ Hosp Dept Med Res Taichung Taiwan Univ Kebangsaan Malaysia Fac Econ & Management Bangi Malaysia ZhangJiakou Wind Photovolta & Energy Storage Demo Zhangjiakou 075061 Peoples R China Hajee Mohammad Danesh Sci & Technol Univ Dept Math Dinajpur 5200 Bangladesh

The contribution is to propose a novel hybrid model based on modal reconstruction to predict short-term photovoltaic (PV) power. PV power generation large-scale grid connection causes the impact on the power system due to the instability and intermittence, and PV curtailment measures are taken to reduce the impact from voltage fluctuation. Accurate forecast is necessary to make a reasonable generation plan. A novel hybrid forecasting model is proposed, and an enhanced gray wolf optimization algorithm is proposed to improve the convergence ability and to solve the influence of extreme learning machine random parameters. The proposed algorithm has stronger convergence stability and higher convergence accuracy compared with the existing algorithms. The ensemble empirical mode decomposition algorithm is used to decompose the PV power under different weather conditions. The complexity of each component is calculated by the sample entropy, and the components are reconstructed to reduce the computational cost of forecasting models. The results revealed that mean absolute percentage error and root mean square error of the proposed model are smaller than 3% and 0.36 under various weather conditions. Meanwhile, the determination coefficient of the proposed model is more than 98%.

关键词： Photovoltaic power forecasting Extreme learning machine gray wolf optimization algorithm Modal reconstruction Ensemble empirical mode decomposition

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Weak fault feature extraction for polycrystalline diamond compact bit based on ensemble empirical mode decomposition and adaptive stochastic resonance

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MEASUREMENT 2021年 178卷 109304-109304页

作者： Gao, Kangping Xu, Xinxin Li, Jiabo Jiao, Shengjie Shi, Ning Changan Univ Natl Engn Lab Highway Maintenance Equipment Xian 710064 Peoples R China Henan Gaoyuan Maintenance Technol Highway Co Ltd Xinxiang 453003 Henan Peoples R China

Aiming at the problem of weak early fault signals of rotating machinery, a feature extraction method combining ensemble empirical mode decomposition (EEMD) and adaptive stochastic resonance (ASR) is proposed. First, the original vibration signal is decomposed into a series of intrinsic mode functions (IMF) through EEMD, and the main IMF components are selected using the correlation coefficient and the root mean square principle. Next, the selected components are reconstructed and used as the input of the ASR system based on the gray wolf algorithm. Finally, to evaluate the performance of ASR, a new evaluation index-weighted power spectrum kurtosis (WPSK) is defined. The results of experimental analysis on the simulation signals and vibration signals are used to verify the feasibility and superiority of the proposed method. Compared with the GA-SR, the proposed method increases the WPSK value by 34.5% when detecting weak signals of worn polycrystalline diamond compact bits.

关键词： Weak signal detection Feature extraction Adaptive stochastic resonance Ensemble empirical mode decomposition gray wolf optimization algorithm Polycrystalline diamond compact bit

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Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

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JOURNAL OF FOOD PROCESS ENGINEERING 2023年第11期46卷 e14422-e14422页

作者： Zhang, Hao Li, Lixia Tian, Liquan Zhang, Fujie Shi, Lei Wu, Wenqiang Zhan, Zicheng Kunming Univ Sci & Technol Fac Modern Agr Engn Kunming Peoples R China Key Lab Crop Harvesting Equipment Technol Zhejiang Jinhua Peoples R China Kunming Univ Sci & Technol Fac Modern Agr Engn Kunming 650500 Peoples R China

Cracks in walnuts during processing and storage can adversely affect their quality and cause economic losses. To achieve efficient identification of cracked walnuts, this study proposed a method of walnut crack identification based on acoustic vibration and feature fusion. First, the sound signals of intact and cracked walnuts were collected using an acoustic signal acquisition system, and 44 time-domain features, 13 frequency-domain features, and 768 Mel spectrogram features (the number of pixel frequencies corresponding to the gray-scale values of R, G, and B channels) of the sound signals were extracted. Then, the classification models of support vector machines (SVM), least squares support vector machines (LSSVM), and extreme learning machines (ELM) were established based on single class features data and fusion of different feature groups data respectively. The results indicated that the LSSVM model with the fusion of the three feature sets was optimal, with an accuracy of 85% in the testing set. Next, three feature selection methods were employed to reduce the dimensionality of the best fused feature data. Subsequently, the LSSVM classification model was established based on the feature selection data. Finally, arithmetic optimization algorithm (AOA), particle swarm optimization (PSO), and gray wolf optimization (GWO) were introduced to optimize the parameters c and delta(2) of the classification model. The results indicated that the best classification model was VISSA-IRIV-GWO-LSSVM, with 95% accuracy in the testing set. This study provides theoretical support for the research and development of online detection equipment for walnut crack in Yunnan Yangbi. Practical applications Cracks in walnut during harvesting, transportation and peeling may lead to economic losses and food safety problems. Aiming at the difficulty and low accuracy of crack identification in Yunnan walnut, this paper proposed a method for crack detection based on acoustic vibration identif

关键词： acoustic vibration identification feature fusion gray wolf optimization algorithm least squares support vector machine walnut cracks

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Application of GWO-ELM Model to Prediction of Caojiatuo Landslide Displacement in the Three Gorge Reservoir Area

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WATER 2020年第7期12卷 1860-1860页

作者： Zhang, Liguo Chen, Xinquan Zhang, Yonggang Wu, Fuwei Chen, Fei Wang, Weiting Guo, Fei Liaoning Tech Univ Coll Mines Fuxin 123000 Peoples R China Lijiang Anda Civil Blasting Serv Co Ltd Lijiang 674100 Peoples R China Xiamen Anneng Construct Co Ltd Xiamen 361000 Peoples R China Tongji Univ Key Lab Geotech & Underground Engn Minist Educ Shanghai 200092 Peoples R China Tongji Univ Dept Geotech Engn Shanghai 200092 Peoples R China Tongji Univ Dept Struct Engn Shanghai 200092 Peoples R China Univ Sydney Fac Engn Sydney NSW 2006 Australia Shanghai Peoples Procuratorate Huxi Dist Shanghai 200092 Peoples R China China Three Gorges Univ Key Lab Disaster Prevent & Mitigat Hubei Prov Yichang 443002 Peoples R China

In order to establish an effective early warning system for landslide disasters, accurate landslide displacement prediction is the core. In this paper, a typical step-wise-characterized landslide (Caojiatuo landslide) in the Three Gorges Reservoir (TGR) area is selected, and a displacement prediction model of Extreme Learning Machine with gray wolf optimization (GWO-ELM model) is proposed. By analyzing the monitoring data of landslide displacement, the time series of landslide displacement is decomposed into trend displacement and periodic displacement by using the moving average method. First, the trend displacement is fitted by the cubic polynomial with a robust weighted least square method. Then, combining with the internal evolution rule and the external influencing factors, it is concluded that the main external trigger factors of the periodic displacement are the changes of precipitation and water level in the reservoir area. gray relational degree (GRG) analysis method is used to screen out the main influencing factors of landslide periodic displacement. With these factors as input items, the GWO-ELM model is used to predict the periodic displacement of the landslide. The outcomes are compared with the nonoptimized ELM model. The results show that, combined with the advantages of the GWO algorithm, such as few adjusting parameters and strong global search ability, the GWO-ELM model can effectively learn the change characteristics of data and has a better and relatively stable prediction accuracy.

关键词： landslide displacement prediction gray wolf optimization algorithm extreme learning machine GWO-ELM model the Three Gorges Reservoir area

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An improved sub-harmonic to harmonic ratio method for pitch estimation and Shadja detection

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第7期35卷 e7604-e7604页

作者： Kumaraswamy, Balachandra Poonacha, P. G. BMS Coll Engn Dept Elect & Telecommun Engn Bengaluru India Int Inst Informat Technol CEEMS Bangalore India BMS Coll Engn Dept Elect & Telecommun Engn Bengaluru 560019 Karnataka India

In this paper, we have presented an improved Sub-Harmonic to Harmonic ratio (SHR) algorithm using the Genetic algorithm (GA) for pitch estimation of audio recordings of various ragas. Then we study the problem of Shadja identification using machine learning with the help of feature extraction and classification. The extraction of features from the raga signal is done with the help of statistical analysis of pitch estimation and these features are classified using a Neural Network (NN). Here, the training of NN is accomplished by gray wolf optimization (GWO) algorithm for, determining the weights. Performance of the proposed Sa detection algorithm is analyzed by comparing the developed NN using gray wolf optimization (GWO) models with the conventional models such as Levenberg Marquardt based NN (LM-NN), Gradient Descent based (GD-NN), Particle Swarm optimization based NN (PSO-NN) and FireFly based NN (FF-NN) in terms of positive and negative performance measures.

关键词： gray wolf optimization algorithm neural network pitch estimation raga Shadja detection sub-harmonic to harmonic ratio

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Optimal Operation of Distributed Generations Considering Demand Response in a Microgrid Using GWO algorithm

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Computer Systems Science & Engineering 2023年第10期47卷 809-822页

作者： Hassan Shokouhandeh Mehrdad Ahmadi Kamarposhti William Holderbaum Ilhami Colak Phatiphat Thounthong Department of Electrical Engineering Semnan UniversitySemnanIran Department of Electrical Engineering Jouybar BranchIslamic Azad UniversityJouybarIran School of Science Engineering and EnvironmentUniversity of SalfordSalfordUK Department of Electrical and Electronics Engineering Faculty of Engineering and ArchitecturesNisantasi UniversityIstanbulTurkey Renewable Energy Research Centre(RERC) Department of Teacher Training in Electrical EngineeringFaculty of Technical EducationKing Mongkut’s University of Technology North Bangkok1518Pracharat 1 RoadBangsueBangkok10800Thailand

The widespread penetration of distributed energy sources and the use of load response programs,especially in a microgrid,have caused many power system issues,such as control and operation of these networks,to be *** control and operation of many small-distributed generation units with different performance characteristics create another challenge for the safe and efficient operation of the *** this paper,the optimum operation of distributed generation resources and heat and power storage in a microgrid,was performed based on real-time pricing through the proposed gray wolf optimization(GWO)algorithm to reduce the energy supply cost with the *** generation resources such as solar panels,diesel generators with battery storage,and boiler thermal resources with thermal storage were used in the studied ***,a combined heat and power(CHP)unit was used to produce thermal and electrical energy *** the simulations,in addition to the gray wolf algorithm,some optimization algorithms have also been *** the results of 20 runs for each algorithm confirmed the high accuracy of the proposed GWO *** results of the simulations indicated that the CHP energy resources must be managed to have a minimum cost of energy supply in the microgrid,considering the demand response program.

关键词： Microgrid demand response program cost reduction gray wolf optimization algorithm

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