检索结果-内蒙古大学图书馆

Optimal parameter identification strategy applied to lithium-ion battery model for electric vehicles using drive cycle data

引用

ENERGY REPORTS 2024年 11卷 2049-2058页

作者： Ghadbane, Houssam Eddine Rezk, Hegazy Ferahtia, Seydali Barkat, Said Al-Dhaifallah, Mujahed Univ 8 Mai 1945 Electrotech & Automat Engn Dept Elect Engn Lab Guelma LGEG Guelma 24000 Algeria Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Alddawasir Dept Elect Engn Al Kharj Saudi Arabia Univ Nantes Ind Engn Dept IREENA Lab Nantes France Univ Msila Elect Engn Lab Elect Engn Dept Msila Algeria King Fahd Univ Petr & Minerals Control & Instrumentat Engn Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals IRC Sustainable Energy Syst IRC SES Dhahran 31261 Saudi Arabia

The optimal parameter identification of lithium -ion (Li -ion) battery models is essential for accurately capturing battery behavior and performance in electric vehicle (EV) applications. Traditional methods for parameter identification often rely on manual tuning or trial-and-error approaches, which can be time-consuming and yield suboptimal results. In recent years, metaheuristic optimization algorithms have emerged as powerful tools for efficiently searching and identifying optimal parameter values. This paper proposes an optimal parameter identification strategy using a metaheuristic optimization algorithm applied to a Shepherd model for EV applications. The identification technique that was based on the Self -adaptive Bonobo Optimizer (SaBO) performed extremely well when it came to the process of identifying the battery's unidentified properties. Because of this, the overall voltage error of the suggested identification technique has been lowered to 4.2377 x 10-3, and the root mean square error (RMSE) between the model and the data has been calculated to be 8.64 x 10-3. In addition, compared to the other optimization methods, the optimization efficiency was able to attain 96.6%, which validated its efficiency.

关键词： Electric vehicles Li-ion battery metaheuristic optimization algorithms Parameters identification

来源：评论

学校读者我要写书评

暂无评论

Techno-economic feasibility of stand-alone hybrid energy systems fora remote Australian optimization and

引用

RENEWABLE ENERGY 2025年 241卷

作者： Roy, Tushar Kanti Mahmud, Md. Apel Oo, Amanullah Maung Than Macquarie Univ Sch Engn Sydney NSW 2109 Australia Flinders Univ S Australia Coll Sci & Engn Tonsley SA 5042 Australia

This research work investigates the techno-economic feasibility and optimal design of stand-alone hybrid energy systems (HESs) for electricity and hydrogen production in a remote Australian community. It evaluates three configurations - PV/BESS, PV/FC/EL/HT, and PV/BESS/FC/EL/HT - incorporating photovoltaic (PV) panels, battery energy storage systems (BESS), fuel cells (FC), electrolyzers (EL), and hydrogen tanks (HT). The systems are assessed based on net present cost (NPC), levelized cost of electricity (LCOE), and levelized cost of hydrogen (LCOH), utilizing advanced metaheuristic optimization algorithms and HOMER Pro simulations. The PV/BESS configuration demonstrates the lowest NPC ($888,833) and LCOE ($0.2903/kWh), making it the most cost-effective for electricity generation, but it proves inefficient in utilizing renewable energy due to 54% excess electricity. The PV/FC/EL/HT system emerges as the optimal hydrogen-integrated configuration, achieving an NPC of $964,440.97, an LCOE of $0.3326/kWh, and an LCOH of $6.0264/kg using the cuckoo search algorithm (CSA). Although the PV/BESS/FC/EL/HT configuration offers enhanced operational flexibility, its higher costs, with an NPC of $1,388,303 and LCOH of $8.90/kg, limit its economic competitiveness. optimization algorithms, particularly CSA, consistently outperform HOMER Pro by achieving up to 20% reductions in NPC and 12% in LCOE through optimized component sizing and interactions. Trade-offs appear with the harmony search algorithm (HSA), which prioritizes hydrogen production and renewable energy utilization at significantly higher costs. The sensitivity analysis reveals that reducing FC and EL capital costs by 50% decreases NPC by 78.64% and LCOE/LCOH by 45.46%, while a 15% increase in solar irradiation reduces NPC by 18% and LCOE/LCOH by 4.08% and 3.72%, respectively. Longer project lifetimes and lower discount rates significantly improve unit cost metrics, with lower financing costs emerging as critical for a

关键词： Battery energy storage system Hydrogen storage Hybrid energy system Levelized cost of electricity Levelized cost of hydrogen metaheuristic optimization algorithms Renewable energy resources

来源：评论

学校读者我要写书评

暂无评论

GOOSE algorithm: a powerful optimization tool for real-world engineering challenges and beyond

引用

EVOLVING SYSTEMS 2024年第4期15卷 1249-1274页

作者： Hamad, Rebwar Khalid Rashid, Tarik A. Erbil Polytech Univ Erbil Tech Engn Coll Dept Informat Syst Engn Erbil Iraq Univ Kurdistan Hewler Comp Sci & Engn Dept Erbil Iraq

This study proposes the GOOSE algorithm as a novel metaheuristic algorithm based on the goose's behavior during rest and foraging. The goose stands on one leg and keeps his balance to guard and protect other individuals in the flock. The GOOSE algorithm is benchmarked on 19 well-known benchmark test functions, and the results are verified by a comparative study with genetic algorithm (GA), particle swarm optimization (PSO), dragonfly algorithm (DA), and fitness dependent optimizer (FDO). In addition, the proposed algorithm is tested on 10 modern benchmark functions, and the gained results are compared with three recent algorithms, such as the dragonfly algorithm, whale optimization algorithm (WOA), and salp swarm algorithm (SSA). Moreover, the GOOSE algorithm is tested on 5 classical benchmark functions, and the obtained results are evaluated with six algorithms, such as fitness dependent optimizer (FDO), FOX optimizer, butterfly optimization algorithm (BOA), whale optimization algorithm, dragonfly algorithm, and chimp optimization algorithm (ChOA). The achieved findings attest to the proposed algorithm's superior performance compared to the other algorithms that were utilized in the current study. The technique is then used to optimize Welded beam design and Economic Load Dispatch Problems, Pressure vessel Design Problems, and the Pathological IgG Fraction in the Nervous System, four renowned real-world challenges. The outcomes of the engineering case studies illustrate how well the suggested approach can optimize issues that arise in the real-world.

关键词： GOOSE algorithm metaheuristic optimization algorithms Evaluation study Benchmark test functions Real-World engineering challenges Pathological IgG fraction in the nervous system

来源：评论

学校读者我要写书评

暂无评论

A new integrated intelligent computing paradigm for predicting joints shear strength

引用

Geoscience Frontiers 2024年第6期15卷 176-193页

作者： Shijie Xie Zheyuan Jiang Hang Lin Tianxing Ma Kang Peng Hongwei Liu Baohua Liu School of Resources and Safety Engineering Central South UniversityChangsha 410083HunanChina Jiangsu Key Laboratory of Urban Underground Engineering and Environmental Safety Institute of Geotechnical EngineeringSoutheast UniversityNanjing 210096JiangsuChina Ocean College Zhejiang UniversityZhoushan 316021China

Joints shear strength is a critical parameter during the design and construction of geotechnical engineering *** prevailing models mostly adopt the form of empirical functions,employing mathematical regression techniques to represent experimental *** an alternative approach,this paper proposes a new integrated intelligent computing paradigm that aims to predict joints shear *** metaheuristic optimization algorithms,including the chameleon swarm algorithm(CSA),slime mold algorithm,transient search optimization algorithm,equilibrium optimizer and social network search algorithm,were employed to enhance the performance of the multilayered perception(MLP)*** comparisons were conducted between the proposed CSA-MLP model and twelve classical models,employing statistical indicators such as root mean square error(RMSE),correlation coefficient(R2),mean absolute error(MAE),and variance accounted for(VAF)to evaluate the performance of each *** sensitivity analysis of parameters that impact joints shear strength was ***,the feasibility and limitations of this study were *** results revealed that,in comparison to other models,the CSA-MLP model exhibited the most appropriate performance in terms of R2(0.88),RMSE(0.19),MAE(0.15),and VAF(90.32%)*** result of sensitivity analysis showed that the normal stress and the joint roughness coefficient were the most critical factors influencing joints shear *** paper presented an efficacious attempt toward swift prediction of joints shear strength,thus avoiding the need for costly in-site and laboratory tests.

关键词： Rock discontinuities Joints shear strength metaheuristic optimization algorithms Machine learning

来源：评论

学校读者我要写书评

暂无评论

Evaluation of students' performance during the academic period using the XG-Boost Classifier-Enhanced AEO hybrid model

引用

EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartD期238卷

作者： Cheng, Biqian Liu, Yuping Jia, Yunjian China Univ Polit Sci & Law Postdoctoral Stn Beijing 100088 Peoples R China China Univ Polit Sci & Law Sch Criminal Justice Beijing 100088 Peoples R China Chongqing Univ Coll Commun Engn Chongqing 400044 Peoples R China

The proactive prediction and systematic classification of students' academic performance empower educational administrators with the invaluable capability to not only pinpoint potential challenges but also to craft targeted strategies and interventions that are tailored to the unique needs of students. This multifaceted approach enables educational institutions to proactively address issues within the education system, fostering a more equitable and effective learning environment for all, while simultaneously fostering a culture of continuous improvement and accountability in the pursuit of educational excellence. Hence, the current investigation aims to classify and predict the students' performance by examining and comparing the machine learning and artificial neural network assessments. Five methods of the Random Forest Classifier, the Decision Tree Classifier, the K Neighbors Classifier, the MLP Classifier, and the XG-Boost Classifier are used. These methods' performances are compared through the accuracy, precision, recall, and F1-score indicators. This comparison is applied to the base data and balanced data, which is carried out by the SVM-SMOTE technique. Finally, five metaheuristic algorithms are applied to the selected method to evaluate the performance indicators of the hybrid models. The results indicate that applying the SVM-SMOTE technique improves the methods' performance, in which the XG-Boost represented the best performance. As a result, the metaheuristic algorithms are applied to the XG-Boost, yielding to 9.33%, 8.44%, 9.33%, and 9.27% enhancement of the Accuracy, Precision, Recall and F1-Score values. Subsequently, the Enhanced Artificial Ecosystem-Based optimization + XG-Boost hybrid method provides the accuracy and F1-score values of 0.9417 and 0.9413. These results underscore the potential of combining machine learning techniques with metaheuristic algorithms to enhance the accuracy and effectiveness of predicting and classifying students' per

关键词： Student performance evaluation Machine learning methods metaheuristic optimization algorithms XG-Boost Classifier

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Review on Stochastic Optimal Power Flow Problems and Solution Methodologies

引用

IETE TECHNICAL REVIEW 2024年第2期41卷 147-174页

作者： Maheshwari, Ankur Sood, Yog Raj Jaiswal, Supriya Natl Inst Technol Elect Engn Hamirpur Himachal Prades India Natl Inst Technol Dept Elect Engn Hamirpur Himachal Prades India

The deregulation of the electricity market has been accompanied by the growing utilization of unpredictable renewable energy sources (RESs) such as solar, wind, and hydropower plants. Additionally, advancements in energy storage technologies and new energy demands have further contributed to this trend. As a result, the planning and operation of power systems are now surrounded by a higher level of uncertainty. In order to ensure the proper operation of power systems integrated with RESs, modern power systems are equipped with specific vital tools such as optimal power flow (OPF), which regulates generation and demand to achieve specific objectives. Hence, this paper conducts a comprehensive review of recently published research articles focusing on various solution strategies to address OPF problems in the presence of stochastic RESs and power demand. The review encompasses diverse solution methodologies, objective functions, constraints, and distinct techniques to simulate the stochastic behavior of RESs and dynamic loads. Additionally, the paper explores fundamental challenges, identifies critical research gaps, and highlights unexplored areas pertaining to optimal power system operation in the future. This review is essential for system operators who need to assess and pre-plan flexibility competency for their power systems to ensure practical and cost-effective operation under high RESs penetration.

关键词： metaheuristic optimization algorithms Optimal power flow Probability density functions Renewable energy sources Renewable uncertainty

来源：评论

学校读者我要写书评

暂无评论

Global performance of metaheuristic optimization tools for water distribution networks

引用

AIN SHAMS ENGINEERING JOURNAL 2021年第1期12卷 223-239页

作者： Djebedjian, Berge Abdel-Gawad, Hossam A. A. Ezzeldin, Riham M. Mansoura Univ Fac Engn Mech Power Engn Dept Mansoura Egypt Mansoura Univ Fac Engn Irrigat & Hydraul Engn Dept Mansoura Egypt

Numerous metaheuristic optimization algorithms are used for optimal design of water distribution networks. Each algorithm shows dissimilar characteristics depending on the network properties and the sensitivity analysis of the algorithm control variables. New performance metrics of metaheuristic optimization methods are proposed using simple but robust refined metrics and were applied to the available literature data for different algorithms which have previously been used for three popular benchmark water distribution networks. In general, recent performance metrics are devoted to measure effectiveness, efficiency, and reliability in a separate manner, which made some confusion, which is the best?. In the present work, the proposed metrics are used to calculate both of best global and average global performance of different optimization algorithms. The results show that the present metrics have a good distinctive performance between different algorithms. The Fittest individual referenced Differential Evolution is found to be the best algorithm. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University.

关键词： optimization Algorithm performance Water distribution networks metaheuristic optimization algorithms Performance metrics

来源：评论

学校读者我要写书评

暂无评论

Optimal placement of FACTS devices and power-flow solutions for a power network system integrated with stochastic renewable energy resources using new metaheuristic optimization techniques

引用

INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2021年第13期45卷 18786-18809页

作者： Nusair, Khaled Alasali, Feras Hayajneh, Ali Holderbaum, William Natl Elect Power Co Protect & Metering Dept Amman Jordan Hashemite Univ Dept Elect Engn Zarqa 13113 Jordan Aston Univ Aston Inst Mat Res Birmingham W Midlands England

Flexible AC transmission systems (FACTS) and optimal power-flow (OPF) solutions play an important role in solving power operation problems. The volatile nature of the power generation profiles from renewable energy sources, solar and wind systems, and determining the optimal locations and sizes of FACTS devices increase the complexity of the OPF problems in modern power network models, such as transmission power loss, power generation operation cost and voltage deviation, as a highly nonlinear-nonconvex optimization problem. Therefore, this article introduces and employs four new independent, reliable and efficient optimization algorithms inspired by nature and biological nature, namely: Slime Mould Algorithm (SMA), Artificial Ecosystem-based optimization (AEO), Marine Predators Algorithm (MPA) and Jellyfish Search (JS), for solving both multi- and single-OPF objective problems for a power network incorporating FACTS and stochastic renewable energy sources. The proposed new metaheuristic optimization techniques are compared to the common and available alternatives in the literature, Particle Swarm optimization (PSO), Moth Flame optimization (MFO) and Grey Wolf Optimizer (GWO), using IEEE 30-bus test system. To consider and address the challenges of the OPF in modern power network models, the proposed optimization techniques tested under different operation cases such as an increasing in the load, with and without FCTAS and renewable energy sources, different renewable energy sources locations on the network. The result showed that the MPA, SMA, JS and AEO algorithms are more effective solvers for the OPF problems cases compared to the PSO, GWO and MFO algorithms. For example, the AEO obtained 0.0844 p.u. in case of minimizing the voltage deviation compared to 0.1155 p.u. for PSO, which means that the AEO algorithm improved the voltage deviation term by 27% compared to the PSO algorithm.

关键词： flexible AC transmission systems (FACTS) metaheuristic optimization algorithms optimal power-flow (OPF) renewable energy sources

来源：评论

学校读者我要写书评

暂无评论

A survey on advanced machine learning and deep learning techniques assisting in renewable energy generation

引用

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第41期30卷 93407-93421页

作者： Revathi, B. Sri Vellore Inst Technol Sch Elect Engn Chennai 600127 Tamil Nadu India

The sustainability of the earth depends on renewable energy. Forecasting the output of renewable energy has a big impact on how we operate and manage our power networks. Accurate forecasting of renewable energy generation is crucial to ensuring grid dependability and permanence and reducing the risk and cost of the energy market and infrastructure. Although there are several approaches to forecasting solar radiation on a global scale, the two most common ones are machine learning algorithms and cloud pictures combined with physical models. The objective is to present a summary of machine learning-based techniques for solar irradiation forecasting in this context. Renewable energy is being used more and more in the world's energy grid. Numerous strategies, including hybrids, physical models, statistical approaches, and artificial intelligence techniques, have been developed to anticipate the use of renewable energy. This paper examines methods for forecasting renewable energy based on deep learning and machine learning. Review and analysis of deep learning and machine learning forecasts for renewable energy come first. The second paragraph describes metaheuristic optimization techniques for renewable energy. The third topic was the open issue of projecting renewable energy. I will wrap up with a few potential future job objectives.

关键词： Renewable energy Machine learning Deep learning metaheuristic optimization algorithms Neural network Power prediction

来源：评论

学校读者我要写书评

暂无评论

Modified bald eagle search algorithm for lithium-ion battery model parameters extraction

引用

ISA TRANSACTIONS 2023年 134卷 357-379页

作者： Ferahtia, Seydali Rezk, Hegazy Djerioui, Ali Houari, Azeddine Motahhir, Saad Zeghlache, Samir Univ Msila Lab Genie Elect Dept Elect Engn Msila Algeria Prince Sattam bin Abdulaziz Univ Coll Engn Wadi Alddawasir Dept Elect Engn Al Kharj Saudi Arabia Minia Univ Fac Engn Al Minya Egypt Nantes Univ IREENA Lab Nantes France USMBA ENSA Fes Morocco Univ Msila Dept Elect Engn Lab Anal Signaux & Syst Msila Algeria

Bald eagle search algorithm (BES) is a recent metaheuristic algorithm based on bald eagle hunting behavior. Like other metaheuristic algorithms, the BES algorithm is prone to entangle in local optimums due to limited diversity, sluggish convergence rate, or improper equilibrium between exploitation and exploration. Thus, adaptive parameters are injected into the original BES to overcome these shortcomings. These parameters are a function of the current and the max number of iterations. They provide the eagle with more diversity during the exploration and exploitation phases. The modified BES is tested on test functions provided by Congress on Evolutionary Computation 2020 and Congress on Evolutionary Computation 2022. The obtained results are compared to that of other reliable and recent algorithms. In addition, analysis of variance and Tuckey tests are utilized to confirm the results' significance. Due to its benefits, lithium-ion batteries are employed in more and more applications. However, extracting its parameters is challenging due to its complex model. Hence, the proposed algorithm will handle this task to approve its performance in complex problems. The significant benefit of this extraction method is its excellent precision, with fitness value declining (root mean square error) to 0.89 x 10-3 compared to the original BES (1.013 x 10-3) with a standard deviation of 1.12 x 10-3. To confirm the performance of mBES, a second battery was tested with the New European Driving Cycle profile. The mBES has the lowest fitness values (0.058896) and the least standard deviation (5.89 x 10-7).(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Bald eagle search algorithm (BES) metaheuristic optimization algorithms (MAs) Lithium-ion battery model Parameters identification

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：