检索结果-内蒙古大学图书馆

Advanced Parameter Identification in Electric Vehicles Lithium-Ion Batteries With Marine Predators Algorithm-Based optimization

引用

INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2025年第1期2025卷

作者： Ghadbane, Houssam Eddine Rezk, Hegazy Alhumade, Hesham Univ 8 Mai 1945 Dept Electrotech & Automatique Lab Genie Elect Guelma LGEG Guelma 24000 Algeria Prince Sattam bin Abdulaziz Univ Coll Engn Wadi Alddawasir Dept Elect Engn Al Kharj Saudi Arabia Minia Univ Fac Engn Elect Engn Dept Al Minya 61517 Egypt King Abdulaziz Univ Fac Engn Chem & Mat Engn Dept Jeddah Saudi Arabia King Abdulaziz Univ Ctr Excellence Desalinat Technol Jeddah 21589 Saudi Arabia

Accurate parameter identification of lithium-ion (Li-ion) battery models is critical for understanding battery behavior and optimizing performance in electric vehicle (EV) applications. Traditional methods often rely on manual adjustments or trial-and-error processes, leading to inefficiencies and suboptimal outcomes. This study introduces a novel parameter identification approach using the marine predators algorithm (MPA), applied to a Shepherd model for EV applications. The proposed technique was validated under various dynamic test conditions, including the urban dynamic driving cycle (UDDC), the new European driving cycle (NEDC), and the worldwide harmonized light vehicles test procedure (WLTP). The MPA-based method systematically identifies optimal parameters, achieving a voltage error of 2.743 x 10-3, a state of charge (SOC) error of 0.7693 x 10-3, and a root mean square error (RMSE) of 8.37 x 10-3 between the model and real data. Compared to other optimization techniques, the MPA demonstrated superior performance, achieving an optimization efficiency of 97.69%. These results validate the robustness and reliability of the method for accurately capturing battery dynamics under realistic driving conditions. These results highlight the potential of the MPA-based approach in improving the accuracy of Li-ion battery parameter identification, leading to more efficient energy management in EVs and contributing to enhanced battery performance and reliability.

关键词： Li-ion battery marine predators algorithm metaheuristic optimization algorithms parameter identification

来源：评论

学校读者我要写书评

暂无评论

Optimal Power Flow: A Review of State-of-the-Art Techniques and Future Perspectives

引用

IEEE ACCESS 2025年 13卷 60012-60039页

作者： Babiker, Ahmed Ahmad, Sulaiman S. Ahmed, Ijaz Khalid, Muhammad Abido, Mohammad A. Saleh Al-Ismail, Fahad King Fahd Univ Petr & Minerals KFUPM Elect Engn Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals KFUPM Interdisciplinary Res Ctr Sustainable Energy Syst Dhahran 31261 Saudi Arabia

The Optimal Power Flow (OPF) problem has become increasingly pivotal in the planning and operation of modern power systems. With the expansion of the grid scale, the advent of smart grid technologies, and the unpredictable nature of renewable energy sources (RESs), interest in OPF has surged. These challenges with new energy storage have introduced a heightened level of uncertainty into the power system's operation as well as planning. Because of this, OPF is seen as an important tool for achieving different goals, such as optimizing the distribution of resources, making electrical networks more efficient, and so on. However, the OPF problem is inherently difficult to solve because of its non-linear characteristics. Different constraints and limitations intrinsic to real power system grids further accentuate this complexity. Moreover, modern power systems have incorporated new constraints, which make the OPF problem more complex in terms of mathematical formulation and solution. This paper offers a comprehensive and foundational review of OPF, covering the main concept, mathematical formulation, OPF types, comprehensive OPF optimization problem concepts, and the various methods developed to solve it. Additionally, it explores the evolution of these methods from conventional approaches to advanced and recent techniques, including mathematical methods and artificial intelligence methods, which include metaheuristic (search-based) and machine learning algorithms (data-driven). The paper also discusses various types of convex relaxation methods in depth. Ultimately, the paper highlights key gaps, challenges, and opportunities for future research.

关键词： Power system stability Load flow Machine learning algorithms Renewable energy sources Reactive power Power systems Generators Power system dynamics Uncertainty Stochastic processes Optimal power flow (OPF) machine learning metaheuristic optimization algorithms OPF convex relaxation artificial intelligence methods renewable energy systems economic dispatch formulation

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Hybrid and Ensemble Machine Learning Approaches in Predicting Football Player Transfer Values

引用

COGNITIVE COMPUTATION 2025年第2期17卷 1-25页

作者： Zhang, Wenjing Cao, Dan LiaoNing Finance & Trade Coll Inst Phys Educ Huludao 125105 Liaoning Peoples R China Shenyang Med Coll Phys Educ Dept Shenyang 110032 Liaoning Peoples R China

In football economics, a player's transfer market value extends beyond performance metrics, with popularity playing a crucial role in clubs' decisions. Reputation indexes, reflecting a player's standing in the industry, are derived from various sources. Traditional metrics include goals, assists, and defensive prowess, while social media activity (likes on Facebook and Instagram), press citations, and Wikipedia page views add a new dimension. This study utilized F & eacute;d & eacute;ration Internationale de Football Association 19 data and a real-world statistical dataset, encompassing 54 features for 491 players across various leagues. After adding valuable data and removing ineffective features and outliers, two filtering-based feature selection methods identified the 20 most critical features for predicting market value. The study applied Extreme Gradient Boosting and Adaptive Boosting regression models, along with their hybrid forms optimized by metaheuristic algorithms. The Extreme Gradient Boosting optimized with the Ali Baba and Forty Thieves algorithm model showed the best performance, with a 99% match to actual values and a misestimation of around 1.9 million. Ensemble models, averaging predictions from all hybrid models, provided reliable market value estimates. These insights help managers make informed decisions to improve team performance and secure financial benefits for the club.

关键词： Market valuation International reputation index Football superstars Filtering feature selection Boosting Tree Regression metaheuristic optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Optimal loop power flow control of power distribution system using advanced meta-heuristic algorithms

引用

ELECTRICAL ENGINEERING 2025年第1期107卷 419-431页

作者： Vy, Huynh Tuyet Anh, Ho Pham Huy Ind Univ Ho Chi Minh City Fac Elect Technol Ho Chi Minh City Vietnam Ho Chi Minh City Univ Technol HCMUT 268 Ly Thuong Kiet St Dist 10 Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City VNU HCM Linh Trung Ward Ho Chi Minh City Vietnam

This study uses advanced metaheuristic algorithms to solve energy-saving problems in the loop distribution network. An initiative energy management system (EMS) is suggested to deliver optimal commands to the loop power controller (LPC) whose operation is optimized by combining a metaheuristic algorithm with a ladder iterative technique. Here, the performance of state-of-the-art metaheuristic algorithms applied in the proposed EMS-LPC solution is compared and evaluated by simulation in a real-world case study. Simulation results show that all metaheuristic algorithms meet the optimization objective of reducing distribution loss in which the artificial ecosystem-based optimization (AEO) algorithm outperforms with both the lowest optimum value and the fastest convergence time. Consequently, the proposed EMS-LPC method results in a daily energy savings of 18% in comparison with the base case. Then, the proposed solution has potential for real-time applications to save grid power and reduce operating expenses due to the perfect performance of the suggested EMS-LPC approach.

关键词： Loop power controller (LPC) metaheuristic optimization algorithms Power distribution system Ladder iterative technique Energy management system (EMS) Proposed EMS-LPC controller Grid energy loss saving

来源：评论

学校读者我要写书评

暂无评论

A physics-data-driven method for predicting surface and building settlement induced by tunnel construction

引用

COMPUTERS AND GEOTECHNICS 2025年 179卷

作者： Wang, You Fan, Qianjun Dai, Fang Wang, Rui Ding, Bosong Cent South Univ Sch Civil Engn Changsha 410075 Peoples R China

Surface and surrounding building settlement is frequently caused by soil disturbance during subway tunnel construction, significantly impacting construction safety and structural stability. Traditional machine learning models have shown some effectiveness in settlement prediction but often fail to capture the underlying physical mechanisms. This study proposed a novel physics-informed optimized extreme learning machine (PIOELM) to enhance prediction accuracy and physical interpretability. Based on the extreme learning machine (ELM), the model integrated the chaos adaptive sparrow search algorithm (CASSA) for parameter optimization and incorporated the Pasternak foundation model using automatic differentiation. The model's accuracy was validated using precise engineering data and compared against the physics-informed neural network (PINN), physicsinformed extreme learning machine (PIELM), and traditional data-driven models. The results show that the PIOELM model outperforms others in handling extreme values and maintains high accuracy across various scales of data prediction. Prediction accuracy improved by up to 85.29%, with a minimum improvement of 30.68%, demonstrating strong stability and generalization capabilities.

关键词： Physics-data-driven Physics informed optimized extreme learning Pasternak foundation model metaheuristic optimization algorithms Shield tunnelling machine (PIOELM)

来源：评论

学校读者我要写书评

暂无评论

Boundary Strategy for optimization-based Structural Damage Detection Problem using metaheuristic algorithms

引用

PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2021年第1期65卷 150-167页

作者： Kaveh, Ali Hosseini, Seyed Milad Zaerreza, Ataollah Iran Univ Sci & Technol Sch Civil Engn Tehran *** Iran

The present paper proposes a new strategy namely Boundary Strategy (BS) in the process of optimization-based damage detection using metaheuristic algorithms. This strategy gradually neutralizes the effects of structural elements that are healthy in the optimization process. BS causes the optimization method to find the optimum solution better than conventional methods that do not use the proposed BS. This technique improves both aspects of the accuracy and convergence speed of the algorithms in identifying and quantifying the damage. To evaluate the performance of the developed strategy, a new damage-sensitive cost function, which is defined based on vibration data of the structure, is optimized utilizing the Shuffled Shepherd optimization Algorithm (SSOA). Different examples including truss, beam, and frame are investigated numerically in order to indicate the applicability of the proposed technique. The proposed approach is also applied to other well-known optimization algorithms including TLBO, GWO, and MFO. The obtained results illustrate that the proposed method improves the performance of the utilized algorithms in identifying and quantifying of the damaged elements, even for noise-contaminated data.

关键词： structural damage detection Boundary Strategy metaheuristic optimization algorithms Shuffled Shepherd optimization Algorithm vibration data

来源：评论

学校读者我要写书评

暂无评论

ANFIS-MOH: Systematic exploration of hybrid ANFIS frameworks via metaheuristic optimization hybridization with evolutionary and swarm-based algorithms

引用

APPLIED SOFT COMPUTING 2024年 167卷

作者： Wang, Haoyu Chen, Bin Sun, Hangling Li, Anji Zhou, Chenyu Univ Shanghai Sci & Technol Sch Mech Engn Shanghai Peoples R China Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai Peoples R China Hengtu Imalligent Technol Shanghai Co Ltd Shanghai Peoples R China Abbott Labs Shanghai Co Ltd Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China Xinjiang Univ Sch Comp Sci & Technol Urumqi Peoples R China Tsinghua Univ Beijing Peoples R China

The adaptive neuro-fuzzy inference system (ANFIS) has shown promising performance in modeling nonlinear problems, leveraging the strengths of both neural networks and fuzzy inference systems. However, as the problem scale increases, the growing number of tunable parameters in ANFIS can make it challenging to optimize via traditional gradient-based methods alone. This study introduces ANFIS-MOH, a novel framework that synergistically integrates ANFIS with metaheuristic optimization algorithms to address these challenges. By leveraging the global search capabilities of metaheuristics such as ant colony optimization (ACO), particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA), ANFIS-MOH enhances the parameter tuning process of ANFIS models. We evaluate ANFIS-MOH on benchmark datasets including Boston Housing and Wine Quality, demonstrating significant improvements in prediction accuracy and generalization compared to traditional ANFIS and neural network approaches. The proposed framework achieves up to 20% reduction in Mean Squared Error and 15% increase in R2 2 scores, particularly excelling in handling high- dimensional, noisy data. This work contributes to the field of hybrid intelligent systems by introducing effective ways to combine the strengths of ANFIS with powerful metaheuristic optimization algorithms. The findings suggest that such hybrid approaches can be effective in tackling challenging nonlinear modeling problems. Our code is available at https://***/AmbitYuki/metaheuristic-Adaptive-ANFIS.

关键词： Adaptive neuro-fuzzy inference metaheuristic optimization algorithms Nonlinear modeling and regression Hybrid intelligent systems

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

Excess energy management and techno-economic analysis of optimal designed isolated microgrid with reliability and environmental aspects

引用

ENERGY CONVERSION AND MANAGEMENT 2025年 333卷

作者： Yadav, Subhash Kumar, Pradeep Kumar, Ashwani Natl Inst Technol Kurukshetra Dept Elect Engn Kurukshetra India JSS Acad Tech Educ Dept Elect Engn Noida India

Isolated microgrids generate excess energy (Pexg) up to 70.1% of total generation, disturbing supply reliability and protection systems. This study presents the Pexg management, optimal design, and techno-economicenvironmental analysis in a Hybrid Renewable Energy System (HRES) based isolated microgrid. The optimal sizing is obtained with the minimization of Levelized Cost Of Energy (LCOE), subject to Deficiency of Power Supply Probability (DPSP), and Percentage of Excess Power Generation (PEPG) to maintain the supply reliability and restrict the Pexg generation. The outage rate of Solar Photovoltaic (SPV) and Wind Turbine (WT) units is also considered to obtain the microgrid design. The proposed model is optimized using the African Vultures optimization Algorithm (AVOA), Dragonfly Algorithm (DA), and Grey Wolf optimization algorithm (GWO). Results show that GWO performs superior to AVOA and DA in standings of execution time and accuracy. The proposed microgrid with energy management techniques restricts the Pexg at 4.84% and 9.64% for Case-A and Case-B, respectively. The minimized LCOE of most techno-economical-environmentally friendly configuration SPVWT-BG-BES is 0.2414 $/kWh and 0.1133 $/kWh for Case-A and Case-B, respectively, obtained with GWO. This configuration reduces the GHG emissions by 76.09% and 89.33% for Case-A and Case-B, respectively. The sensitivity analysis shows that LCOE varies significantly with the growth in load demand and capital costs. The CO2 emissions increase almost linearly with the raise in load growth. Thus, the proposed isolated microgrid design offers a techno-economically-environmental friendly system as it offers minimum LCOE, lowest Pexg, high supply reliability, and 100% Renewable Energy Fraction (REF).

关键词： Biogas generator Wind and solar energy Battery energy storage Hybrid renewable energy system Levelized cost of energy Excess energy management metaheuristic optimization algorithms

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

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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：