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Internal Fuzzy Hybrid Charger System for a Hybrid Electrical Vehicle

JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME 2018年第1期140卷 012003-012003页

作者： Aymen, Flah Univ Gabes Natl Engn Sch Gabes Res Unit Photovolta Wind & Geothermal Syst Gabes 6072 Tunisia

Controlling the charging power system in an electrical vehicle, presents a serious challenge for the engineer in order to find the best solution that guarantee the system effectiveness and performance. Related to this objective, this paper is presented to offer an intelligent power management algorithm, which guarantees the best process of power extraction and injection, respectively, from an electrical generator (EG) linked to an internal combustion engine (ICE) to a system of batteries via a direct current to alternative current power converter. This intelligent process was based on the fuzzy technology and the system tuning is made after a various test. Obtaining the necessary power in the exact moment and in the specific condition, that presents the goal of the presented algorithm. For obtaining the best instruction from the present intelligent process, the state of charge (SOC) of the battery, the measured output voltage from the battery and the acceleration decision of the user, are used as a real's input parameters for having a real statue of the electrical vehicle. This new process will be an asset to the highway electrical vehicle for optimizing the power consumption. To evaluate the algorithm performance MATLAB/SIMULINK is used and a simulation results are presented and discussed.

关键词： electrical vehicles storage energy power management MATLAB intelligent algorithms fuzzy controller

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Research on Cable Tension Prediction Based on Neural Network

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BUILDINGS 2024年第6期14卷 1723-1723页

作者： Zhang, Hongbin Hu, Weihao Dalian Univ Sch Civil Engn Dalian 116622 Peoples R China

Conventional methods for calculating tension currently suffer from an excessive simplification of boundary conditions and a vague definition of effective cable length, both of which cause inaccurate cable tension calculations. Therefore, this study utilizes bridge field data to establish a BP neural network for tension prediction, with design cable length, line density, and frequency as the input parameters and with cable tension as the output parameter. After disregarding the selection of effective cable length and innovatively integrating the particle swarm optimization-back propagation (PSO-BP) neural network for tension prediction, it is found that the MAPE between the predicted results of the BP neural network and the actual tension values is 7.93%. After optimization using the particle swarm optimization algorithm, the mean absolute percentage error (MAPE) of the neural network prediction is reduced to 2.78%. Both of these values significantly outperform those obtained from the theoretical equations of string vibration. Moreover, the MAPE of PSO-BP also surpasses that of the optimized calculation formulas in the literature. Utilizing the PSO-BP neural network for tension prediction avoids inaccuracies in tension calculation caused by an excessive simplification of boundary conditions and a vague definition of effective cable length;thus, it possesses certain engineering practical value.

关键词： cable tension prediction optimization-back propagation neural network particle swarm optimization intelligent algorithms

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A fixed charge transportation problem with damageable items under uncertain environment

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2021年 581卷 126234-126234页

作者： Zhu, Kai Ji, Kaiyuan Shen, Jiayu Jiangsu Univ Technol Sch Automobile & Traff Engn Changzhou 213001 Jiangsu Peoples R China China State Shipbldg Corp 723th Res Inst Yangzhou 225101 Jiangsu Peoples R China Nanjing Vocat Univ Ind Technol Dept Publ Basic Courses Nanjing 210023 Jiangsu Peoples R China

An uncertain mathematical model for a fixed charge transportation problem is created in this paper. To make it closer to actuality, item damage during the transportation is considered. Besides, as some indeterministic factors may appear in the problem because of the incompletion of historical data, several parameters in this model such as purchasing price, supplies, demands and so on are regarded as uncertain variables based on uncertainty theory. After that, the original model is analyzed in two criteria of expected value criterion and chance-constrained one, and two kinds of uncertain models as well as their corresponding deterministic forms are constructed. Then, two intelligent algorithms like the genetic algorithm (GA) and the particle swarm optimization algorithm (PSO) are employed to figure out the equivalent forms of the models. Finally, the applications of the models are displayed via several numerical examples. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Fixed charge Uncertainty Uncertain variables intelligent algorithms

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Operational Optimization of Integrated Regional Energy Systems Considering Master-Slave Games and Electric Heating Equipment Testers: An Improved Coati Optimization Algorithm

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SENSORS AND MATERIALS 2023年第12期35卷 4439-4457页

作者： Liu, Guanchen Yuan, Jianping Lin, Cheng-Jian Qu, Linan Li, Zhongtao Li, Lingling Power China Huadong Engn Corp Ltd Hangzhou 310000 Peoples R China Hangzhou Huachen Elect Power Control Co Ltd Hangzhou 310014 Peoples R China Natl Chin Yi Univ Technol Dept Comp Sci Informat Engn Taichung 411 Taiwan Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300401 Peoples R China Hebei Univ Technol Key Lab Elect Field & Elect Apparat Reliabil Hebe Tianjin 300401 Peoples R China

The integrated regional energy system has significant advantages in realizing multi-energy complementarity and improving system energy utilization, which has gradually become a hot research topic nowadays. In this study, a corresponding optimal operation model is established for the integrated regional energy system. Moreover, the game relationship between the microgrid operator and the user aggregator in the integrated regional microgrid considering user-side electric heating equipment is studied and analyzed, and a two-layer game model between the microgrid operator and the user aggregator is proposed. Meanwhile, sensor devices, such as temperature sensors, thermal sensors, and energy sensors, are included in the integrated energy system and are used to provide real-time data to help monitor the state of the system and improve the control strategy and rationality and intelligence of the system. Finally, the electricity and heat prices of the upper-level microgrid operator are updated using the improved coati optimization algorithm (ICOA), and the lower-level optimization problem is modeled and solved by the solver. The results show that the proposed two-tier planning approach can considerably improve the revenue of the lower tier user side while ensuring the revenue of the upper tier microgrid operator. The revenue of the user aggregator increases by 23.1%, and the total system revenue increases by $29, which realizes a win-win situation for both the microgrid operator and the user aggregator and has long-term significance for improving the operational stability and economy of the regional integrated microgrid system.

关键词： integrated regional energy system game theory two-tier planning demand response intelligent algorithms

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The Research on Complex Lithology Identification Based on Well Logs: A Case Study of Lower 1st Member of the Shahejie Formation in Raoyang Sag

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ENERGIES 2023年第4期16卷 1748页

作者： Song, Zhaojing Xiao, Dianshi Wei, Yongbo Zhao, Rixin Wang, Xiaocheng Tang, Jiafan Shandong Prov Key Lab Deep Oil & Gas Qingdao 266580 Peoples R China China Univ Petr East China Sch Geosci Qingdao 266580 Peoples R China Explorat & Dev Res Inst Daqing Oilfield Co Ltd Daqing 163712 Peoples R China

Lithology identification is the basis for sweet spot evaluation, prediction, and precise exploratory deployment and has important guiding significance for areas with low exploration degrees. The lithology of the shale strata, which are composed of fine-grained sediments, is complex and varies regularly in the vertical direction. Identifying complex lithology is a typical nonlinear classification problem, and intelligent algorithms can effectively solve this problem, but different algorithms have advantages and disadvantages. Compared were the three typical algorithms of Fisher discriminant analysis, BP neural network, and classification and regression decision tree (C&RT) on the identification of seven lithologies of shale strata in the lower 1st member of the Shahejie Formation (Es-1(L)) of Raoyang sag. Fisher discriminant analysis method is linear discriminant, the recognition effect is poor, the accuracy is 52.4%;the accuracy of the BP neural network to identify lithology is 82.3%, but it belongs to the black box and can not be visualized;C&RT can accurately identify the complex lithology of Es-1(L), the accuracy of this method is 85.7%, and it can effectively identify the interlayer and thin interlayer in shale strata.

关键词： lithology identification intelligent algorithms well logs shale strata Raoyang sag

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Optimizing Energy Consumption in Transportation: Literature Review, Insights, and Research Opportunities

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ENERGIES 2020年第5期13卷 1115-1115页

作者： Corlu, Canan G. de la Torre, Rocio Serrano-Hernandez, Adrian Juan, Angel A. Faulin, Javier Boston Univ Metropolitan Coll Adm Sci Dept Boston MA 02215 USA Univ Publ Navarra INARBE Inst Pamplona 31006 Spain Univ Publ Navarra Inst Smart Cities Pamplona 31006 Spain Univ Oberta Catalunya Dept Comp Sci Internet Interdisciplinary Inst IN3 Barcelona 08018 Spain Euncet Business Sch Barcelona 08018 Spain

From airplanes to electric vehicles and trains, modern transportation systems require large quantities of energy. These vast amounts of energy have to be produced somewhere-ideally by using sustainable sources-and then brought to the transportation system. Energy is a scarce and costly resource, which cannot always be produced from renewable sources. Therefore, it is critical to consume energy as efficiently as possible, that is, transportation activities need to be carried out with an optimal intake of energetic means. This paper reviews existing work on the optimization of energy consumption in the area of transportation, including road freight, passenger rail, maritime, and air transportation modes. The paper also analyzes how optimization methods-of both exact and approximate nature-have been used to deal with these energy-optimization problems. Finally, it provides insights and discusses open research opportunities regarding the use of new intelligent algorithms-combining metaheuristics with simulation and machine learning-to improve the efficiency of energy consumption in transportation.

关键词： transportation systems optimization problems efficient energy consumption intelligent algorithms sustainability

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A novel approach to pore pressure modeling based on conventional well logs using convolutional neural network

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年第0期211卷 110156-110156页

作者： Matinkia, Morteza Amraeiniya, Ali Behboud, Mohammad Mohammadi Mehrad, Mohammad Bajolvand, Mahdi Gandomgoun, Mohammad Hossein Gandomgoun, Mehdi Islamic Azad Univ Dept Petr Engn Omidiyeh Branch Omidiyeh Iran Islamic Azad Univ Fac Engn Dept Petr Engn Cent Tehran Branch Tehran Iran Shahrood Univ Technol Fac Min Petr & Geophys Engn Shahrood Iran Sahand Univ Technol Petr Engn Fac Tabriz Iran

Accurate prediction of pore pressure (PP) is among the most critical concerns to the design of drilling operation because of the remarkable role of this parameter in preventing particular drilling problems such as wellbore instability, drilling pipe stuck, mud loss, kicks, and even blow outs. Given the limitations of PP measurement through in-hole well tests, a number of analytic and intelligent techniques have been developed to estimate the PP from conventionally available petrophysical logs at offset wells. In this contribution, analytic equations are combined with intelligent algorithms (IAs) in an integrated workflow for estimating the PP. For this purpose, we collected the required data from two wells (herein referred to as Wells A and B) penetrating a carbonate reservoir in two fields in southwestern Iran. The collected data included full-set petrophysical log data at a total of 2850 points as well as 15 measured PPs using the RFT tool. In order to model and validate the results, the data from Well A was used to train the model, with the Well-B data used for validation. Once finished with data collection, a noise attenuation stage was implemented through median filtering. Subsequently, PP estimation was practiced using a couple of popular analytic models, namely modified Eaton's, Bowers', and compressibility models, with the results compared to the measured PPs. Next, a feature selection phase was conducted where depth (Depth), gamma ray log (CGR), density log (RHOB), resistivity log (RT), pore compressibility (Cp), and slowness log (DT) were selected as the most effective parameters for estimating the PP out of the 8 parameters studied at Well A. Feature selection was performed using the second version of nondominated-sorting genetic algorithm (NSGA-II) combined with multilayer perceptron (MLP) neural network (NN). Next, deep learning techniques, simple form of the least square support vector machine (LSSVM) and its hybrid forms with particle swarm optimizati

关键词： Pore pressure prediction Eaton 's model Bowers ' model Compressibility model intelligent algorithms Feature selection

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Modeling and Optimization of NO2 Stations in the Smart City of Barcelona

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APPLIED SCIENCES-BASEL 2024年第22期14卷 10355页

作者： Soriano-Gonzalez, Raquel Martin, Xabier A. Perez-Bernabeu, Elena Carracedo, Patricia Univ Politecn Valencia Res Ctr Prod Management & Engn Alcoy 03802 Spain

The growing problem of nitrogen dioxide (NO2) pollution in urban environments is driving cities to adopt smart and sustainable approaches to address this challenge. To quantify and compare the effect of environmental policies, cities must be able to make informed decisions with real-time data that reflect the actual situation. Therefore, the objective of this work is threefold: The first is to study the behavior of the key performance indicator (KPI) of NO2 concentrations per station in Barcelona through exploratory analysis and clustering. The second is to predict NO2 concentration behavior, considering meteorological data. Lastly, a new distribution of current and new stations will be proposed using an optimization algorithm that maximizes the distance between them and covers the largest area of the city. As a result of this study, the importance of the location of measurement points and the need for better distribution in the city are highlighted. These new spatial distributions predict an 8% increase in NO2 concentrations. In conclusion, this study is a comprehensive tool for obtaining an accurate representation of NO2 concentrations in the city, contributing to informed decision-making, helping to improve air quality, and promoting a more sustainable urban environment.

关键词： NO2 concentration smart cities KPI environmental machine learning intelligent algorithms

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Optimal operation of electricity-gas-heating-cooling integrated energy systems with SCPR-based carbon trading using a novel SMABC algorithm

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RENEWABLE ENERGY 2024年 232卷

作者： Liu, Jing Zhao, Tong China Univ Min & Technol Beijing Sch Mech & Elect Engn Beijing 100083 Peoples R China

With further aggravation of climate change and energy crisis, more and more attention has been focused on integrated energy systems which can reduce carbon emission and increase accommodation of renewable energy. At present, research on optimal operation of integrated energy systems has been widely carried out. However, intelligent optimization algorithms for solving the optimal operation problems still needs to be further studied. To gap this issue, the slime mould-artificial bee colony (SMABC) algorithm is proposed for the first time to solve optimal operation of the integrated electricity-gas-heating-cooling energy systems which contains renewable energy, power to hydrogen (P2H), power to gas (P2G), hydrogen fuel cells, gas-fueled units, gas boiler, electrical chiller and waste heat recovery units. Additionally, a carbon trading mechanism with the stepped carbon penalty response (SCPR) is presented in the optimal operation model to cut down operation cost as well as to reduce carbon emission considering power load, gas load, heating load and cooling load. Further, four case studies were carried out on an integrated energy systems using the proposed SMABC algorithm. The results illustrate that SMABC algorithm has great advantages than the traditional particle swarm optimization (PSO) algorithm and the slime mould (SM) algorithm in terms of better economic/environmental benefit (7.58 % and 2.82 % decline in operation cost as well as 21.02 % and 12.19 % reduction in carbon emission, respectively). Meanwhile, renewable energy curtailment is reduced significantly using SMABC algorithm than using PSO algorithm and SM algorithm (decreased by 77.27 % and 63.43 %, respectively). Moreover, the SCPR contributes to less carbon emission (reduced by 21.93 %). The presented optimal operation model considering carbon trading mechanism with SCPR as well as the proposed SMABC algorithm are respected to be applied to other integrated energy systems.

关键词： Integrated energy systems Slime mould-artificial bee colony algorithm (SMABC) Carbon emission Stepped carbon penalty response (SCPR) intelligent algorithms

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Location-routing optimization problem for electric vehicle charging stations in an uncertain transportation network: An adaptive co-evolutionary clustering algorithm

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ENERGY 2024年 304卷

作者： Wu, Jiabin Li, Qihang Bie, Yiming Zhou, Wei Foshan Univ Business Sch Foshan 528000 Peoples R China Sun Yat sen Univ Sch Intelligent Syst Engn Guangzhou 510006 Guangdong Peoples R China Jilin Univ Sch Transportat Changchun 130022 Peoples R China Natl Univ Singapore Dept Civil & Environm Engn Singapore 117576 Singapore

The rapid growth of Electric Vehicles (EVs) has led to issues such as insufficient and unevenly distributed charging stations, posing an increasingly severe challenge. This not only means higher economic and time costs for EV users traveling to charging stations, but also indicates that EVs need to incur additional energy consumption. To address these challenges, this paper first establishes an extended space-time-state network to describe the temporal variability and uncertainty of the traffic environment. Secondly, considering the impact of the energy consumption incurred by EVs traveling to charging stations (CSs) on the layout of CSs, a coevolutionary optimization model of the location-routing problem is proposed. To solve the model, a two-stage Adaptive Co-evolutionary Clustering Algorithm (ACECA) integrating an adaptive clustering framework and a co-evolutionary mechanism is designed. Finally, the experiment results indicate that there exists a game between a short-term economic investment and long-term benefits of energy conservation and emission reduction. Moreover, ACECA demonstrates stronger solution performance and robustness compared to other algorithms, with the resulting CS location schemes showing superior advantages in energy conservation and in cost saving for users. The research findings can provide theoretical support for the planning of charging station locations for electric vehicles.

关键词： Electric vehicles Public charging stations Location-routing optimization problem intelligent algorithms Energy conservation

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