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1

Voynarenko, Mykhaylo, Mariia V. Dykha, Oksana Mykoliuk, Ludmyla Yemchuk, and Anastasiia Danilkova. "Assessment of an enterprise’s energy security based on multi-criteria tasks modeling." Problems and Perspectives in Management 16, no. 4 (2018): 102–16. http://dx.doi.org/10.21511/ppm.16(4).2018.10.

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Today Ukrainian business entities operate in conditions of macroeconomic instability, environmental disturbance, energy dependence on risk of instable and interrupted supply and high cost of energy resources, excessive energy consumption and inefficient use of fuel and energy resources, which requires immediate actions as for finding solutions to ensure energy security. The goal of the article is to solve multi-criteria tasks focused on making managerial decisions regarding the development of enterprise energy security system based on evaluation of influence of numerous factors. As a result of this study, main components of energy security of the enterprise and most important influence factors are determined. The mathematical model of the hierarchy of factors in terms of their influence on the energy security of the enterprise with the use of graph theory is developed. Use of iterative procedure to determine the levels of hierarchy of factors allowed to assess the importance/priority of their influence on energy security of the enterprise. Thus, the developed model of hierarchy of factors based on the applied scientific and methodical approach to determine their influence on energy security of the enterprise provides the opportunity to get a detailed idea of factors interaction, interconnections and influence on energy security of the enterprise, which ultimately leads to elaboration of complex optimal/agreed managerial decisions in context of development and implementation of energy security system of the enterprise.
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2

Xie, Ming, and Walter H. Gerstle. "Energy-Based Cohesive Crack Propagation Modeling." Journal of Engineering Mechanics 121, no. 12 (1995): 1349–58. http://dx.doi.org/10.1061/(asce)0733-9399(1995)121:12(1349).

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3

Wang, Yu, Charlie C. L. Wang, and Matthew M. F. Yuen. "Fast energy-based surface wrinkle modeling." Computers & Graphics 30, no. 1 (2006): 111–25. http://dx.doi.org/10.1016/j.cag.2005.10.010.

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4

Gonzalez de Durana, José María, Oscar Barambones, Enrique Kremers, and Liz Varga. "Agent based modeling of energy networks." Energy Conversion and Management 82 (June 2014): 308–19. http://dx.doi.org/10.1016/j.enconman.2014.03.018.

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5

Umar, Mariam, Shirley V. Moore, Jeremy S. Meredith, Jeffrey S. Vetter, and Kirk W. Cameron. "Aspen-based performance and energy modeling frameworks." Journal of Parallel and Distributed Computing 120 (October 2018): 222–36. http://dx.doi.org/10.1016/j.jpdc.2017.11.005.

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6

Biberacher, Markus. "GIS‐based modeling approach for energy systems." International Journal of Energy Sector Management 2, no. 3 (2008): 368–84. http://dx.doi.org/10.1108/17506220810892937.

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7

Jeong, WoonSeong, Jong Bum Kim, Mark J. Clayton, Jeff S. Haberl, and Wei Yan. "Translating Building Information Modeling to Building Energy Modeling Using Model View Definition." Scientific World Journal 2014 (2014): 1–21. http://dx.doi.org/10.1155/2014/638276.

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This paper presents a new approach to translate between Building Information Modeling (BIM) and Building Energy Modeling (BEM) that uses Modelica, an object-oriented declarative, equation-based simulation environment. The approach (BIM2BEM) has been developed using a data modeling method to enable seamless model translations of building geometry, materials, and topology. Using data modeling, we created a Model View Definition (MVD) consisting of a process model and a class diagram. The process model demonstrates object-mapping between BIM and Modelica-based BEM (ModelicaBEM) and facilitates the definition of required information during model translations. The class diagram represents the information and object relationships to produce a class package intermediate between the BIM and BEM. The implementation of the intermediate class package enables system interface (Revit2Modelica) development for automatic BIM data translation intoModelicaBEM. In order to demonstrate and validate our approach, simulation result comparisons have been conducted via three test cases using (1) the BIM-based Modelica models generated fromRevit2Modelicaand (2) BEM models manually created using LBNL Modelica Buildings library. Our implementation shows thatBIM2BEM(1) enables BIM models to be translated intoModelicaBEMmodels, (2) enables system interface development based on the MVD for thermal simulation, and (3) facilitates the reuse of original BIM data into building energy simulation without an import/export process.
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8

Elsherbiny, E., M. T. Elhagry, M. S. M. Soliman, and Essam Abu Elzahab. "Design and Modeling of a Novel Micro-Power Generator based on Harvesting Omni-Directional Vibration Energy." International Journal of Engineering Research 4, no. 8 (2015): 437–41. http://dx.doi.org/10.17950/ijer/v4s8/807.

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9

Heendeniya, Charitha Buddhika. "Agent-based modeling of a rule-based community energy sharing concept." E3S Web of Conferences 239 (2021): 00001. http://dx.doi.org/10.1051/e3sconf/202123900001.

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In an energy community, the prosumers’ interactions are critical to ensure efficient use of renewable resources. Local energy sharing concepts where a coordinating agent typically regulates the energy transactions between prosumers intend to achieve such a sharing economy. The coordinating agent, known as the market agent or energy sharing agent, acts according to a set of rules to match the prosumers’ renewable energy surpluses and deficits. This paper describes an agent-based modeling strategy and a case study to demonstrate the interactions in an energy sharing community where each agent individually and collectively attempts to maximize renewable energy self-consumption. The prosumers attempt to achieve their individual and collective objectives by following a two-step rule-based strategy. In the first step, a building-integrated battery storage operation strategy based on a schedule improves the prosumer-level self-consumption while providing grid-friendly behavior. The next step involves an energy sharing strategy and an operating strategy for community-scale battery storage that maximizes the collective selfconsumption. Throughout the transactions, prosumers have no visibility of other prosumers; therefore, the energy sharing coordinator has the sole responsibility of orchestrating the energy exchanges between prosumers. Finally, we discuss the significance and future research outlook for energy interaction modeling at a community scale.
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10

Han, Xiao, Jing Qiu, Lingling Sun, Wei Shen, Yuan Ma, and Dong Yuan. "Low‐carbon energy policy analysis based on power energy system modeling." Energy Conversion and Economics 1, no. 1 (2020): 34–44. http://dx.doi.org/10.1049/enc2.12005.

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11

Meyer, H., J. Plössnig, B. Weißenberger, and B. Vogel-Heuser. "Energy Management based on a Hybrid Modeling Approach." IFAC Proceedings Volumes 46, no. 9 (2013): 158–61. http://dx.doi.org/10.3182/20130619-3-ru-3018.00056.

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12

Miguel, Eder, David Miraut, and Miguel A. Otaduy. "Modeling and Estimation of Energy-Based Hyperelastic Objects." Computer Graphics Forum 35, no. 2 (2016): 385–96. http://dx.doi.org/10.1111/cgf.12840.

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13

Xie, Ming, Walter H. Gerstle, and Pakal Rahulkumar. "Energy-Based Automatic Mixed-Mode Crack-Propagation Modeling." Journal of Engineering Mechanics 121, no. 8 (1995): 914–23. http://dx.doi.org/10.1061/(asce)0733-9399(1995)121:8(914).

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14

Esplin, J. J., Benjamin J. Kim, Michael P. Kinzel, and R. L. Culver. "Bulk cavitation extent modeling: An energy-based approach." Journal of the Acoustical Society of America 140, no. 4 (2016): 3121. http://dx.doi.org/10.1121/1.4969773.

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15

Renger, Thomas, Mohamed El-Amine Madjet, Marcel Schmidt am Busch, Julian Adolphs, and Frank Müh. "Structure-based modeling of energy transfer in photosynthesis." Photosynthesis Research 116, no. 2-3 (2013): 367–88. http://dx.doi.org/10.1007/s11120-013-9893-3.

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16

Shin, Sungho, Carleton Coffrin, Kaarthik Sundar, and Victor M. Zavala. "Graph-Based Modeling and Decomposition of Energy Infrastructures." IFAC-PapersOnLine 54, no. 3 (2021): 693–98. http://dx.doi.org/10.1016/j.ifacol.2021.08.322.

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17

Ma, Tieju, and Yoshiteru Nakamori. "Modeling technological change in energy systems – From optimization to agent-based modeling." Energy 34, no. 7 (2009): 873–79. http://dx.doi.org/10.1016/j.energy.2009.03.005.

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18

Zang, Han Shu, Qing Chao Sun, Jun Liang Wu, and Chuan Lei Wang. "The Research of Product Concept Feature Modeling Based on Energy Saving." Advanced Materials Research 712-715 (June 2013): 2917–24. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.2917.

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Product conceptual feature modeling based on energy saving is established. Through having decomposed the general functions, product design information of every function unit is extracted and converted into energy eigenvector. The combination of every function unit becomes functional chain and the function model is got. Using the bond graph and state space theory, principal analysis and behavior modeling is developed. Structure modeling is established based on function modeling and behavior modeling, comprehensively considered energy consumption influencing factors in structure modeling. Function-behavior-Structure modeling is put forward and product conceptual feature modeling based on energy saving is realized.
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19

Li, Peng, Bo Dong, Hao Yu, et al. "A Unified Energy Bus Based Multi-energy Flow Modeling Method of Integrated Energy System." Energy Procedia 159 (February 2019): 418–23. http://dx.doi.org/10.1016/j.egypro.2018.12.066.

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20

Shahbakhsh, Arash, and Astrid Nieße. "Modeling multimodal energy systems." at - Automatisierungstechnik 67, no. 11 (2019): 893–903. http://dx.doi.org/10.1515/auto-2019-0063.

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Abstract Information and communication technology (ICT) and the technology of coupling points including power-to-gas (PtG), power-to-heat (PtH) and combined heat and power (CHP) reshape future energy systems fundamentally. To study the resulting multimodal smart energy system, a proposed method is to separate the behavior of the component layer from the control layer. The component layer includes pipelines, power-lines, generators, loads, coupling points and generally all components through which energy flows. In the work at hand, a model is presented to analyze the operational behavior of the component layer. The modeling problem is formulated as state and phase transition functions, which present the external commands and internal dynamics of system. Phase transition functions are approximated by ordinary differential equations, which are solved with integral methods. State transition functions are nonlinear algebraic functions, which are solved numerically and iteratively with a modified Newton–Raphson method. In a proof-of-concept case study, a scenario shows the expected multi-sector effects based on evaluated models.
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21

Li, Qian, Siyuan Wang, Xiaoyan Zhou, Anan Zhang, and Roksana Zaman. "Modeling and Optimization of RIES Based on Composite Energy Pipeline Energy Supply." IEEE Transactions on Applied Superconductivity 31, no. 8 (2021): 1–5. http://dx.doi.org/10.1109/tasc.2021.3090340.

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22

Li, Ming, and Zhe Wang. "Modeling and Optimization of Integrated Energy System Based on Energy Circuit Theory." IEEJ Transactions on Electrical and Electronic Engineering 16, no. 5 (2021): 696–703. http://dx.doi.org/10.1002/tee.23349.

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23

Manasa, Krishna. "Modeling of Vortex Induced Vibration based Hydrokinetic Energy Converter." IOSR Journal of Electrical and Electronics Engineering 6, no. 6 (2013): 26–31. http://dx.doi.org/10.9790/1676-0662631.

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24

Azizi, Abdolhamid, Hamed Adibi, Seyed Mehdi Rezaei, and Hamid Baseri. "Modeling of Specific Grinding Energy Based on Wheel Topography." Advanced Materials Research 325 (August 2011): 72–78. http://dx.doi.org/10.4028/www.scientific.net/amr.325.72.

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Grinding performance is evaluated mainly in terms of specific grinding energy. The number of active grits per unit area and their slope is considered as the two grinding wheel topographical key parameters for studying grinding performance. To provide a view on how various parameters influence specific energy and the importance of wheel topography and grit workpiece interaction, a specific grinding energy model is developed. Inputs to this model are workpiece parameters, grinding process parameters, and, in particular, the grinding wheel topographical parameters. This model has been validated by experimental results. The theoretical values considering the complexity of the grinding process reasonably compare with the experimental results. The effect of number of active grits per unit area and their slope on specific grinding energy and then metal removal mechanism is investigated. The results revealed that the number of active grits per unit area has less effect on specific grinding energy than grits slope.
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25

GAO, Bing-Tuan. "Dynamical Modeling and Energy-based Control Design for TORA." Acta Automatica Sinica 34, no. 9 (2009): 1221–24. http://dx.doi.org/10.3724/sp.j.1004.2008.01221.

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26

Mittal, Anuj, Caroline C. Krejci, Michael C. Dorneich, and David Fickes. "An agent-based approach to modeling zero energy communities." Solar Energy 191 (October 2019): 193–204. http://dx.doi.org/10.1016/j.solener.2019.08.040.

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27

Yalcinkaya, Tuncay, and Giovanni Lancioni. "Energy-based Modeling of Localization and Necking in Plasticity." Procedia Materials Science 3 (2014): 1618–25. http://dx.doi.org/10.1016/j.mspro.2014.06.261.

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28

Hocaoglu, Fatih Onur, and Fatih Karanfil. "A time series-based approach for renewable energy modeling." Renewable and Sustainable Energy Reviews 28 (December 2013): 204–14. http://dx.doi.org/10.1016/j.rser.2013.07.054.

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29

SKOREK-OSIKOWSKA, Anna, Wojciech UCHMAN, and Sebastian WERLE. "MODELING OF ENERGY CROPS GASIFICATION BASED ON EXPERIMENTAL DATA." Architecture, Civil Engineering, Environment 10, no. 3 (2017): 135–41. http://dx.doi.org/10.21307/acee-2017-044.

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30

Jia, Yan-Bin. "Three-dimensional impact: energy-based modeling of tangential compliance." International Journal of Robotics Research 32, no. 1 (2012): 56–83. http://dx.doi.org/10.1177/0278364912457832.

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31

Montegiglio, Pasquale, Claudio Maruccio, and Giuseppe Acciani. "Nonlinear Physics-based Modeling of a Piezoelectric Energy Harvester." IFAC-PapersOnLine 51, no. 2 (2018): 707–12. http://dx.doi.org/10.1016/j.ifacol.2018.03.120.

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32

Göke, Leonard. "A graph-based formulation for modeling macro-energy systems." Applied Energy 301 (November 2021): 117377. http://dx.doi.org/10.1016/j.apenergy.2021.117377.

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33

Kim, Gyeong-Min, Sol-Young Jung, Seung-Chan Oh, Jae-Gul Lee, Hun-Young Shin, and Jin Hur. "Probabilistic Security Analysis based on Renewable Energy Scenario Modeling." Transactions of The Korean Institute of Electrical Engineers 70, no. 8 (2021): 1075–80. http://dx.doi.org/10.5370/kiee.2020.70.8.1075.

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34

Huang, Yujian, Zhun (Jerry) Yu, Jun Li, Yaping Zhou, Jin Zhou, and Guoqiang Zhang. "A Novel Energy Benchmarking Methodology Based on an Agent-Based Modeling Method." Procedia Engineering 205 (2017): 1725–32. http://dx.doi.org/10.1016/j.proeng.2017.10.014.

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35

Li, Jingchao, Yulong Ying, Xingdan Lou, Juanjuan Fan, Yunlongyu Chen, and Dongyuan Bi. "Integrated Energy System Optimization Based on Standardized Matrix Modeling Method." Applied Sciences 8, no. 12 (2018): 2372. http://dx.doi.org/10.3390/app8122372.

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Aiming at the optimization of an integrated energy system, a standardized matrix modeling method and optimization method for an integrated energy system is proposed. Firstly, from the perspective of system engineering, the energy flow between energy conversion devices is used as a state variable to deal with nonlinear problems caused by the introduction of scheduling factors, and a standardized matrix model of the integrated energy system is constructed. Secondly, based on the proposed model, the structural optimization (i.e., energy flow structure and equipment type), design optimization (i.e., equipment capacity and quantity), and operation optimization for the integrated energy system can be achieved. The simulation case studies have shown that the proposed integrated energy system standardized matrix modeling method and optimization method are both simple and efficient, and can be effectively used to decide the system components and their interconnections, and the technical characteristics and daily operating strategy of the system components.
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36

Kaur, Pardeep, Preeti Singh, and Balwinder S. Sohi. "Traffic Models for Energy Harvesting Based Wireless Sensor Networks." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 13, no. 2 (2020): 219–26. http://dx.doi.org/10.2174/1872212113666190306145721.

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Background: Energy consumption is an important parameter in wireless sensor networks since it affects the lifetime of sensor nodes. Methods: Battery powered wireless sensor networks cannot sustain for long hence impractical for real-time applications. With energy harvesting and relevant protocols, this issue of extending the lifetime of nodes has been solved largely. The performance can be enhanced further if proper traffic analysis and modeling are done as a proactive approach. Results: A proper understanding of the traffic dynamics provides a base for further network optimization and detection of anomalies within the network. Much of the reported work in energy harvesting based WSN till now, is to design the efficient protocols only, traffic analysis and modeling is the ignored parameter. In this paper traffic models appropriate for the energy harvesting based system have been analyzed and their performance is evaluated for a MAC protocol. Conclusion: Results show that Weibull distribution is the most useful model for traffic modeling in energy harvesting based wireless sensor networks.
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37

Zhu, Jisong, Zhaoxia Jing, Tianyao Ji, and Nauman Ali Larik. "Energy–Economy Coupled Simulation Approach and Simulator Based on Invididual-Based Model." Energies 13, no. 11 (2020): 2771. http://dx.doi.org/10.3390/en13112771.

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An integrated energy system, referred to specifically as a heterogeneous energy system that combines cooling, heating, power, etc., is a dynamic system containing continuous as well as discrete behaviors on both technical and economic levels. Currently, the comprehensive utilization of multiple forms of energy and the implementation of the energy market have made the simulation of such a system very complicated, which is reflected in two aspects. First, the simulation model becomes complex and varied. Second, the time-varying characteristics of the models are quite diverse. Therefore, a standard and normative modeling and simulation method is urgently needed. This work aims to obtain a compatible modeling and simulation method for the energy economy coupling system. The individual-based model is widely used to describe organisms in an ecology system that are similar to the energy–economy coupled system. Inspired by this, a general simulation approach based on the individual-based model is proposed in this paper to overcome these existing problems. The standard formal expression model is built, then its structure and elements explained in detail, and multi-scale time simulation supported to model and simulate an integrated energy system that is coupled with markets. In addition, a simulator is designed and implemented based on multi-agent framework and model-view-controller architecture. Finally, a simulation case of a conceived scenario was designed and executed, and the results analysis proved the validity and versatility of the proposed approach. The proposed method has the advantages of model standardization, multi-scale time compatibility, distributed simulation capability, and privacy protection. These advantages support and strengthen each other. Through these studies, a systematic approach was formed that could improve the standardization of modeling and simulation in the energy–economy research area.
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38

Ma, Tengfei, Junyong Wu, and Liangliang Hao. "Energy flow modeling and optimal operation analysis of the micro energy grid based on energy hub." Energy Conversion and Management 133 (February 2017): 292–306. http://dx.doi.org/10.1016/j.enconman.2016.12.011.

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39

Omijeh, B. O. "Modeling of GSM-Based Energy Recharge Scheme for Prepaid Meter." IOSR Journal of Electrical and Electronics Engineering 4, no. 1 (2013): 46–53. http://dx.doi.org/10.9790/1676-0414653.

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40

Zhao, Yi, Valentin Gies, and Jean-Marc Ginoux. "WSN BASED THERMAL MODELING: A NEW INDOOR ENERGY EFFICIENT SOLUTION." International Journal on Smart Sensing and Intelligent Systems 8, no. 2 (2015): 869–95. http://dx.doi.org/10.21307/ijssis-2017-787.

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41

Kang, Tae-Wook, Han-Soo Ryu, Jeong-Lim Ko, and Hyun-Sang Choi. "Process Reference Model for modeling BIM/GIS-based Energy Information." Journal of the Korea Academia-Industrial cooperation Society 16, no. 4 (2015): 2791–98. http://dx.doi.org/10.5762/kais.2015.16.4.2791.

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42

Cai, Ziyue, Weijie Li, Wenxuan Xu, and Zihan Zhao. "Research on Energy Saving of Charging Based on Mathematical Modeling." Journal of Applied Mathematics and Physics 08, no. 03 (2020): 555–83. http://dx.doi.org/10.4236/jamp.2020.83044.

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43

Su, Shaopu, and Laurent Stainier. "Energy-based variational modeling of adiabatic shear bands structure evolution." Mechanics of Materials 80 (January 2015): 219–33. http://dx.doi.org/10.1016/j.mechmat.2014.04.013.

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44

Bako, Zeinabou Nouhou, Mahamadou Abdou Tankari, Gilles Lefebvre, and Amadou Seidou Maiga. "Experiment-Based Methodology of Kinetic Battery Modeling for Energy Storage." IEEE Transactions on Industry Applications 55, no. 1 (2019): 593–99. http://dx.doi.org/10.1109/tia.2018.2866148.

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45

Wang, Nan, Wei Liang, Yanan Cheng, and Yunfei Mu. "Battery Energy Storage System Information Modeling Based on IEC 61850." Journal of Power and Energy Engineering 02, no. 04 (2014): 233–38. http://dx.doi.org/10.4236/jpee.2014.24033.

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46

Neelin, J. David, and Isaac M. Held. "Modeling Tropical Convergence Based on the Moist Static Energy Budget." Monthly Weather Review 115, no. 1 (1987): 3–12. http://dx.doi.org/10.1175/1520-0493(1987)115<0003:mtcbot>2.0.co;2.

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47

Yang, Mo, Dinghua Zhang, Baohai Wu, and Yunpeng Zhang. "Energy Consumption Modeling for EDM Based on Material Removal Rate." IEEE Access 8 (2020): 173267–75. http://dx.doi.org/10.1109/access.2020.3024748.

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48

Sarkar, Sidhartha, Rahul Bhattacharya, Gour Gopal Roy, and Prodip Kumar Sen. "Modeling MIDREX Based Process Configurations for Energy and Emission Analysis." steel research international 89, no. 2 (2017): 1700248. http://dx.doi.org/10.1002/srin.201700248.

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49

Hill, F. A., T. F. Havel, and C. Livermore. "Modeling mechanical energy storage in springs based on carbon nanotubes." Nanotechnology 20, no. 25 (2009): 255704. http://dx.doi.org/10.1088/0957-4484/20/25/255704.

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50

Dhananjay Rao, K., Subhojit Ghosh, Shantanu Das, and Mano Ranjan Kumar. "Transient Behavior Modeling-Based Hysteresis-Dependent Energy Estimation of Ultracapacitor." IEEE Transactions on Instrumentation and Measurement 69, no. 9 (2020): 6455–64. http://dx.doi.org/10.1109/tim.2020.2971290.

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