Volume 2, Issue 1, No.4 PDF DOWNLOAD
  • Title:
  • Strategies for managing models regarding environmental confidence and complexity involved in intelligent control of energy systems - A review
  • Author:

    Adel Razek

  • Author Affiliation:

    Group of Electrical Engineering – Paris (GeePs), CNRS, University of Paris-Saclay and Sorbonne University, Gif sur Yvette, France

  • Received:Mar.14, 2023
  • Accepted:Apr.12, 2023
  • Published:Apr.26, 2023
Abstract
This assessment aims to analyze, illustrate, and examine the complexity and confidence problem associated with monitoring dynamic energy systems and managing them through model coupling and reduction. The confidence problem, which is related to the proximity of models to reality, can be reduced in general by considering neglected secondary domains by coupling their models to that of the main domain. Moreover, the complexity must be properly accounted for in the system models without decreasing the monitoring efficiency, which can be done through appropriate numerical model reduction techniques. In the article, after having posed the problem to be solved, we discussed and analyzed the automated procedures involved in energy systems. The notions of complexity and confidence in these systems are then illustrated and analyzed. In this framework, a complete coupled physical model reducing the confidence problem is then discussed and demonstrated. Model reduction strategies needed to optimize matching in automated procedures are then reviewed and analyzed. Finally, the pairing behavior of digital twins involving complex procedures is discussed and assessed, using a literature review. At the end of the paper, the case of electric and intelligent vehicles is discussed as an example of energy systems. 
Keywords

Energy systems, smart control, dynamics; complexity, confidence, coupled models, online matching, model reduction.

References

[1] Perrow, C. Normal Accidents: Living with High Risk Technologies - Updated Edition. Princeton University Press, New Jersey, United States. 2011 doi: 10.2307/j.ctt7srgf

[2] Yang, H.; Fujii, Y.; Zhang, Y.; Haria, H. et al. Uncertainty Quantification of Wet Clutch Actuator Behaviors in P2 Hybrid Engine Start Process. SAE Technical Paper 2022, 01, 0652. https://doi.org/10.4271/2022-01-0652

[3] Memon, Z.A.; Trinchero, R.; Manfredi, P.; Canavero, F.; Stievano, I.S. Compressed Machine Learning Models for the Uncertainty Quantification of Power Distribution Networks. Energies 2020, 13, 4881. https://doi.org/10.3390/en13184881

[4] Taha, A. F.; Gatsis, N.; Summers, T.; Nugroho, S. A. Time-Varying Sensor and Actuator Selection for Uncertain Cyber-Physical Systems. IEEE Transactions on Control of Network Systems 2019, 6(2), 750-762.doi: 10.1109/TCNS.2018.2873229.

[5] Granig, W.; Faller, LM.; Zangl, H. Sensor System Optimization to meet Reliability Targets. Microelectronics Reliability 2018, 87, 113-124. DOI: 10.1016/j.microrel.2018.06.00

[6] Luo, Q.; Peng, Y.; Peng, X.; Saddik, A.E. Uncertain Data Clustering-Based Distance Estimation in Wireless Sensor Networks. Sensors 2014, 14, 6584-6605. https://doi.org/10.3390/s140406584

[7] Tzagkarakis, G.; Seliniotaki, A.; Christophides, V.; Tsakalides, P. Uncertainty-Aware Sensor Data Management and Early Warning for Monitoring Industrial Infrastructures. Int. J. Monit. Surveillance Technol. Res. 2014, 2 (4), 1–24. http:// DOI: 10.4018/IJMSTR.2014100101

[8] Xavier, M.; Tawk, C.; Zolfagharian, A.; Pinskier, J.; Howard, G.; Young, T.; Lai, J.; Harrison, S.; Yong, Y. K.; Bodaghi, M.; Fleming, A. Soft Pneumatic Actuators: A Review of Design, Fabrication, Modeling, Sensing, Control and Applications. IEEE Access 2022, 1-1. 10.1109/ACCESS.2022.3179589.

[9] Xu, D.; Wang, B.; Zhang, G.; Wang, G.; Yu, Y. A review of sensorless control methods for AC motor drives. CES Transactions on Electrical Machines and Systems 2020, 2(1), 104–115. doi:10.23919/tems.2018.8326456.

[10] Soto, G. G.; Mendes, E.; Razek, A. Reduced-order observers for rotor flux, rotor resistance and speed estimation for vector-controlled induction motor drives using the extended Kalman filter technique. IEE Proceedings-Electric Power Applications 1999, 146(3), 282-288. doi:10.1049/ip-epa:19990293.

[11] Alonge, F.; D’Ippolito, F.; Sferlazza, A. Sensorless control of induction-motor drive based on robust kalman filter and adaptive speed estimation. IEEE Trans on Ind. Elec. 2014, 61(3), 1444–1453. doi:10.1109/TIE.2013.2257142.

[12] El Moucary, C.; Mendes, E.; Razek, A. Decoupled direct control for PWM inverter-fed induction motor drives. IEEE transactions on industry applications 2002, 38(5), 1307-1315. doi:10.1109/TIA.2002.803010.

[13] Holtz, J.; Quan, J. Drift- and parameter-compensated flux estimator for persistent zero-stator-frequency operation of sensorless-controlled induction motors. IEEE Transactions on Industry Applications 2003, 39(4), 1052–1060. doi:10.1109/tia.2003.813726.

[14] Ortega, R.; Aranovskiy, S.; Pyrkin, A. A.; Astolfi, A.; Bobtsov, A. A. New Results on Parameter Estimation via Dynamic Regressor Extension and Mixing: Continuous and Discrete-Time Cases. IEEE Transactions on Automatic Control 2021, 66(5), 2265–2272. doi:10.1109/TAC.2020.3003651.

[15] Mendes, E.; Baba, A.; Razek, A. Losses minimization of a field oriented controlled induction machine. IEEE Conference Publication 1995, 412, 310–314). doi:10.1049/cp:19950885.

[16] Razek, A. Towards an image-guided restricted drug release in friendly implanted therapeutics. EPJ Applied Physics 2018, 82(3), 31401. doi:10.1051/epjap/2018180201

[17] Guo, Z.; Yan, S.; Xu, X.; Chen, Z.; Ren, Z. Twin-Model Based on Model Order Reduction for Rotating Motors. IEEE Transactions on Magnetics 2022, 58(9), 1-4, 8206304.doi: 10.1109/TMAG.2022.3187620.

[18] Besselink, B.; Tabak,; Lutowska, A.; van de Wouw, N.; Nijmeijer, H.; Rixen, D.J.; Hochstenbach, M.E.;. Schilders, W.H.A A comparison of model reduction techniques from structural dynamics, numerical mathematics and systems and control.Journal of Sound and Vibration 2013, 332(19), 4403-4422. https://doi.org/10.1016/j.jsv.2013.03.025.

[19] Tol, H.J.; de Visser, C.C.; Kotsonis, M. Model reduction of parabolic PDEs using multivariate splines. International Journal of Control 2019, 92 (1) 175-190. https://doi.org/10.1080/00207179.2016.1222554 

[20] Nunes, A. S.; Dular, P.; Chadebec, O.; Kuo-Peng, P. Subproblems Applied to a 3-D Magnetostatic Facet FEM Formulation. IEEE Transactions on Magnetics 2018, 54(8), 1-9, 7402209. doi:10.1109/TMAG.2018.2828786..

[21] Benner, P.; Ohlberger, M.; Cohen, A.; Wilcox, K. Model Reduction and Approximation: Theory and Algorithms. Published by SIAM-Society for Industrial & App. Math. 2017. https://doi.org/10.1137/1.9781611974829 

[22] Tolk, A. Simulation-Based Optimization: Implications of Complex Adaptive Systems and Deep Uncertainty. Information 2022, 13, 469. https://doi.org/10.3390/info13100469

[23] Wen, J.; Gabrys, B.; Musial, K. Towards Digital Twin Oriented Modeling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey. arXiv 2022, arXiv:2202.09363. https://doi.org/10.48550/arXiv.2202.09363

[24] Oliveira Monteiro, L. M.; Saraiva, J. P.; Brizola Toscan, R.; Stadler, P. F.; Silva-Rocha, R.; Nunes da Rocha, U. PredicTF: prediction of bacterial transcription factors in complex microbial communities using deep learning. Env. microbiome 2022, 17(1), 7. https://doi.org/10.1186/s40793-021-00394-x

[25] Helbing, D.; S'anchez-Vaquerizo, J.A. Digital Twins: Potentials, Ethical Issues, and Limitations. SSRN Electronic Journal 2022, arXiv:2208.04289. https://doi.org/10.48550/arXiv.2208.04289

[26] Polasky, S. et al. Corridors of Clarity: Four Principles to Overcome Uncertainty Paralysis in the Anthropocene. Bioscience 2020, 70(12), 1139–1144. https://doi.org/10.1093/biosci/biaa115

[27] Pelz, P.F. et al. Types of Uncertainty. In: Pelz, P.F., Groche, P., Pfetsch, M.E., Schaeffner, M. (eds) Mastering Uncertainty in Mechanical Engineering. Springer Tracts in Mechanical Engineering 2021. Springer, Cham. https://doi.org/10.1007/978-3-030-78354-9_2

[28] Hüllermeier, E.; Waegeman, W. Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. Mach Learn 110 2021, 457–506. https://doi.org/10.1007/s10994-021-05946-3

[29] Razek, A. Coupled Models in Electromagnetic and Energy Conversion Systems from Smart Theories Paradigm to That of Complex Events: A Review. Appl. Sci. 2022, 12, 4675. https://doi.org/10.3390/app12094675

[30] Craig Jr., R.R.; Bampton, M.C.C. Coupling of substructures for dynamic analyses. AIAA Journal 1968, 6 (7), 1313–1319. https://doi.org/10.2514/3.4741

[31] Géradin, M.; Rixen, D. Mechanical Vibrations: Theory and Application to Structural Dynamics, ISBN: 978-1-118-90020-8, 2nd ed., John Wiley & Sons, 1997.

[32] Enns, D.F. Model reduction with balanced realizations: an error bound and a frequency weighted generalization. Proceedings of the 23rd IEEE Conference on Decision and Control, Las Vegas, USA, 1984, 127–132. doi:10.1109/CDC.1984.272286.

[33] Glover, K. All optimal Hankel-norm approximations of linear multivariable systems and their L ∞ - error bounds. International Journal of Control 1984, 39(6), 1115 – 1193. https://doi.org/10.1080/00207178408933239

[34] Moore, B.C. Principal component analysis in linear systems—controllability, observability, and model reduction, IEEE Transactions on Automatic Control 1981, AC-26 (1), 17–32. doi: 10.1109/TAC.1981.1102568

[35] Imran, M.; Imran M.; Ahmad, M. I. Development of Frequency Weighted Model Order Reduction Techniques for Discrete-Time One-Dimensional and Two-Dimensional Linear Systems with Error Bounds. IEEE Access 2022, 10, 15096-15117. doi: 10.1109/ACCESS.2022.3146394.

[36] Sarkar, A; Scherpen, J. M. A. Extended Differential Balancing for Nonlinear Dynamical Systems. IEEE Control Systems Letters 2022, 6, 3170-3175. doi: 10.1109/LCSYS.2022.3183528.

[37] Gao, Y.; Liu, J.; Li, M.; Tian, Z.; Li, C. Design of Asynchronous Motor Controller Based on Controlled Lagrangians Method. Mathematical Problems in Engineering 2022, 4275946. https://doi.org/10.1155/2022/4275946

[38] Borja, P.; Scherpen, J. M. A; Fujimoto, K. Extended Balancing of Continuous LTI Systems: A Structure-Preserving Approach. IEEE Transactions on Automatic Control 2023, 68(1), 257-271. doi: 10.1109/TAC.2021.3138645.

[39] Feldmann, P.; Freund, R.W. Efficient linear circuit analysis by Padé approximation via the Lanczos process. IEEE Trans on Comp-Aided Design of Integrated Circuits and Systems 1995, 14 (5), 639–649.doi: 10.1109/43.384428.

[40] Gugercin, S.; Antoulas, A.C. A survey of model reduction by balanced truncation and some new results. International Journal of Control 2004, 77 (8), 748–766. DOI: 10.1080/00207170410001713448

[41] Pillage, L.T; Rohrer, R.A. Asymptotic waveform evaluation for timing analysis. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 1990, 9 (4) 352–366. doi: 10.1109/43.45867.

[42] Alfke, D; Feng, L; Lombardi, L; Antonini, G; Benner, P. Model order reduction for delay systems by iterative interpolation. Int J Numer Methods Eng. 2021; 122, 684– 706. https://doi.org/10.1002/nme.6554

[43] Grieves, M.; Vickers, J. Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In: Kahlen, J., Flumerfelt, S., Alves, A. (eds) Transdisciplinary Perspectives on Complex Systems 2017, 85-113. Springer, Cham. https://doi.org/10.1007/978-3-319-38756-7_4

[44] Tao, F.; Sui, F.; Liu, A.; Qi, Q.; Zhang, M.; Song, B.; Guo, Z.; Lu, S. C. Y.; Nee, A.Y.C. Digital twin-driven product design framework. Int. Jour. of Prod. Research 2019, 57(12),3935–3953. doi:10.1080/00207543.2018.1443229.

[45] He, B.; Bai, K. J. Digital twin-based sustainable intelligent manufacturing: A review. Advances in Manufac. 2021, 9(1), 1-21. doi:10.1007/s40436-020-00302-5.55

[46] Cai, Y.et al Sensor Data and Information Fusion to Construct Digital-twins Virtual Machine Tools for Cyber-physical Manufacturing. Procedia Manufacturing 2017, 10, 1031–1042.doi:10.1016/j.promfg.2017.07.094.

[47] Selçuk, Ş.Y.; Ünal, P.; Albayrak, Ö.; Jomâa, M. A Workflow for Synthetic Data Generation and Predictive Maintenance for Vibration Data. Information 2021, 12, 386. https://doi.org/10.3390/info12100386

[48] Montero Jimenez, J. J. et al. Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics. Jour of Manuf Sys 2020, 56, 539–557. https://doi.org/10.1016/j.jmsy.2020.07.008.

[49] Nacchia, M.; Fruggiero, F.; Lambiase, A.; Bruton, K. A Systematic Mapping of the Advancing Use of Machine Learning Techniques for Predictive Maintenance in the Manufacturing Sector. Appl. Sci. 2021, 11, 2546. https://doi.org/10.3390/app11062546

[50] Liu, Z.; Meyendorf, N.; Mrad, N. The role of data fusion in predictive maintenance using digital twin. AIP Conference Proceedings 2018, 1949, 020023. https://doi.org/10.1063/1.5031520 

[51] Liu, Y.et al. A Novel Cloud-Based Framework for the Elderly Healthcare Services Using Digital Twin. IEEE Access 2019, 7, 49088–49101.doi:10.1109/ACCESS.2019.2909828.

[52] Kamel Boulos, M. N.; Zhang, P. Digital twins: From personalised medicine to precision public health. Journal of Personalized Medicine 2021, 11(8), 745. doi:10.3390/jpm11080745.

[53] Holmes, D., et al. Digital Twins and Cyber Security – solution or challenge? Comp Eng, Computer Networks and Social Media Conference (SEEDA-CECNSM, 1–8) 2021.doi:10.1109/seeda-cecnsm53056.2021.9566277.

[54] Gehrmann, C.; Gunnarsson, M. A digital twin based industrial automation and control system security architecture. IEEE Transactions on Industrial Informatics 2020, 16(1), 669–680. doi:10.1109/TII.2019.2938885.

[55] Boschert, S.; Rosen, R. Digital Twin—the Simulation Aspect. In: Hehenberger, P., Bradley, D. (eds) Mechatronic Futures 2016. Springer, Cham. https://doi.org/10.1007/978-3-319-32156-1_5 

[56] Shirowzhan, S.et al. Digital twin and Cyber GIS for improving connectivity and measuring the impact of infrastructure construction planning in smart cities. ISPRS Int Jour of Geo-Inf 2020, 9(4), 240,. doi:10.3390/ijgi904024014

[57] Bhatti, G.; Mohan, H.; Raja Singh, R. Towards the future of smart electric vehicles: Digital twin technology. Renewable and Sustainable Energy Reviews 2021,141,110801. doi:10.1016/j.rser.2021.110801.

[58] Chen, X.; Min, X.; Li, N.; Cao, W.; Xiao, S.; Du, G.; Zhang, P. Dynamic safety measurement￾control technology for intelligent connected vehicles based on digital twin system. Vibro engineering Procedia 2021, 37, 78–85. doi:10.21595/vp.2021.21990

[59] Liu, S.; Wang, X.V.; Wang, L. Digital twin-enabled advance execution for human-robot collaborative assembly. CIRP Annals 2022, 71(1), 25-28, ISSN 0007-8506, https://doi.org/10.1016/j.cirp.2022.03.024.

[60] Razek, A. Review of Pairing Exercises Involving a Real Event and its Virtual Model up to the Supervision of Complex Procedures. Journal of Human, Earth, and Future 2021, 2 (4), Doi: 10.28991/HEF-2021-02-04-010

[61] Nie, X.; Min, C.; Pan, Y.; Li, K.; Li, Z. Deep-Neural-Network-Based Modelling of Longitudinal-Lateral Dynamics to Predict the Vehicle States for Autono. Driving. Sensors 2022, 22, 2013. https://doi.org/10.3390/s22052013

[62] Farroni, F.; Sakhnevych, A. multiphysical modeling for the analysis of thermal and wear sensitivity on vehicle objective dynamics and racing performances. Simulation Modelling Practice and Theory 2022, 117,102517. https://doi.org/10.1016/j.simpat.2022.102517.

[63] Mosconi, L.; Farroni, F; Sakhnevych, A.; Timpone, F.; Gerbino, F. Adaptive vehicle dynamics state estimator for onboard automotive applications and performance analysis, Vehicle System Dynamics 2022, DOI: 10.1080/00423114.2022.2158567 

[64] Hulagu, S.; Celikoglu, H. B. An Electric Vehicle Routing Problem With Intermediate Nodes for Shuttle Fleets. IEEE Trans on Intelligent Transportation Systems 2022, 23(2), 1223-1235.doi: 10.1109/TITS.2020.3023673.

[65] Bellocchi, S.; Klöckner, K.; Manno, M.; Noussan, M.; Vellini, M. On the role of electric vehicles towards low-carbon energy systems: Italy and Germany in comparison, Applied Energy 2019, 255, 0306-2619, https://doi.org/10.1016/j.apenergy.2019.113848.

[66] Qiu, D.; Wang, Y.; Zhang, T.; Sun, M.; Strbac, G. Hybrid Multiagent Reinforcement Learning for Electric Vehicle Resilience Control Towards a Low-Carbon Transition. IEEE Transactions on Industrial Informatics 2022, 18(11), 8258-8269.doi: 10.1109/TII.2022.3166215.

[67] Shaukat, N.S; Ali, M; Mehmood, C.A; Khan, B.; Jawad, M.; Farid, U.; Ullah, Z.; Anwar, S.M.; Majid, M. A survey on consumers empowerment, communication technologies, and renewable generation penetration within Smart Grid. Renewable and Sustainable Energy Reviews 2018, 81(1), 1453-1475. https://doi.org/10.1016/j.rser.2017.05.208.

[68] Yang, Z.; Li, K.; Foley, A. Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review. Renewable and Sustainable Energy Reviews 2015, 51, 396-416 .https://doi.org/10.1016/j.rser.2015.06.007.

[69] Venegas, F.G.; Petit, M.; Perez, Y. Active integration of electric vehicles into distribution grids: Barriers and frameworks for flexibility services. Renewable and Sustainable Energy Reviews 2021, 145, 11060. https://doi.org/10.1016/j.rser.2021.111060.

[70] Yan, L.; Chen, X.; Chen, Y.; Wen, J. A Cooperative Charging Control Strategy for Electric Vehicles Based on Multiagent Deep Reinforcement Learning. IEEE Transactions on Industrial Informatics 2022, 18(12), 8765-8775. doi: 10.1109/TII.2022.3152218.

[71] Sadeghianpourhamami, N; Deleu, J; Develder, C. Definition and Evaluation of Model-Free Coordination of Electrical Vehicle Charging with Reinforcement Learning. IEEE Transactions on Smart Grid 2020, 11(1), 203-214.doi: 10.1109/TSG.2019.2920320.

[72] Jin, J.; Xu, Y. Optimal Policy Characterization Enhanced Actor-Critic Approach for Electric Vehicle Charging Scheduling in a Power Distribution Network. IEEE Transactions on Smart Grid 2021, 12(2), 1416-1428.doi: 10.1109/TSG.2020.3028470.

[73] Zhang, F.; Yang, Q.; An, D. CDDPG: A Deep-Reinforcement-Learning-Based Approach for Electric Vehicle Charging Control. IEEE Internet of Things Journal 2021, 8(5), 3075-3087. doi: 10.1109/JIOT.2020.3015204.

[74] Wang, R.; Chen, Z.; Xing, Q.; Zhang, Z.; Zhang, T. A Modified Rainbow-Based Deep Reinforcement Learning Method for Optimal Scheduling of Charging Station. Sustainability 2022, 14, 1884. https://doi.org/10.3390/su1403188415

[75] Wang, S.; Bi, S.; Zhang, Y. A. Reinforcement Learning for Real-Time Pricing and Scheduling Control in EV Charging Stations. IEEE Transactions on Industrial Informatics 2021, 17(2), 849-859. doi: 10.1109/TII.2019.2950809.

[76] Zhao, Z.; Lee, C. K. M. Dynamic Pricing for EV Charging Stations: A Deep Reinforcement Learning Approach. IEEE Transactions on Transportation Electrification 2022, 8(2), 2456-2468. doi: 10.1109/TTE.2021.3139674.

[77] Xu, P.; Zhang, J.; Gao, T.; Chen, S.; Wang, X.; Jiang, H. et al. Real-time fast charging station recommendation for electric vehicles in coupled power-transportation networks: A graph reinforcement learning method. International Journal of Electrical Power & Energy Systems 2022, 141,108030. https://doi.org/10.1016/j.ijepes.2022.108030.

[78] Qiu, D.; Wang, Y.; Hua, W.; Strbac, G. Reinforcement learning for electric vehicle applications in power systems: A critical review, Renewable and Sustainable Energy Reviews 2023, 173, 113052. https://doi.org/10.1016/j.rser.2022.113052.

[79] Dorokhova, M.; Martinson, Y.; Ballif, C.; Wyrsch, N. Deep reinforcement learning control of electric vehicle charging in the presence of photovoltaic generation. Applied Energy 2021, 301, 117504.https://doi.org/10.1016/j.apenergy.2021.117504.

[80] Yang, A.; Sun, H.; Zhang, X. Deep Reinforcement Learning Strategy for Electric Vehicle Charging Considering Wind Power Fluctuation. Journal of Engineering Science and Technology Review 2021, 14. 103-110. 10.25103/jestr.143.12.

[81] Sarigiannidis, A. G.; Beniakar, M. E.; Kladas, A. G. Fast Adaptive Evolutionary PM Traction Motor Optimization Based on Electric Vehicle Drive Cycle. IEEE Transactions on Vehicular Technology 2017, 66(7), 5762-5774. doi: 10.1109/TVT.2016.2631161.

[82] Naseri, F.; Schaltz, E.; Lu, K.; Farjah, E. Real-time open-switch fault diagnosis in automotive permanent magnet synchronous motor drives based on Kalman filter. IET Power Electronics 2020, 13, 2450-2460. https://doi.org/10.1049/iet-pel.2019.1498

[83] Razek, A. Review of Contactless Energy Transfer Concept Applied to Inductive Power Transfer Systems in Electric Vehicles. Appl. Sci. 2021, 11, 3221. https://doi.org/10.3390/app11073221

[84] Ibrahim, M.; Bernard, L.; Pichon, L.; Razek, A.; Houivet, J.; Cayol, O. Advanced modeling of a 2-kw series–series resonating inductive charger for real electric vehicle. IEEE Trans. Veh. Technol. 2015, 64, 421–430. doi: 10.1109/TVT.2014.2325614. 

[85] Cirimele, V.; Torchio, R.; Villa, J.L.; Freschi, F.; Alotto, P.; Codecasa, L.; Di Rienzo, L. Uncertainty Quantification for SAE J2954 Compliant Static Wireless Charge Components. IEEE Access 2020, 8, 171489–171501. DOI:10.1109/ACCESS.2020.3025052

[86] Cirimele, V.; Diana, M.; Bellotti, F.; Berta, R.; El Sayed, N.; Kobeissi, A.; Guglielmi, P.; Ruffo, R.; Khalilian, M.; La Ganga, A.; et al. The Fabric ICT Platform for Managing Wireless Dynamic Charging Road Lanes. IEEE Trans. Veh. Technol. 2020, 69, 2501–2512. doi: 10.1109/TVT.2020.2968211.

[87] Zhang, B; Wang, X; Lu, C; Lu, Y; Xu, W. A wireless power transfer system for an autonomous underwater vehicle based on lightweight universal variable ring-shaped magnetic coupling. Int J Circ Theor Appl. 2023, 1, 20. doi:10.1002/cta.3568

[88] Ding, P.; Bernard, L.; Pichon, L.; Razek, A. Evaluation of Electromagnetic Fields in Human Body Exposed to Wireless Inductive Charging System. IEEE Trans. Magn. 2014, 50, 1037–1040. doi: 10.1109/TMAG.2013.2284245.

[89] Asa, E; Mohammad, M.; Onar, O. C.; Pries, J.; Galigekere, V.; Su, G. -J. Review of Safety and Exposure Limits of Electromagnetic Fields (EMF) in Wireless Electric Vehicle Charging (WEVC) Applications. 2020 IEEE Transportation Electrification Conference & Expo (ITEC), Chicago, IL, USA, 2020, 17-24, doi: 10.1109/ITEC48692.2020.9161597.

Copyright 2018 - 2023 Sanderman Publishing House