Performance and energy efficiency in electric vehicles using an induction motor through active disturbance rejection and optimal control strategies

ilustraciones (principalmente a color), diagramas, fotografías

Autores:: Quecan Herrera, Juan Sebastian

Tipo de recurso:

Fecha de publicación:: 2024

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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repository_id_str
dc.title.eng.fl_str_mv	Performance and energy efficiency in electric vehicles using an induction motor through active disturbance rejection and optimal control strategies
dc.title.translated.spa.fl_str_mv	Desempeño y eficiencia energética en vehículos eléctricos utilizando un motor de inducción a través de estrategias de rechazo activo de perturbaciones y control óptimo
title	Performance and energy efficiency in electric vehicles using an induction motor through active disturbance rejection and optimal control strategies
spellingShingle	Performance and energy efficiency in electric vehicles using an induction motor through active disturbance rejection and optimal control strategies 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Vehículos electrícos Motores eléctricos de inducción Control automático Máquinas eléctricas Electric vehicles Electric motors, induction Automatic control Electric machines Extended state observer Induction motor Active Disturbance Rejection Control Metaheuristic techniques Electric vehicles Optimization Motor de inducción Control por rechazo activo de perturbaciones Optimización Técnicas Metaheuristicas Vehículos Eléctricos Observador de estado extendido
title_short	Performance and energy efficiency in electric vehicles using an induction motor through active disturbance rejection and optimal control strategies
title_full	Performance and energy efficiency in electric vehicles using an induction motor through active disturbance rejection and optimal control strategies
title_fullStr	Performance and energy efficiency in electric vehicles using an induction motor through active disturbance rejection and optimal control strategies
title_full_unstemmed	Performance and energy efficiency in electric vehicles using an induction motor through active disturbance rejection and optimal control strategies
title_sort	Performance and energy efficiency in electric vehicles using an induction motor through active disturbance rejection and optimal control strategies
dc.creator.fl_str_mv	Quecan Herrera, Juan Sebastian
dc.contributor.advisor.none.fl_str_mv	Cortés Romero, John Alexander Neira-García, Jorge Enrique
dc.contributor.author.none.fl_str_mv	Quecan Herrera, Juan Sebastian
dc.contributor.researchgroup.spa.fl_str_mv	Electrical Machines and Drives
dc.contributor.orcid.spa.fl_str_mv	Quecan Herrera, Juan Sebastian [0009000823321487]
dc.contributor.cvlac.spa.fl_str_mv	Quecan Herrera, Juan Sebastian [0001993729]
dc.contributor.researchgate.spa.fl_str_mv	Quecan Herrera, Juan Sebastian [Juan_Quecan_Herrera]
dc.subject.ddc.spa.fl_str_mv	620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
topic	620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Vehículos electrícos Motores eléctricos de inducción Control automático Máquinas eléctricas Electric vehicles Electric motors, induction Automatic control Electric machines Extended state observer Induction motor Active Disturbance Rejection Control Metaheuristic techniques Electric vehicles Optimization Motor de inducción Control por rechazo activo de perturbaciones Optimización Técnicas Metaheuristicas Vehículos Eléctricos Observador de estado extendido
dc.subject.lemb.spa.fl_str_mv	Vehículos electrícos Motores eléctricos de inducción Control automático Máquinas eléctricas
dc.subject.lemb.eng.fl_str_mv	Electric vehicles Electric motors, induction Automatic control Electric machines
dc.subject.proposal.eng.fl_str_mv	Extended state observer Induction motor Active Disturbance Rejection Control Metaheuristic techniques Electric vehicles Optimization
dc.subject.proposal.spa.fl_str_mv	Motor de inducción Control por rechazo activo de perturbaciones Optimización Técnicas Metaheuristicas Vehículos Eléctricos Observador de estado extendido
description	ilustraciones (principalmente a color), diagramas, fotografías
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-07-02T21:04:28Z
dc.date.available.none.fl_str_mv	2024-07-02T21:04:28Z
dc.date.issued.none.fl_str_mv	2024
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/publishedVersion
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dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	publishedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/86353
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/86353 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
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Proceedings of 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2020, pages 1032–1035, 2020. Y. Xu, S. Huang, Z. Wang, Y. Ren, Z. Xie, J. Guo, and Z. Zhu. Optimization based on tabu search algorithm for optimal sizing of hybrid PV/energy storage system: Ef fects of tabu search parameters. Sustainable Energy Technologies and Assessments, 53(PC):102662, 2022. M. V. Wyk and J. Bekker. Application of metaheuristics in multi-product polymer production scheduling : A case study. Systems and Soft Computing, 5(October):200063, 2023. A. Vasile, I. C. Coropet,chi, D. M. Constantinescu, Sorohan, and C. R. Picu. Simulated Annealing Algorithms Used for Microstructural Design of Composites. 38th DanubiaAdria Symposium on Advances in Experimental Mechanics, DAS 2022, (xxxx), 2022. M. Swargiary, J. Dey, and T. K. Saha. Optimal speed control of induction motor based on Linear Quadratic Regulator theory. 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015, 4(2):1–6, 2016 M. Srinivas and L. M. Patnaik. Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms. IEEE Transactions on Systems, Man and Cybernetics, 24(4):656– 667, 1994 S. Siva Sathya and M. V. Radhika. Convergence of nomadic genetic algorithm on benchmark mathematical functions. Applied Soft Computing Journal, 13(5):2759–2766, 2013. . Sira-Ramirez, Hebertt Luviano-Juárez, M. Ramírez-Neria, and E. W. Zurita Bustamante. Active Disturbance Rejection Control of Dynamic Systems, volume 1. Mexico City, 1 edition, 2017. H. Sira-Ramirez, F. Gonzalez-Montanez, J. A. Cortes-Romero, and A. Luviano-Juarez. A robust linear field-oriented voltage control for the induction motor: Experimental results. IEEE Transactions on Industrial Electronics, 60(8):3025–3033, 2013. H. Sira-Ramírez and S. K. Agrawal. Differentially Flat Systems. Marcel Dekker, Inc., New York, 1 edition, 2004. H. Sira-Ramírez. Differentially Flat Systems. Marcel Dekker, Inc., 1 edition, 2004. Y. Shi and R. C. Eberhart. Empirical study of particle swarm optimization. Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999, 3:1945–1950, 1999. D . P. C. SEN. PRINCIPLES OF ELECTRIC MACHINES AND POWER ELEC TRONICS. John Wiley & Sons, Inc., 3 edition, 2013. L. Sean. Essentials of Metaheuristics: A Set of Undergraduate Lecture Notes. 2010. M. Sayed, S. M. Gharghory, and H. A. Kamal. Gain tuning PI controllers for boiler turbine unit using a new hybrid jump PSO. Journal of Electrical Systems and Information Technology, 2(1):99–110, 2015. R. Saidur. A review on electrical motors energy use and energy savings. Renewable and Sustainable Energy Reviews, 14(3):877–898, apr 2010. A. K. Sahoo and D. R. Mishra. Parametric optimization of response parameter of Nd YAG laser drilling for basalt-PTFE coated glass fibre using genetic algorithm. Journal of Engineering Research, (April), 2023. C. Rao and B. Yan. Study on the interactive influence between economic growth and environmental pollution. Environmental Science and Pollution Research, 27(31):39442– 39465, 2020. N. Patel and N. Padhiyar. Modified genetic algorithm using Box Complex method: Application to optimal control problems. Journal of Process Control, 26:35–50, 2015. G. Park, S. Lee, S. Jin, and S. Kwak. Integrated modeling and analysis of dynamics for electric vehicle powertrains. Expert Systems with Applications, 41(5):2595–2607, 2014. M. Nssibi, G. Manita, and O. Korbaa. Advances in nature-inspired metaheuristic optimization for feature selection problem: A comprehensive survey. Computer Science Review, 49:100559, 2023. J. E. Neira García. Control de un motor de inducción sin sensores de velocidad con rechazo activo de perturbaciones para aplicaciones en vehículos eléctricos. PhD thesis, Universidad Nacional de Colombia, 2022. J. Morris, W. Wang, T. Plaisted, C. J. Hansen, and A. V. Amirkhizi. Optimizing graded metamaterials via genetic algorithm to control energy transmission. International Journal of Mechanical Sciences, (June):108775, 2023 E. Mojica-nava. Optimización y Control en Grafos. 2020. mohamed Ismail, basem Elhady, and ahmed Bendary. Variable Voltage Control of Three-Phase Induction Motor for Energy Saving. ERJ. Engineering Research Journal, 44(4):377–383, 2021. Z. Lu, A. Martínez-Gavara, J. K. Hao, and X. Lai. Solution-based tabu search for the capacitated dispersion problem. Expert Systems with Applications, 223(March), 2023. C. Liu, F. Zhang, H. Zhang, Z. Shi, and H. Zhu. Optimization of assembly sequence of building components based on simulated annealing genetic algorithm. Alexandria Engineering Journal, 62:257–268, 2023. C. Liu, K. T. Chau, C. H. Lee, and Z. Song. A Critical Review of Advanced Elec tric Machines and Control Strategies for Electric Vehicles. Proceedings of the IEEE, 109(6):1004–1028, 2021. E. E. Kuruoglu, C. L. Kuo, and W. K. V. Chan. Sparse neural network optimization by Simulated Annealing. Franklin Open, 4(August):100037, 2023. M. Kohler, M. M. Vellasco, and R. Tanscheit. PSO+: A new particle swarm optimization algorithm for constrained problems. Applied Soft Computing, 85:105865, 2019. D. E. Kirk. Optimal Control Theory. An Introduction, 2004. A. Khatir, R. Capozucca, S. Khatir, E. Magagnini, B. Benaissa, C. Le Thanh, and M. Abdel Wahab. A new hybrid PSO-YUKI for double cracks identification in CFRP cantilever beam. Composite Structures, 311(March 2022):116803, 2023. Y. Jiang, H. Qian, Y. Chu, J. Liu, Z. Jiang, F. Dong, and L. Jia. Convergence analysis of ABC algorithm based on difference model. Applied Soft Computing, 146:110627, 2023. S. Janous, J. Talla, V. Smidl, and Z. Peroutka. Constrained LQR Control of Dual Induction Motor Single Inverter Drive. IEEE Transactions on Industrial Electronics, 68(7):5548–5558, 2021. M. A. Hannan, M. M. Hoque, A. Hussain, Y. Yusof, and P. J. Ker. State-of-the-Art and Energy Management System of Lithium-Ion Batteries in Electric Vehicle Applications: Issues and Recommendations. IEEE Access, 6:19362–19378, 2018. J. Han. From PID to active disturbance rejection control. IEEE Transactions on Industrial Electronics, 56(3):900–906, 2009. A. Haddoun, M. E. H. Benbouzid, D. Diallo, R. Abdessemed, J. Ghouili, and K. Srairi. A loss-minimization DTC scheme for EV induction motors. IEEE Transactions on Vehicular Technology, 56(1):81–88, 2007. C. Guo, C. Fu, R. Luo, and G. Yang. Energy-oriented car-following control for a front and rear-independent-drive electric vehicle platoon. Energy, 257:124732, 2022. Y. Gao. PID-based search algorithm: A novel metaheuristic algorithm based on PID algorithm. Expert Systems with Applications, 232(December 2022):120886, 2023. D. Fredette, C. Pavlich, and U. Ozguner. Development of a UDDS-comparable frame work for the assessment of connected and automated vehicle fuel saving techniques. IFAC-PapersOnLine, 28(15):306–312, 2015. F. J. Ferreira, G. Baoming, and A. T. De Almeida. Reliability and Operation of High Efficiency Induction Motors. IEEE Transactions on Industry Applications, 52(6):4628– 4637, 2016. R. C. Eberhart and Y. Shi. Particle swarm optimization: Developments, applications and resources. Proceedings of the IEEE Conference on Evolutionary Computation, ICEC, 1:81–86, 2001. C. Du, Z. Yin, Y. Zhang, J. Liu, X. Sun, and Y. Zhong. Research on Active Disturbance Rejection Control With Parameter Autotune Mechanism for Induction Motors Based on Adaptive Particle Swarm Optimization Algorithm With Dynamic Inertia Weight. IEEE Transactions on Power Electronics, 34(3):2841–2855, 2019. N. Di Cesare, D. Chamoret, and M. Domaszewski. A new hybrid PSO algorithm based on a stochastic Markov chain model. Advances in Engineering Software, 90:127–137, 2015. A. Demirören, S. Ekinci, B. Hekimoğlu, and D. Izci. Opposition-based artificial electric field algorithm and its application to FOPID controller design for unstable magnetic ball suspension system. Engineering Science and Technology, an International Journal, 24(2):469–479, 2021. J. Chiasson. Modeling and High-Performance Control of Electric Machines. John Wiley & Sons. Inc, New York, 2005. C. L. Chan and C. L. Chen. A cautious PSO with conditional random. Expert Systems with Applications, 42(8):4120–4125, 2015. C. C. Chan. The state of the art of electric, hybrid, and fuel cell vehicles. Proceedings of the IEEE, 95(4):704–718, 2007. S. Chakrabarty, R. S. Vishwakarma, and T. P. Selvam. A Simulated Annealing optimization technique to obtain uniform dose distribution in gamma irradiators. Radiation Physics and Chemistry, 209(March):110959, 2023. C. Cattaneo. Internal and external barriers to energy efficiency: which role for policy interventions? Energy Efficiency, 12(5):1293–1311, 2019. R. Carter, A. Cruden, and P. J. Hall. Optimizing for efficiency or battery life in a battery/supercapacitor electric vehicle. IEEE Transactions on Vehicular Technology, 61(4):1526–1533, 2012. J.-F. Camacho-Vallejo, C. Corpus, and J. G. Villegas. Metaheuristics for bilevel opti mization: A comprehensive review. Computers & Operations Research, (June):106410, 2023. S. L. Brunton, B. W. Brunton, J. L. Proctor, and J. N. Kutz. Koopman invariant subspaces and finite linear representations of nonlinear dynamical systems for control. PLoS ONE, 11(2), 2016. S. Boyd and L. Vandenberghe. Convex Optimization, volume 3. 2006. A. G. Boulanger, A. C. Chu, S. Maxx, and D. L. Waltz. Vehicle electrification: Status and issues. Proceedings of the IEEE, 99(6):1116–1138, 2011. J. Bertsimas, Dimitris; N. Tsitsiklis. Introduction to Linear Optimization. Number 1. 1997. I. Baboselac, T. Benšić, and Ž. Hederić. MatLab simulation model for dynamic mode of the Lithium-Ion batteries to power the EV. Tehnički glasnik, 11(1-2):7–13, 2017. J. A.Pétrowski and P. E.Taillard. Metaheuristics for Hard Optimization. 2006. R. Antonello, F. Tinazzi, and M. Zigliotto. Energy efficiency measurements in IM: The non-trivial application of the norm IEC 60034-2-3:2013. Proceedings - 2015 IEEE Workshop on Electrical Machines Design, Control and Diagnosis, WEMDCD 2015, pages 248–253, 2015. S. Aguilar. Metodología de control óptimo para sistemas no lineales diferencialmente planos basado en control por rechazo activo de perturbaciones (ADRC) y control predictivo basado en modelo (MPC). PhD thesis, Universidad Nacional de Colombia, 2022.
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dc.rights.license.spa.fl_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ingeniería - Maestría en Ingeniería - Automatización Industrial
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Cortés Romero, John Alexanderd4c4ad5497c404645297a4b48010bf01600Neira-García, Jorge Enrique844831159ab0bbd7ec0b772d74690e28600Quecan Herrera, Juan Sebastian21a9ec35c313784e2b06c215322b46b3Electrical Machines and DrivesQuecan Herrera, Juan Sebastian [0009000823321487]Quecan Herrera, Juan Sebastian [0001993729]Quecan Herrera, Juan Sebastian [Juan_Quecan_Herrera]2024-07-02T21:04:28Z2024-07-02T21:04:28Z2024https://repositorio.unal.edu.co/handle/unal/86353Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones (principalmente a color), diagramas, fotografíasCurrently, energy efficiency holds significant importance in society and its energetic transition proposals, including vehicular technology topics. Regarding electric vehicles, a critical challenge lies in addressing autonomy issues. To tackle this concern, it becomes imperative to optimize various components and associated strategies for these vehicles. A critical component is the electric motor, and induction motors are popular for their cost-effectiveness and well-established techniques. Despite its advantages, the induction motor also experiences intrinsic losses, demanding performance enhancement that can be accomplished with control strategies. This research adopts a modified active disturbance rejection control approach to address the uncertainties and complex dynamics, along with optimal control concepts for the efficiency requirements of the induction motor. The modification involves including a disturbance rejection weight, developing a weighted cost function, and tuning all the controller parameters with metaheuristic techniques. A comparative analysis of the optimization approaches, considering the disturbance rejection, and its weighted version is conducted. The modified ADRC effectively reduced the cost function value when compared to the classic ADRC approach. The study's findings are validated through experimentation with an induction motor and a DC generator simulating electric vehicle conditions. This suggests that the proposed control strategy, rooted in partial disturbance rejection within the ADRC scheme, can deliver superior performance based on a cost function, although increasing the complexity for the parameter tuning (Texto tomado de la fuente).Actualmente, la eficiencia energética cobra gran importancia en la sociedad y sus propuestas de transición energética, incluyendo los temas de tecnología vehicular. En cuanto a los vehículos eléctricos, un desafío crítico reside en abordar las cuestiones de autonomía. Para abordar esta preocupación, se vuelve imperativo optimizar varios componentes y estrategias asociadas para estos vehículos. Un componente crítico es el motor eléctrico, y los motores de inducción son ampliamente usados por su rentabilidad y sus técnicas bien establecidas. A pesar de sus ventajas, el motor de inducción también experimenta pérdidas intrínsecas, lo que exige una mejora del rendimiento que se puede lograr con estrategias de control. Esta investigación adopta un enfoque modificado de control basado en rechazo activo de perturbaciones para abordar las incertidumbres y la dinámica compleja, junto con conceptos de control óptimos para los requisitos de eficiencia del motor de inducción. La modificación implica incluir una ponderación en el rechazo de perturbaciones, desarrollar una función de costo ponderada y ajustar todos los parámetros del controlador considerando técnicas metaheurísticas. Se realiza un análisis comparativo de los enfoques de optimización considerando el rechazo de perturbaciones y su versión ponderada. El ADRC modificado redujo efectivamente el valor de la función de costos en comparación con el enfoque ADRC clásico. Los hallazgos del estudio se validan mediante la experimentación con un motor de inducción y un generador de DC que simula las condiciones de un vehículo eléctrico. Esto sugiere que la estrategia de control propuesta, basada en el rechazo parcial de perturbaciones dentro del esquema ADRC, puede ofrecer un rendimiento superior basado en una función de costos, aunque aumenta la complejidad para el ajuste de los parámetros (Texto tomado de la fuente).MaestríaMagíster en Ingeniería - Automatización IndustrialTeoría y aplicación de ControlIngeniería Mecánica y Mecatrónica.Sede Bogotáxvi, 107 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Automatización IndustrialFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaVehículos electrícosMotores eléctricos de inducciónControl automáticoMáquinas eléctricasElectric vehiclesElectric motors, inductionAutomatic controlElectric machinesExtended state observerInduction motorActive Disturbance Rejection ControlMetaheuristic techniquesElectric vehiclesOptimizationMotor de inducciónControl por rechazo activo de perturbacionesOptimizaciónTécnicas MetaheuristicasVehículos EléctricosObservador de estado extendidoPerformance and energy efficiency in electric vehicles using an induction motor through active disturbance rejection and optimal control strategiesDesempeño y eficiencia energética en vehículos eléctricos utilizando un motor de inducción a través de estrategias de rechazo activo de perturbaciones y control óptimoTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/TMD. Yu and X. He. A bibliometric study for DEA applied to energy efficiency: Trends and future challenges. Applied Energy, 268(March):115048, 2020C. Yu, N. Lahrichi, and A. Matta. Optimal budget allocation policy for tabu search in stochastic simulation optimization. Computers and Operations Research, 150(September 2022):106046, 2023.Z. Yin, C. Du, J. Liu, X. Sun, and Y. Zhong. Research on Autodisturbance-Rejection Control of Induction Motors Based on an Ant Colony Optimization Algorithm. IEEE Transactions on Industrial Electronics, 65(4):3077–3094, 2018.Z. Yang, F. Shang, I. P. Brown, and M. Krishnamurthy. Comparative study of interior permanent magnet, induction, and switched reluctance motor drives for EV and HEV applications. IEEE Transactions on Transportation Electrification, 1(3):245–254, 2015Z. Yan and Y. Zhou. Application to Optimal Control of Brushless DC Motor with ADRC Based on Genetic Algorithm. Proceedings of 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2020, pages 1032–1035, 2020.Y. Xu, S. Huang, Z. Wang, Y. Ren, Z. Xie, J. Guo, and Z. Zhu. Optimization based on tabu search algorithm for optimal sizing of hybrid PV/energy storage system: Ef fects of tabu search parameters. Sustainable Energy Technologies and Assessments, 53(PC):102662, 2022.M. V. Wyk and J. Bekker. Application of metaheuristics in multi-product polymer production scheduling : A case study. Systems and Soft Computing, 5(October):200063, 2023.A. Vasile, I. C. Coropet,chi, D. M. Constantinescu, Sorohan, and C. R. Picu. Simulated Annealing Algorithms Used for Microstructural Design of Composites. 38th DanubiaAdria Symposium on Advances in Experimental Mechanics, DAS 2022, (xxxx), 2022.M. Swargiary, J. Dey, and T. K. Saha. Optimal speed control of induction motor based on Linear Quadratic Regulator theory. 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015, 4(2):1–6, 2016M. Srinivas and L. M. Patnaik. Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms. IEEE Transactions on Systems, Man and Cybernetics, 24(4):656– 667, 1994S. Siva Sathya and M. V. Radhika. Convergence of nomadic genetic algorithm on benchmark mathematical functions. Applied Soft Computing Journal, 13(5):2759–2766, 2013.. Sira-Ramirez, Hebertt Luviano-Juárez, M. Ramírez-Neria, and E. W. Zurita Bustamante. Active Disturbance Rejection Control of Dynamic Systems, volume 1. Mexico City, 1 edition, 2017.H. Sira-Ramirez, F. Gonzalez-Montanez, J. A. Cortes-Romero, and A. Luviano-Juarez. A robust linear field-oriented voltage control for the induction motor: Experimental results. IEEE Transactions on Industrial Electronics, 60(8):3025–3033, 2013.H. Sira-Ramírez and S. K. Agrawal. Differentially Flat Systems. Marcel Dekker, Inc., New York, 1 edition, 2004.H. Sira-Ramírez. Differentially Flat Systems. Marcel Dekker, Inc., 1 edition, 2004.Y. Shi and R. C. Eberhart. Empirical study of particle swarm optimization. Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999, 3:1945–1950, 1999.D . P. C. SEN. PRINCIPLES OF ELECTRIC MACHINES AND POWER ELEC TRONICS. John Wiley & Sons, Inc., 3 edition, 2013.L. Sean. Essentials of Metaheuristics: A Set of Undergraduate Lecture Notes. 2010.M. Sayed, S. M. Gharghory, and H. A. Kamal. Gain tuning PI controllers for boiler turbine unit using a new hybrid jump PSO. Journal of Electrical Systems and Information Technology, 2(1):99–110, 2015.R. Saidur. A review on electrical motors energy use and energy savings. Renewable and Sustainable Energy Reviews, 14(3):877–898, apr 2010.A. K. Sahoo and D. R. Mishra. 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PhD thesis, Universidad Nacional de Colombia, 2022.BibliotecariosEstudiantesInvestigadoresMaestrosProveedores de ayuda financiera para estudiantesPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86353/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53ORIGINAL1072715942.2024.pdf1072715942.2024.pdfTesis de Maestría en Ingeniería-Automatización Industrial - Performance and energy efficiencyapplication/pdf3692210https://repositorio.unal.edu.co/bitstream/unal/86353/4/1072715942.2024.pdfb9aa2e8b34da8a24f63f0e9c14e307a6MD54THUMBNAIL1072715942.2024.pdf.jpg1072715942.2024.pdf.jpgGenerated Thumbnailimage/jpeg4997https://repositorio.unal.edu.co/bitstream/unal/86353/5/1072715942.2024.pdf.jpga2c4f25296aec6d81ee8cf8b3e03fff5MD55unal/86353oai:repositorio.unal.edu.co:unal/863532024-07-02 23:05:02.141Repositorio Institucional Universidad Nacional de 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Performance and energy efficiency in electric vehicles using an induction motor through active disturbance rejection and optimal control strategies

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