Modelo de degradación de batería de li-ion para aplicaciones en microrredes eléctricas

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dc.contributor.advisorTrujillo Rodríguez, César Leonardo
dc.contributor.advisorSantamaría Piedrahita, Francisco
dc.contributor.authorSantos Leon, Andres Ignacio
dc.contributor.orcidTrujillo Rodríguez, César Leonardo [0000-0002-0985-1472]
dc.contributor.orcidSantamaría Piedrahita, Francisco [0000-0002-0391-4508]
dc.date.accessioned2025-03-17T15:03:02Z
dc.date.available2025-03-17T15:03:02Z
dc.date.created2024-12-05
dc.descriptionLas fuentes no convencionales de energía, como la energía fotovoltaica y la energía eólica, constituyen el inicio de la transformación del sistema energético tradicional, al pasar de una generación centralizada y alejada de los centros de consumo, a un sistema con generación distribuida, la cual está más cerca de las cargas finales, y una matriz energética más diversa que reduce la dependencia a unas pocas fuentes de energía. Una de las formas en las que las fuentes no convencionales de energía se pueden integrar al sistema tradicional de generación - transmisión/distribución de energía eléctrica son las microrredes eléctricas. Las microrredes eléctricas son sistemas controlables y gestionables donde las fuentes no convencionales de energía, las cargas y los sistemas de almacenamiento interactúan a través de interfaces conformadas por dispositivos de electrónica de potencia. Los sistemas de almacenamiento en las microrredes eléctricas son clave para el balance entre la generación y consumo de energía, la estabilidad, la autonomía del sistema, el almacenamiento y posterior venta de excedentes de generación, entre otros. Según la tecnología utilizada para almacenar la energía, los sistemas de almacenamiento pueden dividirse en eléctricos, mecánicos, electroquímicos y químicos. Las baterías son una forma de sistema de almacenamiento electroquímico, existen diferentes tipos de baterías utilizadas en microrredes eléctricas, como por ejemplo beterías de plomo ácido y baterías de ion de litio (Li-ion), estas últimas ofrecen características de alta densidad de potencia y energía en comparación con otros tipos de baterías. Uno de los grandes retos de los sistemas electroquímicos es la degradación que sufren con el tiempo, dicha variable impacta directamente en las microrredes eléctricas en términos de confiabilidad y estabilidad. En este proyecto de investigación se propone un modelo de degradación de batería de Li-ion en el contexto de una microrred eléctrica, el modelo está basado en una red neuronal NARX la cual se entrenó con un grupo de datos disponible en la literatura, el miso grupo de datos permite determinar los parámetros circuitales de un modelo circuital de batería que sirve de conexión entre el modelo de microrred y el modelo de degradación. El modelo de degradación de batería de Li-ion propuesto es evaluó encontrando un error menor a 2%, considerando el MAE (error absoluto medio) y RMSE (raíz del error cuadrático medio).
dc.description.abstractNon-conventional sources of energy, such as photovoltaic energy and wind energy, constitute the beginning of the transformation of the traditional energy system, moving from centralized generation away from consumption centers, to a system with distributed generation, which is closer to final loads, and a more diverse energy matrix that reduces dependence on a few energy sources. One of the ways in which non-conventional sources of energy can be integrated into the traditional system of generation-transmission/distribution of electrical energy are electrical microgrids. Electrical microgrids are controllable and manageable systems where non-conventional energy sources, loads and storage systems interact through interfaces made up of power electronics devices. Storage systems in electricity microgrids are key to the balance between energy generation and consumption, stability, system autonomy, storage and subsequent sale of surplus generation, among others. Depending on the technology used to store the energy, storage systems can be divided into electrical, mechanical, electrochemical, and chemical. Batteries are a form of electrochemical storage system, there are different types of batteries used in electrical microgrids, such as lead acid batteries and lithium-ion (Li-ion) batteries, the latter offering high power and energy density characteristics compared to other types of batteries. One of the great challenges of electrochemical systems is the degradation they suffer over time, this variable directly impacts electrical microgrids in terms of reliability and stability. In this research project a Li-ion battery degradation model is proposed in the context of an electrical microgrid, the model is based on a NARX neural network which was trained with a set of data available in the literature, the same set of data allows to determine the circuit parameters of a battery circuit model that serves as a connection between the microgrid model and the degradation model. The proposed Li-ion battery degradation model was evaluated by finding an error of less than 2%, considering the MAE (mean absolute error) and RMSE (root mean square error).
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dc.identifier.urihttp://hdl.handle.net/11349/93725
dc.relation.referencesAbdali, A., Noroozian, R., & Mazlumi, K. (2019). Simultaneous control and protection schemes for DC multi microgrids systems. Electrical Power and Energy Systems, 104, 230-245. https://doi.org/10.1016/j.ijepes.2018.06.054
dc.relation.referencesAnseán, D., García, V. M., González, M., Blanco-Viejo, C., Viera, J. C., Pulido, Y. F., & Sánchez, L. (2019). Lithium-Ion Battery Degradation Indicators Via Incremental Capacity Analysis. IEEE Transactions on Industry Applications, 55(3), 2992-3002. https://doi.org/10.1109/TIA.2019.2891213
dc.relation.referencesAnvari-Moghaddam, A., Dragicevic, T., Vasquez, J. C., & Guerrero, J. M. (2015). Optimal utilization of microgrids supplemented with battery energy storage systems in grid support applications. 2015 IEEE 1st International Conference on Direct Current Microgrids, ICDCM 2015, 57-61. https://doi.org/10.1109/ICDCM.2015.7152010
dc.relation.referencesB Saha & K Goebel. (2007). Battery Data Set. NASA Ames Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA. https://www.nasa.gov/content/prognostics-center-of-excellence-data-set-repository
dc.relation.referencesBamati, S., & Chaoui, H. (2022). Lithium-ion batteries long horizon health prognostic using machine learning. IEEE Transactions on Energy Conversion, 37(2), 1176-1186. https://doi.org/10.1109/TEC.2021.3111525
dc.relation.referencesBao, M., Liu, D., Wu, Y., Wang, Z., Yang, J., Lan, L., & Ru, Q. (2024). Interpretable machine learning prediction for li-ion battery’s state of health based on electrochemical impedance spectroscopy and temporal features. Electrochimica Acta, 494, 144449. https://doi.org/10.1016/j.electacta.2024.144449
dc.relation.referencesBoicea, V. A. (2014). Energy Storage Technologies: The Past and the Present. Proceedings of the IEEE, 102(11), 1777-1794. https://doi.org/10.1109/JPROC.2014.2359545
dc.relation.referencesBustos, R., Gadsden, S. A., Biglarbegian, M., AlShabi, M., & Mahmud, S. (2024). Battery State of Health Estimation Using the Sliding Interacting Multiple Model Strategy. Energies, 17(2), Article 2. https://doi.org/10.3390/en17020536
dc.relation.referencesCui, Z., Wang, C., Gao, X., & Tian, S. (2021). State of health estimation for lithium-ion battery based on the coupling-loop nonlinear autoregressive with exogenous inputs neural network. Electrochimica Acta, 393, 139047. https://doi.org/10.1016/j.electacta.2021.139047
dc.relation.referencesDing, Z., Wu, W., & Leung, M. K. H. (2022). On the rational development of advanced thermochemical thermal batteries for short-term and long-term energy storage. Renewable and Sustainable Energy Reviews, 164, 112557. https://doi.org/10.1016/j.rser.2022.112557
dc.relation.referencesDoyle, M., Fuller, T. F., & Newman, J. (1993). Modeling of Galvanostatic Charge and Discharge of the Lithium/Polymer/Insertion Cell. Journal of The Electrochemical Society, 140(6), 1526-1533. https://doi.org/10.1149/1.2221597
dc.relation.referencesDragičević, T., Lu, X., Vasquez, J. C., & Guerrero, J. M. (2016). DC Microgrids—Part I: A Review of Control Strategies and Stabilization Techniques. IEEE Transactions on Power Electronics, 31(7), 4876-4891. https://doi.org/10.1109/TPEL.2015.2478859
dc.relation.referencesEberleh, B., & Raiser, S. (2017). Lithium Batteries for Commercial Vehicles The Workhorses among Energy Storage Units. ATZ worldwide, 119(11), 26-31. https://doi.org/10.1007/s38311-017-0118-9
dc.relation.referencesEcker, M., Gerschler, J. B., Vogel, J., Käbitz, S., Hust, F., Dechent, P., & Sauer, D. U. (2012). Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data. Journal of Power Sources, 215, 248-257. https://doi.org/10.1016/j.jpowsour.2012.05.012
dc.relation.referencesEleftheriadis, P., Gangi, M., Leva, S., Rey, A. V., Groppo, E., & Grande, L. (2024). Comparative study of machine learning techniques for the state of health estimation of Li-Ion batteries. Electric Power Systems Research, 235, 110889. https://doi.org/10.1016/j.epsr.2024.110889
dc.relation.referencesEltoumi, F., Badji, A., Becherif, M., & Ramadan, H. S. (2018). Experimental identification using equivalent circuit model for lithium-ion battery. International Journal of Emerging Electric Power Systems, 19(3), 20170210. https://doi.org/doi:10.1515/ijeeps-2017-0210
dc.relation.referencesEnair. (s. f.-a). Aeronogenerador E30PRO - La última tecnología. Recuperado 23 de mayo de 2023, de https://www.enair.es/es/aerogeneradores/e30pro
dc.relation.referencesEnair. (s. f.-b). Estimación de producción eólica y solar. Recuperado 23 de mayo de 2023, de https://www.enair.es/es/app
dc.relation.referencesFan, G., Pan, K., & Canova, M. (2015). A comparison of model order reduction techniques for electrochemical characterization of Lithium-ion batteries. 2015 54th IEEE Conference on Decision and Control (CDC), 3922-3931. https://doi.org/10.1109/CDC.2015.7402829
dc.relation.referencesFerraro, P., Crisostomi, E., & Milano, F. (2018). Evaluation of the Impact of the Size of Storage Devices in Grid-Connected Microgrids. 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe), 1-5. https://doi.org/10.1109/EEEIC.2018.8494377
dc.relation.referencesFotouhi, A., Auger, D. J., Propp, K., & Longo, S. (2018). Accuracy versus simplicity in online battery model identification. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(2), 195-206. https://doi.org/10.1109/TSMC.2016.2599281
dc.relation.referencesGao, D. W. (2015a). Chapter 1—Basic Concepts and Control Architecture of Microgrids. En D. W. Gao (Ed.), Energy Storage for Sustainable Microgrid (pp. 1-34). Academic Press. https://doi.org/10.1016/B978-0-12-803374-6.00001-9
dc.relation.referencesGao, D. W. (2015b). Chapter 2—Applications of ESS in Renewable Energy Microgrids. En D. W. Gao (Ed.), Energy Storage for Sustainable Microgrid (pp. 35-77). Academic Press. https://doi.org/10.1016/B978-0-12-803374-6.00002-0
dc.relation.referencesGao, Y., Jiang, J., Zhang, C., Zhang, W., Ma, Z., & Jiang, Y. (2017). Lithium-ion battery aging mechanisms and life model under different charging stresses. Journal of Power Sources, 356, 103-114. https://doi.org/10.1016/j.jpowsour.2017.04.084
dc.relation.referencesGou, B., Xu, Y., & Feng, X. (2020a). State-of-health estimation and remaining-useful-life prediction for lithium-ion battery using a hybrid data-driven method. IEEE Transactions on Vehicular Technology, 69(10), 10854-10867. https://doi.org/10.1109/TVT.2020.3014932
dc.relation.referencesGou, B., Xu, Y., & Feng, X. (2020b). State-of-health estimation and remaining-useful-life prediction for lithium-ion battery using a hybrid data-driven method. IEEE Transactions on Vehicular Technology, 69(10), 10854-10867. https://doi.org/10.1109/TVT.2020.3014932
dc.relation.referencesHadjipaschalis, I., Poullikkas, A., & Efthimiou, V. (2009). Overview of current and future energy storage technologies for electric power applications. Renewable and Sustainable Energy Reviews, 13(6), 1513-1522. https://doi.org/10.1016/j.rser.2008.09.028
dc.relation.referencesHajiaghasi, S., Salemnia, A., & Hamzeh, M. (2019). Hybrid energy storage system for microgrids applications: A review. Journal of Energy Storage, 21, 543-570. https://doi.org/10.1016/j.est.2018.12.017
dc.relation.referencesHe, R., He, Y., Xie, W., Guo, B., & Yang, S. (2023). Comparative analysis for commercial li-ion batteries degradation using the distribution of relaxation time method based on electrochemical impedance spectroscopy. Energy, 263, 125972. https://doi.org/10.1016/j.energy.2022.125972
dc.relation.referencesImamura, W., Eda, N., Tanaka, K., Horie, H., & Akimoto, H. (2011). Capacity fade model of Lithium-ion batteries for Practical use. IMPROVING COMPLEX SYSTEMS TODAY, 441-448. https://doi.org/10.1007/978-0-85729-799-0_52
dc.relation.referencesJafari, M., Khan, K., & Gauchia, L. (2018). Deterministic models of Li-ion battery aging: It is a matter of scale. Journal of Energy Storage, 20, 67-77. https://doi.org/10.1016/j.est.2018.09.002
dc.relation.referencesKhaleghi, S., Karimi, D., Beheshti, S. H., Hosen, Md. S., Behi, H., Berecibar, M., & Van Mierlo, J. (2021). Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network. Applied Energy, 282, 116159. https://doi.org/10.1016/j.apenergy.2020.116159
dc.relation.referencesKhodadoost Arani, A. A., B. Gharehpetian, G., & Abedi, M. (2019). Review on Energy Storage Systems Control Methods in Microgrids. International Journal of Electrical Power and Energy Systems, 107(January), 745-757. https://doi.org/10.1016/j.ijepes.2018.12.040
dc.relation.referencesKim, T., & Qiao, W. (2011). A hybrid battery model capable of capturing dynamic circuit characteristics and nonlinear capacity effects. IEEE Transactions on Energy Conversion, 26(4), 1172-1180. https://doi.org/10.1109/TEC.2011.2167014
dc.relation.referencesKomarnicki, P., Lombardi, P., & Styczynski, Z. (2017). Electric Energy Storage System. En Electric Energy Storage Systems: Flexibility Options for Smart Grids (pp. 37-95). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-53275-1_2
dc.relation.referencesKumar, J., Agarwal, A., & Agarwal, V. (2019). A review on overall control of DC microgrids. Journal of Energy Storage, 21, 113-138. https://doi.org/10.1016/j.est.2018.11.013
dc.relation.referencesLam, F., Allam, A., Joe, W. T., Choi, Y., & Onori, S. (2021). Offline multiobjective optimization for fast charging and reduced degradation in lithium-ion battery cells using electrochemical dynamics. IEEE Control Systems Letters, 5(6), 2066-2071. https://doi.org/10.1109/LCSYS.2020.3046378
dc.relation.referencesLam, L., & Bauer, P. (2013). Practical Capacity Fading Model for Li-Ion Battery Cells in Electric Vehicles. IEEE Transactions on Power Electronics, 28(12), 5910-5918. https://doi.org/10.1109/TPEL.2012.2235083
dc.relation.referencesLi, C., Cui, N., Cui, Z., Wang, C., & Zhang, C. (2022). Novel equivalent circuit model for high-energy lithium-ion batteries considering the effect of nonlinear solid-phase diffusion. Journal of Power Sources, 523, 230993. https://doi.org/10.1016/j.jpowsour.2022.230993
dc.relation.referencesLi, J.-F., Lin, Y.-S., Lin, C.-H., & Chen, K.-C. (2017). Three-Parameter Modeling of Nonlinear Capacity Fade for Lithium-Ion Batteries at Various Cycling Conditions. Journal of The Electrochemical Society, 164(12), A2767--A2776.
dc.relation.referencesLiu, K., Gao, Y., Zhu, C., Li, K., Fei, M., Peng, C., Zhang, X., & Han, Q.-L. (2022). Electrochemical modeling and parameterization towards control-oriented management of lithium-ion batteries. Control Engineering Practice, 124, 105176. https://doi.org/10.1016/j.conengprac.2022.105176
dc.relation.referencesLiu, K., Hu, X., Yang, Z., Xie, Y., & Feng, S. (2019). Lithium-ion battery charging management considering economic costs of electrical energy loss and battery degradation. Energy Conversion and Management, 195, 167-179. https://doi.org/10.1016/j.enconman.2019.04.065
dc.relation.referencesLong, B., Li, X., Gao, X., & Liu, Z. (2019). Prognostics Comparison of Lithium-Ion Battery Based on the Shallow and Deep Neural Networks Model. ENERGIES, 12(17). https://doi.org/10.3390/en12173271
dc.relation.referencesLucu, M., Martinez-Laserna, E., Gandiaga, I., & Camblong, H. (2018). A critical review on self-adaptive Li-ion battery ageing models. Journal of Power Sources, 401, 85-101. https://doi.org/10.1016/j.jpowsour.2018.08.064
dc.relation.referencesMa, Z., Wang, X., Davenport, P., Gifford, J., Cook, K., Martinek, J., Schirck, J., Morris, A., Lambert, M., & Zhang, R. (2022). System and component development for long-duration energy storage using particle thermal energy storage. Applied Thermal Engineering, 216, 119078. https://doi.org/10.1016/j.applthermaleng.2022.119078
dc.relation.referencesMarongiu, A., Roscher, M., & Sauer, D. U. (2015). Influence of the vehicle-to-grid strategy on the aging behavior of lithium battery electric vehicles. Applied Energy, 137, 899-912. https://doi.org/10.1016/j.apenergy.2014.06.063
dc.relation.referencesMathworks. (s. f.-a). Gensim—Generar un bloque de Simulink para la simulación de redes neuronales superficiales—MATLAB - MathWorks América Latina. Recuperado 2 de noviembre de 2024, de https://la.mathworks.com/help/deeplearning/ref/network.gensim.html#mw_abb93ea8-1a7a-409c-bd3e-7859253adcf1
dc.relation.referencesMathworks. (s. f.-b). Lsqcurvefit—Solve nonlinear curve-fitting (data-fitting) problems in least-squares sense—MATLAB. Recuperado 15 de octubre de 2024, de https://www.mathworks.com/help/optim/ug/lsqcurvefit.html
dc.relation.referencesNASA. (s. f.). POWER | Data Access Viewer. Recuperado 23 de mayo de 2023, de https://power.larc.nasa.gov/data-access-viewer/
dc.relation.referencesNing, G., White, R. E., & Popov, B. N. (2006). A generalized cycle life model of rechargeable Li-ion batteries. Electrochimica Acta, 51(10), 2012-2022. https://doi.org/10.1016/j.electacta.2005.06.033
dc.relation.referencesPadmanaban, S., Sharmeela, C., Sivaraman, P., & Holm-Nielsen, J. B. (Eds.). (2022). Residential microgrids and rural electrifications. Academic Press.
dc.relation.referencesPetit, M., Prada, E., & Sauvant-Moynot, V. (2016). Development of an empirical aging model for Li-ion batteries and application to assess the impact of Vehicle-to-Grid strategies on battery lifetime. Applied Energy, 172, 398-407. https://doi.org/10.1016/j.apenergy.2016.03.119
dc.relation.referencesPowerTech-Systems-SAS. (s. f.-a). Lithium-Ion Battery 48V - 105Ah—5.38kWh—PowerBrick+ / LFP battery. Recuperado 23 de mayo de 2023, de https://www.powertechsystems.eu/home/products/48v-lithium-ion-battery-pack/48v-105ah-5-38kwh-lithium-ion-battery-pack-powerbrick/
dc.relation.referencesPowerTech-Systems-SAS. (s. f.-b). PowerBrick+: 12V Lithium Iron Phosphate Battery | 12V LiFePO4 Battery. Recuperado 23 de mayo de 2023, de https://www.powertechsystems.eu/home/products/12v-lithium-battery-pack-powerbrick/
dc.relation.referencesPurohit, I., Sundaray, S., & Motiwala, S. (2018). Familiarization with Energy Storage Technologies and Their Relevance for Renewable Energy (RE) Based Power Generation. En A. Sharma, A. Shukla, & L. Aye (Eds.), Low Carbon Energy Supply: Trends, Technology, Management (pp. 273-326). Springer Singapore. https://doi.org/10.1007/978-981-10-7326-7_14
dc.relation.referencesSabry, A. H., Shallal, A. H., Hameed, H. S., & Ker, P. J. (2020). Compatibility of household appliances with DC microgrid for PV systems. Heliyon, 6(12), e05699. https://doi.org/10.1016/j.heliyon.2020.e05699
dc.relation.referencesSapunkov, O., Pande, V., Khetan, A., Choomwattana, C., & Viswanathan, V. (2015). Quantifying the promise of ‘beyond’ Li–ion batteries. Translational Materials Research, 2(4), 045002. https://doi.org/10.1088/2053-1613/2/4/045002
dc.relation.referencesSchmalstieg, J., Käbitz, S., Ecker, M., & Sauer, D. U. (2014). A holistic aging model for Li(NiMnCo)O2 based 18650 lithium-ion batteries. Journal of Power Sources, 257, 325-334. https://doi.org/10.1016/j.jpowsour.2014.02.012
dc.relation.referencesSechilariu, M., & Locment, F. (2016). General Introduction. En M. Sechilariu & F. Locment (Eds.), Urban DC Microgrid (pp. xxi-xxx). Butterworth-Heinemann. https://doi.org/10.1016/B978-0-12-803736-2.02001-6
dc.relation.referencesShah, A. A., Yu, F., Xing, W. W., & Leung, P. K. (2022). Machine learning for predicting fuel cell and battery polarisation and charge–discharge curves. Energy Reports, 8, 4811-4821. https://doi.org/10.1016/j.egyr.2022.03.191
dc.relation.referencesShen, J., Dusmez, S., & Khaligh, A. (2014). Optimization of Sizing and Battery Cycle Life in Battery/Ultracapacitor Hybrid Energy Storage Systems for Electric Vehicle Applications. IEEE Transactions on Industrial Informatics, 10(4), 2112-2121. https://doi.org/10.1109/TII.2014.2334233
dc.relation.referencesSiva Suriya Narayanan, S., & Thangavel, S. (2022). Machine learning-based model development for battery state of charge–open circuit voltage relationship using regression techniques. Journal of Energy Storage, 49, 104098. https://doi.org/10.1016/j.est.2022.104098
dc.relation.referencesSong, H., Cao, Z., Chen, X., Lu, H., Jia, M., Zhang, Z., Lai, Y., Li, J., & Liu, Y. (2013). Capacity fade of LiFePO4/graphite cell at elevated temperature. Journal of Solid State Electrochemistry, 17(3), 599-605. https://doi.org/10.1007/s10008-012-1893-2
dc.relation.referencesSun, B., He, X., Zhang, W., Ruan, H., Su, X., & Jiang, J. (2021). Study of Parameters Identification Method of Li-Ion Battery Model for EV Power Profile Based on Transient Characteristics Data. IEEE Transactions on Intelligent Transportation Systems, 22(1), 661-672. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2020.3032447
dc.relation.referencesSuri, G., & Onori, S. (2016). A control-oriented cycle-life model for hybrid electric vehicle lithium-ion batteries. Energy, 96, 644-653. https://doi.org/10.1016/j.energy.2015.11.075
dc.relation.referencesSwierczynski, M., Stroe, D. I., Stan, A. I., Teodorescu, R., & Vikelgaard, H. (2013). Selection and impedance based model of a lithium ion battery technology for integration with virtual power plant. 2013 15th European Conference on Power Electronics and Applications (EPE), 1-10. https://doi.org/10.1109/EPE.2013.6634755
dc.relation.referencesTan, K. M., Babu, T. S., Ramachandaramurthy, V. K., Kasinathan, P., Solanki, S. G., & Raveendran, S. K. (2021). Empowering smart grid: A comprehensive review of energy storage technology and application with renewable energy integration. Journal of Energy Storage, 39, 102591. https://doi.org/10.1016/j.est.2021.102591
dc.relation.referencesTanim, T. R., & Rahn, C. D. (2015). Aging formula for lithium ion batteries with solid electrolyte interphase layer growth. Journal of Power Sources, 294, 239-247. https://doi.org/10.1016/j.jpowsour.2015.06.014
dc.relation.referencesTian, Y., Li, X., Zhu, Y., & Xia, R. (2018). Optimal capacity allocation of multiple energy storage considering microgrid cost Optimal capacity allocation of multiple energy storage considering microgrid cost. 1074, 012126. https://doi.org/10.1088/1742-6596/1074/1/012126
dc.relation.referencesTrina Solar. (2017, febrero 19). Vertex S DE09.08. Trina Solar. https://www.trinasolar.com/en-glb/resources/downloads
dc.relation.referencesTu, H., Moura, S., Wang, Y., & Fang, H. (2023). Integrating physics-based modeling with machine learning for lithium-ion batteries. Applied Energy, 329, 120289. https://doi.org/10.1016/j.apenergy.2022.120289
dc.relation.referencesVega-Escobar, A.-M. (2018). Gestión de la energía eléctrica domiciliaria con base en la gestión activa de la demanda [Universidad Distrital Francisco José de Caldas - Biblioteca UDFJC]. http://repository.udistrital.edu.co/handle/11349/7587
dc.relation.referencesVetter, J., Novák, P., Wagner, M. R., Veit, C., Möller, K.-C., Besenhard, J. O., Winter, M., Wohlfahrt-Mehrens, M., Vogler, C., & Hammouche, A. (2005). Ageing mechanisms in lithium-ion batteries. Journal of Power Sources, 147(1), 269-281. https://doi.org/10.1016/j.jpowsour.2005.01.006
dc.relation.referencesWaag, W., Käbitz, S., & Sauer, D. U. (2013). Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application. Applied Energy, 102, 885-897. https://doi.org/10.1016/j.apenergy.2012.09.030
dc.relation.referencesWang, P., Dan, X., & Yang, Y. (2019). A multi-scale fusion prediction method for lithium-ion battery capacity based on ensemble empirical mode decomposition and nonlinear autoregressive neural networks. International Journal of Distributed Sensor Networks, 15(3), 1550147719839637. https://doi.org/10.1177/1550147719839637
dc.relation.referencesWang, Q., Mao, B., Stoliarov, S. I., & Sun, J. (2019). A review of lithium ion battery failure mechanisms and fire prevention strategies. Progress in Energy and Combustion Science, 73, 95-131. https://doi.org/10.1016/j.pecs.2019.03.002
dc.relation.referencesWelder, L., Stenzel, P., Ebersbach, N., Markewitz, P., Robinius, M., Emonts, B., & Stolten, D. (2019). Design and evaluation of hydrogen electricity reconversion pathways in national energy systems using spatially and temporally resolved energy system optimization. International Journal of Hydrogen Energy, 44(19), 9594-9607. https://doi.org/10.1016/j.ijhydene.2018.11.194
dc.relation.referencesXiong, X., Wang, Y., Li, K., & Chen, Z. (2023). State of health estimation for lithium-ion batteries using Gaussian process regression-based data reconstruction method during random charging process. Journal of Energy Storage, 72, 108390. https://doi.org/10.1016/j.est.2023.108390
dc.relation.referencesXu, B., Oudalov, A., Ulbig, A., Andersson, G., & Kirschen, D. S. (2018). Modeling of lithium-ion battery degradation for cell life assessment. IEEE Transactions on Smart Grid, 9(2), 1131-1140. https://doi.org/10.1109/TSG.2016.2578950
dc.relation.referencesYuan, M., Fu, Y., Mi, Y., Li, Z., & Wang, C. (2019). Hierarchical control of DC microgrid with dynamical load power sharing. https://doi.org/10.1016/j.apenergy.2019.01.081
dc.relation.referencesZhang, F., Meng, C., Yang, Y., Sun, C., Ji, C., Chen, Y., Wei, W., Qiu, H., & Yang, G. (2015). Advantages and challenges of DC microgrid for commercial building: A case study from Xiamen university DC microgrid. 2015 IEEE 1st International Conference on Direct Current Microgrids, ICDCM 2015, 355-358. https://doi.org/10.1109/ICDCM.2015.7152068
dc.relation.referencesZhang, Y., Wang, C.-Y., & Tang, X. (2011). Cycling degradation of an automotive LiFePO4 lithium-ion battery. Journal of Power Sources, 196(3), 1513-1520. https://doi.org/10.1016/j.jpowsour.2010.08.070
dc.rights.accesoAbierto (Texto Completo)
dc.rights.accessrightsOpenAccess
dc.subjectBatería de ion de litio
dc.subjectMicrorred
dc.subjectAprendizaje automático
dc.subjectEstimación de capacidad
dc.subjectEnvejecimiento
dc.subjectDegradación
dc.subject.keywordLi-ion battery
dc.subject.keywordMicrogrid
dc.subject.keywordMachine learning
dc.subject.keywordNeural network
dc.subject.keywordCapacity estimation
dc.subject.keywordAging
dc.subject.keywordDegradation
dc.subject.lembMaestría en Ingeniería - Énfasis en Ingeniería Electrónica -- Tesis y disertaciones académicas
dc.subject.lembSistemas eléctricos
dc.subject.lembBaterías de litio -- Almacenamiento de energía
dc.subject.lembNARX -- Redes neurales
dc.subject.lembIngeniería electrónica
dc.titleModelo de degradación de batería de li-ion para aplicaciones en microrredes eléctricas
dc.title.titleenglishLi-ion battery degradation model for microgrid applications
dc.typemasterThesis
dc.type.degreeProducción Académica

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