Machine Learning Based System for Detecting Battery State-of-Health
- Michelena, Álvaro
- Díaz-Longueira, Antonio
- Timiraos, Míriam
- Zayas-Gato, Francisco
- Quintián, Héctor
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Fernández, Natalia Prieto
1
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Alaiz-Moretón, Héctor
1
- Calvo-Rolle, José Luis
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García-Ordás, María Teresa
1
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1
Universidad de León
info
ISSN: 2367-3370, 2367-3389
ISBN: 9783031425288, 9783031425295
Argitalpen urtea: 2023
Orrialdeak: 165-173
Mota: Liburuko kapitulua
Erreferentzia bibliografikoak
- Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324/METRICS, https://link.springer.com/article/10.1023/A:1010933404324
- Caínzos López, V., et al.: Intelligent model for power cells state of charge forecasting in EV. Processes 10(7), 1406 (2022)
- Calvo-Rolle, J.L., Quintian-Pardo, H., Corchado, E., del Carmen Meizoso-López, M., García, R.F.: Simplified method based on an intelligent model to obtain the extinction angle of the current for a single-phase half wave controlled rectifier with resistive and inductive load. J. Appl. Log. 13(1), 37–47 (2015)
- Cortes, C., Vapnik, V., Saitta, L.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1007/BF00994018, https://link.springer.com/article/10.1007/BF00994018
- Energy, R.: Powering up: Global battery demand to surge by 2030, supply headaches on the horizon (2022)
- Fernandez-Serantes, L., Casteleiro-Roca, J., Calvo-Rolle, J.: Hybrid intelligent system for a half-bridge converter control and soft switching ensurement. Revista Iberoamericana de Automática e Informática industrial (2022)
- Fix, E., Hodges, J.L.: Discriminatory analysis - nonparametric discrimination: consistency properties. Int. Stat. Rev. 57(3), 238 (1989). https://doi.org/10.2307/1403797
- Jove, E., Casteleiro-Roca, J.L., Quintián, H., Zayas-Gato, F., Vercelli, G., Calvo-Rolle, J.L.: A one-class classifier based on a hybrid topology to detect faults in power cells. Logic J. IGPL 30(4), 679–694 (2022)
- LiFeBATT: Lifebatt x–1p 8ah 38123 cell. http://www.solarvan.co.uk/Life/LiFeBATT8Ah.pdf
- Martin, G., Rentsch, L., Höck, M., Bertau, M.: Lithium market research-global supply, future demand and price development. Energy Storage Mater. 6, 171–179 (2017)
- Martins, L.S., Guimarães, L.F., Junior, A.B.B., Tenório, J.A.S., Espinosa, D.C.R.: Electric car battery: an overview on global demand, recycling and future approaches towards sustainability. J. Environ. Manage. 295, 113,091 (2021)
- Michelena, A., Zayas-Gato, F., Jove, E., Fontenla-Romero, O., Calvo-Rolle, J.L.: Comparative study of anomaly detection techniques for monitoring lithium iron phosphate-lifepo4 batteries. In: Proceedings of V XoveTIC Conference. XoveTIC, vol. 14, pp. 80–82 (2023)
- Porras, S., Jove, E., Baruque, B., Calvo-Rolle, J.L.: A comparative analysis of intelligent techniques to predict energy generated by a small wind turbine from atmospheric variables. Logic J. IGPL (2022). https://doi.org/10.1093/jigpal/jzac031
- Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. Gaussian Processes for Machine Learning (2005). https://doi.org/10.7551/MITPRESS/3206.001.0001, https://direct.mit.edu/books/book/2320/Gaussian-Processes-for-Machine-Learning
- Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986). https://doi.org/10.1038/323533A0
- Stampatori, D., Raimondi, P.P., Noussan, M.: Li-ion batteries: a review of a key technology for transport decarbonization. Energies 13(10), 2638 (2020)
- Xu, C., Dai, Q., Gaines, L., Hu, M., Tukker, A., Steubing, B.: Future material demand for automotive lithium-based batteries. Commun. Mater. 1(1), 99 (2020)
- Zayas-Gato, F., et al.: Intelligent model for active power prediction of a small wind turbine. Logic J. IGPL (2022). https://doi.org/10.1093/jigpal/jzac040
- Zayas-Gato, F., et al.: A distributed topology for identifying anomalies in an industrial environment. Neural Comput. Appl. 34(23), 20463–20476 (2022). https://doi.org/10.1007/s00521-022-07106-7, https://link.springer.com/10.1007/s00521-022-07106-7
- Zayas-Gato, F., Michelena, Quintián, H., Jove, E., Casteleiro-Roca, J.L., Leitão, P., Luis Calvo-Rolle, J.: A novel method for anomaly detection using beta Hebbian learning and principal component analysis. Logic J. IGPL 31(2), 390–399 (2022). https://doi.org/10.1093/jigpal/jzac026
- Zayas-Gato, F., et al.: A novel method for anomaly detection using beta hebbian learning and principal component analysis. Logic J. IGPL 31(2), 390–399 (2023). https://doi.org/10.1093/jigpal/jzac026, https://academic.oup.com/jigpal/article/31/2/390/6532160