Hybrid Intelligent Model for Fault Detection of a Lithium Iron Phosphate Power Cell Used in Electric Vehicles

Quintián, Héctor; Casteleiro‐Roca, José‐Luis; Castelo, Francisco Javier Pérez; Calvo-Rolle, José Luís; Corchado, Emilio

doi:10.1007/978-3-319-32034-2_63

Cited by 16 publications

(4 citation statements)

References 34 publications

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“…Es por ello que, una alternativa interesante a los laboratorios reales son los laboratorios virtuales. Hoy en día, muchos de los sistemas basados en técnicas de inteligencia artificial como los que se explican en [31,1,9,17,39,7,28,26,33,13,45,29,36,40,44,34,27,4,47,4,16,14] se pueden verificar, en las primeras fases de diseño, mediante este tipo de laboratorios.…”

Section: Introductionunclassified

Implementación virtual de prácticas de asignaturas de control como alternativa a las prácticas de laboratorio presenciales

Michelena¹,

Zayas‐Gato²,

Jove³

et al. 2021

Xlii Jornadas De Automática : Libro De Actas

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La enfermedad del coronavirus (COVID-19) ha provocado una grave crisis de salud con consecuencias socio-económicas sin precedentes. Desde la declaración de pandemia por parte de la Organización Mundial de la Salud, la mayoría de los gobiernos aplicaron políticas de control como el confinamiento, la restriccón de la movilidad, el distanciamiento social, etc. En este contexto, adaptar la planificación educativa a ese escenario conlleva un gran desafío para la comunidad educativa global. Además, dado que las prácticas presenciales de laboratorio en la educación superior fueron canceladas temporalmente, la creación de escenarios virtuales realistas mediante modernas aplicaciones software representa una alternativa interesante y necesaria. Este enfoque práctico se puede lograr mediante el uso de dos herramientas de software interesantes, como Node-RED y Factory I/O. En este trabajo se propone la implementación de una planta de laboratorio virtual utilizando las herramientas mencionadas anteriormente, con características muy similares a las plantas de laboratorio reales de la Facultad de Ingeniería de la Universidad de A Coruña.

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Section: Introductionunclassified

Implementación virtual de prácticas de asignaturas de control como alternativa a las prácticas de laboratorio presenciales

Michelena¹,

Zayas‐Gato²,

Jove³

et al. 2021

Xlii Jornadas De Automática : Libro De Actas

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“…The definition of the term smart city is under constant review [2,3]. Taking this fact into account, a smart city could be described as a city that uses the potential of technology and innovation, along with other resources that help make effective the use of this potential, promotes sustainable development, and ultimately, improves the quality of life of its citizens [4][5][6]. Smart cities are made up of a group of many elements, tangible or intangible [7].…”

Section: Introductionmentioning

confidence: 99%

Solar Thermal Collector Output Temperature Prediction by Hybrid Intelligent Model for Smartgrid and Smartbuildings Applications and Optimization

et al. 2020

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Currently, there is great interest in reducing the consumption of fossil fuels (and other non-renewable energy sources) in order to preserve the environment; smart buildings are commonly proposed for this purpose as they are capable of producing their own energy and using it optimally. However, at times, solar energy is not able to supply the energy demand fully; it is mandatory to know the quantity of energy needed to optimize the system. This research focuses on the prediction of output temperature from a solar thermal collector. The aim is to measure solar thermal energy and optimize the energy system of a house (or building). The dataset used in this research has been taken from a real installation in a bio-climate house located on the Sotavento Experimental Wind Farm, in north-west Spain. A hybrid intelligent model has been developed by combining clustering and regression methods such as neural networks, polynomial regression, and support vector machines. The main findings show that, by dividing the dataset into small clusters on the basis of similarity in behavior, it is possible to create more accurate models. Moreover, combining different regression methods for each cluster provides better results than when a global model of the whole dataset is used. In temperature prediction, mean absolute error was lower than 4 ∘ C.

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“…Hence, in renewable energy systems, or any industrial plant in general terms, the anomaly detection is a crucial task [6,7,8,9,10,11]. These anomalies can be produced by wrong sensor readings, actuator malfunctions or changes in plant parameters, in general terms [12,13,14,15,16,17,18]. Focusing on the sensors performance, the occasional reading errors can be removed and recovered, making the systems more fault tolerant and robust [19,20,21,22,23,24,25].…”

Section: Introductionmentioning

confidence: 99%

A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques

Aláiz‐Moretón

Castejón-Limas

Casteleiro‐Roca

et al. 2019

Sensors

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This paper proposes a methodology for dealing with an issue of crucial practical importance in real engineering systems such as fault detection and recovery of a sensor. The main goal is to define a strategy to identify a malfunctioning sensor and to establish the correct measurement value in those cases. As study case, we use the data collected from a geothermal heat exchanger installed as part of the heat pump installation in a bioclimatic house. The sensor behaviour is modeled by using six different machine learning techniques: Random decision forests, gradient boosting, extremely randomized trees, adaptive boosting, k-nearest neighbors, and shallow neural networks. The achieved results suggest that this methodology is a very satisfactory solution for this kind of systems.

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Hybrid Intelligent Model for Fault Detection of a Lithium Iron Phosphate Power Cell Used in Electric Vehicles

Cited by 16 publications

References 34 publications

Implementación virtual de prácticas de asignaturas de control como alternativa a las prácticas de laboratorio presenciales

Implementación virtual de prácticas de asignaturas de control como alternativa a las prácticas de laboratorio presenciales

Solar Thermal Collector Output Temperature Prediction by Hybrid Intelligent Model for Smartgrid and Smartbuildings Applications and Optimization

A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques

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