A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems

A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems

A model-based virtual sensor for assessing the health of rechargeable batteries for cyber-physical vehicle systems (CPVSs) is presented that can exploit coarse data streamed from on-vehicle sensors of current, voltage, and temperature. First-principle-based models are combined with knowledge acquire...

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Main Authors: Luciano Sánchez, Inés Couso, José Otero, Yuviny Echevarría, David Anseán
Format: Article
Language:English
Published: Hindawi Limited 2017-01-01
Series:Journal of Sensors
Online Access:http://dx.doi.org/10.1155/2017/9643279
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spelling doaj-9278c806a8b843c09b78604a816feb1e2020-11-24T20:47:25ZengHindawi LimitedJournal of Sensors1687-725X1687-72682017-01-01201710.1155/2017/96432799643279A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle SystemsLuciano Sánchez0Inés Couso1José Otero2Yuviny Echevarría3David Anseán4Computer Science Department, Universidad de Oviedo, Oviedo, SpainStatistics Department, Universidad de Oviedo, Oviedo, SpainComputer Science Department, Universidad de Oviedo, Oviedo, SpainComputer Science Department, Universidad de Oviedo, Oviedo, SpainElectrical Engineering Department, Universidad de Oviedo, Oviedo, SpainA model-based virtual sensor for assessing the health of rechargeable batteries for cyber-physical vehicle systems (CPVSs) is presented that can exploit coarse data streamed from on-vehicle sensors of current, voltage, and temperature. First-principle-based models are combined with knowledge acquired from data in a semiphysical arrangement. The dynamic behaviour of the battery is embodied in the parametric definition of a set of differential equations, and fuzzy knowledge bases are embedded as nonlinear blocks in these equations, providing a human understandable reading of the State of Health of the CPVS that can be easily integrated in the fleet through-life management.http://dx.doi.org/10.1155/2017/9643279
collection DOAJ
language English
format Article
sources DOAJ
author Luciano Sánchez
Inés Couso
José Otero
Yuviny Echevarría
David Anseán
spellingShingle Luciano Sánchez
Inés Couso
José Otero
Yuviny Echevarría
David Anseán
A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems
Journal of Sensors
author_facet Luciano Sánchez
Inés Couso
José Otero
Yuviny Echevarría
David Anseán
author_sort Luciano Sánchez
title A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems
title_short A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems
title_full A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems
title_fullStr A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems
title_full_unstemmed A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems
title_sort model-based virtual sensor for condition monitoring of li-ion batteries in cyber-physical vehicle systems
publisher Hindawi Limited
series Journal of Sensors
issn 1687-725X
1687-7268
publishDate 2017-01-01
description A model-based virtual sensor for assessing the health of rechargeable batteries for cyber-physical vehicle systems (CPVSs) is presented that can exploit coarse data streamed from on-vehicle sensors of current, voltage, and temperature. First-principle-based models are combined with knowledge acquired from data in a semiphysical arrangement. The dynamic behaviour of the battery is embodied in the parametric definition of a set of differential equations, and fuzzy knowledge bases are embedded as nonlinear blocks in these equations, providing a human understandable reading of the State of Health of the CPVS that can be easily integrated in the fleet through-life management.
url http://dx.doi.org/10.1155/2017/9643279
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