Energy-management system for a hybrid electric vehicle, using ultracapacitors and neural networks

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dc.contributor.authorMoreno de la Carrera, Jorge Alejandro
dc.contributor.authorOrtúzar Dworsky, Micah Etan
dc.contributor.authorDixon Rojas, Juan
dc.date.accessioned2022-05-18T14:39:52Z
dc.date.available2022-05-18T14:39:52Z
dc.date.issued2006
dc.description.abstractA very efficient energy-management system for hybrid electric vehicles (HEVs), using neural networks (NNs), was developed and tested. The system minimizes the energy requirement of the vehicle and can work with different primary power sources like fuel cells, microturbines, zinc-air batteries, or other power supplies with a poor ability to recover energy from a regenerative braking, or with a scarce power capacity for a fast acceleration. The experimental HEV uses lead-acid batteries, an ultracapacitor (UCAP) bank, and a brushless dc motor with nominal power of 32 kW, and a peak power of 53 kW. The digital signal processor (DSP) control system measures and stores the following parameters: primary-source voltage, car speed, instantaneous currents in both terminals (primary source and UCAP), and actual voltage of the UCAP. When UCAPs were installed on the vehicle, the increase in range was around 5.3% in city tests. However, when optimal control with NN was used, this figure increased to 8.9%. The car used for this experiment is a Chevrolet light utility vehicle (LUV) truck, similar in shape and size to Chevrolet S-10, which was converted to an electric vehicle (EV) at the Universidad Catolica de Chile. Numerous experimental tests under different conditions are compared and discussed.
dc.fuente.origenIEEE
dc.identifier.doi10.1109/TIE.2006.870880
dc.identifier.issn1557-9948
dc.identifier.urihttps://doi.org/10.1109/TIE.2006.870880
dc.identifier.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1614145
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/64187
dc.identifier.wosidWOS:000236776900030
dc.information.autorucEscuela de ingeniería ; Moreno de la Carrera, Jorge Alejandro ; S/I ; 14452
dc.information.autorucEscuela de ingeniería ; Ortúzar Dworsky, Micah Etan ; S/I ; 4366
dc.information.autorucEscuela de ingeniería ; Dixon Rojas, Juan ; S/I ; 99537
dc.issue.numero2
dc.language.isoen
dc.nota.accesoContenido parcial
dc.pagina.final623
dc.pagina.inicio614
dc.revistaIEEE Transactions on Industrial Electronics
dc.rightsacceso restringido
dc.subjectHybrid electric vehicles
dc.subjectSupercapacitors
dc.subjectNeural networks
dc.subjectSystem testing
dc.subjectBattery powered vehicles
dc.subjectFuel cell vehicles
dc.subjectFuel cells
dc.subjectPower supplies
dc.subjectAcceleration
dc.subjectBrushless DC motors
dc.subject.ods13 Climate action
dc.subject.ods07 Affordable and clean energy
dc.subject.ods11 Sustainable cities and communities
dc.subject.odspa13 Acción por el clima
dc.subject.odspa07 Energía asequible y no contaminante
dc.subject.odspa11 Ciudades y comunidades sostenibles
dc.titleEnergy-management system for a hybrid electric vehicle, using ultracapacitors and neural networkses_ES
dc.typeartículo
dc.volumen53
sipa.codpersvinculados14452
sipa.codpersvinculados4366
sipa.codpersvinculados99537
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