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dc.contributor.authorSeria, Sebastián
dc.contributor.authorEspinoza, Pablo
dc.contributor.authorL. Quintero, Vanessa
dc.contributor.authorPerez, Aramis
dc.contributor.authorJaramillo, Francisco
dc.contributor.authorOrchard, Marcos
dc.contributor.authorBenavides, Matías
dc.date.accessioned2019-07-02T17:49:55Z
dc.date.accessioned2019-07-02T17:49:55Z
dc.date.available2019-07-02T17:49:55Z
dc.date.available2019-07-02T17:49:55Z
dc.date.issued2017-09-06
dc.date.issued2017-09-06
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/6154
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/6154
dc.descriptionThis research proposes a method for energy management in electric bicycles with Lithium-Ion batteries. This method optimizes the way energy is consumed to maximize the rider’s comfort, subject to constraints on the battery State-of-Charge once destination is reached. The algorithm considers the elevation profile of the route chosen by the rider, predicting the battery energy consumption based on physical parameters of the user and the bicycle. The route is partitioned into equispaced segments, and the optimization problem is then formulated to decide when to pedal or when to use the bicycle electric motor. Binary Particle Swarm Optimization (BPSO) is used to solve the optimization problem, while particle-filter-based estimators are used to determine the initial battery State-of-Charge. We surmise that management of the variability associated with the State-of-Charge swing range, in a systematic manner, will help to increase the battery life.en_US
dc.description.abstractThis research proposes a method for energy management in electric bicycles with Lithium-Ion batteries. This method optimizes the way energy is consumed to maximize the rider’s comfort, subject to constraints on the battery State-of-Charge once destination is reached. The algorithm considers the elevation profile of the route chosen by the rider, predicting the battery energy consumption based on physical parameters of the user and the bicycle. The route is partitioned into equispaced segments, and the optimization problem is then formulated to decide when to pedal or when to use the bicycle electric motor. Binary Particle Swarm Optimization (BPSO) is used to solve the optimization problem, while particle-filter-based estimators are used to determine the initial battery State-of-Charge. We surmise that management of the variability associated with the State-of-Charge swing range, in a systematic manner, will help to increase the battery life.en_US
dc.languageeng
dc.language.isoengen_US
dc.publisherAnnual Conference of the Prognostics and Health Management Society 2017en_US
dc.publisherAnnual Conference of the Prognostics and Health Management Society 2017
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectLi-ion Batteryen_US
dc.subjectBinary Particle Swarm Optimizationen_US
dc.subjectElectric Bicycleen_US
dc.subjectEnergy Managementen_US
dc.subjectLi-ion Battery
dc.subjectBinary Particle Swarm Optimization
dc.subjectElectric Bicycle
dc.subjectEnergy Management
dc.titleElectric Bicycle Energy Management Given an Elevation Traveling Profileen_US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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