Show simple item record

dc.contributor.authorHernández de la Iglesia, Daniel
dc.contributor.authorVillarrubia González, Gabriel 
dc.contributor.authorPaz Santana, Juan Francisco de 
dc.contributor.authorBajo Pérez, Javier
dc.date.accessioned2017-12-22T12:54:35Z
dc.date.available2017-12-22T12:54:35Z
dc.date.issued2017
dc.identifier.citationIglesia, D.H. de la., Villarrubia, G., Paz, J. de., Bajo, J. (2017). Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm. Sensors, 17 (11)es_ES
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10366/135825
dc.description.abstract[EN]The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route.es_ES
dc.format.mimetypeapplication/pdf
dc.language.isoenges_ES
dc.publisherMDPI Publishing (Basilea, Suiza)es_ES
dc.rightsAttribution-NonCommercial-NoDerivs 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectIntelligent transport systemses_ES
dc.subjectInformation fusiones_ES
dc.subjectVehicular sensor networkes_ES
dc.subjectEnergy efficiencyes_ES
dc.titleMulti-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithmes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publishversionhttp://dx.doi.org/10.3390/s17112501
dc.subject.unesco1203 Ciencia de los ordenadoreses_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivs 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 4.0 International