Regenerative Intelligent Brake Control for Electric Motorcycles

dc.contributor.authorCastillo-Aguilar, Juan Jesús
dc.contributor.authorPérez-Fernández, Javier
dc.contributor.authorVelasco García, Juan María
dc.contributor.authorCabrera-Carrillo, Juan Antonio
dc.date.accessioned2025-01-16T13:49:10Z
dc.date.available2025-01-16T13:49:10Z
dc.date.issued2017
dc.departamentoIngeniería Mecánica, Térmica y de Fluidos
dc.description.abstractVehicle models whose propulsion system is based on electric motors are increasing in number within the automobile industry. They will soon become a reliable alternative to vehicles with conventional propulsion systems. The main advantages of this type of vehicles are the non-emission of polluting gases and noise and the effectiveness of electric motors compared to combustion engines. Some of the disadvantages that electric vehicle manufacturers still have to solve are their low autonomy due to inefficient energy storage systems, vehicle cost, which is still too high, and reducing the recharging time. Current regenerative systems in motorcycles are designed with a low fixed maximum regeneration rate in order not to cause the rear wheel to slip when braking with the regenerative brake no matter what the road condition is. These types of systems do not make use of all the available regeneration power, since more importance is placed on safety when braking. An optimized regenerative braking strategy for two-wheeled vehicles is described is this work. This system is designed to recover the maximum energy in braking processes while maintaining the vehicle’s stability. In order to develop the previously described regenerative control, tyre forces, vehicle speed and road adhesion are obtained by means of an estimation algorithm. A based-on-fuzzy-logic algorithm is programmed to carry out an optimized control with this information. This system recuperates maximum braking power without compromising the rear wheel slip and safety. Simulations show that the system optimizes energy regeneration on every surface compared to a constant regeneration strategy.es_ES
dc.identifier.citationCastillo Aguilar, J.J.; Pérez Fernández, J.; Velasco García, J.M.; Cabrera Carrillo, J.A. Regenerative Intelligent Brake Control for Electric Motorcycles. Energies 2017, 10, 1648. https://doi.org/10.3390/en10101648es_ES
dc.identifier.doi10.3390/en10101648
dc.identifier.urihttps://hdl.handle.net/10630/36438
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución 4.0 Internacional*
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectVehículos eléctricoses_ES
dc.subject.otherRegenerationes_ES
dc.subject.otherElectric vehicleses_ES
dc.subject.otherAntilock brake system (ABS)es_ES
dc.subject.otherFuzzy logices_ES
dc.titleRegenerative Intelligent Brake Control for Electric Motorcycleses_ES
dc.typejournal articlees_ES
dc.type.hasVersionVoRes_ES
dspace.entity.typePublication
relation.isAuthorOfPublication8efbe5d8-de15-4512-a14c-7d705e278163
relation.isAuthorOfPublication2c50a2bd-cff0-4ae1-a333-183439902173
relation.isAuthorOfPublication.latestForDiscovery8efbe5d8-de15-4512-a14c-7d705e278163

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