Intelligent Torque Vectoring Approach For Electric Vehicles With Per-Wheel Motors
dc.contributor.author | Parra Delgado, Alberto | |
dc.contributor.author | Zubizarreta Pico, Asier ![]() | |
dc.contributor.author | Pérez Rastelli, Joshue Manuel | |
dc.contributor.author | Dendaluce Jahnke, Martín | |
dc.date.accessioned | 2018-07-05T12:00:55Z | |
dc.date.available | 2018-07-05T12:00:55Z | |
dc.date.issued | 2018-02-25 | |
dc.identifier.citation | Complexity 2018 : (2018) // Article ID 7030184 | es_ES |
dc.identifier.issn | 1076-2787 | |
dc.identifier.issn | 1099-0526 | |
dc.identifier.uri | http://hdl.handle.net/10810/27923 | |
dc.description.abstract | Transport electrification is currently a priority for authorities, manufacturers, and research centers around the world. The development of electric vehicles and the improvement of their functionalities are key elements in this strategy. As a result, there is a need for further research in emission reduction, efficiency improvement, or dynamic handling approaches. In order to achieve these objectives, the development of suitable Advanced Driver-Assistance Systems (ADAS) is required. Although traditional control techniques have been widely used for ADAS implementation, the complexity of electric multimotor powertrains makes intelligent control approaches appropriate for these cases. In this work, a novel intelligent Torque Vectoring (TV) system, composed of a neuro-fuzzy vertical tire forces estimator and a fuzzy yaw moment controller, is proposed, which allows enhancing the dynamic behaviour of electric multimotor vehicles. The proposed approach is compared with traditional strategies using the high fidelity vehicle dynamics simulator Dynacar. Results show that the proposed intelligent Torque Vectoring system is able to increase the efficiency of the vehicle by 10%, thanks to the optimal torque distribution and the use of a neuro-fuzzy vertical tire forces estimator which provides 3 times more accurate estimations than analytical approaches. | es_ES |
dc.description.sponsorship | The research leading to these results has been supported by the ECSEL Joint Undertaking under Grant agreement no. 662192 (3Ccar). This Joint Undertaking receives support from the European Union Horizon 2020 research and innovation program and the ECSEL member states. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Hindawi | es_ES |
dc.relation | European Comission | |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | fuzzy inference system | es_ES |
dc.subject | forces estimation | es_ES |
dc.subject | stability control | es_ES |
dc.subject | tire forces | es_ES |
dc.subject | simulation | es_ES |
dc.subject | anfis | es_ES |
dc.title | Intelligent Torque Vectoring Approach For Electric Vehicles With Per-Wheel Motors | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Attribution 4.0 International (CC BY 4.0) You are free to: Share — copy and redistribute the material in any medium or format Adapt — remix, transform, and build upon the material for any purpose, even commercially. | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://www.hindawi.com/journals/complexity/2018/7030184/ | es_ES |
dc.identifier.doi | 10.1155/2018/7030184 | |
dc.departamentoes | Ingeniería de sistemas y automática | es_ES |
dc.departamentoeu | Sistemen ingeniaritza eta automatika | es_ES |
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Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material for any purpose, even commercially.