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dc.contributor.authorParra, Alberto
dc.contributor.authorZubizarreta, Asier
dc.contributor.authorPérez, Joshué
dc.date.accessioned2021-02-02T10:26:53Z
dc.date.available2021-02-02T10:26:53Z
dc.date.issued2021-01-13
dc.identifier.citationParra, Alberto, Asier Zubizarreta, and Joshué Pérez. “An Energy Efficient Intelligent Torque Vectoring Approach Based on Fuzzy Logic Controller and Neural Network Tire Forces Estimator.” Neural Computing and Applications (January 13, 2021). doi:10.1007/s00521-020-05680-2.en
dc.identifier.issn0941-0643en
dc.identifier.urihttp://hdl.handle.net/11556/1071
dc.description.abstractIn electric vehicles (EVs) with multiple motors, torque vectoring (TV) control can effectively enhance the cornering response and safety. Moreover, TV systems can also improve the overall efficiency through an optimal torque distribution that also considers the power consumption. For such a complex control system with multiple objectives, intelligent control techniques have demonstrated to be one of the best alternatives. However, the works proposed in the literature do not handle both vehicle dynamics behavior and energy efficiency, and generally do not consider the real-time implementability of the developed controllers. To overcome the aforementioned isues, in this work, a novel torque vectoring approach is proposed, which uses a neural network-based vertical tire forces estimator and considers the regenerative braking capabilities of EVs. Moreover, the implementability of the controller in a heterogenous (FPGA and microcontroller) automotive suitable system on chip is addressed, ensuring its real-time capabilities. For the sake of validating the proposed approach, a set of experiments have been carried out in a hardware in the loop setup. The performance of the proposed TV approach has been compared with other two TV approaches from the literature, evaluating them in several challenging manoeuvres in high and low tire-road friction coefficient scenarios. Results show that the proposed approach not only is able to enhance the vehicle dynamics behavior but also to decrease the energy consumption about 13%.en
dc.description.sponsorshipAuthors wants to thank to the ECSEL HIPERFORM Project (with Grant Number 783174) and to the H2020 ACHILES Project (with Grant Number 824311) for their support in the development of this work.en
dc.language.isoengen
dc.publisherSpringeren
dc.titleAn energy efficient intelligent torque vectoring approach based on fuzzy logic controller and neural network tire forces estimatoren
dc.typearticleen
dc.identifier.doi10.1007/s00521-020-05680-2en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/783174/EU/ACHILESen
dc.rights.accessRightsembargoedAccessen
dc.subject.keywordsElectric vehiclesen
dc.subject.keywordsTorque vectoringen
dc.subject.keywordsNeural networksen
dc.subject.keywordsIntelligent controlen
dc.subject.keywordsFPGAen
dc.identifier.essn1433-3058en
dc.journal.titleNeural Computing and Applicationsen


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