dc.contributor.author | Parra Delgado, Alberto | |
dc.contributor.author | Tavernini, Davide | |
dc.contributor.author | Gruber, Patrick | |
dc.contributor.author | Sorniotti, Aldo | |
dc.contributor.author | Zubizarreta Pico, Asier | |
dc.contributor.author | Pérez Rastelli, Joshue Manuel | |
dc.date.accessioned | 2021-05-04T08:13:43Z | |
dc.date.available | 2021-05-04T08:13:43Z | |
dc.date.issued | 2021-01 | |
dc.identifier.citation | IEEE Transactions On Vehicular Technology 70(1) : 173-188 (2021) | es_ES |
dc.identifier.issn | 0018-9545 | |
dc.identifier.issn | 1939-9359 | |
dc.identifier.uri | http://hdl.handle.net/10810/51296 | |
dc.description.abstract | A recently growing literature discusses the topics of direct yaw moment control based on model predictive control (MPC), and energy-efficient torque-vectoring (TV) for electric vehicles with multiple powertrains. To reduce energy consumption, the available TV studies focus on the control allocation layer, which calculates the individual wheel torque levels to generate the total reference longitudinal force and direct yaw moment, specified by higher level algorithms to provide the desired longitudinal and lateral vehicle dynamics. In fact, with a system of redundant actuators, the vehicle-level objectives can be achieved by distributing the individual control actions to minimize an optimality criterion, e.g., based on the reduction of different power loss contributions. However, preliminary simulation and experimental studies - not using MPC - show that further important energy savings are possible through the appropriate design of the reference yaw rate. This paper presents a nonlinear model predictive control (NMPC) implementation for energy-efficient TV, which is based on the concurrent optimization of the reference yaw rate and wheel torque allocation. The NMPC cost function weights are varied through a fuzzy logic algorithm to adaptively prioritize vehicle dynamics or energy efficiency, depending on the driving conditions. The results show that the adaptive NMPC configuration allows stable cornering performance with lower energy consumption than a benchmarking fuzzy logic TV controller using an energy-efficient control allocation layer | es_ES |
dc.description.sponsorship | This work was supported in part by the Horizon 2020 Programme of the European Commission under Grant Agreements 769944 (STEVE project) and 824311 (ACHILES project). The review of this article was coordinated by Dr. Dongpu Cao | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | IEEE-Institute of Electrical and Electronics Engineers | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/769944 | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/824311 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | TV | es_ES |
dc.subject | mechanical power transmission | es_ES |
dc.subject | energy efficiency | es_ES |
dc.subject | tires | es_ES |
dc.subject | torque | es_ES |
dc.subject | resource management | es_ES |
dc.subject | wheels | es_ES |
dc.subject | torque-vectoring | es_ES |
dc.subject | nonlinear model predictive control | es_ES |
dc.subject | powertrain power loss | es_ES |
dc.subject | tire slip power loss | es_ES |
dc.subject | reference yaw rate | es_ES |
dc.subject | control allocation | es_ES |
dc.subject | weight adaptation | es_ES |
dc.title | On Nonlinear Model Predictive Control for Energy-Efficient Torque-Vectoring | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | This work is licensed under a Creative Commons Attribution License (CC BY 4.0) | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://ieeexplore-ieee-org.ehu.idm.oclc.org/document/9186728 | es_ES |
dc.identifier.doi | 10.1109/TVT.2020.3022022 | |
dc.contributor.funder | European Commission | |
dc.departamentoes | Ingeniería de sistemas y automática | es_ES |
dc.departamentoeu | Sistemen ingeniaritza eta automatika | es_ES |