Reference : Fast Stochastic Non-linear Model Predictive Control for Electric Vehicle Advanced Dri...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Multidisciplinary, general & others
Security, Reliability and Trust
http://hdl.handle.net/10993/31798
Fast Stochastic Non-linear Model Predictive Control for Electric Vehicle Advanced Driver Assistance Systems
English
Sajadi Alamdari, Seyed Amin mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Voos, Holger [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit > ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Darouach, Mohamed [Universit ́e de Lorraine, IUT de Longwy, 186 rue de Lorraine, F-54400 Cosnes et Romain, France. > Centre de Recherche en Automatique de Nancy (CRAN) UMR-CNRS 7039]
27-Jun-2017
13th IEEE International Conference on Vehicular Electronics and Safety, Vienna, Austria 27-28 June 2017
91-96
Yes
No
International
13th IEEE International Conference on Vehicular Electronics and Safety
27-06-2017 to 28-06-2017
[en] Stochastic Optimisation ; Electric Vehicle ; Predictive Control
[en] Semi-autonomous driving assistance systems have a high potential to improve the safety and efficiency of the battery electric vehicles that are enduring limited cruising range. This paper presents an ecologically advanced driver assistance system to extend the functionality of the adaptive cruise control system. A real-time stochastic non-linear model predictive controller with probabilistic constraints is presented to compute on-line the safe and energy-efficient cruising velocity profile. The individual chance-constraint is reformulated into a convex second-order cone constraint which is robust for a general class of probability distributions. Finally, the performance of proposed approach in terms of states regulation, constraints fulfilment, and energy efficiency is evaluated on a battery electric vehicle.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Automation & Robotics Research Group
Fonds national de la Recherche
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/31798
FnR ; FNR7041503 > Seyed Amin Sajadi Alamdari > SEDAS > Stochastic Model Predictive Control For Eco-Driving Assistance Systems In Electric Vehicles > 15/06/2014 > 14/06/2018 > 2013

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