Reference : Stochastic Optimum Energy Management for Advanced Transportation Network
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/10993/36173
Stochastic Optimum Energy Management for Advanced Transportation Network
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 [Centre de Recherche en Automatique de Nancy (CRAN) > Universite de Lorraine]
19-Jul-2018
15th IFAC Symposium on Control in Transportation Systems CTS 2018
Elsevier
51
317-322
Yes
No
International
Savona
Italy
5th IFAC Symposium on Control in Transportation Systems CTS 2018
06-06-2018 to 08-06-2018
Savona
Italy
[en] Stochastic Model Predictive Control ; Transportation Network ; Energy Management
[en] Smart and optimal energy consumption in electric vehicles has high potential to improve the limited cruising range on a single battery charge. The proposed concept is a semi-autonomous ecological advanced driver assistance system which predictively plans for a safe and energy-efficient cruising velocity profile autonomously for battery electric vehicles. However, high entropy in transportation network leads to a challenging task to derive a computationally efficient and tractable model to predict the traffic flow. Stochastic optimal control has been developed to systematically find an optimal decision with the aim of performance improvement. However, most of the developed methods are not real-time algorithms. Moreover, they are mainly risk-neutral for safety-critical systems. This paper investigates on the real-time risk-sensitive nonlinear optimal control design subject to safety and ecological constraints. This system improves the efficiency of the transportation network at the microscopic level. Obtained results demonstrate the effectiveness of the proposed method in terms of states regulation and constraints satisfaction.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Automation & Robotics Research Group
Fonds National de la Recherche - FnR
Stochastic Model Predictive Control for Eco-Driving Assistance Systems in Electric Vehicles
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/36173
10.1016/j.ifacol.2018.07.052
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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