Reference : Stochastic Model Predictive Control for Eco-Driving Assistance Systems in Electric Ve...
Dissertations and theses : Doctoral thesis
Engineering, computing & technology : Multidisciplinary, general & others
Security, Reliability and Trust
http://hdl.handle.net/10993/36164
Stochastic Model Predictive Control for Eco-Driving Assistance Systems in Electric Vehicles
English
Sajadi Alamdari, Seyed Amin mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
25-Apr-2018
University of Luxembourg, ​​Luxembourg
Doctor of Engineering
263
Voos, Holger mailto
Hadji-Minaglou, Jean-Régis mailto
Darouach, Mohamed mailto
Viti, Francesco mailto
Wang, Meng mailto
[en] Stochastic Model Predictive Control ; Driver Assistance System ; Electric Vehicles
[en] Electric vehicles are expected to become one of the key elements of future sustainable transportation systems. The first generation of electric cars are already commercially available but still, suffer from problems and constraints that have to be solved before a mass market might be created. Key aspects that will play an important role in modern electric vehicles are range extension, energy efficiency, safety, comfort as well as communication. An overall solution approach to integrating all these aspects is the development of advanced driver assistance systems to make electric vehicles more intelligent. Driver assistance systems are based on the integration of suitable sensors and actuators as well as electronic devices and software-enabled control functionality to automatically support the human driver. Driver assistance for electric vehicles will differ from the already used systems in fuel-powered cars such as electronic stability programs, adaptive cruise control etc. in a way that they must support energy efficiency while the system itself must also have a low power consumption. In this work, an eco-driving functionality as the first step towards those new driver assistance systems for electric vehicles will be investigated. Using information about the internal state of the car, navigation information as well as advanced information about the environment coming from sensors and network connections, an algorithm will be developed that will adapt the speed of the vehicle automatically to minimize energy consumption. From an algorithmic point of view, a stochastic model predictive control approach will be applied and adapted to the special constraints of the problem. Finally, the solution will be tested in simulations as well as in first experiments with a commercial electric vehicle in the SnT Automation & Robotics Research Group (SnT ARG).
SnT - Interdisciplinary Centre for Security, Reliability and Trust
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/10993/36164
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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