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Fast stochastic non-linear model predictive control for electric vehicle advanced driver assistance systems

Abstract : 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.
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https://hal.archives-ouvertes.fr/hal-01643970
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Submitted on : Tuesday, November 21, 2017 - 6:35:10 PM
Last modification on : Wednesday, November 3, 2021 - 7:56:35 AM

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Seyed Amin Sajadi-Alamdari, Holger Voos, Mohamed Darouach. Fast stochastic non-linear model predictive control for electric vehicle advanced driver assistance systems. IEEE International Conference on Vehicular Electronics and Safety, ICVES 2017, Jun 2017, Vienna, Australia. ⟨10.1109/ICVES.2017.7991907⟩. ⟨hal-01643970⟩

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