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Reference: Ober-Blöbaum, S, Eckstein, J, Peitz, S et al., (2016). A comparison of two predictive approaches to control the longitudinal dynamics of electric vehicles.Citable link to this page:

 

A comparison of two predictive approaches to control the longitudinal dynamics of electric vehicles

Abstract: In this contribution we compare two different approaches to the implementation of a Model Predictive Controller in an electric vehicle with respect to the quality of the solution and real-time applicability. The goal is to develop an intelligent cruise control in order to extend the vehicle range, i.e. to minimize energy consumption, by computing the optimal torque profile for a given track. On the one hand, a path-based linear model with strong simplifications regarding the vehicle dynamics is used. On the other hand, a nonlinear model is employed in which the dynamics of the mechanical and electrical subsystem are modeled.

Peer Review status:Peer reviewedPublication status:PublishedVersion:Accepted manuscript Funder: Intelligente Technische Systeme Ostwestfalen-Lippe   Funder: German Federal Ministry of Education and Research   Conference Details: SysInt 2016: 3rd International Conference on System-Integrated IntelligenceNotes:© 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Bibliographic Details

Publisher: Elsevier

Publisher Website: http://www.elsevier.com

Host: SysInt 2016: 3rd International Conference on System-Integrated Intelligencesee more from them

Publication Website: http://www.journals.elsevier.com/procedia-technology/

Issue Date: 2016-10Identifiers

Urn: uuid:f864f1b4-3dc0-44c6-923d-2714e5781273

Source identifier: 627550

Issn: 2212-0173

Doi: https://doi.org/10.1016/j.protcy.2016.08.059 Item Description

Type: Conference;

Version: Accepted manuscriptKeywords: Model Predictive Control Automotive Battery Electric Vehicle Cruise Control Tiny URL: pubs:627550

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Author: Ober-Blöbaum, S - institutionUniversity of Oxford Oxford, MPLS, Engineering Science - - - Eckstein, J - - - Peitz, S - - - Schä

Source: https://ora.ox.ac.uk/objects/uuid:f864f1b4-3dc0-44c6-923d-2714e5781273



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