Open AccessA Comparison of two Predictive Approaches to Control the Longitudinal Dynamics of Electric Vehicles
Author(s) -
Julian Eckstein,
Sebastian Peitz,
Kai Schäfer,
Patrick Friedel,
Ulrich Köhler,
Mirko Hessel-von Molo,
Sina OberBlöbaum,
Michael Dellnitz
Publication year - 2016
Publication title -
procedia technology
Language(s) - English
Resource type - Journals
ISSN - 2212-0173
DOI - 10.1016/j.protcy.2016.08.059
Subject(s) - model predictive control , cruise control , electric vehicle , torque , nonlinear system , control theory (sociology) , control engineering , controller (irrigation) , range (aeronautics) , vehicle dynamics , engineering , track (disk drive) , driving range , computer science , control (management) , automotive engineering , aerospace engineering , mechanical engineering , artificial intelligence , agronomy , power (physics) , physics , quantum mechanics , biology , thermodynamics
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
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