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Automated longitudinal control based on nonlinear recursive B-spline approximation for battery electric vehicles

Jauch, J. 1; Bleimund, F. 1; Frey, M. ORCID iD icon 1; Gauterin, F. ORCID iD icon 1
1 Institut für Fahrzeugsystemtechnik (FAST), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

This works presents a driver assistance system for energy-efficient ALC of a BEV. The ALC calculates a temporal velocity trajectory from map data. The trajectory is represented by a cubic B-spline function and results from an optimization problem with respect to travel time, driving comfort and energy consumption. For the energetic optimization we propose an adaptive model of the required electrical traction power. The simple power train of a BEV allows the formulation of constraints as soft constraints. This leads to an unconstrained optimization problem that can be solved with iterative filter-based data approximation algorithms. The result is a direct trajectory optimization method of which the effort grows linearly with the trajectory length, as opposed to exponentially as with most other direct methods. We evaluate ALC in real test drives with a BEV. We also investigate the energy-saving potential in driving simulations with ALC compared to MLC. On the chosen reference route the ALC saves up to 3.4% energy compared to MLC at same average velocity, and achieves a 2.6% higher average velocity than MLC at the same energy consumption

Verlagsausgabe §
DOI: 10.5445/IR/1000098758
Veröffentlicht am 09.10.2019
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Fahrzeugsystemtechnik (FAST)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2019
Sprache Englisch
Identifikator ISSN: 2032-6653
KITopen-ID: 1000098758
Erschienen in World electric vehicle journal
Verlag MDPI
Band 10
Heft 3
Seiten 52
Vorab online veröffentlicht am 05.09.2019
Schlagwörter automated longitudinal control; battery electric vehicle; trajectory optimization; B-spline approximation
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