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**Harvard**

Hoxell, F. (2016) *Predictive Longitudinal Control of Heavy-Duty Vehicles Using a Novel Genetic Algorithm and Road Topography Data*. Göteborg : Chalmers University of Technology (Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, nr: 2016:08).

** BibTeX **

@misc{

Hoxell2016,

author={Hoxell, Fredrik},

title={Predictive Longitudinal Control of Heavy-Duty Vehicles Using a Novel Genetic Algorithm and Road Topography Data},

abstract={Fuel costs account for approximately one third of the total cost of haulage contractors.
This makes it very lucrative from both the contractors’ and hence Scanias’
perspective to reduce the vehicles’ fuel consumption. With limited power-to-mass ratio
of heavy-duty vehicles, anticipatory control is crucial for fuel- and time-efficient
manoeuvring. Solutions addressing this problem are already in production, but
with ever-increasing system complexity the usefulness of conventional mathematical
methods is suffering. As an alternative approach, this thesis is aimed at investigating
the applicability of a real-time genetic algorithm (GA) to the domain of longitudinal
control of heavy-duty vehicles for fuel-saving adaption to road topography data.
Known to be computationally heavy, an as lightweight as possible algorithm is developed
and aimed at optimising the engine torque by model predictive control. The
final algorithm uses a vehicle prediction model of fuel-consumption data including
a gear prediction model. Validated through simulation this novel approach displays
a clear improvement over a similar MPC-controller utilising a QP-solver and a cost
function similar to that of the GA.},

publisher={Institutionen för tillämpad mekanik, Fordonsteknik och autonoma system, Chalmers tekniska högskola,},

place={Göteborg},

year={2016},

series={Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, no: 2016:08},

keywords={Adaptive, Look-ahead, Cruise Control, Genetic Algorithm, Quadratic Programming, Heavy-Duty Vehicles, Model Predictive Control},

}

** RefWorks **

RT Generic

SR Electronic

ID 238897

A1 Hoxell, Fredrik

T1 Predictive Longitudinal Control of Heavy-Duty Vehicles Using a Novel Genetic Algorithm and Road Topography Data

YR 2016

AB Fuel costs account for approximately one third of the total cost of haulage contractors.
This makes it very lucrative from both the contractors’ and hence Scanias’
perspective to reduce the vehicles’ fuel consumption. With limited power-to-mass ratio
of heavy-duty vehicles, anticipatory control is crucial for fuel- and time-efficient
manoeuvring. Solutions addressing this problem are already in production, but
with ever-increasing system complexity the usefulness of conventional mathematical
methods is suffering. As an alternative approach, this thesis is aimed at investigating
the applicability of a real-time genetic algorithm (GA) to the domain of longitudinal
control of heavy-duty vehicles for fuel-saving adaption to road topography data.
Known to be computationally heavy, an as lightweight as possible algorithm is developed
and aimed at optimising the engine torque by model predictive control. The
final algorithm uses a vehicle prediction model of fuel-consumption data including
a gear prediction model. Validated through simulation this novel approach displays
a clear improvement over a similar MPC-controller utilising a QP-solver and a cost
function similar to that of the GA.

PB Institutionen för tillämpad mekanik, Fordonsteknik och autonoma system, Chalmers tekniska högskola,

T3 Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, no: 2016:08

LA eng

LK http://publications.lib.chalmers.se/records/fulltext/238897/238897.pdf

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