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Abstract |
Look-ahead Control of Heavy Vehicles
Trucks are responsible for the major part of inland freight and so,
they are a backbone of the modern economy but they are also a large
consumer of energy. In this context, a dominating vehicle is a truck
with heavy load on a long trip. The aim with look-ahead control is to
reduce the energy consumption of heavy vehicles by utilizing
information about future conditions focusing on the road topography
ahead of the vehicle. The possible gains with look-ahead control are evaluated by
performing experiments with a truck on highway. A real-time control
system based on receding horizon control (RHC) is set up where the
optimization problem is solved repeatedly on-line for a certain
horizon ahead of the vehicle. The experimental results show that
significant reductions of the fuel consumption are achieved, and that
the controller structure, where the algorithm calculates set points
fed to lower level controllers, has satisfactory robustness to perform
well on-board in a real environment. Moreover, the controller behavior
has the preferred property of being intuitive, and the behavior is
perceived as comfortable and natural by participating drivers and
passengers. A well-behaved and efficient algorithm is developed, based on
dynamic programing, for the mixed-integer nonlinear minimum-fuel
problem. A modeling framework is formulated where special attention
is given to properly include gear shifting with physical models. Fuel
equivalents are used to reformulate the problem into a tractable form
and to construct a residual cost enabling the use of a shorter horizon
ahead of the vehicle. Analysis of errors due to discretization of the
continuous dynamics and due to interpolation shows that an energy
formulation is beneficial for reducing both error sources. The result
is an algorithm giving accurate solutions with low computational
effort for use in an on-board controller for a fuel-optimal velocity
profile and gear selection. The prevailing approach for the look-ahead problem is RHC where
main topics are the approximation of the residual cost and the choice
of the horizon length. These two topics are given a thorough
investigation independent of the method of solving the optimal control
problem in each time step. The basis for the fuel equivalents and the
residual cost is formed from physical intuition as well as
mathematical interpretations in terms of the Lagrange multipliers used
in optimization theory. Measures for suboptimality are introduced
that enables choosing horizon length with the appropriate compromise
between fuel consumption and trip time. Control of a hybrid electric powertrain is put in the framework
together with control of velocity and gear. For an efficient solution
of the minimum-fuel problem in this case, more fuel equivalence
factors and an energy formulation are employed. An application is
demonstrated in a design study where it is shown how the optimal
trade-off between size and capacity of the electrical system depends
on road characteristics, and also that a modestly sized electrical
system achieves most of the gain. The contributions develop algorithms, create associated design
tools, and carry out experiments. Altogether, a feasible framework is
achieved that pave the way for on-board fuel-optimal look-ahead
control.
Erik Hellström
2010
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Senast uppdaterad: 2021-11-10
