Evaluation, Generation, and Transformation of Driving Cycles
Driving cycles are important components for evaluation and design of
vehicles. They determine the focus of vehicle manufacturers, and
indirectly they affect the environmental impact of vehicles since the
vehicle control system is usually tuned to one or several driving
cycles. Thus, the driving cycle affects the design of the vehicle
since cost, fuel consumption, and emissions all depend on the driving
cycle used for design. Since the existing standard driving cycles
cannot keep up with the changing road infrastructure, the changing
vehicle fleet composition, and the growing number of vehicles on the
road, which do all cause changes in the driver behavior, the need to
get new and representative driving cycles are increasing. A research
question is how to generate these new driving cycles so that they are
both representative and at the same time have certain equivalence
properties, to make fair comparisons of the performance results.
Besides generation, another possibility to get more driving cycles is
to transform the existing ones into new, different, driving cycles
considering equivalence constraints.
With the development of new powertrain concepts the need for
evaluation will increase, and an interesting question is how to
utilize new developments in dynamometer technology together with new
possibilities for connecting equipment. Here a pedal robot is
developed to be used in a vehicle mounted in a chassis dynamometer and
the setup is used for co-simulation together with a moving base
simulator that is connected with a communication line. The results
show that the co-simulation can become a realistic driving experience
and a viable option for dangerous tests and a complement to tests on a
dedicated track or on-road tests, if improvements on the braking and
the vehicle feedback to the driver are implemented.
The problem of generating representative driving cycles, with
specified excitation at the wheels, is approached with a combined
two-step method. A Markov chain approach is used to generate candidate
driving cycles that are then transformed to equivalent driving cycles
with respect to the mean tractive force components, which are the used
measures. Using an optimization methodology the transformation of
driving cycles is formulated as a nonlinear program with constraints
and a cost function to minimize. The nonlinear program formulation
can handle a wide range of constraints, e.g., the mean tractive force
components, different power measures, or available energy for
recuperation, and using the vehicle jerk as cost function gives good
drivability.
In conclusion, methods for driving cycle design have been proposed
where new driving cycles can either be generated from databases, or
given driving cycles can be transformed to fulfill certain equivalence
constraints, approaching the important problem of similar but not the
same. The combination of these approaches yields a stochastic and
general method to generate driving cycles with equivalence properties
that can be used at several instances during the product development
process of vehicles. Thus, a powerful and effective engineering tool
has been developed.
Peter Nyberg
2015
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Last updated: 2021-11-10
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