Using Real-World Driving Databases to Generate Driving Cycles with Equivalence Properties
Due to the increasing complexity of vehicle design, understanding
driver behavior and driving patterns is becoming increasingly more
important. Therefore, a large amount of test driving is performed,
which together with recordings of normal driving, results in large
databases of recorded drives. A fundamental question is how to make
best use of these data to devise driving cycles suitable in the
development process of vehicles. One way is to generate driving cycles
that are representative for the data or for a suitable subset of the
data, e.g., regarding geographical location, driving distance, speed
range, or many other possible selection variables. Further, to make a
fair comparison on two such driving cycles possible, another
fundamental requirement is that they should have similar excitation of
the vehicle. A key contribution here is an algorithm that combines the
two given objectives. A formulation with Markov processes is used to
obtain a condensed and effective characterization of the database and
to generate candidate driving cycles (CDCs). In addition to that is a
method transforming a candidate to an equivalent driving cycle (EqDC)
with desired excitation. The method is a general approach but is here
based on the components of the mean tractive force (MTF), and this is
motivated by a hardware-in-the-loop experiment showing the strong
relevance of these MTF components regarding fuel consumption. The
result is a new method that combines the generation of driving cycles
using real-world driving cycles with the concept of EqDCs.
Peter Nyberg, Erik Frisk and Lars Nielsen
IEEE Transactions on Vehicular Technology,
2016
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