Driving Cycle Generation Using Statistical Analysis and Markov Chains
A driving cycle is a velocity profile over time. Driving cycles can be
used for environmental classification of cars and to evaluate vehicle
performance. The benefit by using stochastic driving cycles instead of
predefined driving cycles, i.e. the New European Driving Cycle, is for
instance that the risk of cycle beating is reduced. Different methods
to generate stochastic driving cycles based on real-world data have
been used around the world, but the representativeness of the
generated driving cycles has been difficult to ensure.
The possibility to generate stochastic driving cycles that captures
specific features from a set of real-world driving cycles is
studied. Data from more than 500 real-world trips has been processed
and categorized. The driving cycles are merged into several transition
probability matrices (TPMs), where each element corresponds to a
specific state defined by its velocity and acceleration. The TPMs are
used with Markov chain theory to generate stochastic driving
cycles. The driving cycles are validated using percentile limits on a
set of characteristic variables, that are obtained from statistical
analysis of real-world driving cycles.
The distribution of the generated driving cycles is investigated and
compared to real-world driving cycles distribution. The generated
driving cycles proves to represent the original set of real-world
driving cycles in terms of key variables determined through
statistical analysis. Four different methods are used to determine
which statistical variables that describes the features of the
provided driving cycles. Two of the methods uses regression
analysis. Hierarchical clustering of statistical variables is proposed
as a third alternative, and the last method combines the cluster
analysis with the regression analysis.
The entire process is automated and a graphical user interface is
developed in Matlab to facilitate the use of the software.
Emil Torp and Patrik Önnegren
Senast uppdaterad: 2021-11-10