Synthesis and validation of representative real world driving cycles for Plug-In HEVs
Driving cycles play an important role in analysis and
design of hybrid propulsion systems. Their role is magnified in
case of Plug-In Hybrids, since driving patterns have a strong
impact on specific energy consumption, ability of the vehicle to
provide pure electric operation, range of charge depleting
operation, and finally assessments of the state-of-charge (SOC) at
the beginning of charging. Certification schedules are commonly
used, but in case of PHEV standard cycles are repeated many
times to study the charge depleting operation. While this is useful
for comparisons with results in literature, it is not representative
of real-world driving. Therefore, characterizing the naturalistic
driving patterns and generating representative real-world
schedules is essential for PHEV design and control work, studies
of technology adoption by real consumers, and assessments of the
impact on the grid. This work presents analysis of naturalistic
driving data collected in a Midwest region of US, followed by a
procedure for synthesizing real-world driving cycles based on that
data. The characteristic of the real-world cycles are captured
with transition probability matrices (TPMs), and a regression
analysis is applied to select significant explanatory variables for
determining the most representative synthetic cycles. Validation
indicates the ability to generate representative real-world driving
cycles for any arbitrary driving distance by combining a
stochastic process and statistical methodology.
Index Terms—Real-world driving cycle, synthesis, validation,
stochastic process, Markov chain, statistical methodology, plug-in
hybrid electric vehicle.