  The energy demand of vehicles, particularly in unsteady drive cycles, is
affected by complex dynamics internal to the engine and other powertrain
components. Yet, in many applications, particularly macroscopic traffic flow
modeling and optimization, structurally simple approximations to the complex
vehicle dynamics are needed that nevertheless reproduce the correct effective
energy behavior. This work presents a systematic model reduction pipeline that
starts from complex vehicle models based on the Autonomie software and derives
a hierarchy of simplified models that are fast to evaluate, easy to disseminate
in open-source frameworks, and compatible with optimization frameworks. The
pipeline, based on a virtual chassis dynamometer and subsequent approximation
strategies, is reproducible and is applied to six different vehicle classes to
produce concrete explicit energy models that represent an average vehicle in
each class and leverage the accuracy and validation work of the Autonomie
software.
