Among the myriad of potential hybrid powertrain architectures, selecting the
optimal for an application is a daunting task. Whenever available, computer
models greatly assist in it. However, some aspects, such as pollutant emissions,
are difficult to model, leaving no other option than to test. Validating
plausible options before building the powertrain prototype has the potential of
accelerating the vehicle development even more, doing so without shipping
components around the world. This work concerns the design of a system to
virtually couple—that is, avoiding physical contact—geographically distant test
rigs in order to evaluate the components of a powertrain. In the past, methods
have been attempted, either with or without assistance of mathematical models of
the coupled components (observers). Existing methods are accurate only when the
dynamics of the systems to couple are slow in relation to the communication
delay. Also, existing methods seem to overlook the implications of operating a
distributed system without a common time frame. In order to overcome the
inherent latency arising from long-range communication, the proposed design
combines two features: The exploitation of synchronized clocks for the
simultaneous introduction of setpoint commands and the use of observers
generated through machine learning algorithms. This novel design is subsequently
tested in two scenarios: A simple one, involving the virtual coupling of two
parts of an elementary device formed by three rotating inertias, and a more
complex one, the coupling between an internal combustion engine and an electric
motor/generator as representative of a series or parallel hybrid powertrain.
Although the results are heavily influenced by the quality of the data-generated
observers, the architecture improves the fidelity of the coupling by nearly an
order of magnitude compared to the alternative of directly transmitting the
signals. It also opens a niche application that leverages the accuracy of
low-fidelity models.
