  The term NeuralODE describes the structural combination of an Artifical
Neural Network (ANN) and a numerical solver for Ordinary Differential Equations
(ODEs), the former acts as the right-hand side of the ODE to be solved. This
concept was further extended by a black-box model in the form of a Functional
Mock-up Unit (FMU) to obtain a subclass of NeuralODEs, named NeuralFMUs. The
resulting structure features the advantages of first-principle and data-driven
modeling approaches in one single simulation model: A higher prediction
accuracy compared to conventional First Principle Models (FPMs), while also a
lower training effort compared to purely data-driven models. We present an
intuitive workflow to setup and use NeuralFMUs, enabling the encapsulation and
reuse of existing conventional models exported from common modeling tools.
Moreover, we exemplify this concept by deploying a NeuralFMU for a consumption
simulation based on a Vehicle Longitudinal Dynamics Model (VLDM), which is a
typical use case in automotive industry. Related challenges that are often
neglected in scientific use cases, like real measurements (e.g. noise), an
unknown system state or high-frequent discontinuities, are handled in this
contribution. For the aim to build a hybrid model with a higher prediction
quality than the original FPM, we briefly highlight two open-source libraries:
FMI.jl for integrating FMUs into the Julia programming environment, as well as
an extension to this library called FMIFlux.jl, that allows for the integration
of FMUs into a neural network topology to finally obtain a NeuralFMU.
