NeuralFMU: presenting a workflow for integrating hybrid neuralODEs into real-world applications

The term NeuralODE describes the structural combination of an Artificial Neural Network (ANN) and a numerical solver for Ordinary Differential Equations (ODE), 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 the first-principle and data-driven modeling approaches in one single simulation model: a higher prediction accuracy compared to conventional First-Principle Models (FPMs) and also a lower training effort compared to purely data-driven models. We present an intuitive workflow to set up 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 the automotive industry. Related challenges that are often neglected in scientific use cases, such as real measurements (e.g., noise), an unknown system state or high-frequency discontinuities, are handled in this contribution. To build a hybrid model with a higher prediction quality than the original FPM, we briefly highlight two open-source libraries: FMI.jl, which allows for the import of FMUs into the Julia programming language, as well as the library FMIFlux.jl, which enables the integration of FMUs into neural network topologies to obtain a NeuralFMU

SystemC-AMS Simulation of Energy Management of Electric Vehicles

Electric vehicles (EV) are rapidly invading the market, since they are clean, quiet and energy efficient. However, there are many factors that discourage EVs for current and potential customers. Among them, driving range is one of the most critical issues: running out of battery charge while driving results in serious inconvenience even comparable to vehicle breakdown, as an effect of long fuel recharging times and lack of charging facilities. As a result, the dimensioning of the energy subsystem of an EV is a crucial activity. The choice of the power components and of the adopted policies should thus be validated at design time through simulations, that estimate the vehicle driving range under reference driving profiles. It is thus necessary to build a simulation framework that takes into account an EV power consumption model, dependent on the characteristics of the vehicle and of the driving route, plus accurate models for all power components, including batteries and green power sources. The goal of this paper is to achieve early EV simulation, so that the designer can estimate at design time the driving range of the vehicle, validate the adopted components and policies and evaluate alternative configurations

Electrified Powertrain Development: Distributed Co-Simulation Protocol Extension for Coupled Test Bench Operations

The increasingly stringent CO2 emissions standards require innovative solutions in the vehicle development process. One possibility to reduce CO2 emissions is the electrification of powertrains. The resulting increased complexity, as well as the increased competition and time pressure make the use of simulation software and test benches indispensable in the early development phases. This publication therefore presents a methodology for test bench coupling to enable early testing of electrified powertrains. For this purpose, an internal combustion engine test bench and an electric motor test bench are virtually interconnected. By applying and extending the Distributed Co-Simulation Protocol Standard for the presented hybrid electric powertrain use case, real-time-capable communication between the two test benches is achieved. Insights into the test bench setups, and the communication between the test benches and the protocol extension, especially with regard to temperature measurements, enable the extension to be applied to other powertrain or test bench configurations. The shown results from coupled test bench operations emphasize the applicability. The discussed experiences from the test bench coupling experiments complete the insights

Using Modelica for advanced Multi-Body modelling in 3D graphical robotic simulators

This paper describes a framework to extend the 3D robotic simulation environment Gazebo, and similar ones, with enhanced, tailor-made, multi-body dynamics specified in the Modelica language. The body-to-body interaction models are written in Modelica, but they use the sophisticated collision detection capabilities of the Gazebo engine. This contribution is a first step toward the simulation of complex robotics systems integrating detailed physics modelling and realistic sensors such as lidar and cameras. A proof-of-concept implementation is described in the paper integrating Gazebo collider and the Modelica MultiBody library, and the results obtained when simulating the interaction of an elastic sphere with a rigid plane are shown