Search Results (15)

In automotive software development, testing and validation workloads are often concentrated at the end of the development cycle, leading to delays and late-stage issue discovery. To address this, virtual Electronic Control Units (vECUs) have gained attention for enabling earlier-stage verification. In our previous work, we developed a Level-4 vECU using a hardware-level emulator. However, when simulating multiple vECUs with independent clocks across distributed emulators, we observed poor timing reproducibility due to the lack of explicit synchronization. To solve this, we implemented an integration layer compliant with the functional mock-up interface (FMI), a widely used standard for simulation tool interoperability. The layer enables synchronized simulation between a centralized simulation master and independently running vECUs. We also developed a virtual CAN bus model to simulate message arbitration and validate inter-vECU communication behavior. Simulation results show that our framework correctly reproduces CAN arbitration logic and significantly improves timing reproducibility compared to conventional Linux-based interfaces. To improve simulation performance, the FMI master algorithm was parallelized, resulting in up to 85.2% reduction in simulation time with eight vECUs. These contributions offer a practical solution for synchronizing distributed Level-4 vECUs and lay the groundwork for future cloud-native simulation of automotive systems. Full article

A novel approach for the robust integration of Functional Mock-up Units (FMUs) into Modelica is proposed, which maintains the computational robustness of the Modelica base model and minimizes the simulation time. Using a control-like approach, the base model is retained and mimics the FMU outputs. On the one hand, the controller can be interpreted as a numerical tool designed to provide a correct steady-state solution and minimize transient errors. On the other hand, the additional low-pass filter can also be used to represent the inertia of a system. The application of this easy-to-implement approach is demonstrated for a digital twin of the overall thermal management system (TMS) of a future hybrid electrical regional aircraft, which aims at identifying critical conditions and flight cases in advance of hardware tests and virtually demonstrating the behavior of the TMS during complete flight missions. To this end, a base model of the TMS is first set up using the Thermofluid Stream Modelica Library, which focuses on computational robustness, in order to define the boundaries and interfaces of the different subsystems. Then, the subsystems are gradually replaced by validated FMUs to enable virtual demonstrations, where the novel control-like approach proves to be crucial. Full article

Currently, innovations in mechatronic products often occur at the system level, requiring consideration of component interactions throughout the entire development process. In the earlier phases of development, this is accomplished by coupling virtual prototypes such as simulation models. As the development progresses and real prototypes of certain system components become available, real-virtual prototypes (RVPs) are established with the help of network communication. However, network effects—all of which can be interpreted as latencies in simplified terms—distort the system behavior of RVPs. To reduce these distortions, we propose a coupling method for RVPs that compensates for latencies. We present an easily applicable approach by introducing a generic coupling algorithm based on error space extrapolation. Furthermore, we enable online learning by transforming coupling algorithms into feedforward neural networks. Additionally, we conduct a frequency domain analysis to assess the impact of coupling faults and algorithms on the system behavior of RVPs and derive a method for optimally designing coupling algorithms. To demonstrate the effectiveness of the coupling method, we apply it to a hybrid vehicle that is productively used as an RVP in the industry. We show that the optimally designed and trained coupling algorithm significantly improves the credibility of the RVP. Full article

The concept of cellular energy systems of the German Association for Electrical, Electronic & Information Technologies (VDE) proposes sector coupled energy networks for energy transition based on cellular structures. Its decentralized control approach radically differs from that of existing networks. Deeply integrated information and communications technologies (ICT) open opportunities for increased resilience and optimizations. The exploration of this concept requires a comprehensive simulation tool. In this paper, we investigate simulation techniques for cellular energy systems and present a concept based on co-simulation. We combine simulation tools developed for different domains. A classical tool for studying physical aspects of energy systems (Modelica, TransiEnt library) is fused with a state-of-the-art communication networks simulator (OMNeT++) via the standardized functional mock-up interface (FMI). New components, such as cell managers, aggregators, and markets, are integrated via remote procedure calls. A special feature of our concept is that the communication simulator coordinates the co-simulation as a master and integrates other components via a proxy concept. Model consistency across different domains is achieved by a common description of the energy system. Evaluation proves the feasibility of the concept and shows simulation speeds about 20 times faster than real time for a cell with 111 households. Full article

This paper presents an overview and formalization of the Functional Mock-up Interface (FMI) 3.0. The formalization captures the new FMI 3.0 standard and is intended to be used as an introduction for conceptualizing the use of clocks in the FMI standard to support the simulation of event-based cyber-physical systems. The FMI 3.0 standard supports two kinds of clock-based simulations: Synchronous Clocked Simulation to ensure predictable systems scheduling with multiple simultaneous events and scheduled execution to facilitate real-time simulations comprising multiple black-box models by allowing fine-grained control over the computation time of submodels. The formalization is a basis for developing tools for orchestrating, verifying and validating the composition of multiple FMUs. The formalization is provided as an accessible VDM-SL specification. Full article

This work introduces a process to develop a tool-independent, high-fidelity, ray tracing-based light detection and ranging (LiDAR) model. This virtual LiDAR sensor includes accurate modeling of the scan pattern and a complete signal processing toolchain of a LiDAR sensor. It is developed as a functional mock-up unit (FMU) by using the standardized open simulation interface (OSI) 3.0.2, and functional mock-up interface (FMI) 2.0. Subsequently, it was integrated into two commercial software virtual environment frameworks to demonstrate its exchangeability. Furthermore, the accuracy of the LiDAR sensor model is validated by comparing the simulation and real measurement data on the time domain and on the point cloud level. The validation results show that the mean absolute percentage error $\left(MAPE\right)$ of simulated and measured time domain signal amplitude is $1.7\%$. In addition, the $MAPE$ of the number of points $N_{points}$ and mean intensity $I_{mean}$ values received from the virtual and real targets are $8.5\%$ and $9.3\%$, respectively. To the author’s knowledge, these are the smallest errors reported for the number of received points $N_{points}$ and mean intensity $I_{mean}$ values up until now. Moreover, the distance error $d_{error}$ is below the range accuracy of the actual LiDAR sensor, which is 2 cm for this use case. In addition, the proving ground measurement results are compared with the state-of-the-art LiDAR model provided by commercial software and the proposed LiDAR model to measure the presented model fidelity. The results show that the complete signal processing steps and imperfections of real LiDAR sensors need to be considered in the virtual LiDAR to obtain simulation results close to the actual sensor. Such considerable imperfections are optical losses, inherent detector effects, effects generated by the electrical amplification, and noise produced by the sunlight. Full article

Establishing interoperability is an essential aspect of the often-pursued shift towards Model-Based Systems Engineering (MBSE) in, for example, aircraft development. If models are to be the primary information carriers during development, the applied methods to enable interaction between engineering domains need to be modular, reusable, and scalable. Given the long life cycles and often large and heterogeneous development organizations in the aircraft industry, a piece to the overall solution could be to rely on open standards and tools. In this paper, the standards Functional Mock-up Interface (FMI) and System Structure and Parameterization (SSP) are exploited to exchange data between the disciplines of systems simulation and geometry modeling. A method to export data from the 3D Computer Aided Design (CAD) Software (SW) CATIA in the SSP format is developed and presented. Analogously, FMI support of the Modeling & Simulation (M&S) tools OMSimulator, OpenModelica, and Dymola is utilized along with the SSP support of OMSimulator. The developed technology is put into context by means of integration with the M&S methodology for aircraft vehicle system development deployed at Saab Aeronautics. Finally, the established interoperability is demonstrated on two different industrially relevant application examples addressing varying aspects of complexity. A primary goal of the research is to prototype and demonstrate functionality, enabled by the SSP and FMI standards, that could improve on MBSE methodology implemented in industry and academia. Full article

The increasing requirements to further reduce pollutant emissions, particularly with regard to the upcoming Euro 7 (EU7) legislation, cause further technical and economic challenges for the development of internal combustion engines. All the emission reduction technologies lead to an increasing complexity not only of the hardware, but also of the control functions to be deployed in engine control units (ECUs). Virtualization has become a necessity in the development process in order to be able to handle the increasing complexity. The virtual development and calibration of ECUs using hardware-in-the-loop (HiL) systems with accurate engine models is an effective method to achieve cost and quality targets. In particular, the selection of the best-practice engine model to fulfil accuracy and time targets is essential to success. In this context, this paper presents a physically- and chemically-based stochastic reactor model (SRM) with tabulated chemistry for the prediction of engine raw emissions for real-time (RT) applications. First, an efficient approach for a time-optimal parametrization of the models in steady-state conditions is developed. The co-simulation of both engine model domains is then established via a functional mock-up interface (FMI) and deployed to a simulation platform. Finally, the proposed RT platform demonstrates its prediction and extrapolation capabilities in transient driving scenarios. A comparative evaluation with engine test dynamometer and vehicle measurement data from worldwide harmonized light vehicles test cycle (WLTC) and real driving emissions (RDE) tests depicts the accuracy of the platform in terms of fuel consumption (within 4% deviation in the WLTC cycle) as well as NOx and soot emissions (both within 20%). Full article

With the advance in technology in driving vehicles, there is currently more emphasis on developing advanced control systems for better road handling stability and ride comfort. However, one of the challenging problems in the design and implementation of intelligent suspension systems is that there is currently no solution supporting the export of generic suspension models and control components for integration into embedded Electronic Control Units (ECUs). This significantly limits the usage of embedded suspension components in automotive production code software as it requires very high efforts in implementation, manual testing, and fulfilling coding requirements. This paper introduces a new dynamic model of full-car suspension system with semi-active suspension behavior and provides a hybrid sliding mode approach for control of full-car suspension dynamics such that the road handling stability and ride comfort characteristics are ensured. The semi-active suspension model and the hybrid sliding mode controller are implemented as Functional Mock-Up Units (FMUs) conforming to the Functional Mock-Up Interface for embedded systems (eFMI) and are calibrated with a set experimental tests using a 1/5 Soben-car test bench. The methods and prototype implementation proposed in this paper allow both model and controller re-usability and provide a generic way of integrating models and control software into embedded ECUs. Full article

An accurate prediction of the State of Charge (SOC) of an Electric Vehicle (EV) battery is important when determining the driving range of an EV. However, the majority of the studies in this field have either been focused on the standard driving cycle (SDC) or the internal parameters of the battery itself to predict the SOC results. Due to the significant difference between the real driving cycle (RDC) and SDC, a proper method of predicting the SOC results with RDCs is required. In this paper, RDCs and deep learning methods are used to accurately estimate the SOC of an EV battery. RDC data for an actual driving route have been directly collected by an On-Board Diagnostics (OBD)-II dongle connected to the author’s vehicle. The Global Positioning System (GPS) data of the traffic lights en route are used to segment each instance of the driving cycles where the Dynamic Time Warping (DTW) algorithm is adopted, to obtain the most similar patterns among the driving cycles. Finally, the acceleration values are predicted from deep learning models, and the SOC trajectory for the next trip will be obtained by a Functional Mock-Up Interface (FMI)-based EV simulation environment where the predicted accelerations are fed into the simulation model by each time step. As a result of the experiments, it was confirmed that the Temporal Attention Long–Short-Term Memory (TA-LSTM) model predicts the SOC more accurately than others. Full article

High-level modeling languages facilitate system modeling and the development of control systems. This is mainly achieved by the automated handling of differential algebraic equations which describe the dynamics of the modeled systems across different physical domains. A wide selection of model libraries provides additional support to the modeling process. Nevertheless, deployment on embedded targets poses a challenge and usually requires manual modification and reimplementation of the control system. The novel proposed eFMI Standard (Functional Mock-up Interface for embedded systems) introduces a workflow and an automated toolchain to simplify the deployment of model-based control systems on embedded targets. This contribution describes the application and verification of the eFMI workflow using a vertical dynamics control problem with an automotive application as an example. The workflow is exemplified by a control system design process which is supported by the a-causal, multi-physical, high-level modeling language Modelica. In this process, the eFMI toolchain is applied to a model-based controller for semi-active dampers and demonstrated using an eFMI-based nonlinear prediction model within a nonlinear Kalman filter. The generated code was successfully tested in different validation steps on the dedicated embedded system. Additionally, tests with a low-volume production electronic control unit (ECU) in a series-produced car demonstrated the correct execution of the controller code under real-world conditions. The novelty of our approach is that it automatically derives an embedded software solution from a high-level multi-physical model with standardized eFMI methodology and tooling. We present one of the first full application scenarios (covering all aspects ranging from multi-physical modeling up to embedded target deployment) of the new eFMI tooling. Full article

The simulation of an aero-engine control system involves numerous disciplines due to its complex functions and architecture, which generally consist of mechanical, hydraulic and electrical, and electronic systems. For each discipline, the modeling and simulation are usually dependent on different commercial software and tools, which makes the simulation, integration, and verification of system-level models very difficult. To meet this challenge, a multi-domain co-simulation method based on the Functional Mock-up Interface (FMI) standard is proposed to integrate models developed by different software or tools. The simulation and testing results demonstrate that multi-disciplinary model integration and cross-platform simulation based on the FMI standard can be realized for an aero-engine control system, which lays a foundation for high-fidelity control system design, simulation, integration, and testing. Full article

Rigorous model-based design and control for intelligent vehicle suspension systems play an important role in providing better driving characteristics such as passenger comfort and road-holding capability. This paper investigates a new technique for modelling, simulation and control of semi-active suspension systems supporting both ride comfort and road-holding driving characteristics and implements the technique in accordance with the functional mock-up interface standard FMI 2.0. Firstly, we provide a control-oriented hybrid model of a quarter car semi-active suspension system. The resulting quarter car hybrid model is used to develop a sliding mode controller that supports both ride comfort and road-holding capability. Both the hybrid model and controller are then implemented conforming to the functional mock-up interface standard FMI 2.0. The aim of the FMI-based implementation is to serve as a portable test bench for control applications of vehicle suspension systems. It fully supports the exchange of the suspension system components as functional mock-up units (FMUs) among different modelling and simulation platforms, which allows re-usability and facilitates the interoperation and integration of the suspension system components with embedded software components. The concepts are validated with simulation results throughout the paper. Full article

A new approach to lightning transient studies including complex grounding grids is presented in this paper. The grounding system is modeled in Matlab/Simulink based on the transmission line theory. Using a bottom-up approach and considering the properties of the fundamental elements, a detailed view of measurement values will be presented and analyzed. The Matlab/Simulink grounding system models are interfaced for co-simulation with EMTP-RV trough Functional Mock-up Interface (FMI) 2.0. This modeling approach allows the use of the full component library and network design by EMTP-RV to evaluate and analyze the effects of the grounding system and transmission network simultaneously in Matlab/Simulink. The results present a simplified transmission system where a surge is injected, Conseil International des Grands Réseaux Électriques (CIGRE) 1 kA 1.2/50, in far-end of a transmission line. When reaching a substation, the surge is injected into the grounding system through a surge arrester. Full article

Input shaping is an Optimal Control feedforward strategy whose ability to define how and when a flexible dynamical system defined by Ordinary Differential Equations (ODEs) and computer controlled would move into its operative space, without command induced unwanted dynamics, has been exhaustively demonstrated. This work examines the issue of Embedded Internet of Things (IoT) Input Shaping with regard to real time control of multibody oscillatory systems whose dynamics are better described by differential algebraic equations (DAEs). An overhead crane hanging a double link multibody payload has been appointed as a benchmark case; it is a multibody, multimode system. This might be worst scenario to implement Input Shaping. The reasons can be found in the wide array of constraints that arise. Firstly, the reliability of the multibody model was tested on a Functional Mock-Up Interface (FMI) with the two link payload suspended from the trolley by comparing the experimental video tapping signals in time domain faced with the signals extracted from the multibody model. The FFTs of the simulated and the experimental signal contain the same frequency harmonics only with somewhat different power due to the real world light damping in the joints. The application of this approach may be extended to other cases i.e., the usefulness of mobile hydraulic cranes is limited because the payload is supported by an overhead cable under tension that allows oscillation to occur during crane motion. If the payload size is not negligible small when compared with the cable length may introduce an additional oscillatory mode that creates a multibody double pendulum. To give the insight into the double pendulum dynamics by Lagrangian methods two slender rods as payloads are analyzed dealing with the overhead crane and a composite revolute-revolute joint is proposed to model the cable of the hydraulic crane, both assumptions facilitates an affordable analysis. This allows developing a general study of this type of multibody payloads dynamics including its normal modes, modes ratios plus ranges of frequencies expected. Input Shapers were calculated for those multimodes of vibration by convolving Specified Insensitivity (SI) shapers for each mode plus a novel Direct SI-SI shaper well suited to reduce the computational requirements, i.e., the number of the shaper taps, to carry out the convolution sum in real time by the IoT device based on a single microcontroller working as the command generator. Several comparisons are presented for the shaped and unshaped responses using both the multibody model, the experimental FMI set-up and finally a real world hydraulic crane under slewing motion commanded by an analog Joystick connected by two RF modules 802.15.4 to the IoT device that carry out the convolution sum in real time. Input Shaping improves the performances for all the cases. Full article