Browse Topic: Hardware-in-the-loop (HIL)
ABSTRACT BAE Systems Combat Simulation and Integration Labs (CSIL) are a culmination of more than 14 years of operational experience at our SIL facility in Santa Clara. The SIL provides primary integration and test functions over the entire life cycle of a combat vehicle’s development. The backbone of the SIL operation is the Simulation-Emulation-Stimulation (SES) process. The SES process has successfully supported BAE Systems US Combat Systems (USCS) SIL activities for many government vehicle development programs. The process enables SIL activities in vehicle design review, 3D virtual prototyping, human factor engineering, and system & subsystem integration and test. This paper describes how CSIL applies the models, software, and hardware components in a hardware-in-the-loop environment to support USCS combat vehicle development in the system integration lab
ABSTRACT The cannon Concept Technology Demonstrator is a U.S. military proof of concept 155 mm self-propelled howitzer platform. It demonstrated fully automated ammunition handling, weapon stabilization, and mobility in a 24-ton test platform. The next generation Concept Technology Demonstrator served as a transfer mechanism of capabilities from a heavyweight howitzer platform to a notional future lightweight self-propelled howitzer. Simulation model data of the demonstration platform vehicle response during weapon firing was contrasted with the initial notional lightweight system’s firing stability analysis. The results of this comparison stimulated an updated correlation effort. This correlation effort utilized test firings without chassis stabilizing spades to reveal physics-based simulation model fidelity requirements for future programs. Observations of simulation and system performance were used to define a systematic approach to simulation model fidelity improvements and
Summary This paper discusses the latest techniques in vehicle modeling and simulation to support ground vehicle performance and fuel economy studies, enable system design optimization, and facilitate detailed control system design. The Autonomie software package, developed at Argonne National Laboratory, is described with emphasis on its capabilities to support Model-in-the-Loop, Software-in-the-Loop (SIL), Component-in-the-Loop (CIL), and Hardware-in-the-Loop simulations. Autonomie supports Model-Based Systems Engineering, which is growing in use as ground vehicles become more sophisticated and complex, with many more subsystems interacting within the vehicle and the environmental conditions in which the vehicles operate becoming more challenging and varied. With the advent of hybrid powertrains, the additional dimension of vehicle architecture has become one of the design variables that must be considered. This complexity results in the need for a simulation tool that is capable of
Proprietary, black box, and other hard-to-model subsystems are a leading source of schedule and labor cost across simulation supported analysis and lifecycle management. Using AI/ML technologies to rapidly develop and deploy digital twins of Hardware in the Loop (HWIL) and software systems reduces the Non-Recurring Engineering (NRE) in Modeling and Simulation (M&S) and supports validation of existing software digital twins. This approach also allows for portability of obsolete or proprietary components into a broader range of simulations or applications without exposing critical technologies. We present results of multiple case studies applying AI to black box components of interest to the ground vehicle community
Traditional live testing of autonomous ground vehicles can be augmented through use of digital twins of the test environment, the vehicle mobility models, and the vehicle sensors. These digital twins combined with the autonomous software under test allow testers to inject faults, weather, obstacles, find edge case scenarios, and collect information to understand the decision making of the autonomous software under test. With this new capability, autonomous ground vehicles can now be tested in four stages. The first stage is testing the autonomous software using digital twins. In this stage with the help of a High-Performance Computer thousands of scenarios can be run. Once issues are communicated and addressed, stage two, hardware in the loop testing can begin. Hardware in the loop uses simulators that already exist to test systems such as autonomous convoys with a virtual leader and a live follower. Stage three employs a live virtual constructive approach by using one vehicle to test
Hardware-in-the-loop (HIL) testing is part of automotive V-design which is commonly used in automotive industries for the development of Electronic Control Unit (ECU). HIL test platform provides the capacity to test the ECU in a controlled environment even with scenarios that would be too dangerous or impractical to test on real situation, also the ECU can be tested even before the actual plant under building. This paper presents a HIL test platform for the validation of a seat ECU. The HIL platform can also be used for control and diagnostics algorithm development. The HIL test platform consists of three parts: a real time target machine (dSPACE SCALEXIO AutoBox), an ECU (Magna Seating M12 Module), and a signal conditioning unit (Load Box). The ECU produces the control commands to the real-time target machine through load box. The real time target machine hosts the plant model of the power seat which includes the kinematics and dynamics of the seat movements. The virtual model within
Validation plays a crucial role in any Electronic Development process. This is true in the development of any automotive Electronic Control Unit (ECU) that utilizes the Automotive V process. From Research and Development (R&D) to End of Line (EOL), every automotive module goes through a plethora of Hardware (HW) and Software (SW) testing. This testing is tedious, time consuming, and inefficient. The purpose of this paper is to show a way to streamline validation in any part of the automotive V process using Python as a driving force to automate and control Hardware-in-the-loop (HIL) / Model-in-the-loop (MIL) / Software-in-the-loop (SIL) validation. The paper will propose and outline a framework to control test equipment, such as power supplies and oscilloscopes, load boxes, and external HW. The framework includes the ability to control CAN communication signals and messages. A visual Graphical User Interface (GUI) has also been created to provide simplified operation to the user
Autonomous Emergency Braking (AEB) systems play a critical role in ensuring vehicle safety by detecting potential rear-end collisions and automatically applying brakes to mitigate or prevent accidents. This paper focuses on establishing a framework for the Verification & Validation (V&V) of Advanced Driver Assistance Systems (ADAS) by testing & verifying the functionality of a RADAR-based AEB ECU. A comprehensive V&V approach was adopted, incorporating both virtual and physical testing. For virtual testing, closed-loop Hardware-in-Loop (HIL) simulation technique was employed. The AEB ECU was interfaced with the real-time hardware via CAN. Data for the relevant target such as the target position, velocity etc. was calculated using an ideal RADAR sensor model running on the real-time hardware. The methodology involved conducting a series of test scenarios, including various driving speeds, obstacle types, and braking distances. Automation was leveraged to perform automated testing and
The evolution of automotive Electronic Control Unit (ECU) technology brings the additional safety, comfort, and control to the vehicle. With an exponential increase in the complexity involved in modern-day ECU, it is very important to verify and validate robustness, functionality, and reliability of ECU software [1]. As of now, Hardware in loop [HIL] and Vehicle in Loop validations are well known software functional validation methods. However, these methods require physical setup, which can incur more cost and time during the development phase. In recent years, ECU virtualization gained attention for development and validation of automotive ECUs [2]. The goal is to minimize the effort on software testing. This paper focuses on virtualization of Electric Vehicle (EV) powertrain system using SIL approach. The objective is to provide an adaptable EV-virtualization environment for virtual-ECU (vECU) verification and validation. This paper focuses on standardization of SIL simulation setup
System engineering-based approach is now ubiquitous in the automotive industry. It is a disciplined approach that ensures that targets are clearly defined and met through a structured and holistic approach. In this paper, we report an application of a systems engineering-based methodology for developing seating system features. It starts with a Business Requirement Document (BRD), which enlists the business requirements of a feature. We then developed a Logical Architecture Diagram (LAD) on a Simulink environment, which is an initial proposal for designing the logic to realize the desired functionality. As a next step, we perform Functional Failure Analysis (FFA) on the LAD to identify potential failure modes. We propose a few ways to mitigate the identified failures or modify the design so that these failures are rendered inconsequential to the end user. Based on the updated LAD, a System Requirement Document (SRD) is created, which contains all the requirements corresponding to the
Automotives are provided with a lot of intelligence that monitors, controls, actuates, and diagnose the various aspects of vehicle functionalities. One of the critical parameters required to monitor is Vehicle fuel level. Fuel level in the vehicle is a key input for engine performance, drivability, and fuel level indication in Instrumentation cluster for customer. Most economic and reliable fuel level sensor is resistive sensor with float. The purpose of this paper is to address the wrong fuel level indication in Vehicle level. Wrong fuel level indication may be due to malfunction of Instrumentation cluster signal input or Fuel level sensor function. To verify this, Instrumentation cluster is tested with HIL system instead of real time Fuel level sensor. By configuring the HIL module to analogue resistance channel, cluster is tested for fuel level bar indication. Fuel level sensor is tested by Vehicle level fuel calibration and exact issue is simulated. The failed fuel level sensor is
The advent of BS6 coupled with RDE emission norms has increased the development efforts and costs due to the shear amount of testing and validation on real engines and vehicles which are necessitated by these stringent norms. Front-loading of tasks by moving actual vehicle and engine tasks on to virtual setup, will reduce the development efforts and costs significantly. This front-loading of tasks on to a LABCAR would need real time and highly accurate plant models, tools to parameterize these plant models and accurate data driven models to predict dynamic parameters like emissions. In this collaborative work between Maruti Suzuki India Ltd and ETAS India, ETAS VVTB and ICE plant models were parameterized with the data generated on engine test with ASCMO Global DoE test plan by using ASCMO MOCA. The ASCMO Global test plan also ensures the coverage of data points across the entire engine operating space. These plants models were optimized to an accuracy level of more than 95%. The
Hybrid electric aircraft propulsion is an emerging technology that presents a variety of potential benefits along with technical integration challenges. Developing these new propulsion architectures with their complex control systems, and ultimately proving their benefit, is a multistep process. This process includes concept development and analysis, dynamic simulation, hardware-in-the-loop testing, full-scale testing, and so on. This effort is being revolutionized and indeed enabled by new digital tools that support increasing the technology readiness level throughout the maturation process. As part of this Digital Transformation, NASA has developed a suite of publicly available digital tools that facilitate the path from concept to implementation. This paper describes the NASA-developed tools and puts them in the context of control system development for hybrid electric aircraft propulsion. The three MATLAB®-based software packages are the Toolbox for the Modeling and Analysis of
Automated driving, electrification, cloud computing and the push toward software-defined vehicles are forcing automotive and commercial-vehicle developers to revamp design strategies. Tools suppliers are moving to help engineers develop and verify solutions that address the complete vehicle environment, a task that requires a growing number of design tools. During the recent dSPACE World Conference in Munich, Germany, several vehicle manufacturers described their strategies for coping with these trends. dSPACE, which supplies hardware/software-in-the-loop (HIL/SIL) tools, announced plans to see if tool makers can find a way to help developers by making it easier to integrate data created using different development software
Autonomous vehicle (AV) algorithms need to be tested extensively in order to make sure the vehicle and the passengers will be safe while using it after the implementation. Testing these algorithms in real world create another important safety critical point. Real world testing is also subjected to limitations such as logistic limitations to carry or drive the vehicle to a certain location. For this purpose, hardware in the loop (HIL) simulations as well as virtual environments such as CARLA and LG SVL are used widely. This paper discusses a method that combines the real vehicle with the virtual world, called vehicle in virtual environment (VVE). This method projects the vehicle location and heading into a virtual world for desired testing, and transfers back the information from sensors in the virtual world to the vehicle. As a result, while vehicle is moving in the real world, it simultaneously moves in the virtual world and obtains the situational awareness via multiple virtual
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