Browse Topic: Software-in-the-loop (SIL)
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
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
The Virtual Autonomous Navigation Environment (VANE) is a set of tools that have been developed over a decade to assist autonomy developers in building autonomous systems. VANE has high-fidelity, physics-based sensors and vehicle models that interact with virtual environments built by utilizing decades of experience in characterizing environmental conditions. These models and environments are used in software-in-the-loop simulations to assist in the development and evaluation of autonomous vehicles in a cost-effective and time-sensitive manner. The software-in-the-loop simulations have been verified with data from concurrent physical testing and are used by autonomy developers to improve the safety, scalability, and cost effectiveness of testing autonomous vehicles
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
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
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
The front camera module is a fundamental component of a modern vehicle’s active safety architecture. The module supports many active safety features. Perception of the road environment, requests for driver notification or alert, and requests for vehicle actuation are among the camera software’s key functions. This paper presents a novel method of testing these functions virtually. First, the front camera module software is compiled and packaged in a Docker container capable of running on a standard Linux computer as a software in the loop (SiL). This container is then integrated with the active safety simulation tool that represents the vehicle plant model and allows modeling of test scenarios. Then the following simulation components form a closed loop: First, the active safety simulation tool generates a video data stream (VDS). Using an internet protocol, the tool sends the VDS to the camera SiL and other vehicle channels. The camera SiL performs its functions (e.g., object
Simulation of real time situations is a time tested software validation methodology in the automotive industry and array of simulation technologies have been in use for decades and is widely accepted and been part & parcel of software development cycle. While software that is being developed needs detailed plan, architecture and detailed design, it also matters during its development that, it is built in the right way from the very beginning and is fine tuned constantly. Especially for Software-In-Loop simulation (SIL), plenty of practices/tools/techniques/data are being used for simulation of system/software behavior. When it comes to choosing the right simulation technique and tools to be adopted, often there are discussions revolve around cost, feasibility, effectiveness, man-power, scalability, reusability etc. As automotive software validation is data driven, we deal with myriad of ground truth data for simulations, ranging from vehicle dynamics to vehicle models to environment
Autonomous vehicles (AVs) are self-contained vehicles equipped with control systems to execute various tasks. The Lane Departure Warning (LDW) system is widely employed to prevent the most common cause of vehicle collisions. An autonomous lane-departure system will aid and reduce such collisions. When the vehicle is at risk of drifting or departing its lane, the LDW system monitors its relative position to the lane edge and sends an alarming warning signal to the driver. This work uses an ML-based technique to detect lane markers in an Indian context using a high-resolution camera mounted on the car. Considering that, the LDW system requires three primary operations. The camera geometry information is used to divide the acquired image into two parts: a road part and a non-road component. Then, to circumvent the obstacles caused by the perspective effect, inverse perspective mapping is applied. Then, using a sliding window technique, lane markers are filtered, and Canny edge detection
In this article, a formation flying technique designed for a multiple unmanned aerial vehicles (multi-UAV) system to provide low-cost and efficient solution for civilian and military applications is presented. First, a modular leader-follower formation algorithm was developed to accomplish the formation flying with off-the-shelf low-cost components and sensors. Second, a proportional-integral-derivative (PID) controller was utilized for velocity control of the UAVs to maintain the tight formation. Third, a particle swarm optimization-optimized reciprocal velocity obstacles (PSO-RVO) algorithm was utilized for obstacles avoidance and collision avoidance between the UAVs while navigating, with the aid of sonar ranging sensors onboard. The formation flying algorithm developed was tested through both simulation and experiment using two quadcopters with global positioning system (GPS) signals. For the simulation, the algorithm developed was tested on a virtual quadcopter using an open
Due to the ever increasingly stringent emission regulations for passenger vehicles, the efficiency and performance increase of Spark Ignition (SI) engines have been under the focus of the engine manufacturers. The quest for efficiency and performance increase has led to the development of increasingly complex powertrains and control strategies. The development process requires novel methods that feature a smooth transition between the real and the virtual prototypes. Furthermore, to reduce the development time and cost, developing an engine simulator with a low computational effort and good accuracy, which predicts the engine behavior on the entire operating range, plays a crucial role. This work proposes an Artificial Intelligence-based engine simulator for a Spark Ignition engine. The simulator relies on Neural Networks for the calculation of the main combustion metrics. In the first part of this paper, the data acquired at the engine test cell are analyzed. A shallow neural network
This paper presents the evolution of a series of connected, automated vehicle technologies from simulation to in-vehicle validation for the purposes of minimizing the fuel usage of a class-8 heavy duty truck. The results reveal that an online, hierarchical model-predictive control scheme, implemented via the use of extended horizon driver advisories for velocity and gear, achieves fuel savings comparable to predictions from software-in-the-loop (SiL) simulations and engine-in-the-loop (EiL) studies that operated with a greater degree of powertrain and chassis automation. The work of this paper builds on prior work that presented in detail this predictive control scheme that successively optimizes vehicle routing, arrival and departure at signalized intersections, speed trajectory planning, platooning, predictive gear shifting, and engine demand torque shaping. This paper begins by outlining the controller development progression from a previously published engine-in-the-loop study to
Multispeed eDrive or eAxles arguably benefit the overall performance and efficiency of an electric vehicle. The majority of the benefits can be derived from rightsizing and optimal control over the gear shift sequence under various driving scenarios. This paper focuses on developing an optimal shift schedule and precise shift controls for a special utility three-wheeled electric vehicle using a Model-Based Design approach. The supervisory control logic is implemented using Stateflow. Further, the entire shift mechanism with the controller, stepper motor and driver is modeled in the Matlab-Simulink-Simscape environment. A novel solution of integrating the SolidWorks CAD model of the gearbox provided by the manufacturer with the shift mechanism using Simulink Multibody is presented. Finally, the controller model and C- code test methods are presented to validate the behavior and functional requirements using MIL, SIL and PIL on a prototype microcontroller chip
Automated driving systems (ADS) are one of the key modern technologies that are changing the way we perceive mobility and transportation. In addition to providing significant access to mobility, they can also be useful in decreasing the number of road accidents. For these benefits to be realized, candidate ADS need to be proven as safe, robust, and reliable; both by design and in the performance of navigating their operational design domain (ODD). This paper proposes a multi-pronged approach to evaluate the safety performance of a hypothetical candidate system. Safety performance is assessed through using a set of test cases/scenarios that provide substantial coverage of those potentially encountered in an ODD. This systematic process is used to create a library of scenarios, specific to a defined domain. Beginning with a system-specific ODD definition, a set of core competencies are identified. These core competencies are then considered both in isolation and in conjunction with other
With the enhancements in vehicle electrification and autonomous vehicles, Traffic systems are also being improved at an accelerated rate to aid the development of improving fuel economy standards. For this to be possible, it is essential that traffic can be accurately modeled and predicted. The existing toolsets are proprietary and expensive and traffic modeling is not a trivial task due to its dependence on various factors such as place, time, and weather. To address these issues, an entirely open-source Software-In-Loop (SIL) fleet-focused traffic modeling toolset has been developed with the ability to take environmental factors with powertrain-in-the-loop into account leveraging Simulation of Urban Mobility (SUMO) and python. The proposed SIL toolset encompasses the creation of a microscopic traffic distribution which accounts for the usual traffic trends of a typical day. Parameters such as the number of vehicles entering the network and the speed of all the vehicles at a time of a
Model-based system simulations play a critical role in the development process of the automotive industry. They are highly instrumental in developing embedded control systems during conception, design, validation, and deployment stages. Whether for model-in-the-loop (MiL), software-in-the-loop (SiL) or hardware-in-the-loop (HiL) scenarios, high-fidelity plant models are particularly valuable for generating realistic simulation results that can parallel or substitute for costly and time-consuming vehicle field tests. In this paper, the development of a powertrain plant model and its correlation performance are presented. The focus is on the following modules of the propulsion systems: transmission, driveline, and vehicle. The physics and modeling approach of the modules is discussed, and the implementation is illustrated in Amesim software. The developed model shows good correlation performance against test data in dynamic events such as launch, tip-in, tip-out, and gearshifts. To
The future emission standards, including real driving emissions (RDE) measurements are big challenges for engine and after-treatment development. Also for development of a robust control system, in real driving emissions cycles under varied operating conditions and climate conditions, like low ambient temperature as well as high altitude are advanced physical-based algorithms beneficial in order to realize more precise, robust and efficient control concepts. A fast-running novel physical-based ignition delay model for diesel engine combustion simulation and additionally, for combustion control in the next generation of ECUs is presented and validated in this study. Detailed chemical reactions of the ignition processes are solved by a n-heptane mechanism which is coupled to the thermodynamic simulation of in-cylinder processes during the compression and autoignition phases. All relevant engine operating conditions, like engine speed, in-cylinder charge mass and temperature as well as
This paper outlines the modeling process in SysML (Systems Modeling Language) in context of MBSE (Model Based Software Engineering) as well as the MBD (Model-Based Design) in Simulink and we compare the models to get useful information into software. For this goal, we propose the use of an RM/SM tool (Requirements Management and Systems Modeling) (3SL Cradle) and Matlab/Simulink to model the system, do the system validations, and finally embed the generated code. For automotive systems, the development process is visualized through the V-Model, which leads to the right choice of components, the integration of the system and the project realization. The first step in V-Model handles the requirements management for the development, i.e., the requirements for a project will be collected in respect to the stakeholder’s needs and system limitations. Then, the next steps consist of modeling the system based on its requirements, going through simulation, system validation through Model-In-the
The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is currently going through several modeling and testing stages to develop models that represent the P3 PHEV powertrain the team is building as part the EcoCAR 3 competition. The model development process consists of several major steps. First, Model-in-the-Loop (MIL) testing is conducted to validate a conventional vehicle model, down-select a desired powertrain configuration, and generate initial vehicle technical specifications. HEVT is pursuing a performance powertrain that balances high performance with minimal energy consumption. Initial MIL modeling results yield an IVM-60 mph time of 4.9 seconds and an overall UF-weighted 4-cycle energy consumption of 560 Wh/km. MIL modeling provides an initial reference to compare subsequent vehicle modeling. Following the MIL process, Software-in-the-Loop (SIL) is used to develop a vehicle model from the ground-up that facilitates the transition to Hardware-in-the-Loop (HIL) testing. This
ISO 26262, a functional safety standard for motor vehicles, was published in November 2011. Although motorcycles are not included in the scope of application of the current edition of ISO 26262, it is expected that motorcycles will be included in the next revision. However, it is not appropriate to directly apply automotive safety integrity levels (ASILs) to motorcycles because the situation of usage in practice presumably differs between motorcycles and motor vehicles. In our previous study, we newly defined safety integrity levels for motorcycles (MSILs) and proposed that the levels of MSILs should correspond to levels one step lower than those of ASILs; however, we did not investigate the validity of their connections. Accordingly, in this research, we validated the connections. We defined the difference of levels of SILs between motorcycles and motor vehicles as the difference of target values of random hardware failure rates specified in ISO 26262-5. By taking into account the
Model-Based Design (MBD) has been widely used for automotive embedded software design. Automobile manufacturers and suppliers have often underlined the importance of an unified approach for electrical and electronic (E/E) system design. In this scenario, MBD can provide a mutual benefit for stakeholders due to the share of information, workflow, and tool-chain. In this paper, we highlight MBD application for automotive Exterior Lighting System (ELS) design. In fact, ELS is an event-driven control system typically needed for car lighting and signalization, in particular at night. Furthermore, this system is mandatory for every road vehicle according to current Brazilian laws and legislation. Also, it provides safety drive preventing car accidents and pedestrian injury. In this context, we present how to boost ELS design using MBD concepts. ELS was developed in three MBD workflow (Model-In-the-Loop, Software-In-the-Loop, and Processor-In-the-Loop), from supplier’s viewpoint. The results
Current market drivers for automotive and light commercial engines and powertrain systems are mainly the new CO2 emission regulations all over the world and the pollutant emission reduction in the emerging markets, at minimal system cost. For both reasons, the adoption of a regulated electric low pressure fuel pump is very advantageous for electronically controlled diesel systems, customized for the emerging markets. Usually, the fuel delivery from the feed pump is performed at the maximum flow rate and a pressure regulator discharges the exceeding fuel amount either from the rail or upstream the high pressure pump. Therefore, at part load, the electric feed pump flow is higher than the request for engine power generation. For the purpose of this paper, the low pressure fuel pump is controlled for fuel delivery according to the engine request (reduced fuel consumption), thus avoiding the use of a pressure regulator valve (reduced cost). The development of the system was carried out
Active Safety (AS) and Advanced Driver Assistance Systems (ADAS) can nowadays be considered as distributed embedded software systems where independent microprocessor systems communicate together using different communication protocols. Typical AS or ADAS functionality is then realized by several microprocessors communicating with each other. AS and ADAS systems interact with other Electronic Control Units in a vehicle via communication networks and gather vehicle's surroundings via camera, radar or laser sensors. Quality assurance and safety standards combined with increasing complexity and reliability demands related to vision sensing, radar sensing and data fusion, often together with a short time to market, make the development of such systems challenging. As the number of important road scenarios for the system grows, mathematical modelling and computer simulation become important engineering tasks that aim to assure the required quality and compliance with safety standards. This
A vehicle's suspension system is the basic component which decides its dynamic performance. It is designed to separate the vehicle body and its passengers or payload from vibrations arising due to road disturbances, at the same time to ensure that the tires stay in adequate contact with the road surface. Challenges in suspension design many a time's leads in a compromise between the conflicting demands of ride comfort and road holding. Vehicles having soft suspension isolate the vehicle body from the higher frequencies in suspension but reduce the ability of the dampers to control the wheel movements which leads to poor road holding. Conversely, hard suspension provides more road holding but transmits more of the suspension movement to the body; in turn provide a less comfortable ride. The development of active/ semi active suspension has addressed both these needs and provides optimum level of ride comfort and road holding which results in the safety and driving pleasure. This paper
The Hybrid Electric Vehicle Team of Virginia Tech (HEVT) is participating in the 2012-2014 EcoCAR 2: Plugging in to the Future Advanced Vehicle Technology Competition series organized by Argonne National Lab (ANL), and sponsored by General Motors Corporation (GM) and the U.S. Department of Energy (DOE). The goals of the competition are to reduce well-to-wheel (WTW) petroleum energy consumption (PEU), WTW greenhouse gas (GHG) and criteria emissions while maintaining vehicle performance, consumer acceptability and safety. Following the EcoCAR 2 Vehicle Development Process (VDP), HEVT is designing, building, and refining an advanced technology vehicle over the course of the three year competition using a 2013 Chevrolet Malibu donated by GM as a base vehicle. The team selected a series-parallel Plug-In Hybrid Electric Vehicle (PHEV) with P2 (between engine and transmission) and P4 (rear axle) motors, a lithium-ion battery pack, an internal combustion engine, and an automatic transmission
EcoCAR 2 is the premiere North American collegiate automotive competition that challenges 15 North American universities to redesign a 2013 Chevrolet Malibu to decrease the environmental impact of the Malibu while maintaining its performance, safety, and consumer appeal. The EcoCAR 2 project is a three year competition headline sponsored by General Motors and U.S. Department of Energy. In Year 1 of the competition, extensive modeling guided the Colorado State University (CSU) Vehicle Innovation Team (VIT) to choose an all-electric vehicle powertrain architecture with range extending hydrogen fuel cells, to be called the Malibu H2eV. During this year, the CSU VIT followed the EcoCAR 2 Vehicle Design Process (VDP) to develop the H2eV's electric and hydrogen powertrain, energy storage system (ESS), control systems, and auxiliary systems. From the design developed in Year 1 of the EcoCAR 2 competition, a Malibu donated by General Motors was converted into a concept validating prototype
In the last few years, we have seen a tremendous increase in the rise in product complexity due to advances in technology and aircraft system functionality enhancement. The Model-based Design (MBD) process has helped manage the complexity of these systems while making product development faster by bringing more effective tools and methods to the entire process. Developing software using MBD has required extensive, sophisticated tool-chains that allow for efficient rapid controls prototyping, automatic code generation, and advanced validation and verification techniques using model-in-the-loop (MIL), software-in-the-loop (SIL), and hardware-in-the-loop (HIL) for both component testing and integration testing. However, the MBD process leads to generation of large volumes of data artifacts and work-products throughout the V-Cycle. The various components of these environments, from models to parameters to tests, can be inundating, and variants and versions of these artifacts lead to even
In CVT, it is essential to optimize Clamping Force in Pulley to improve fuel efficiency. Clamping force in pulley is shaft force to control primary pulley and secondary pulley. It is determined by pulley ratio, input engine torque in CVT and safety factor for protecting belt slip. It is difficult to calculate correct clamping force and detect belt slip. Generally speaking, CVT has a tendency to have excessive Safety factor to prevent belt slip. This excessive safety factor in clamping force leads to lower fuel efficiency. In order to find an optimal clamping force, ‘the minimum clamping force’ which will not induce belt slip should be determined even during decreasing clamping force. Furthermore, clamping force should be maintained near the ‘minimum clamping force’. For this, following logics was developed First, the logic to calculate first safety ratio of belt, Second, the logic to detect belt slip state, Third, the logic to calculate the optimal clamping force. This logic was
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