Browse Topic: Hardware-in-the-loop (HIL)
PEM electrolysis system has characteristic of excellent performance such as fast response, high electrolysis efficiency, compact design and wide adjustable power range. It provides a sustainable solution for the production of hydrogen, and is well suited to couple with renewable energy sources. In the development process of PEM electrolysis controller, this article originally applied the V-mode development process, including simulation modeling, RCP testing, and HIL testing, which can provide guidance in the practical application of electrolytic hydrogen production. In this paper, we present modeling and simulation study of PEM water electrolysis system. Model of electrolytic cell, hydrogen production subsystem and thermal management subsystem are constructed in Matlab/Simulink. Controller model was designed based on PI control strategy. A rapid prototyping controller with MPC5744 chip was used to develop the control system of electrolytic hydrogen production system. Hardware in the
SBW(Steer-by-wire) is a steering system that transmits the driver’s request and gives feedback to the driver through electrical signals. This system eliminates the mechanical connection of the traditional steering system, and can realize the decoupling of the steering wheel and the road wheel. In addition, this system has a perfect torque feedback system, which can accurately and delicately feedback the road surface information to the driver. However, vehicle driving deviation is one of the most common failure modes affecting vehicle performance in the automotive aftermarket, this failure mode can exacerbates tire wear, reducing their life cycle, at the same time, the driver must apply a counter torque to the steering wheel for a long time to maintain straight-line travel during driving. This increases the driver’s operational burden and poses safety hazards to the vehicle’s operation. Based on the steer-by-wire system and vehicle driving deviation characteristics, this paper proposes
In the rapidly evolving field of automotive engineering, the drive for innovation is relentless. One critical component of modern vehicles is the automotive ECU. Ensuring the reliability and performance of ECU is paramount, and this has led to the integration of advanced testing methodologies such as Hardware-in-the-Loop (HIL) testing. In conjunction with HIL, the adoption of Continuous Integration (CI) and Continuous Testing (CT) processes has revolutionized how automotive ECU are developed and validated. This paper explores the integration of CI and CT in HIL testing for automotive ECU, highlighting the benefits, challenges, and best practices. Continuous Integration and Continuous Test (CI/CT) are essential practices in software development. Continuous Integration process involves regularly integrating code changes into the main branch, ensuring that it does not interfere with the work of other developers. The CI/CT server automatically build and test code whenever a new commit is
In non-cooperative environments, unmanned aerial vehicles (UAVs) have to land without artificial markers, which is a key step towards achieving full autonomy. However, the existing vision-based schemes have the common problems of poor robustness and generalization, and the LiDAR-based schemes have the disadvantages of low resolution, high power consumption and high weight. In this paper, we propose an UAV landing system equipped with a binocular camera to preform 3D reconstruction and select the safe landing zone. The whole system only consists of a stereo camera, and the innovation of the solution is fusing the stereo matching algorithm and monocular depth estimation(MDE) model to get a robust prediction on the metric depth. The whole landing system consists of a stereo matching module, a monocular depth estimation (MDE) module, a depth fusion module, and a safe landing zone selection module. The stereo matching module uses Semi-Global Matching (SGM) algorithm to calculate the
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
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
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
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
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
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