Browse Topic: Electronic control units
Emergence of Software Defined Vehicles (SDVs) presents a paradigm shift in the automotive domain. In this paper, we explore the application of Model-Based Systems Engineering (MBSE) for comprehensive system simulation within the SDV architecture. The key challenge for developing a system model for SDV using traditional methods is the document centric approach combined with the complexity of SDV. This MBSE approach can help to reduce the complexity involved in Software-Defined Vehicle Architecture making it more flexible, consistent, and scalable. The proposed approach facilitates the definition and analysis of functional, logical, and physical architecture enabling efficient feature and resource allocation and verification of system behaviour. It also enables iterative component analysis based on performance parameters and component interaction analysis (using sequence diagrams).
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
This paper examines the effectiveness of optimizing energy management in hybrid electric vehicles by integrating adaptive machine learning algorithms with the energy management electronic control unit (ECU). Existing traditional rule-based energy management and control strategies of power distribution between internal combustion and battery struggle to adapt to dynamic driving conditions, such as rapid acceleration, frequent stop-and-go traffic, and varying terrain. These scenarios often result in sub-optimal energy utilization and performance, as the fixed rules struggle to account for the immediate demands and inefficiencies that arise in such conditions. In conditions like that, rapid acceleration demands a sudden increase in power, which can lead to inefficient fuel consumption if not managed properly, while frequent stop-and-go traffic conditions can cause the battery to drain and lead to increased fuel consumption. Varying terrain can also lead to improper power distribution
An industry-first 3D laser-based, computer-vision system can monitor and control the application of adhesive beads as tiny in width as two human hairs. This unique inspection system for electronic assemblies operates at speeds of 400 to 1,000 times per second, considerably quicker and more effective than conventional 2D systems. “Difficulty in precisely dispensing adhesives or sealants, especially in extremely small or complex electronic assemblies, can lead to over-application, under-application, bubbles, or incorrect location of the adhesive bead,” Juergen Dennig, president of Ann Arbor, Michigan-headquartered Coherix, told SAE Media. Improper application of joining material on electronic control units (ECUs) and power control units (PCUs) can result in poor adhesion, material voids and short circuits.
Energy efficiency in both internal combustion engine (ICE) and electric vehicles (EV) is a strategic advantage of automotive companies. It provides a better user experience that emanates amongst others from the reduction in operation expenses, particularly critical for fleets, and the increase in range. This is especially important in EVs where customers may experience range anxiety. The energetical impact of using the air conditioning system in vehicles is not negligible with power consumptions in the range of kilowatts, even with a stopped vehicle. This becomes particularly important in areas with high temperature and humidity levels where the usage of the air conditioning systems becomes safety factor. In such areas, drivers are effectively forced to use the air conditioning system continuously. Hence, the air conditioning system becomes an ideal choice to deploy control strategies for optimized energy usage. In this paper, we propose and implement a control strategy that allows a
A new industry-first open platform for developing the software-defined vehicle (SDV) combines processing, vehicle networking and system power management with integrated software. NXP Semiconductors' new S32 CoreRide Platform was designed to run “multiple time-critical, safety-critical, security-critical applications in parallel,” Henri Ardevol, executive vice president and general manager of Automotive Embedded Systems for NXP Semiconductors, told SAE Media. NXP's new foundation platform for SDVs differs from the traditional approach of using multiple electronic control units (ECUs), each designed to handle specific vehicle system control tasks. Since each unit requires its own integration work, the integration workload exponentially increases with each additional ECU on a vehicle.
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
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
Letter from the Special Issue Editors
Next-generation vehicle electrical architectures will be based on highly sophisticated domain controllers called HPCs (high-performance computers). These HPCs are more alike gaming PCs than as the traditional ECUs (electronic control units). Today’s diagnostic communication protocol, e.g., UDS (Unified Diagnostic Services, ISO 14229-1) was developed for ECUs and is not fit to be used for HPCs. There is a new protocol being developed within ASAM, SOVD (service-oriented vehicle diagnostics), which is based on a RESTful API (REpresentational State Transfer Application Programming Interface) sent over http (hypertext transfer protocol). But OBD (OnBoard Diagnostic) under the emissions regulation is not yet updated for this shift of protocols and therefore vehicle manufacturers must support older OBD protocols (e.g., SAE J1979-2) during the transition phase. Another problem is that some of the software packages may fall under the DEC-ECU (diagnostic or emission critical electronic control
This document establishes recommended practices to validate acceptable corrosion performance of metallic components and assemblies used in medium truck, heavy truck, and bus and trailer applications. The focus of the document is methods of accelerated testing and evaluation of results. A variety of test procedures are provided that are appropriate for testing components at various locations on the vehicle. The procedures incorporate cyclic conditions including corrosive chemicals, drying, humidity, and abrasive exposure. These procedures are intended to be effective in evaluating a variety of corrosion mechanisms as listed in Table 1. Test duration may be adjusted to achieve any desired level of exposure. Aggravating conditions such as joint rotation, mechanical stress, and temperature extremes are also considered. This document does not address the chemistry of corrosion or methods of corrosion prevention. For information in these areas, refer to SAE J447 or similar standard.
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