Browse Topic: Electronic control units
The calibration of automotive electronic control units is a critical and resource-intensive task in modern powertrain development. Optimizing parameters such as transmission shift schedules for minimum fuel consumption traditionally requires extensive prototype testing by expert calibrators. This process is costly, time-consuming, and subject to variability in environmental conditions and human judgment. In this paper, an artificial calibrator is introduced – a software agent that autonomously tunes transmission shift maps using reinforcement learning (RL) in a Software-in-the-Loop (SiL) simulation environment. The RL-based calibrator explores shift schedule parameters and learns from fuel consumption feedback, thereby achieving objective and reproducible optimizations within the controlled SiL environment. Applied to a 7-speed dual-clutch transmission (DCT) model of a Mild Hybrid Electric Vehicle (MHEV), the approach yielded significant fuel efficiency improvements. In a case study on
The rapid evolution of electric vehicles (EVs) necessitates advanced electronic control units (ECUs) for enhanced safety, monitoring, and performance. This study introduces an innovative ECU system designed with a modular architecture, incorporating real-time monitoring, cloud connectivity, and crash sensing. The methodology includes cost-effective design strategies, integrating STM32 controllers, CAN bus systems, and widely available sensors for motor RPM and temperature monitoring. Key findings demonstrate that the proposed ECU system improves data reliability, enhances vehicle safety through crash response systems, and enables predictive maintenance via cloud connectivity. This scalable and affordable ECU is adaptable to a broad range of EV models.
Software Defined Vehicle (SDV) is gaining attraction in the automotive industry due to its wide range of benefits like remote software/feature upgrade, scalable functionality, Electronic Control Unit (ECU) commonization, remote diagnostics, increased safety, etc. To obtain all these benefits, ECUs need to be designed accordingly. ECU hardware must be designed to support a range of vehicles with a variety of loading, scalable features, power distribution, levels of processing, and networking architecture. Each domain has unique challenges to make the ECU economical and robust to operating conditions without compromising performance. This paper illustrates the critical hardware design challenges to accommodate a scalable SDV architecture. This paper focuses electrical interface design to support wide range of input/output port loads, scalable functionality, and robust diagnostics. Also, flexibility of microprocessor processing capability, ECU networking, and communication complexity are
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
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).
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.
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
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