Browse Topic: Architecture
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.
The automotive industry faces the challenge of developing vehicles that meet current customer needs while being future-proof. Surveys conducted for this study show that customers are concerned about the financial risks of essential components such as energy storage systems, mainly due to aging and performance degradation, which significantly affect vehicle lifespans. Based on vehicle developer surveys, a clear need for action was identified. Given the rapid technological advancements in electrified drive systems, there is a need for innovative approaches that can easily adapt to changing requirements. Therefore, this paper presents a strategy combining foresight-based planning of system upgrades with product architecture design to create adaptable and sustainable vehicles through modularity. First, dynamic subsystem characteristics are identified to establish future energy storage technology requirements. Subsequently, future energy storage system technologies are examined to determine
The U-Shift IV represents the latest evolution in modular urban mobility solutions, offering significant advancements over its predecessors. This innovative vehicle concept introduces a distinct separation between the drive module, known as the driveboard, and the transport capsules. The driveboard contains all the necessary components for autonomous driving, allowing it to operate independently. This separation not only enables versatile applications - such as easily swapping capsules for passenger or goods transportation - but also significantly improves the utilization of the driveboard. By allowing a single driveboard to be paired with different capsules, operational efficiency is maximized, enabling continuous deployment of driveboards while the individual capsules are in use. The primary focus of U-Shift IV was to obtain a permit for operating at the Federal Garden Show 2023. To achieve this goal, we built the vehicle around the specific requirements for semi-public road
This article introduces a comprehensive cooperative navigation algorithm to improve vehicular system safety and efficiency. The algorithm employs surrogate optimization to prevent collisions with cooperative cruise control and lane-keeping functionalities. These strategies address real-world traffic challenges. The dynamic model supports precise prediction and optimization within the MPC framework, enabling effective real-time decision-making for collision avoidance. The critical component of the algorithm incorporates multiple parameters such as relative vehicle positions, velocities, and safety margins to ensure optimal and safe navigation. In the cybersecurity evaluation, the four scenarios explore the system’s response to different types of cyberattacks, including data manipulation, signal interference, and spoofing. These scenarios test the algorithm’s ability to detect and mitigate the effects of malicious disruptions. Evaluate how well the system can maintain stability and avoid
The global satellite communications (SATCOM) sector is undergoing profound transformation. Fueled by the rapid growth of low Earth-orbit (LEO) constellations, increased government investment, and heightened demand for secure, high-throughput connectivity, the market is projected to expand from $66.75 billion in 2025 to $103.78 billion by 20291, 2. This momentum reflects a broader realignment of priorities across commercial and defense markets: a shift from reliance on legacy geostationary systems toward agile, resilient networks capable of supporting next-generation missions and applications.
Sound power is a commonly used metric to quantify acoustic sources like AC motor in electrified powertrain. Testing for sound power determination is often performed in an anechoic environment to create free-field conditions around the unit. To eliminate the influence of extraneous noise sources, the anechoic facilities must be further isolated from driver and absorber dynamometers. These dynamometers are needed for running the AC motors in the desired speed and load conditions. For early detection of potential issues, it is advantageous to have the capability for engineers to conduct acoustic tests in standard laboratory environments. These may include non-acoustically treated rooms, presence of extraneous noise sources (e.g., driver and absorber dynos), etc. In such environments, sound intensity-based sound power determination methods could be utilized. The sound intensity-based approach is covered in ISO 9614 standard. The norm is to sweep an intensity probe on a sound source in
Demonstrating deadline adherence for real-time tasks is a common requirement in all safety norms. Timing verification has to address two levels: the code level (worst-case execution time) and the scheduling level (worst-case response time). Determining which methodology is suited best depends on the characteristics of the target processor. All contemporary microprocessors try to maximize the instruction-level parallelism by sophisticated performance-enhancing features that make the execution time of a particular instruction dependent on the execution history. On multi-core systems, the execution time additionally is influenced by interference effects on shared resources caused by concurrent activities on the different cores, which are not controlled by the scheduling algorithm. In the avionics domain, the new FAA AC 20-193 / EASA AMC 20-193 guidance documents formalize predictability aspects of multi-core systems and derive adequate measures for timing verification. Timing verification
Researchers from MIT and the Institute of Science and Technology Austria have developed a computational technique that makes it easier to quickly design a metamaterial cell from smaller building blocks like interconnected beams or thin plates, and then evaluate the resulting metamaterial’s properties.
IEEE-1394b, Interface Requirements for Military and Aerospace Vehicle Applications, establishes the requirements for the use of IEEE Std 1394™-2008 as a data bus network in military and aerospace vehicles. The portion of IEEE Std 1394™-2008 standard used by AS5643 is referred to as IEEE-1394 Beta (formerly referred to as IEEE-1394b.) It defines the concept of operations and information flow on the network. As discussed in 1.4, this specification contains extensions/restrictions to “off-the-shelf” IEEE-1394 standards and assumes the reader already has a working knowledge of IEEE-1394. This document is referred to as the “base” specification, containing the generic requirements that specify data bus characteristics, data formats, and node operation. It is important to note that this specification is not designed to be stand-alone; several requirements leave the details to the implementations and delegate the actual implementation to be specified by the network architect/integrator for a
High-efficiency manufacturing involves the transmission of copious amounts of data, exemplified both by trends in the automotive industry and advances in technology. In the automotive industry, products have been growing increasingly complex, owing to multiple SKUs, global supply chains and the involvement of many tier 2 / Just-In Time (JIT) suppliers. On top of that, recalls and incidents in recent years have made it important for OEMs to be able to track down affected vehicles based on their components. All of this has increased the need for OEMs to be able to collect and analyze component data. The advent of Industry 4.0 and IoT has provided manufacturing with the ability to efficiently collect and store large amounts of data, lining up with the needs of manufacturing-based industries. However, while the needs to collect data have been met, corporations now find themselves facing the need to make sense of the data to provide the insights they need, and the data is often unstructured
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