Browse Topic: Architecture
With the continuous improvement of performance requirements for aviation equipment, the importance and complexity of hydraulic systems as the core carrier of flight control are becoming increasingly prominent. The cleanliness of aircraft hydraulic pipelines directly affects the reliability and flight safety of hydraulic systems, and it is necessary to use specialized cleaning and testing equipment during design and manufacturing to achieve efficient cleaning. The design of traditional cleaning equipment relies on experience-driven development, with mechanical, hydraulic, and electrical systems developed independently. There are problems such as unclear requirement definitions, low efficiency of interdisciplinary collaboration, and lagging validation, making it difficult to achieve the goal of forward design. Therefore, this study introduces Model-based Systems Engineering (MBSE) method in the development process of pipeline cleaning test equipment, proposes a modeling process based on RFLP (Requirements-Function-Logical-Physical), and uses SysML system modeling language to construct a top down design model system for aircraft hydraulic pipeline cleaning equipment. Through requirement analysis modeling, functional behavior definition, and system architecture design, the significant advantages of MBSE method in the development of complex aviation test equipment have been verified, effectively improving the bold design capability and top down design efficiency. MBSE method can not only improve the design efficiency of equipment, but also promote the intelligent and efficient operation of equipment, which has important significance for the development of intelligent manufacturing and electromechanical integration technology.
KPIT experts address challenges of maintaining legacy architectures while introducing centralized compute, OTA, new energy platforms and AI layers - driving integration complexity and validation effort. KPIT Technologies is providing the executive leadership for this year's SAE COMVEC, a forum for global leaders in trucking, construction equipment, agricultural machinery and defense vehicles to address the technologies, regulations and innovations impacting transportation today and in the coming years. The theme for COMVEC 2026 (www.sae.org/events/comvec), which takes place in Schaumburg, Illinois, from September 29 to October 1, is “Resolving Current Challenges While Reimagining the Future.” “For commercial and off-highway, this theme captures a structural contradiction the industry lives with every day: transform the entire product architecture while continuing to deliver near-zero downtime, tight margins and proven reliability,” Satish Kumar, senior VP at KPIT, said in a pre-event interview with Truck & Off-Highway Engineering.
For decades, hydraulic systems have been relied upon to do all the heavy lifting in aerospace. They are powerful, reliable, and deeply embedded in how aircraft are designed, to the extent that - for many engineers - they are simply part of the landscape. Now, however, things are beginning to change. From advanced air mobility platforms now entering certification to next-generation commercial aircraft on 10-year horizons, electric and electro-hydraulic actuation is steadily replacing the heavy, centralized hydraulic architectures that have defined flight control for decades. Understanding why means stepping back from the actuator itself and looking at the aircraft as a whole system - and, increasingly, as an integrated motion control challenge.
The turbine hybrid electric propulsion system is an important form of green aviation. Unlike the single form of aviation power scheme, the hybrid energy system is flexible in architecture, uses two or more energy forms, and has diverse energy sources. Under different mission requirements, it needs to meet the requirements of mass balance, energy balance, and power demand, etc. Therefore, The control and distribution management between different energy systems have become the key to hybrid power, and power management technology is one of the key challenges in the development of aviation hybrid power control systems. This paper reviews the current structural forms of aviation turbine hybrid electric propulsion systems, analyzes the current research status of power management technology for aviation hybrid systems, and points out that the online power management method based on optimization is the best power management technology solution for turbine hybrid electric propulsion systems. Establishing a high-precision and realtime on-board power calculation model, breaking through the power management method based on the integrated flight and engine, and improving the applicability of the power management method throughout the service life are important directions for promoting the development of online power management technology.
Software-defined, highly customizable vehicle architectures drastically increase the number of hardware–software constellations that must be validated, especially under safety and timing constraints. Traditional unit and integration testing, as well as current regression and combinatorial methods, cannot practically cover this configuration space or reliably capture emergent effects arising from complex interactions, such as bandwidth contention and non-linear latency behavior. This work presents a proof-of-concept for predictive, situational validation of self-describing hardware and software components within realistic automotive E/E architectures. Proposing a novel Machine Learning- (ML) based method for early systemic feasibility prediction of automotive configurations using Graph Neural Networks (GNNs). Specifically, the subclass Graph Isomorphism Networks (GINs) is applied to predict the compatibility of a randomly composed configuration of software and hardware components, assessing both structural compatibility and functional stability. The trained models achieve recall and accuracy above 90%, even when detailed behavioral metadata is hidden during training, indicating that systemic incompatibilities are learnable from topological features alone. Results were achieved from training on a realistic, synthetic dataset representing less than 10e−27% of all possible permutations without finetuning or further parameter optimization. It demonstrates the potential of GIN-based graph learning to enable early, automated feasibility assessment, substantially reducing testing time and development effort for modular, personalized, and update-capable vehicle architectures.
The UMV Peoplemover 2+2 is part of a modular vehicle family (Urban Modular Vehicle) that includes derivatives for passenger and cargo transport in urban environments. The platform supports automated movers as well as conventionally controlled vehicles with a human driver, ensuring high flexibility across applications. The modular platform enables the extensive use of common parts, allowing the efficient and cost-effective realization of multiple vehicle variants. The increased share of common parts also improves sustainability by reducing derivative-specific parts, material usage, and production complexity. A drivable demonstrator of the UMV Peoplemover 2+2 has already been realized. The vehicle is designed for the automated transport of up to four occupants in a 2+2 vis-à-vis seating arrangement and is targeted at demand-oriented shuttle services. While the drivable demonstrator validated the proof of concept, it lacked the core Level 4 hardware and software stack for automated driving functions. To address this limitation, we deployed a software-defined vehicle architecture to the concept. This paper introduces the novel e/e-architecture and software stack enabling the Peoplemover 2+2 to initiate its first shuttle service at the German Aerospace Center (DLR e.V.) in Stuttgart. We further detail the deployed multi-modal sensor suite, comprising modern solid-state LiDARs and a 4D imaging radar, which were carefully selected to meet the operational design domain requirements while also serving as a versatile research platform for future advanced perception studies. Finally, we analyze the SDV-based modular software stack, which facilitates rapid application development through straightforward switching between commercial, open-source, and in-house software domains, and supports parallel execution of domain-specific functions across all three software sources.
Electrical/Electronic Architectures (EEAs) are continuously evolving to meet newly emerging demands. In recent years, major drivers of this evolution have been the increasing software-defined nature of vehicles and the push toward automated driving. Key technologies such as edge-enhanced functions, vehicle-to-vehicle communication, and service-oriented architectures are therefore the focus of current research efforts. This paper presents a vision of how these technologies can be used to enable cooperation between vehicles, illustrated by using parked vehicles as edge nodes. These are typically seen as obstructions, as they significantly increase the risk of missing or misinterpreting vulnerable road users such as pedestrians or cyclists. Our proposed approach to counteract this problem is the use of the parked vehicles themselves as edge nodes that support object detection or even trajectory planning. Current research primarily considers smart traffic infrastructure, roadside units, and other vehicles as potential edge nodes. Including parked vehicles as edge nodes means that, instead of acting solely as obstacles, we leverage their built-in sensors to contribute to cooperative awareness. While such cooperation will enhance the safety of automated vehicles in urban areas, several challenges arise. In this paper, we discuss how data traceability, decision-making in the presence of conflicting information, and incentive mechanisms for owners of parked vehicles can be addressed. Based on these challenges, the paper outlines requirements for future cooperative architecture and highlights the role of edge-enhanced functions, Vehicle-to-Vehicle (V2V) communication, and service-oriented architectures in enabling fully automated driving.
Humanoid robots have long been the focus of science fiction, but today they are making their way into industrial environments thanks to the simultaneous maturing and convergence of multiple systems. Technology advances have driven the development of humanoid robots that have a wide range of movement and can perform demanding jobs around the clock without tiring. While currently representing a small share of all industrial robot deployments, the humanoid robot market is projected to grow rapidly over the next few years. In fact, estimates suggest the market could reach over $4 billion by 2030. This growth is being driven by factors such as labor shortages, falling costs, and the need for more flexible automation.
Precision agriculture, also known as smart farming, was once reserved for early adopters or large-scale operations, but is now an expectation within the farming industry. Across various regions and farm sizes, smart farming techniques are changing the way crops are planted as well as how they are monitored and harvested. However, farmers today are under increasing pressure to reduce labor, decrease chemical inputs, conserve water and operate in tighter windows. Couple this with factors such as narrow seasonal windows, productivity demands and safety considerations, and the need for smarter decisions becomes imperative. Going one step further, global food demands and environmental pressures are further increasing demand for precise, accurate and intelligent farming solutions.
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