Browse Topic: Data management

Items (11,587)
Reliable component libraries are the foundation of the engineering process and the starting point for all intelligence within CAD tools. In practice, however, libraries created and maintained by librarians often contain incomplete, inconsistent, or outdated data. This paper introduces the component data consistency and relationship inference AI system, developed within Amoeba software, which addresses these challenges by improving component library quality. The system uses AI to infer component attributes such as component type, gender, color, material, etc. Moreover, it can identify relationships such as the family a connector is associated with based on its attributes and geometry. The system improves data consistency in areas such as resolving mismatched wire size constraints imposed by the connector and cavity components. It also utilizes computer vision to identify common connector footprints, cavity sizes, and 2D symbol geometries. Deployed within Amoeba software, the system has
Phan, DungHorvat, Bryan
The useability of development processes in the automotive sector has decreased in the past years to a level at which their application and true benefit to is being questioned. Such degradation can be attributed to new additions to the processes and introduction of FuSa and Cybersecurity standards. The processes try to keep up with the shift from the traditional ‘plan–implement–test–roll-out' methodology to more agile methods. In addition, process departments typically in charge of these processes, focus on compliance to the letter of the standard to achieve certification, often with little thought to the actual implementation and the process they will be used by their engineering teams. Process growth to meet the needs of new and more complex technologies often mandates the use of new tools, which if implemented incorrectly can lead to unnecessary bureaucracy and additional overheads. Furthermore, the language of these new processes is in a form from assessor, making it difficult for
Weber, MatthiasKmiec, MateuszRomijn, MarcelNedkov, Detelin
The development of electric vehicle powertrains is driven by diverse and often conflicting requirements. In early development phases, these requirements are often vague, incomplete, continuously refined and subject to change as development progresses. Moreover, powertrain designs must be competitive regarding multiple key performance indicators (KPIs) such as performance, cost, energy efficiency, and package integration. This challenges engineers to concurrently develop the powertrain design alongside the requirements on which the design is based on. Managing this combination of uncertain requirements and multi-KPI design optimization represents a complex challenge in automotive engineering. The present work introduces a requirements engineering approach based on OPED (Optimization of Electric Drives). OPED digitalizes the transition from requirements to technical solutions by integrating parametric system models with an AI-based evolutionary optimization algorithm. This enables
Hofstetter, MartinLechleitner, Dominik
Many academic institutions are turning to free and accessible gaming platforms such as Unreal Engine and Unity for research and educational purposes. In the Human Factors Group at the University of Michigan Transportation Research Institute (UMTRI), a multidisciplinary team of 19 students is developing an Unreal Engine-based driving simulator as a research tool to investigate the difficulty of driving roads, among other purposes. For those unfamiliar, Unreal Engine is a real-time 3D development platform that provides visual programming via its Blueprint system. Development on Unreal Engine can be done with C++ as well, but that was not commonly the case for this team. Throughout the course of the project, five significant documentation-related pain points were identified: (1) a lack of consistent documentation formatting and guidelines, (2) a lack of structure to keep information searchable and accessible, (3) code fragmentation and redundant logic, (4) a steep learning curve for new
Erturk, SelayStimec, LukaXin, Jared HongyiTasmaan, AshleyMitani, KateZhang, LuyaGreen, Paul
Crashes involving passenger vehicles increasingly include vehicles equipped with infotainment systems that are unsupported by commercial vehicle system forensics hardware and software. Examiners facing these systems must overcome challenges in acquiring and analyzing user data, requiring an understanding of both digital forensics principles and the proprietary characteristics of the modules. This paper presents a methodology for acquiring data from previously unsupported Lexus infotainment modules, including techniques to bypass CMD42 security locks on SD cards and extract data. Once acquired, the paper outlines methods for analyzing user data through data carving techniques, enabling recovery of information from binary images even when the full file system cannot be reconstructed. Emphasis is placed on maintaining the integrity of the evidence and validating findings through controlled testing. These validation procedures ensure that the recovered information is both accurate and
Burgess, Shanon
Global geopolitical volatility is recognized as a critical threat to the resilience of the electric vehicle battery supply chain. Static, manually updated databases are inadequate for capturing the sector’s rapid dynamics, resulting in significant information gaps for strategic planning. To address this, an Artificial Intelligence-driven methodology is proposed for constructing a comprehensive and dynamic database. An automated pipeline was implemented. First, real-time textual data are collected from curated news and industry sources using specialized web crawlers. Then, the unstructured data obtained undergo preprocessing, including deduplication and cleansing, to ensure quality. A core innovation involves the application of Large Language Models (LLMs) for deep semantic parsing and extraction of structured information. These models are utilized to accurately identify key entities—such as corporations, facilities, and production capacities—and to delineate complex multi-tier
Zhu, JuntongLuo, WeiZhang, XiangYang, ZhifengOu, Shiqi(Shawn)He, Xin
This document provides a summary of names commonly used throughout the industry for aircraft fuel system components. It is a thesaurus intended to aid those not familiar with the lexicon of the industry.
AE-5A Aerospace Fuel, Inerting and Lubrication Sys Committee
The scope of this document is to provide considerations, guidelines, and best practices for extracting knowledge from long-term archival data. The document is intended to cover the data generated across all life cycle stages of an aircraft starting from concept to disposal. This document does not standardize the process, nor does it allow regulatory authorities to recognize the document as an acceptable means of compliance. It is only a guideline document to discover, capture, store, retrieve, process, and consume knowledge.
G-31 Digital Transactions for Aerospace
This SAE Standard applies to directional drilling electronics and tracking equipment of the following types: Tracking transmitter Tracking receiver Telemetry device Remote display This type of tracking equipment is typically used with horizontal earthboring machines as defined in SAE J2022.
MTC9, Trenching and Horizontal Earthboring Machines
Automotive OEMs can derive significant cost savings by reducing the quantity of physical crash tests and thereby accelerate product development, when they follow the Euro NCAP Virtual Testing procedure. It helps in optimizing the overall vehicle development process via more efficient simulations, as well as facilitates in early adoption of new safety regulations. In this pursuit, companies must comply with strict Euro NCAP requirements, which includes transparency and traceability of virtual tests. A major challenge therein is model validation – which requires highly precise detailing and extensive use of data for accurately replicating real physics of the problem. Deploying these workflows into an existing simulation process can be a complicated and time-consuming task, particularly when integrating various simulation and testing methods. A powerful simulation process and data management system (SPDM) can thereby assist companies to automate their entire simulation process, ensures
Thiele, MarkoSharma, Harsh
The exponential growth of connected and autonomous vehicles has significantly escalated cybersecurity threats, compelling automotive Original Equipment Manufacturers (OEMs) to adopt robust and structured Cybersecurity Incident Response (CSIR) capabilities. Current automotive cybersecurity regulations, such as AIS 189 in India and UNECE WP.29 globally, mandate precise frameworks for proactive threat detection, timely response, and comprehensive incident documentation. This research presents an innovative, comprehensive CSIR framework specifically tailored to integrate seamlessly into OEM cybersecurity management processes. Leveraging a combination of real-time monitoring systems, structured threat categorization methodologies, and integrated escalation and communication protocols, the proposed CSIR framework ensures efficient incident handling aligned with stringent regulatory compliance. The framework encompasses advanced methodologies including Vehicle Security Operations Center (VSOC
Chaudhary lng, VikashDesai, ManojChatterjee, AvikChatterjee lng, Avik
In the evolving landscape of the automotive industry, this study presents an innovative approach to developing digital twins for driver profiles, establishing a standardized and scalable procedure for collecting and analyzing driving data on a global scale. The proposed methodology centers on the development of a robust cloud infrastructure, including Data Lake and associated services, designed for efficient storage and processing of large volumes of data from multiple markets and vehicle types. The research introduces an adaptable procedure for data collection campaigns, applicable to diverse global markets and encompassing a wide range of vehicles, from internal combustion engines to electric and hybrid models. A key feature of this approach is the establishment of advanced data decoding protocols, enabling precise interpretation of CAN network information from vehicles of different manufacturers and models, even when the CAN structure is not previously known. The study defines
Arturo, RubioMarín Saltó, AnnaDiaz, FranciscoOlivencia, Sergio
The purpose of this report is to identify systematic approach of formation of India specific automotive database matrix. At first the paper reviews the practices used to prepare automotive dataset catalogue with established pattern to showcase automotive dataset from which appropriate data clusters can be picked up judiciously in order to train ADAS algorithms. The work applies this framework which helps to establish strategy to build a grid in which Indian automotive dataset can be contoured and selection of serviceable data bunches can be picked. This would make sure prompt selection of database aiming model training with valid input. This serves the purpose of implementation and evaluation of varied ADAS levels in India which insist upon good quality of distinguished dataset pertaining to Indian scenarios. The paper describes the approach with the example of AEB scenarios and present appropriate matrix readiness comprising of relevant data objects excluding unnecessary junk data
Behere, Sayali RajendraKarle, ManishKarle, Ujjwala
This paper presents a comprehensive technical review of the Software-Defined Vehicle (SDV), a paradigm that is fundamentally reshaping the automotive industry. We analyze the architectural evolution from distributed Electronic Control Units (ECUs) to centralized zonal compute platforms, examining the critical role of Service-Oriented Architectures (SOA), the AUTOSAR standard, and virtualization technologies in enabling this shift. A comparative analysis of leading High-Performance Computing (HPC) platforms, including NVIDIA DRIVE, Tesla FSD, and Qualcomm Snapdragon Ride, is conducted to evaluate the silicon foundation of the SDV. The paper further investigates key enabling technologies such as Over- the-Air (OTA) updates, Digital Twins, and the integration of Artificial Intelligence (AI) for applications ranging from predictive maintenance to software-defined battery management. We scrutinize the competing V2X communication standards (DSRC vs. C-V2X) and address the paramount
Ahmad, AqueelHemanth, KhimavathKumar, OmKumar, RajivHaregaonkar, Rushikesh Sambhaji
This paper examines the challenges and opportunities in homologating AI-driven Automated Driving Systems (ADS). As AI introduces dynamic learning and adaptability to vehicles, traditional static homologation frameworks are becoming inadequate. The study analyzes existing methodologies, such as the New Assessment/Test Methodology (NATM), and how various institutions address AI incorporation into ADS certification. Key challenges identified include managing continuous learning, addressing the "black-box" nature of AI models, and ensuring robust data management. The paper proposes a harmonized roadmap for AI in ADS homologation, integrating safety standards like ISO/TR 4804 and ISO 21448 with AI-specific considerations. It emphasizes the need for explainability, robustness, transparency, and enhanced data management in certification processes. The study concludes that a unified, global approach to AI homologation is crucial, balancing innovation with safety while addressing ethical
Lujan Tutusaus, CarlosHidalgo, Justin
The tailgate, as the rearmost vehicle opening, plays a pivotal role in defining the rear aesthetic theme while ensuring structural durability and maximizing luggage space. Contemporary automotive design trends highlight an increasing demand for Full width tailgate-mounted tail lamp configurations, which deliver a bold and dynamic visual appeal. Enhanced by animated lighting features, these designs cater to the preferences of Gen Z customers, becoming a decisive factor in purchasing decisions. However, integrating these complex tail lamp structures introduces significant engineering challenges, including increased X-dimension lamp volume, thereby providing reduced design space, and intricate mounting schemes constrained by panel stamping limitations. These factors necessitate the development of innovative joinery strategies and structural definitions to maintain durability targets, including achieving 25,000–30,000 slam cycles without failure, while preserving luggage space. This paper
Beryl, JoshuaMohanty, AbhinabUnadkat, SiddharthSelvan, Veera
Simulation-driven product development involves numerous computer aided engineering (CAE) model iterations, where each version represents a critical difference. Usually, these multiple model versions are generated by hundreds of simulation engineers working in teams distributed across the globe, making functional collaboration a key to effective product development. To manage vast amounts of CAE data generated by engineers working simultaneously on a project, it is imperative to have a robust version management system to track changes in the CAE data. A robust version management is the backbone of an effective simulation data management (SDM) system. It involves capturing and documenting model changes at every design iteration. Accurate documentation of the model changes is crucial as it helps in understanding the model evolution and collaboration among engineers. However, documenting is usually considered a boring and tedious task by many engineers. This often leads to bad change
Thiele, MarkoSharma, Harsh
This paper presents an in-depth study on configuration management for civil aircraft electromechanical systems, grounded in process methodologies and practical experience of configuration management. Beginning with the definition and significance of configuration management, the study analyzes existing configuration management practices in domestic and international aviation enterprises. It systematically examines the requirements and frameworks for configuration management in civil aircraft electromechanical systems, refining critical elements through two primary dimensions: the establishment, refinement and implementation of configuration management processes. Critical refined elements are highlighted to offer actionable insights for civil aviation enterprises in advancing their configuration management practices.
Cai, Yiyang
It is necessary to save fuel, shorten flight time and reduce cost in order to achieve maximum economic benefits. In this paper, based on the flight performance of aircraft, a database based on the optimal index of fuel saving is established, and the corresponding four dimension (4D) trajectory prediction information and vertical profile are generated on this basis. Finally, the vertical guidance simulation is carried out to verify the effectiveness of the algorithm. The algorithm can reduce air traffic congestion and improve airport operation efficiency while saving fuel.
Hui, HuihuiLi, Zhiyi
This specification covers particle size classifications and corresponding particle size distribution requirements for metal powder feedstock conforming to a classification.
AMS AM Additive Manufacturing Metals
The global electronics supply chain has always run in cycles — tight supply followed by sudden gluts — but in recent years, the pace and scale of disruption have accelerated. From semiconductor shortages to shifting trade policies and pandemic-driven bottlenecks, OEMs across every sector have been forced to rethink how they source and secure critical components.
This SAE Aerospace Information Report presents a glossary of terms commonly used in the ground delivery of fuel to an aircraft and pertinent terms relating to the aircraft being refueled.
AE-5A Aerospace Fuel, Inerting and Lubrication Sys Committee
Aviation carbon verification plays a crucial role in China’s achievement of its “dual carbon goals”. Traditional manual sampling methods are difficult to meet the timeliness requirements of the rapidly increasing volume of flight data. A rapid verification system for flight carbon emissions designed based on process reengineering relies on three spatio-temporal verification methods: weekly cycle verification, flight segment verification, and flight tail number verification. A comprehensive verification framework that can replace manual sampling has been constructed. The system adopts a modular architecture, integrating the functions of data management and rapid verification. Experimental results show that in scenarios with 100,000 flight data, the average verification time of the system is 0.12 hours. Compared with manual methods, the efficiency has been greatly improved, and the f1 score has remained stable at over 89.5%. These findings confirm that the system has advantages in both
Ding, WeichenChen, Jingjie
This paper introduces a comprehensive solution for predictive maintenance, utilizing statistical data and analytics. The proposed Service Planner feature offers customers real-time insights into the health of machine or vehicle parts and their replacement schedules. By referencing data from service stations and manufacturer advisories, the Service Planner assesses the current health and estimated lifespan of parts based on metrics such as days, engine hours, kilometers, and statistical data. This approach integrates predictive analytics, cost estimation, and service planning to reduce unplanned downtime and improve maintenance budgeting, aligning with SAE expectations for review-ready manuscripts. The user interface displays current part health, replacement due dates, and estimated replacement costs. For example, if air filter replacement is recommended every six months, the solution uses manufacturer advisories to estimate the remaining life of the air filter in terms of days or
Chaudhari, Hemant Ashok
This paper introduces an AI-powered mobile application designed to enhance vehicle warranty management through real-time diagnostics, predictive maintenance, and personalized support. The system supports multi-modal inputs (text, voice, image, video), integrates real-time On-Board Diagnostics (OBD) data, and accesses OEM warranty terms via secure APIs. It employs supervised, unsupervised, and reinforcement learning to deliver accurate fault detection, tailored recommendations, and automated claim decisions. Contextual analysis and continuous learning improve precision over time. The application also provides service cost estimates, part availability, and proactive maintenance alerts. This approach improves customer satisfaction, reduces warranty costs, and streamlines aftersales support. Utilizing advanced AI and machine learning algorithms, the application interprets customer queries through multiple input modes—text, voice, video, and image—and retrieves relevant information from the
Ramekar, Vedant MadhavChaudhari, Hemant
In view of the complexity of railway engineering structure, the systematicness of professional collaboration and the high reliability of operation safety, this paper studied the spatial-temporal information data organization model with all elements in whole domain for Shuozhou-Huanghua Railway from the aspect of Shuozhou-Huanghua Railway spatial-temporal information security. Taking the unique spatial-temporal benchmark as the main line, the paper associated different spatial-temporal information to form an efficient organization model of Shuozhou-Huanghua Railway spatial-temporal information with all elements in the whole domain, so as to implement the effective organization of massive spatial-temporal information in various specialties and fields of Shuozhou-Huanghua Railway; By using GIS (Geographic Information System) visualization technology, spatial analysis technology and big data real-time dynamic rendering technology, it was realized the real-time dynamic visualization display
Liu, KunYu, HongshengZhu, PanfengLiu, WenbinWang, Yaoyao
According to the engineering characteristics and general control management requirements of large rail transit depots, this paper establishes a set of modular general control management system based on information model through the division of engineering management modules, the application of BIM model of design and construction integration and the application of multi module control network.The relevant engineering application practice shows that the system can effectively solve the problem of the decomposition of the general control module of large-scale complex projects and the scientific estimation of the control management indexs, and has a significant role in improving the integrated management and information management level of large-scale rail transit depot projects.
Zhou, YuweiFeng, WeiminLiu, JinboZhang, GuanglinPeng, Zhonghua
Knowledge of real-world driving behavior is fundamental to the development of drive systems. The derivation of representative requirements or driving cycles for use case-specific vehicle use allows a customer-centered drive system design. These datasets contain data such as distance, standstill times, average accelerations or a customer driving style estimation. In addition, the real-world data can be used for regulatory purposes such as the definition of utility factors or the definition of real driving emission cycles. In a research project funded by FVV e.V., we have developed a universal database software including data storage, user interface and general data plausibility functions for real driving data. The database contains detailed time series measurement data on component and vehicle level such as torque and speed of electric motors and internal combustion engines as well as general mobility data such as driving distance statistics. A key objective of the database development
Sander, MarcelSturm, Axel WolfgangMartínez Medina, ÓscarHenze, RomanKühne, UlfEilts, Peter
An important characteristic of battery electric vehicles (BEVs) is their noise signature. Besides tire and wind noise, noise from auxiliaries as pumps, the electric drive unit (EDU) is one of the major contributors. The dynamic and acoustic behavior of EDUs can be significantly affected by production tolerances. The effects that lead to these scatter bands must be understood to be able to control them better and thus guarantee a consistently high quality of the products and a silent and pleasant drive. The paper discusses a simulation driven approach to investigate production tolerances and their effect on the NVH behavior of the EDU, using high precision transient multi-body dynamic analysis. This approach considers the main effects, influences, and the interaction from elastic structures of electric motor and transmission with accurate gear contact models in a fully coupled way. It serves as virtual end of line test, applicable in all steps of a new EDU development, by increasing
Klarin, BorislavSchweiger, ChristophResch, Thomas
This terminology document is intended to provide a common nomenclature for use in publishing road vehicle aerodynamics data and reports.
Road Vehicle Aerodynamics Forum Committee
Reliable antenna performance is crucial for aircraft communication, navigation, and radar detection systems. However, an aircraft's structure can detune the antenna input impedance and obstruct radiation, creating a range of potential problems from a low-quality experience for passengers who increasingly expect connectivity while in the air, to violating legal requirements around strict compliance standards. Determining appropriate antenna placement during the design phase can reduce risk of costly problems arising during physical testing stages. Engineers traditionally use a variety of CAD and electromagnetic simulation tools to design and analyze antennas. The use of multiple software tools, combined with globally distributed aircraft development teams, can result in challenges related to sharing models, transferring data, and maintaining the associativity of design and simulation results. To address these challenges, aircraft OEMs and suppliers are implementing unified modeling and
Assessing the effect of road grade on the performance evaluation and testing of heavy-duty vehicles (HDVs) requires the efficient construction of a high-quality multi-parameter driving cycle of HDVs. However, existing pure random heuristic methods fail to preserve the driving characteristics of the original driving cycles, resulting in poor-quality outputs. In addition, the randomness inherent in multiple heuristic approaches limits the search efficiency. To address these issues, this study proposes a novel Monte Carlo tree search heuristic method (MCTSHM) for efficiently constructing multi-parameter driving cycles of HDVs. First, a satisfactory criterion model was used to design the objective function for the multi-parameter driving cycle, ensuring the evaluation indices satisfy given constraints. Next, heuristics were designed to maintain the dynamic transition characteristics of driving cycles. An improved Monte Carlo tree search was conducted to efficiently select heuristics more
Zhang, ManPei, ZhenlongHe, SiyuanQian, Xueming
This paper introduces a secure and cost-effective framework for integrating Commercial Off-the-Shelf (COTS) Generative Artificial Intelligence (GenAI) technology into government enterprise solutions. It explores key aspects of GenAI, emphasizing its transformative role in enhancing efficiency and decision-making within government operations. Central to the discussion is a GenAI Feasibility Study [1] conducted by Booz Allen for the Director, Operational Test & Evaluation (DOT&E), which outlines the development of the AI-Enabled Test & Evaluation Module (ATEM) GenAI Knowledge Assistant. The paper also examines critical factors for successful implementation, including use case definition, model selection, data quality, and prompt engineering.
Vandrovec, BryanKruger, JohnBirr, CalvinMazzara, MarkMossy, GlennHimmel, MaxBarnhart, JamesSenger, Jeff
Brake wear emissions are a significant contributor to particle mass (PM) emissions originating from road transport. In Europe, this is taken into consideration by including emission limits for brake wear particles in the legislation. UN GTR (United Nations Global Technical Regulation) No.24 is a technical description of how to measure the particle number (PN) and PM emissions of brakes. PN measurement includes solid particle number (SPN) and total particle number (TPN), meaning excluding and including the volatile particle matter, respectively. In this study, we examine over 500 TPN and SPN emission factors, in terms of SPN-TPN ratio. To interpret the emission factor data, we present results of a characterization of SPN and TPN measurement instruments in a laboratory setting. We discuss the benefits of using a flow splitter in the PN measurement and present an experimental demonstration of its suitability for measurement of brake wear PN. Combining the results of this investigation
Martikainen, SampsaPramstrahler, MadlenWeidinger, ChristophRainer, AndreasEngler, DieterHuber, Michael
Since the torque converter and fluid coupling are commonly used components of automatic transmissions in industry, SAE appointed a committee to standardize terminology, test procedures, data recording, design symbols, and so forth in this field. The following committee recommendations will facilitate a clear understanding for engineering discussions, comparisons, and the preparation of technical papers. The recommended usages represent the predominant practice or the acceptable practice. Where agreement is not complete, alternates have been included for clarification. This SAE Recommended Practice deals only with the physical parts and dimensions and does not attempt to standardize the design considerations, such as the actual fluid flow angle resulting from the physical blade shape.
Automatic Transmission and Transaxle Committee
Terminology within this document is limited to the dynamics and handling characteristics of single track, two-wheeled vehicles.
Motorcycle Technical Steering Committee
To provide standard terminology and definitions with regard to ignition systems for spark-ignited internal combustion engines.
Ignition Standards Committee
In today’s electric age, the definition of ‘high-performance’ is being rewritten, courtesy of electric sports cars, supercars, and hypercars pushing limits that were once thought impossible to reach. Even Formula 1, quite surprisingly to many, has embraced electrification by integrating hybrid electric systems at the pinnacle of motorsport. Every jaw-dropping 0 to 60 mph time or record-breaking lap is backed by a battery system engineered with precision. Increasingly that precision is driven by simulation technology.
The following definitions and illustrations are intended to establish common nomenclature and terminology for driveshafts and their articulating joints used in various drivetrain applications. In addition, useful guidelines are included for the application of driveshafts and their joints. For more specific details, refer to AE-07.
Drivetrain Standards Committee
The advent of EVs, ride sharing, global events such as the pandemic, chip shortage, and increasing dependency on suppliers are just some factors reshaping the automotive business. Consumer sentiment moving from product to experience resulted in more variants being launched at a record pace. Consequently, product development processes need to be more agile and yet more rigorous while bringing about cohesion and alignment across cross-functional teams to launch vehicles on time, on quality, and in budget. Automotive companies have been using Product Lifecycle Management (PLM) solutions for years to manage CAD, change, and BOMs. With changing business scenarios and increasing complexity of products, the sphere of influence of PLM solutions has expanded significantly over the last decade to manage all aspects of product development. Traditionally PLM software focused on integrating with different authoring tools and managing data in a central repository. The PLM solution had multiple such
Prasad, Ajay
Computer-aided synthesis and development tools are essential for discovering and optimizing innovative concepts. Evaluating different concepts and making informed decisions relies heavily on accurate assessments of drive system properties. Estimating these properties in the early stages of development is challenging due to the depth of modelling required. In addition, defined requirements play a critical role in drive system sizing. This paper presents a tool chain for the synthesis of new electrified drive concepts, with emphasis on requirements definition and modelling. The requirements definition method combines market analysis with a generalized calculation and estimation approach, providing a novel perspective. In addition, we introduce mass and cost modelling capabilities integrated into the tool chain. The mass model achieves high accuracy, with deviations of only 1.6 % at the vehicle level and 6.1 % at the component level. Finally, the paper examines the mass and cost
Sturm, AxelHenze, Roman
The decoupling of software from hardware in automotive systems, driven by the rising share of software in modern vehicles, has introduced a paradigm shift, enabling various software configurations on identical hardware platforms. Consequently, ensuring the correct functionality and reliability of the electric and electronic hardware components, testing and commissioning processes in the vehicle production have grown in importance and complexity. However, the efficiency of these processes relies on diverse datasets, for example parameterization data that allows tailored testing based on the vehicle’s equipment configuration. Therefore, the availability and accuracy of this data need to be guaranteed. Data for testing and commissioning, influenced by the digitization of production processes and their planning, is not only facing the challenges of greater software volumes and faster update cycles, but also those arising from legacy processes or the integration of various IT systems into
El Asad, AimanKöhler, KatjaHahn, MichaelReuss, Hans-Christian
Modern vehicles, increasingly electrified and automated, have effectively become computers on wheels, intensifying product complexity and competitive pressure. Concurrently, increasing digitization offers opportunities to derive customer insights from large-scale vehicle data using Knowledge Discovery in Databases (KDD) and Data Mining (DM). Among these techniques, cluster analysis can reveal hidden subgroups that inform more customer-oriented product solutions. However, cluster analysis lacks a definitive ground truth, making it necessary to test numerous parameter settings, preprocessing steps, and clustering algorithms, and then interpret all plausible results. The complexity of real-world customer data such as heterogeneous, privacy-constrained vehicle usage signals further complicates the selection of appropriate methodologies. Each combination of preprocessing and clustering steps must be analyzed to uncover patterns or groups, significantly increasing the time and manual effort
Wegener, Janvan Putten, SebastiaanNeubeck, JensWagner, Andreas
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