Browse Topic: Product development

Items (3,913)
The rear swing arm, a crucial motorcycle component, connects the frame and wheel, absorbing the vehicle’s load and various road impacts. Over time, these forces can damage the swing arm, highlighting the need for robust design to ensure safety. Identifying potential vulnerabilities through simulation reduces the risk of failure during the design phase. This study performs a detailed fatigue analysis of the swing arm across different road conditions. Data for this research were collected from real-vehicle experiments and simulation analyses, ensuring accuracy by comparing against actual performance. Following CNS 15819-5 standards, road surfaces such as poorly maintained, bumpy, and uneven roads were tested. Using Motion View, a comprehensive multi-body dynamic model was created for thorough fatigue analysis. The results identified the most stress-prone areas on the swing arm, with maximum stress recorded at 109.6N on poorly maintained roads, 218.3N on bumpy surfaces, and 104.8N on
Chiou, Yi-HauHwang, Hsiu-YingHuang, Liang-Yu
The two-wheeler industry features a diverse range of transmission systems catering to varied riding preferences and market demands. Manual transmissions offer direct gear control, favored by enthusiasts for its precision and customizable performance. Automatic transmissions simplify riding, especially in urban settings, eliminating manual gear shifts and reducing rider fatigue. Understanding the dynamics of transmission systems in the two-wheeler space is crucial for manufacturers, engineers, policymakers, and riders alike. It informs product development, regulatory compliance efforts, and market positioning initiatives in an increasingly competitive and innovation-driven industry landscape. DCT (Dual Clutch Transmission) and manual transmissions represent extremes in rider engagement, automation, and cost. While DCT offers seamless gear changes and convenience at a higher price point, manual transmissions provide direct control and a tactile experience with lower initial costs. Riders
Kundu, Prantik
Hybrid powertrain for motorcycles has not been widely adopted to date but has recently shown significant increased interest and it is believed to have great potential for fuel economy containment in real driving conditions. Moreover, this technology is suitable for the expected new legislations, reduced emissions and enables riding in Zero Emissions Zones, so towards a more carbon neutral society while still guaranteeing “motorcycle passion” for the product [1, 2]. Several simulation tools and methods are available for the concept phase of the hybrid system design, allowing definition of the hybrid components and the basic hybrid strategies, but they are not able to properly represent the real on-road behaviour of the hybrid vehicle and its specific control system, making the fine tuning and validation work very difficult. Motorcycle riders are used to expect instant significant torque delivery on their demand, that is not properly represented in legislative cycles (e.g. WMTC); rider
Antoniutti, ChristianSweet, DavidHounsham, Sandra
China Automotive Engineering Research Institute Co., Ltd (CAERI) has completed a new vehicle aero-acoustic wind tunnel (AAWT), which is located in Chongqing, China, and has been in operation for 5 years. To help addressing the Chinese vehicle market’s need to improve fuel economy, reduce exhaust emissions, and decrease product development period, the wind tunnel was designed and implemented to achieve a high degree of automation for vehicle testing next to a high aerodynamic and acoustic test accuracy for product development. The CAERI wind tunnel was in operation in June 2019, achieving a top speed of 250 km/h. A 5-belt rolling road system with a long center belt for proper wake simulation is installed inside, a test section with very low static pressure gradient and background noise. Wind tunnel calibration and customized measurement activities can be performed with an overhead traversing system. In the present paper, the main facilities of the AAWT are described next to necessary
Xu, LeiZhu, XijiaWang, QingyangBu, HanPeng, ChaoShi, FengYang, ChaoHuang, TaoZeng, YiZeng, XiangyiWallmann, SteffenMünstermann, HenningWittmeier, FelixMercker, EdzardBlumrich, Reinhard
Physical testing is required to assess multiple vehicles in different conditions, specially to validate those related to regulations. The acoustic evaluations have difficulties and limitations in physical test; cost and time represent important considerations every time. Additionally, the physical validation happens once a prototype has been built, this takes place in a later phase of the development. Sound pressure is measured to validate different requirements in a vehicle, horn sound is one of these and it is related to a regulation of united nations (ECE28). Currently the validation happens in physical test only and the results vary depending on the location of the horn inside the front end of every vehicle. [7] In this article, the work for approaching a virtual validation method through CAE is presented with the intention to get efficiency earlier in product development process.
Alonso, LilianaCruz, RacielAlvarez, Ezequiel
Interest in Battery-Driven Electric Vehicles (EVs) has significantly grown in recent years due to the decline of traditional Internal Combustion Engines (ICEs). However, malfunctions in Lithium-Ion Batteries (LIBs) can lead to catastrophic results such as Thermal Runaway (TR), posing serious safety concerns due to their high energy release and the emission of flammable gases. Understanding this phenomenon is essential for reducing risks and mitigating its effects. In this study, a digital twin of an Accelerated Rate Calorimeter (ARC) under a Heat-Wait-and-Seek (HWS) procedure is developed using a Computational Fluid Dynamics (CFD) framework. The CFD model simulates the heating of the cell during the HWS procedure, pressure build-up within the LIB, gas venting phenomena, and the exothermic processes within the LIB due to the degradation of internal components. The model is validated against experimental results for an NCA 18650 LIB under similar conditions, focusing on LIB temperature
Gil, AntonioMonsalve-Serrano, JavierMarco-Gimeno, JavierGuaraco-Figueira, Carlos
Electric vehicles rely on accurate estimation of battery states to operate safely and efficiently. Traditionally, the state estimation is pack level and based on empirical models developed to capture the dynamics of a representative battery pack and hence falls short in accounting for cell-to-cell variations. These variations become more pronounced as the cells age within a battery pack under non-homogeneous mechanical, thermal, manufacturing, and electrical conditions. It is challenging to adapt the traditional physics-based model to changing battery dynamics in real-time. To improve the state estimation at the cell level, a data-driven approach utilizing streamed data from vehicles enabled by connectivity has been shown in this paper. While traditional data-driven approaches result in large models and require large quantities of data for training, the proposed method relies on combining the underlying physics of the electrochemical model with novel data-driven modeling techniques
Gupta, ShobhitHegde, BharatkumarHaskara, IbrahimShieh, Su-YangChang, Insu
Model-Based Systems Engineering (MBSE) enables requirements, design, analysis, verification, and validation associated with the development of complex systems. Obtaining data for such systems is dependent on multiple stakeholders and has issues related to communication, data loss, accuracy, and traceability which results in time delays. This paper presents the development of a new process for requirement verification by connecting System Architecture Model (SAM) with multi-fidelity, multi-disciplinary analytical models. Stakeholders can explore design alternatives at a conceptual stage, validate performance, refine system models, and take better informed decisions. The use-case of connecting system requirements to engineering analysis is implemented through ANSYS ModelCenter which integrates MBSE tool CAMEO with simulation tools Motor-CAD and Twin Builder. This automated workflow translates requirements to engineering simulations, captures output and performs validations. System
Upase, BalasahebShroff, Roopesh
This paper presents a Digital Twin approach based on Machine Learning (ML), aimed at creating software-based sensors to reduce the auxiliary devices of the vehicle and enabling predictive maintenance, thus reducing carbon footprint. The solution is applied to the electric Lubrication Oil Pump (eLOP), a crucial component within a vehicle's powertrain system. The proposed eLOP Digital Twin integrates ML-based sensors to estimate critical parameters such as temperature, pressure and flow rate, reducing the reliance on physical sensors and associated hardware. This approach minimizes manufacturing complexity and cost, enhancing energy efficiency during both production and operation. Furthermore, the Digital Twin facilitates predictive maintenance by continuously monitoring the component's performance, enabling early detection of potential failures and optimizing maintenance schedules. This leads to lower energy consumption and reduced emissions throughout the component's lifecycle. The
Khan, JalalD'Alessandro, StefanoTramaglia, FedericoFauda, Alessandro
Automotive chassis components are considered as safety critical components and must meet the durability and strength requirements of customer usage. The cases such as the vehicle driving through a pothole or sliding into a curb make the design (mass efficient chassis components) challenging in terms of the physical testing and virtual simulation. Due to the cost and short vehicle development time requirement, it is impractical to conduct physical tests during the early stages of development. Therefore, virtual simulation plays the critical role in the vehicle development process. This paper focuses on virtual co-simulation of vehicle chassis components. Traditional virtual simulation of the chassis components is performed by applying the loads that are recovered from multi-body simulation (MBD) to the Finite Element (FE) models at some of the attachment locations and then apply constraints at other selected attachment locations. In this approach, the chassis components are assessed
Behera, DhirenLi, FanTasci, MineSeo, Young-JinSchulze, MartinKochucheruvil, Binu JoseYanni, TamerBhosale, KiranAluru, Phani
In the modern automotive industry, improving fuel efficiency while reducing carbon emissions is a critical challenge. To address this challenge, accurately measuring a vehicle’s road load is essential. The current methodology, widely adopted by national guidelines, follows the coastdown test procedure. However, coastdown tests are highly sensitive to environmental conditions, which can lead to inconsistencies across test runs. Previous studies have mainly focused on the impact of independent variables on coastdown results, with less emphasis on a data-driven approach due to the difficulty of obtaining large volumes of test data in a short period, both in terms of time and cost. This paper presents a road load energy prediction model for vehicles using the XGBoost machine learning technique, demonstrating its ability to predict road load coefficients. The model features 27 factors, including rolling, aerodynamic, inertial resistance, and various atmospheric conditions, gathered from a
Song, HyunseungLee, Dong HyukChung, Hyun
Electric motors are critical components in Electric Vehicle (EV) & industrial applications. In case of EVs electric motor has a direct impact on the functionality, range and general user experience. Traditional maintenance procedures have several major limitations such as, leaving no choice but to use the expensive warranty claims, restricted predictive maintenance, unavailability of useful data, reducing resale value, and ultimately poor customer satisfaction. The process of building a virtual duplicate of an actual motor that can replicate the physical system in real time is known as the Digital Twin (DT) technology. Here, the DT technology-based monitoring and maintenance is initiated on permanent magnet synchronous motor (PMSM) used in traction, thus helping to overcome the drawbacks of traditional maintenance system. To provide a holistic approach to real time motor monitoring, motor management, ensuring enhanced reliability, efficiency, and predictive maintenance capabilities
Valiyil, RinshaR, BharathNair, AnushPuthiyapurayil, ShamalRavi, Reshma
Over recent years, BorgWarner has intensified its efforts to explore and leverage trending technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to enhance products and processes. This includes digital twin technology, which has potential use cases for system behavior analysis, product optimization and predictive maintenance. This paper outlines the development process of a digital twin for a commercial vehicle battery, which serves as a demonstrator and learning platform for this technology. In order to assess the feasibility as well as hard- and software requirements, a cloud-based digital twin demonstrator was developed, integrating vehicle telemetry data with physics-based battery electric and thermal models, and an aging prediction algorithm. The key components are an Internet of Things (IoT) gateway, simulation models, data processing and ingestion pipelines, a machine learning algorithm for anomaly detection, and visualizations of telemetry and simulation
Bongards, AnitaLiu, XiaobingBeemer, MariaGajowski, DanielRama, NeerajShah, KeyaFallahdizcheh, Amirhossein
As global warming and environmental problems are becoming more serious, tires are required to achieve a high level of performance trade-offs, such as low rolling resistance, wet braking performance, driving stability, and ride comfort, while minimizing wear, noise, and weight. However, predicting tire wear life, which is influenced by both vehicle and tire characteristics, is technically challenging so practical prediction method has long been awaited. Therefore, we propose an experimental-based tire wear life prediction method using measured tire characteristics and the wear volume formula of polymer materials. This method achieves practical accuracy for use in the early stages of vehicle development without the need for time-consuming and costly real vehicle tests. However, the need for improved quietness and compliance with dust regulations due to vehicle electrification requires more accuracy, leading to an increase in cases requiring judgment through real vehicle tests. To address
Ando, Takashi
The intensive use of software applications in modern vehicles has highlighted the critical role of Systems Engineering (SE) in the automotive industry. These “computers on wheels” are thoroughly interconnected, by their own connections and with the cloud, due to the advancement of Electronic Control Units (ECU) technologies and the widespread use of sensors transmitting real-time data. This interconnectedness and the level of software abstraction that are known today, significantly escalates the complexity of these systems. This has made it necessary to adopt an approach that is flexible to change, structured, agile, and traceable. The modern approach to SE, now model-based, offers numerous advantages over the previous paradigm, which was predominantly document-based. MBSE (Model-Based Systems Engineering) emerges as a contemporary approach, providing the scalability needed for engineering teams to develop robust products. Its “model-based” essence ensures that the model acts as the
Mendes de Oliveira, Arthur HendricksReis, Pedro AlmeidaAnunciação, GabrielVinícius Carlos de Lima, JonathanSarracini Júnior, FernandoGarcia, Matias Ezequiel
Following early adoption, the BEV market has shifted towards a mass market strategy, emphasizing on crucial attributes, such as system cost reduction and range extension. System efficiency is crucial in BEV product development, where efficiency metric influenced greatly vehicle range and cost. For instance, higher iDM efficiency reduces the need for larger battery, cutting cost, or extends range with the same battery size. BorgWarner adopted Digital Twin technology to optimize Integrated Drive Module (iDM) within a vehicle ecosystem. Digital Twin comprises high-fidelity physics based numerical tool suites offering greater degree of freedom to engineers in designing, sizing, optimizing a component versus system benefit tradeoff, thus enabling most efficient product design within economic constraints. BorgWarner’s Analytical System Development (ASD) plan used as framework provides a global unified process for tool development and validation, ensuring the digital print of a real product
Bossi, AdrienBourniche, EricLeblay, ArnaudDavid, PascalNanjundaswamy, Harsha
The automotive industry is amidst an unprecedented multi-faceted transition striving for more sustainable passenger mobility and freight transportation. The rise of e-mobility is coming along with energy efficiency improvements, greenhouse gas and non-exhaust emission reductions, driving/propulsion technology innovations, and a hardware-software-ratio shift in vehicle development for road-based electric vehicles. Current R&D activities are focusing on electric motor topologies and designs, sustainability, manufacturing, prototyping, and testing. This is leading to a new generation of electric motors, which is considering recyclability, reduction of (rare earth) resource usage, cost criticality, and a full product life-cycle assessment, to gain broader market penetration. This paper outlines the latest advances of multiple EU-funded research projects under the Horizon Europe framework and showcases their complementarities to address the European priorities as identified in the 2Zero
Armengaud, EricRatz, FlorianMuñiz, ÁngelaPoza, JavierGarramiola, FernandoAlmandoz, GaizkaPippuri-Mäkeläinen, JenniClenet, StéphaneMessagie, MaartenD’amore, LeaLavigne Philippot, MaevaRillo, OriolMontesinos, DanielVansompel, HendrikDe Keyser, ArneRomano, ClaudioMontanaro, UmbertoTavernini, DavideGruber, PatrickRan, LiaoyuanAmati, NicolaVagg, ChristopherHerzog, MaticWeinzerl, MartinKeränen, JanneMontonen, Juho
In the automotive industry, there have been many efforts of late in using Machine Learning tools to aid crash virtual simulations and further decrease product development time and cost. As the simulation world grapples with how best to incorporate ML techniques, two main challenges are evident. There is the risk of giving flawed recommendations to the design engineer if the training data has some suspect data. In addition, the complexity of porting simulation data back and forth to a Machine Learning software can make the process cumbersome for the average CAE engineer to set up and execute a ML project. We would like to put forth a ML workflow/platform that a typical CAE engineer can use to create training data, train a PINN (Physics Informed Neural Network) ML model and use it to predict, optimize and even synthesize for any given crash problem. The key enabler is the use of an industry first data structure named mwplot that can store diverse types of training data - scalars, vectors
Krishnan, Radha
E-mobility is revolutionizing the automotive industry by improving energy-efficiency, lowering CO2 and non-exhaust emissions, innovating driving and propulsion technologies, redefining the hardware-software-ratio in the vehicle development, facilitating new business models, and transforming the market circumstances for electric vehicles (EVs) in passenger mobility and freight transportation. Ongoing R&D action is leading to an uptake of affordable and more energy-efficient EVs for the public at large through the development of innovative and user-centric solutions, optimized system concepts and components sizing, and increased passenger safety. Moreover, technological EV optimizations and investigations on thermal and energy management systems as well as the modularization of multiple EV functionalities result in driving range maximization, driving comfort improvement, and greater user-centricity. This paper presents the latest advancements of multiple EU-funded research projects under
Ratz, FlorianBäuml, ThomasKompara, TomažKospach, AlexanderSimic, DraganJan, PetraMöller, SebastianFuse, HiroyukiParades Barros, EstebanArmengaud, EricAmati, NicolaSorniotti, AldoLukesch, Walter
The Tour engine is a novel split-cycle internal combustion engine (ICE) that divides the four-stroke Otto cycle of a conventional ICE between two separate cylinders, an intake and compression cylinder and a second expansion and exhaust cylinder, interconnected by an innovative charge transfer mechanism. The engine working fluid, air and fuel, is inducted into the engine and compressed by a dedicated compression cylinder, transferred with minimal pressure loss via an input port to a specifically designed combined spool shuttle transfer mechanism and combustion chamber. It is then ignited and then transferred from the combustion chamber via an exit port to a separate expansion cylinder where it is expanded and exhausted from the engine. The primary advantage of the Tour engine is that it provides the engineering freedom to independently design, control and optimize the compression, combustion, and expansion processes within a slider-crank piston engine. By decoupling the compression
Tour, OdedCho, KukwonHofman, YehoramAnderson, BradleyKemmet, RyanMorris, DanielWahl, MichaelBhanage, PratikSivan, EhudTour, GiladAtkinson, ChrisTour, Hugo
The vehicle wake region is of high importance when analyzing the aerodynamic performance of a vehicle. It is characterized by turbulent separated flow and large low-pressure regions that contribute significantly to drag. In some cases, the wake region can oscillate between different modes which can pose an engineering challenge during vehicle development. Vehicles that exhibit bimodal wake behavior need to have their drag values recorded over a sufficient time period to take into account the low frequency shift in drag signal, therefore, simulating such vehicle configurations in CFD could consume substantial CPU hours resulting in an expensive and inefficient vehicle design iterations process. As an alternative approach to running simulations for long periods of time, the impact of adding artificial turbulence to the inlet on wake behavior and its potential impact on reduced runtime for design process is investigated in this study. By adding turbulence to the upstream flow, the wake
DeMeo, MichaelParenti, GuidoMartinez Navarro, AlejandroShock, RichardFougere, NicolasRazi, PooyanOliveira, DaniloLindsey, CraigYu, ChenxingBreglia Sales, Flavio
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