Browse Topic: Product development

Items (3,898)
European Initiatives addressing High efficiency and low cost electric motors for circularity and low use of rare resources2025-01-8806To be published on 04/01/2025
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, CO2 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 SRIA . The E
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
To facilitate the construction of a robust transport infrastructure, it is essential to implement a digital transformation of the current highway system. The concept of digital twins, which are virtual replicas of physical assets, offers a novel approach to enhancing the operational efficiency and predictive maintenance capabilities of highway networks. The present study begins with an exhaustive examination of the demand for the smart highway digital twin model, underscoring the necessity for a comprehensive framework that addresses the multifaceted aspects of digital transformation. The framework, as proposed, is composed of six integral components: spatiotemporal data acquisition and processing, multidimensional model development, model integration, application layer construction, model iteration, and model governance. Each element is critical in ensuring the fidelity and utility of the digital twin, which must accurately reflect the dynamic nature of highway systems. The
Zhang, YawenCai, Xianhua
In electric vehicles development, manufacturing variations pose big challenge in designing various mechanical components as these variations directly impact various customer perceivable performance outputs. If the manufacturing variations can be included in design phase itself, overall robustness of the design can be enhanced. This paper delineates machine learning based methods to include manufacturing variations in designing drive units for electric vehicles. In an electric vehicle, the drive unit transfers power or torque from a battery through an inverter to wheels. The drive units are subjected to different types of loads under various vehicle maneuvers. To evaluate the drive unit system virtually, system level simulations are performed. Traditionally, nominal values of the several inputs such as bearing parameters, gear parameters and clearances etc. are used. However, the drive unit must be designed in such a way that outputs meet target considering all the variations of inputs
Penumatsa, Venkata Ramana RajuKatha, VenkateswarBlack, DerrickJain, SachinMaddipati, Seshagiri
The increasing reliance on lithium-ion batteries in manufacturing necessitates advanced monitoring techniques to ensure their longevity and reliability. Cloud technology offers a solution by enabling real-time data collection, analysis, and accessibility, facilitating thorough monitoring and predictive maintenance. Digital twin technology, creating a virtual replica of the physical battery system, provides a platform for simulating real-world conditions and predicting potential issues before they arise. By integrating sensor data and historical usage patterns, the digital twin model can accurately predict battery degradation, aiding in timely maintenance strategies. This proactive approach enhances battery operational efficiency and extends lifespan, leading to cost savings and improved safety. The paper explores using cloud-based monitoring systems to enhance the health estimation and management of lithium-ion batteries. A comprehensive feasibility study on adopting battery digital
Zeeshan, MohammadAkre, Vineet
Noise, Vibration, and Harshness (NVH) simulations of vehicle bodies are crucial for assessing performance during the design phase. However, these simulations typically require detailed computer-aided design (CAD) models and are time-consuming. In the early stages of vehicle development, when only high-level vehicle sections are available, designing the body-in-white (BIW) structure to meet target values for bending and torsional stiffness is challenging and often requires multiple iterations. To address these challenges, this study deploys a reduced-order beam modelling approach. This method involves identifying the beam-like sections and major joints within the BIW and calculating their sectional properties (area, area moments of inertia along the plane’s independent axes, and torsion constant). These components form a simplified skeleton model of the BIW. Load and boundary conditions are applied to the suspension mount locations at the front and rear of the vehicle, and torsional and
Khan, Mohd Zishan AliThanapati, AlokDeshmukh, Chandrakant
India has seen a significant boost in automotive research and development, specific to Vehicle Dynamics active safety systems and ADAS. To develop these systems, without excessive reliance on full working prototypes, vehicle manufacturers are relying on virtual models to better fine tune the design parameters. For this, there is a real requirement of digital twins of the proving grounds. This virtual testing surfaces will help in reducing test costs, test times and increase iteration counts, leading to fine-tuned prototype vehicle and finally a market leading product. National Automotive Test Tracks (NATRAX) is already playing a crucial role in the testing and development of these technologies, on its test tracks. Recognizing the need to assist in virtual testing for Indian automotive manufacturers, NATRAX is taking steps to develop virtual proving grounds to complement physical testing and reduce the development time. This paper targets a comparative analysis of dynamic parameters
S J, SrihariUmorya, DivyanshPatidar, DeepeshJaiswal, Manish
The path towards clean mobility points in the direction of battery electric vehicles (BEVs) as a possible transportation solution. Despite a growing market penetration worldwide, emerging countries are struggling to successfully adopt BEV with current vehicle models. The literature presents an embracing discussion about BEV barriers but lacks into suggesting practical actions into BEV design. Based on a product development methodology and value analysis, this research aims to review factors holding back the BEV adoption in developing countries and to apply these factors into BEV features and design specifications. The literature was systematically reviewed based on the Brazilian case scenario to cast customer requirements for numerical evaluation through the Mudge Method. These were later translated into design requirements and ranked according to their relative importance with the quality function deployment (QFD). The results show that vehicle safety, pricing, and range anxiety are
Colpo, Leonardo R.Nora, Macklini DallaRomano, Leonardo N.Glufke, Ronaldo M.Rech, Cassiano
Autonomous vehicles for mining operations offer increased productivity, reduced total cost of ownership, decreased maintenance costs, improved reliability, and reduced operator exposure to harsh mining environments. A large flow of data exists between the remote operation and the ore haul vehicle, and part of the data becomes information for the maintenance sector which it monitors the operating conditions of various systems. One of the systems deserving attention is the suspension system, responsible for keeping the vehicle running and within a certain vibration condition to keep the asset operational and productive. Thus, this work aims to develop a digital twin-assisted system to evaluate the harmonic response of the vehicle’s body. Two representations were created based on equations of motion that modeled the oscillatory behavior of a mass-damper system. One of the representations indicates a quarter of the ore transport truck’s hydraulic system in a healthy state, called a virtual
Rosa, Leonardo OlimpioBranco, César Tadeu Nasser Medeiros
During the early stages of vehicle development, a crucial design attribute to consider is the access of occupants into the vehicle. Access is essential for ensuring comfortable entry and egress, a key factor in occupant satisfaction. The challenge in development lies in arranging various vehicle dimensions to maintain a competitive level of comfort compared to other vehicles in the same segment. Engineering metrics such as the trimmed opening rearward and forward of the Seating Reference Point (SgRP) significantly influence occupants’ perceptions of access comfort. One specific dimension that directly affects satisfaction is entrance height, the focus of this study. This project successfully correlates the entrance height of a vehicle’s first-row door opening with occupant satisfaction under real-world driving conditions. The research involved statistical data analysis and clinics to validate these findings. The results indicate that greater levels of satisfaction are achieved with
Silva, GustavoSantos, Alex CardosoGenaro, PieroTerra, RafaelPádua, AntônioRossini, RafaelBenevente, Rodrigo
In vehicle design, the H point is a theoretical relative location measured in relation to specific characteristics that determines a group of dimensions to define vehicle interior roominess. Based on theoretical H point automakers concept their vehicle and have to make important decisions on vehicle architectural that could result in a bad product for the future customers and during the early phase of vehicle development, one of the key design attributes to consider is in relation to the interior comfort of the user, so that its design and its components enabling a favorable interaction with the occupant. Vehicle interior roominess is one of the key factors for buyers’ satisfaction with certain features such as the shoulder room, headroom and couple distance, among others, may influence the level of satisfaction of the occupants’ comfort. One of these items refers to the rear chair height (H30-2), which is presented while by the distance of rear H-point to the vehicle floor affecting
Santos, Alex CardosoSilva, GustavoTerra, RafaelPádua, AntônioBenevente, RodrigoBuscariolo, FilipeLourenço, Sergio
Throughout the years, the legislations which drive the vehicle development have experimented constant evolutions. Especially when it comes about pollutant emissions and NVH ( Noise, Vibration & Harshness). However, it is complex to understand which calibration strategy promotes the best balance about lowest levels of emissions, vibrations, and noise if considered the number of inputs to be explored, becoming the searching for the optimum calibration a huge challenge for the development engineering team. This work proposes a methodology development in which complex problems can be solved by model based solutions regarding the best balance finding of emissions reduction and noise attenuation. The methodology is based in machine learning approach which provides a virtual behavior of engine phenomena making possible a wider comprehension of the problem and hence the opportunity to explore enhanced solutions. The study case scenario used to apply the method was a 6.4 liters engine which
Ruiz, Rodrigo Peralta MoraesSantos, Lucas ResendeNascif, Gabriel Nobre AlvesOliveira Ribeiro, DouglasPereira, Willyan
During the early phase of vehicle development, one of the key design attributes to consider is the trunk. Trunk is the pillar that is responsible for user’s accommodate their baggage and make into customer needs in engineer metrics. Therefore, it is one of the key requirements to be considered during the vehicle design. Certain internal vehicle trunk characteristics such as the trunk height and length are engineer metrics that influence the occupants’ perception for trunk. One specific characteristic influencing satisfaction is the rear opening width lower for notch back segment, which is the subject of this paper. The objective of this project is to analyze the relationship between the rear opening width lower with the occupant’s satisfaction under real world driving conditions, based on research, statistical data analysis and dynamic clinics.
Santos, Alex CardosoSilva, GustavoGenaro, PieroTerra, RafaelPádua, AntônioBenevente, RodrigoLourenço, Sergio
Systems Engineering is a method for developing complex products, aiming to improve cost and time estimates and ensure product validation against its requirements. This is crucial to meet customer needs and maintain competitiveness in the market. Systems Engineering activities include requirements, configuration, interface, deadlines, and technical risks management, as well as definition and decomposition of requirements, implementation, integration, and verification and validation testing. The use of digital tools in Systems Engineering activities is called Model-Based Systems Engineering (MBSE). The MBSE approach helps engineers manage system complexity, ensuring project information consistency, facilitating traceability and integration of elements throughout the product lifecycle. Its benefits include improved communication, traceability, information consistency, and complexity management. Major companies like Boeing already benefit from this approach, reducing their product
Azevedo, Marcos PauloLahoz, Carlos Henrique Netto
Design validation plays a crucial role in the overall cost and time allocation for product development. This is especially evident in high-value manufacturing sectors like commercial vehicle electric drive systems or e-axles, where the expenses related to sample procurement, testing complexity, and diverse requirements are significant. Validation methodologies are continuously evolving to encompass new technologies, yet they must be rigorously evaluated to identify potential efficiencies and enhance the overall value of validation tests. Simulation tools have made substantial advancements and are now widely utilized in the development phase. The integration of simulation-based or simulation-supported validation processes can streamline testing timelines and sample quantities, all the while upholding quality standards and minimizing risks when compared to traditional methods. This study examines various scenarios where the implementation of advanced techniques has led to a reduction in
Leighton, MichaelTuschkan, AlwinPlayfoot , Ben
Recent advancements in electric vertical take-off and landing (eVTOL) aircraft and the broader advanced air mobility (AAM) movement have generated significant interest within and beyond the traditional aviation industry. Many new applications have been identified and are under development, with considerable potential for market growth and exciting potential. However, talent resources are the most critical parameters to make or break the AAM vision, and significantly more talent is needed than the traditional aviation industry is able to currently generate. One possible solution—leverage rapid advancements of artificial intelligence (AI) technology and the gaming industry to help attract, identify, educate, and encourage current and future generations to engage in various aspects of the AAM industry. Beyond Aviation: Embedded Gaming, Artificial Intelligence, Training, and Recruitment for the Advanced Air Mobility Industry discusses how the modern gaming population of 3.3 million
Doo, Johnny
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).
Navas, AkhilPaul, Annie
With the trend of increasing technological complexity, software content and mechatronic implementation, there are increasing risks from systematic failures and random hardware failures, which is to be considered within the scope of functional safety. ISO 26262 series of standards provides guidance to mitigate these risks by providing appropriate requirements and processes. To develop a safe product with respect to above mentioned complexities, it is very critical to develop a safe system and hence a thorough and robust “Technical Safety Concept” is very important to ensure absence of unreasonable risk due to hazards caused by malfunctions of E/E systems. ISO26262-Part 4 provides guidelines for “Product development at the system level”, to design safety-related systems that include one or more electrical and/or electronic (E/E) systems and that are installed in series production road vehicles. Defining requirements at system level for each individual technology and systematically
Cheni, Dileep KumarDesai, Priyanka Pradeep
As a journey to green initiatives, one of the focus areas for automotive industry is reducing environmental impact especially in case of internal combustion engines. Latest digital twin technology enable modelling complicated, fast and unsteady phenomena including the changes of emission gases concentration and output torque observed during diesel emission and combustion process. This paper presents research on the emission and combustion characteristics of a heavy vehicle diesel engine, elaborating an engineered architecture for prognostics/diagnostics, state monitoring, and performance trending of heavy-duty vehicle engine (HDVE) and after treatment system (ATS). The proposed architecture leverages advanced modeling methodologies to ensure precise predictions and diagnostics, using data-driven techniques, the architecture accurately model’s engine and exhaust system behaviors under various operating conditions. For exhaust system, architecture demonstrates encouraging predictive
Singh, PrabhsharnThakare, UjvalHivarkar, Umesh
Today's battery management systems include cloud-based predictive analytics technologies. When the first data is sent to the cloud, battery digital twin models begin to run. This allows for the prediction of critical parameters such as state of charge (SOC), state of health (SOH), remaining useful life (RUL), and the possibility of thermal runaway events. The battery and the automobile are dynamic systems that must be monitored in real time. However, relying only on cloud-based computations adds significant latency to time-sensitive procedures such as thermal runaway monitoring. Because automobiles operate in various areas throughout the intended path of travel, internet connectivity varies, resulting in a delay in data delivery to the cloud. As a result, the inherent lag in data transfer between the cloud and cars challenges the present deployment of cloud-based real-time monitoring solutions. This study proposes applying a thermal runaway model on edge devices as a strategy to reduce
Sarkar, PrasantaPardeshi, RutujaKharwandikar, AnandKondhare, Manish
A BDT (Battery digital Twin) is a virtual representation of a vehicle's physical battery system, combining electrochemical and machine learning models to provide insights into key battery parameters like State of Charge (SOC), State of Health (SOH), Internal Resistance (IR), and Remaining Useful Life (RUL). This BDT model is calibrated using cell testing throughout its degradation process up to 80% SOH, alongside vehicle data for accurate predictions under diverse conditions. By continuously monitoring the battery under various operating scenarios, the BDT aids in effective battery management, identifying cells that degrade more quickly and the likely causes of this degradation. Current and temperature profiles offer insights into battery usage patterns. The BDT aggregates fleet-wide parameters and analyzes individual cell performance, providing critical information on SOC, SOH, IR, RUL, and voltage. Additionally, the BDT includes prognostic capabilities to alert users of potential
Sasi Kiran, TalabhaktulaKondhare, ManishPatil, SuyogNath, SubhrajyotiCH, Sri RamTank, PrabhuSarkar, Prasanta
As vehicles adopt software-centric architectures, assessing vehicle software behavior becomes more complex, which can lead to the exploitation of overlooked or untreated vulnerabilities. Using these backdoors, attacks frequently targeted automotive products for malicious reasons. Automotive security incident management involves continuous monitoring of incidents and vulnerabilities. However, it faces challenges in reproducing attacks and revalidating security goals. The lack of visualization of attack scenarios, and vectors, and the knowledge required to replicate attacks hinders vulnerability assessment. The proposed approach aims to improve vulnerability assessment and document residual risks. It promotes replicating attack scenarios using cyber digital twins to support threat modeling, risk assessment, and threat analysis. The research paper focuses on utilizing digital twins for cybersecurity incident response, threat monitoring, and vulnerability exploitation by examining elastic
Venkatachalapathy, Sreenikethana
Agile software development aims to create high-quality products while minimizing waste, reducing project costs. Nevertheless, costs are not decreasing despite shorter project cycles and more compact, flexible teams. One area where consideration is being given to reevaluating the stages in software product development is RT. Regression testing is a form of testing done to assure that changes done in Model do not adversely affect the software's functionality. As software develops, test suites typically expand and may become too expensive to run through in their entirety The initial application might have good set of test cases, and running the entire test suite could make testing more expensive. Three ways for reducing the cost of RT include test instance prioritization, test suite minimizing, and regression test selection. In order to verify that software satisfies customer requirements, identify faults or bugs in the code, and determine how to fix these issues to make the software more
Choudhary, Mukund Kumar
Researchers at the Johns Hopkins Applied Physics Laboratory have developed a machine learning method that could have a huge impact on understanding how material is formed during the additive manufacturing process. John Hopkins Applied Physics Laboratory, Laurel, MD Researchers at the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, have demonstrated a novel approach for applying machine learning to predict microstructures produced by a widely used additive manufacturing technique. Their approach promises to dramatically reduce the time and cost of developing materials with tailored physical properties and will soon be implemented on a NASA-funded effort focused on creation of a digital twin. “We anticipate that this new approach will be extremely impactful in helping design and understand material formation during additive manufacturing processes, and this fits into our overarching strategy focused on accelerating materials development for national security,” said
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