Browse Topic: Digital twin

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Real-time range optimization for selected destinations by Dynamic Energy Management Strategy (DEMS) in BEV’s2026-26-01681/16/2026
The automotive industry has undergone significant transformation with the adoption of electric vehicles (EVs). However, to tackle range anxiety, the focus has shifted to energy management strategies for optimal range under different driving conditions. Developing an optimal energy management algorithm is crucial for overcoming optimal range and gaining a competitive edge in the market. This paper introduces a Dynamic Energy Management Strategy (DEMS) for electric vehicles (EVs), designed to optimize battery usage and extend driving range. Utilizing vehicle digital twin model, DEMS estimates energy consumption across Eco, Normal, and Sports driving modes by analyzing vehicle velocity profiles and pedal inputs. By calculating actual battery consumption and pinpointing excess power usage, DEMS operates in a closed loop to periodically assess the power gap based on real-time vehicle conditions, including HV components like the Electric Drive system, DCDC, E-compressor and E-Heater; and
Dey, Supriyo, Venugopal, Karthick Babu, Penta, Amar, KUMAR, Rohit, Arya, Harshita
Integrated Quality Assurance Frameworks for Testing Advanced Automotive Systems in the Era of ADAS and E-Mobility2026-26-05681/16/2026
As the automotive industry rapidly transitions toward intelligent, electrified, and connected mobility, ensuring the safety and performance of advanced vehicle systems has become increasingly complex. Technologies such as Advanced Driver Assistance Systems (ADAS), electric powertrains, and software-defined architectures demand robust quality assurance (QA) frameworks capable of addressing the multi-domain nature of modern vehicle platforms. This paper presents an integrated QA methodology that combines traditional testing protocols with model-based validation, digital twin environments, and real-time system monitoring to comprehensively evaluate the reliability and functionality of next-generation automotive systems. The proposed framework focuses on end-to-end traceability from requirements to validation, encompassing hardware-in-the-loop (HIL), software-in-the-loop (SIL), and over-the-air (OTA) testing strategies. Emphasis is placed on simulating complex scenarios for ADAS, battery
KOMANDURI, ARUN SRINIVAS, Srivastava, Anuj
Use Of Digital Wind Tunnel For Improved Aerodynamic Prediction2026-26-03721/16/2026
In the automotive industry, external aerodynamic evaluations in digital environments are commonly conducted using simplified, large box tunnels with vehicle being static. These approaches are computationally efficient and ensure faster turnaround times. To closely replicate physical wind tunnel testing or real-world conditions, these simulations are often augmented with moving ground and rolling tire configurations. While such setups provide valuable directional feedback for aerodynamic drag improvements, they frequently exhibit significant discrepancies when compared to physical wind tunnel test data. It is observed that key factors such as wind tunnel blockage effects, boundary layer suctions, when not properly accounted for, distort the local flow field dynamics and introduce errors in the simulations. With OEMs aiming to accelerate time-to-market for new vehicle launches, many aspire to minimize reliance on physical testing and maximize use of digital methods for design sign-off
sharma, Sandeep Kumar, Chalipat, Sujit, Maiyya, Sandeep
Vehicle Twin Connectivity and Computing2026-26-04191/16/2026
The evolution of future mobility is predicated on the seamless integration of interconnectivity and high-performance computational frameworks to support advanced functionalities such as Advanced Driver Assistance Systems (ADAS) and autonomous driving. These capabilities necessitate next-generation Electronic Control Units (ECUs) that exhibit both high processing throughput and robust data management capabilities. However, vehicle-embedded computation is constrained by the limitations of on-board perception systems—namely, camera, radar, and LiDAR sensors—which are inherently dependent on line-of-sight (LoS) conditions. These constraints limit environmental awareness and introduce risks in edge-case scenarios, particularly where the probability of false positives in decision-making must be minimized due to real-time safety-critical implications. To address these challenges, we propose a paradigm shift towards cloud-integrated digital twin architectures. Each vehicle would be mirrored by
Rathi, Gopal
Virtual Paint Shop: Automotive E-coating Process2026-26-03651/16/2026
The automotive paint shop is one of the most energy-intensive processes in vehicle manufacturing. This drives automotive OEMs to continuously improve production efficiency and reduce operational costs through digital twin technologies. Additionally, the push for shorter time-to-market emphasizes the need for simulation-based manufacturing processes—such as virtual testing and CAE simulations—to enable faster, data-driven decision-making early in the product development cycle, ultimately reducing cost and development time. Among the various stages in the paint shop, two of the most critical are: 1. Electro-dip coating (E-coating), also known as Electro-Deposition coating, which applies a corrosion-resistant primer to the Body-in-White (BIW). 2. Oven curing, which ensures the primer is properly bonded and cured for long-term protection and finish quality. To optimize these processes, a Dip-Drain-E-Coating simulation was developed using Siemens Simcenter STAR-CCM+. This simulation
Gundavarapu, V S Kumar, p, Vivekaanandan, Garg, Manish, Navelkar, Tanay, BS, Balachandran
Development of a HIL Based Methodology for Over-The-Air Updates Validation2026-26-04801/16/2026
Over-the-Air (OTA) update technology has come forth as a transformative aider in the domain of automotive technology, allowing Original Equipment Manufacturers (OEMs) and Tier-1 suppliers of Electric vehicles (EVs) to frequently make software modifications, enhancements, and bug fixes that are essential to optimize the performance of powertrain components such as the motor controller unit (MCU), Battery Management System (BMS), and Vehicle Control Unit (VCU). This facilitates them to remotely supply updates to the vehicle firmware and software by giving inputs of calibration data without requiring physical access to the vehicle. However, as OTA updates have a direct impact on vehicle’s performance, safety and cybersecurity, a stringent e integration of Hardware-in-the-Loop (HIL) simulation into the OTA validation pipeline as a means to ensure reliability, safety, and functional correctness of updates before they are applied in the field. We present a structured approach of combining
Khare, Shivani, Karle, Ujjwala, Subramaniam, Anand
Testing and Validation of Future Mobility Vehicles using Realistic Digital Twin of Road Corridors and Tires2026-26-05801/16/2026
Recent development in Real Time Modelling & Driving simulator technology has made it possible to real time evaluation of vehicle/ tire performance like NVH, Ride-comfort, HMI and ADAS etc. objectively and subjectively using driving simulator. Many OEMs worldwide are adapting this technology for speeding up their tire and vehicle development by reducing decision making time and optimization of the performance by suitable design changes in initial phase of conceptualization. In order to use this technology, there is requirement of good digital twin of the vehicle, tire, road surfaces Test tracks & realistic Road Corridors. Focus of the current paper is on Digital Twin of Road Corridors. Digital Twin of road corridor comprises of various elements viz. the digitized road profile 3D (X-Y-Z) and road infrastructure elements that includes basic elements like road, barricades, signal, signage etc. along with infrastructure furniture like building, bus stops, flyovers etc. Digital twin of road
Joshi, Omkar Prakash, Pawar, Prashant R
Predictive Modelling of Battery Behavior for Enhanced Energy Management in Electric Vehicles2026-26-03851/16/2026
As electric vehicles (EVs) and smart energy systems continue to evolve, optimizing energy usage and ensuring system reliability has become critical. However, battery pack modelling remains challenging due to its highly dynamic and nonlinear behavior due to the temperature variations, aging, and variable load conditions. This paper presents a 1D digital twin framework designed for predictive monitoring of the battery parameters, including State of Charge (SoC), terminal voltage, temperature, and an indicative assessment of State of Health (SoH). The battery is modelled using a Thevenin equivalent circuit that covers both steady-state and dynamic electrical characteristics. Key parameters such as open-circuit voltage (OCV), internal resistance, and dynamic characteristics were derived through pulse testing conducted in our laboratory. A thermal modelling is done to capture the heat generation and cell temperature. SoC estimation is carried out using the Coulomb counting method and
G, Ayana, Gumma, Muralidhar
Digital Twin-Driven Systems Engineering for Complex Automotive Features2026-26-03791/16/2026
The automotive industry faces increasing challenges in managing vehicle lifecycle complexity, including inefficiencies in design, manufacturing, and maintenance. Traditional reactive maintenance approaches often lead to unexpected downtimes, increased costs, and a diminished customer experience. Moreover, rapidly evolving technologies demand agile and adaptive development processes. The Digital Twin concept which involves leveraging advanced technologies to create virtual representations of physical systems offers a promising solution by enabling real-time simulation, prediction, and optimization throughout the vehicle lifecycle. By bridging the physical and digital realms, Digital Twins provide a powerful tool for improving system efficiency, adaptability, and quality. This paper highlights the tangible benefits of applying Digital Twin principles at the systems engineering level, offering a path toward more resilient, innovative, and customer-centric vehicle systems. This study
Agarwal, Ujjwal, Sabharwal, Shambhavi
AI-Driven Digital Twin for Enhanced Suspension Assembly Testing in Automotive Vehicles2026-26-04451/16/2026
This paper presents the development and application of a digital twin for the suspension assembly of Automotive vehicle, a critical component in evaluating the performance and durability of automotive systems. The digital twin, a virtual replica of the physical suspension assembly, is created using advanced simulation techniques and physical test data integration. By leveraging machine learning algorithms, the digital twin provides a comprehensive and dynamic model that accurately reflects the behavior and performance of the physical system under various conditions. The primary objective of this study is to enhance the accuracy and efficiency of suspension assembly testing by enabling predictive maintenance, real-time monitoring, and optimization of test parameters. The digital twin allows for the identification of potential issues before they occur, reducing downtime and maintenance costs. Additionally, it facilitates the exploration of different test scenarios and conditions without
Sonavane, Pravinkumar, Patil, Amol
Software-Defined Vehicles: Architecting the Future of Intelligent and Connected Mobility2026-26-06911/16/2026
The rise of Software-Defined Vehicles (SDVs) marks a fundamental shift in automotive design, where software, rather than hardware, defines vehicle capabilities. This review explores the SDV paradigm within Intelligent Transportation Systems (ITS), emphasizing centralized computing, over-the-air (OTA) updates, and service-oriented architectures (SOA). Key enablers such as high-performance computing units, middleware platforms (e.g., AUTOSAR Adaptive), and digital twins support scalable, flexible software deployment and real-time functionality. Artificial Intelligence (AI) and Machine Learning (ML) enhance features like predictive maintenance, driver personalization, and autonomous decision-making. However, SDVs also introduce complex challenges, including software validation, real-time performance, and system interoperability. Cybersecurity becomes critical, especially as vehicles interface with cloud platforms, edge computing, and Vehicle-to-Everything (V2X) networks. The paper also
Ahmad, Aqueel, Hemanth, Khimavath, Kumar, Om, Kumar, Rajiv, Haregaonkar, Rushikesh Sambhaji
A Digital Twin Framework for Real-Time Optimization of Solar-Integrated Energy Systems2026-26-04181/16/2026
In the context of increasing global energy demand and climate change concerns, the integration of renewable energy sources with advanced modelling technologies has become essential for achieving sustainable and efficient energy systems. Solar energy, despite its potential, continues to face challenges related to performance variability, limited real-time insights, and reactive maintenance. To overcome these barriers, this work presents a Digital Twin framework aimed at optimizing solar-integrated energy systems through real-time monitoring, predictive analytics, and adaptive control. This work presents a Digital Twin framework designed to address the challenges of designing, operating, maintaining, and estimating renewable energy systems, specifically solar power, based on dynamic load demand. The framework enables the real-time forecasting and prediction of energy outputs, ensuring systems operate efficiently and maintain peak performance across diverse conditions. The proposed
R, Akash, Burud, Priti Raju, Gumma, Muralidhar
A Data-Centric Approach to Identify Optimal NVH simulation Tools and Solvers2026-26-03561/16/2026
The world is rapidly evolving towards a data-driven paradigm, with widespread adoption of tools and techniques such as Artificial Intelligence, Machine Learning, Digital Twins, and Cloud Computing. In the automotive sector, vast amounts of data are being generated through both physical and digital test evaluations. Among these, Computer-Aided Engineering (CAE) stands out as one of the largest contributors to data generation, as physical testing is often cost-prohibitive due to the need for prototypes and complex test setups. The field of Automotive Noise, Vibration, and Harshness (NVH) is advancing exponentially, driven by stringent regulatory norms and growing customer demand for comfort. Digitally advanced techniques are playing a pivotal role in revolutionizing NVH processes. Data generation via CAE tools has become an integral part of engineers' daily operations, with the selection of appropriate CAE software and solvers being critical. This choice impacts factors such as user
Hipparge, Vinod, masurkar, Nikita, Arabale, Vinand, Billade, Dayanand
Reverse Engineering and Digital Twins in Powertrain Design: Enhancing Performance and Sustainability2026-26-00651/16/2026
This study explores the application of reverse engineering (RE) and digital twin (DT) technology in the design and optimization of advanced powertrain systems. Traditional approaches to powertrain development often rely on legacy designs with limited adaptability to modern efficiency and emission standards. In this work, we present a methodology combining 3D scanning, computational modeling, and machine learning to reconstruct, analyze, and enhance internal combustion engines (ICEs) and electric vehicle (EV) drivetrains. By digitizing physical components through RE, we generate high-fidelity DT models that enable virtual testing, performance prediction, and iterative improvement without costly physical prototyping. Key innovations include a novel mesh refinement technique for scanned geometries and a hybrid simulation framework integrating finite element analysis (FEA) and multi-body dynamics (MBD). Our case study demonstrates a 12% increase in thermal efficiency for a retrofitted ICE
Bernikov, Mark Alexandrovich, Kurmaev, Rinat
Optimizing Vehicle Dynamics: A Methodology for Reducing Correlation Gaps2026-26-00911/16/2026
In the highly competitive automotive industry, final design decisions are often made early in the product development cycle. However, physical prototypes may exhibit deviations from nominal specifications due to design tolerances, manufacturing tolerances, stiffness element fitment, and other noise factors. These deviations can lead to significant performance discrepancies (correlation gaps) between real-world performance and the digital twin representation created during the design phase. Repeatedly measuring all system parameters to account for these differences can be both costly and impractical. This research aims to bridge the gap between digital and physical models by identifying key system parameters from system characteristic data generated through controlled dynamic testing. Specifically, dynamic load cases are conducted using a 4-poster test rig, where vehicle responses are recorded at various suspension locations under controlled conditions. By analyzing these target
Verma, Rahul Ranjan, Goli, Naga Aswani Kumar, PRASAD, TEJ PRATAP
Vehicle usage Digital Twins - Mobility analysis test case2026-26-03941/16/2026
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 a 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 a 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, Rubio, Marín Saltó, Anna, Diaz, Francisco, Olivencia, Sergio, Medina, Alba
Transforming Manufacturing: Increasing Throughput and Reducing production Complexity with Digital Twins2026-26-04491/16/2026
In traditional manufacturing, increasing production line capacity often involves physical trials, leading to downtime, high costs, and operational risks. This paper presents a bidirectional digital twin developed for the Fischertechnik Smart Factory Kit, enabling real-time simulation and validation of production line modifications before actual deployment. The digital twin integrates with a Siemens PLC to mirror real-world operations, capturing live production data and visualizing key parameters like cycle time and machine utilization in a 3D environment. Engineers can test various optimization scenarios adjusting conveyor speeds, optimizing robot paths, and modifying buffer sizes to enhance efficiency. The best-performing configurations are identified based on throughput, cycle time, and resource utilization, and validated changes are automatically deployed to the PLC, ensuring seamless implementation. Beyond capacity optimization, this solution improves overall production efficiency
Kumar, Rahul, Singh, Randhir
Digital Twin-Enhanced Chassis Dynamometer for EV Drivetrain Testing2026-26-03631/16/2026
Accurate prediction of drivetrain performance and energy efficiency in electric vehicles (EVs) remains a challenge due to the limitations of conventional chassis dynamometer testing, which often overlooks dynamic vehicle-roller interactions, suspension responses, and regenerative braking effects. This paper presents a novel digital twin-enabled testing framework using a compact, two-roller chassis dynamometer integrated with real-time multibody dynamics and electromechanical co-simulation. The physical test bench is coupled with a virtual model of the EV, including suspension geometry, rear-to-front axle load distribution, tire–roller contact mechanics, and motor-inverter dynamics. By enabling bi-directional data exchange between the physical and virtual domains, the system emulates real-world load shifts, torque demand variations, and energy recovery under standardized drive cycles. This integrated approach enhances simulation fidelity, supports certification-compliant testing, and
Kumar, Akhilesh, V, Yashvati
Transforming Battery Management: The Role of Artificial Intelligence and Machine Learning in Digital Twin Technologies2026-26-03821/16/2026
The traditional Battery Management System (BMS) faces certain limitations in fully utilizing battery capacity and performance during the long cycle life operation of Electric Vehicles (EVs). These constraints include limited real-time data collection, low processing speed, lack of predictive maintenance, and very limited accuracy in predicting health and degradation chemistry. A Battery Digital Twin (BDT) can effectively address these limitations of the BMS. Battery Digital Twins (BDT) can be viewed as a cyber-physical system comprising four key elements: virtual representation, bidirectional connection, simulation, and connection across the life cycle phases of an EV battery. The performance of a Li-ion battery largely depends on the cathode chemistry, component design, and operating conditions. The battery should be manufactured in a manner (such as cylindrical or prismatic cell) that prevents explosion, leakage, and gas generation inside the battery. To enhance the performance and
Chaturvedi, Vikash, M, Venkatesan, Lanke, Siddhi, Subramaniam, Anand, Karle, Manish, Pandit, Rugved, Gupta, Drishti, Karle, Ujjwala Shailesh
Driving Thermal Intelligence – Pioneering Digital Twin-Driven Hardware-In-Loop for Smart Climate Control Modules2026-26-03801/16/2026
The automotive industry is undergoing a transformative shift from hardware-driven vehicle development to software-centric, model-based validation practices. At the heart of this evolution is the demand for smarter, more adaptive cabin climate management systems. The Climate Control Module (CCM) a critical embedded ECU—regulates the vehicle’s Heating, Ventilation, and Air Conditioning (HVAC) functionality based on a fusion of passenger inputs and environmental sensor data. Traditionally, validation of such systems relied heavily on manual testing within climatic chambers or through in-vehicle trials using physical interfaces like the Climate Control Head (CCH) and the Infotainment Head Unit (HU). These legacy methods are increasingly inadequate in addressing rapid development cycles, high functional complexity, and stringent safety and quality standards. This paper introduces a Digital Twin-enabled Hardware-in-the-Loop (HiL) testing methodology using advanced real-time controllers to
More, Shweta, Shinde, Vivek, Turankar, Darshana, Patel, Dafiya, Gosavi, Santosh, GHANWAT, HEMANT
Nexifi11D: A Comprehensive Multidimensional Framework for Futuristic Manufacturing Ecosystems2026-26-02821/16/2026
The manufacturing sector is undergoing a paradigm shift driven by hyper-connectivity, sustainability imperatives, and accelerated digital transformation. Conventional systems, however, remain constrained by their focus on isolated optimizations—such as predictive analytics, IoT-enabled sensing, or workforce scheduling—without providing an integrated operational perspective. To address this fragmentation, Nexifi11D is introduced as a comprehensive, multidimensional framework that consolidates 11 critical dimensions of manufacturing into a unified ecosystem. Nexifi11D advances beyond traditional Industry 4.0 architectures by seamlessly integrating intelligent algorithms, immersive simulation technologies, IoT infrastructure, and predictive systems within a scalable, interoperable mesh. The framework breaks down silos across key manufacturing domains—such as sustainability, cost, operations, and workforce—enabling holistic, data-driven, and proactive decision-making. Each integrated
Kumar, Rahul, Singh, Randhir
Digital Twin for Motor & MCU incorporating Deep learning AI-ML algorithms for intelligent prognostics for Electric vehicles2026-26-04791/16/2026
As electric vehicles (EVs) become more advanced, so ensuring the reliability of critical components like the motor and Motor Control Unit (MCU) is essential. This paper presents a digital twin model designed to predict failures in motor and MCU components using machine learning. The approach focuses on detecting early signs of failure through real-world data and advanced analytics. We collected thermal and performance data from field vehicles, capturing both normal (healthy) and abnormal (faulty) operating conditions. Using this dataset, we developed and trained an Auto Encoder-based machine learning model that learns what “normal” looks like and flags deviations as potential issues. One key outcome of this study is the successful early prediction of Insulated Gate Bipolar Transistor (IGBT) degradation, where the system identified subtle behavioral changes long before any visible failure symptoms appeared. This digital twin acts as a virtual replica of the physical components
Joshi, Pawan, Pandey, Suchit, KONDHARE, Manish, UPADHYAY, ABHAY, JaganMoahanarao, Vana, Tank, Prabhu
Conceptual Framework for Real-Time Battery Health Estimation in Automotive Digital Twins Using Reinforcement Learning2026-26-06581/16/2026
The explosive growth of electric vehicles (EVs) calls forth the need for smart battery management systems that can perform health monitoring and predictive diagnostics in real-time. The conventional battery modeling methods mostly do not cover the complicated, dynamic behaviors coming from different usage patterns. The study outlines a structure that would use Reinforcement Learning (RL)-based AI agent as a part of the Battery Electrical Analogy (BEA) simulation platform. With the help of the AI agent, different health parameters such as State of Health (SOH), State of Charge (SOC), and the signs of early thermal runaway can be predicted in real-time. The suggested design takes advantage of the simulation-based approach to have the agent learn and utilizes a decentralized cloud architecture suitable for scaling and reducing the response time. The RL agent performs an essential role in the process by tagging along with the continuous learning and the adjustment of the battery conditions
Pardeshi, Rutuja Rahul, KONDHARE, MANISH, Sasi Kiran, Talabhaktula
SAE TOMORROW TODAY - Digital Engineering for Defense and Beyond135207/1/2025
If you were to build the perfect use case for the potential of digital engineering, you would be hard-pressed to choose a better sector than the defense industry. That's why the Pentagon isn't just intrigued by digital engineering-it's made it mandatory. As the U.S. military targets groundbreaking technology improvements that safeguard lives and improve defense capabilities, Ansys is helping connect the dots between people, processes, and technology with the predictive power of simulation. Establishing a digital engineering ecosystem, including digital twins, contributes to the resilience and adaptability of innovations across industries. To learn more, we sat down with Dr. Armond E. Sinclair, Sr. Technical Account Executive for Ansys Government Initiatives, to discuss how digital engineering is enabling smarter decision-making, reducing costs, and accelerating innovation at the Department of Defense (DoD) and beyond. We'd love to hear from you. Share your comments, questions and ideas
Hineman, Marcie
Scientists Fuse Simulations and Machine Learning to Accelerate Novel Additively Manufactured MaterialsTBMG-5212712/1/2024
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.
Method And System For Creating Digital Twin Of A Sensor For A Vehicle In Real Time2024-26-02651/16/2024
Modern Vehicles have many sensors equipped with them to give an idea to the control systems of the vehicles related to what is happening in and around the vehicle. These sensors are very costly and they are critical in controlling the vehicle as well as emissions. There are different types of sensors used in a vehicle, which can detect different types of physical parameters such as temperature, pressure, range, speed etc. These readings play an important role in a vehicle as their readings are used to take critical decisions involved in keeping the vehicle running. If certain sensors fail, it can lead to the vehicle being stopped mid operation or increased emission levels. Hence, it is imperative that the sensors have a reliable fail-safe mechanism so that above mentioned problems can be prevented in order to provide a better customer experience. For some of the sensors the mechanism for diagnosing and handling the failure is a legislative requirement itself. However, the existing
Ramesh, Prashanth Mysore, Velichappattil, Anvar Hussain
An AI-based digital twin of the Electric Vehicle2024-26-00931/16/2024
For commercial vehicles, reliability is key since the vehicle is typically linked to the daily earnings of the owner. To ensure continuous vehicle operation, early issue diagnostics and proactive maintenance are important. However, an electric vehicle is a complex and dynamic system consisting of numerous components interacting with each other and with external environments such as road conditions, traffic, weather, and driving behavior. Thus, vehicle operation and performance are highly contextual and for identifying an abnormal operation (diagnostics) the solution must take into account the conditions under which it is driven. To address this, we have built an AI-based digital twin of electric vehicles. The model is an AI-based generative transformer network that generates near-ideal vehicle behavior. The present focus has been on motor, controller and battery. The model is trained using 200 vehicles first 1500 km driven data. To ensure the digital twin model learns near-ideal
Jain, Siddhant, Kumar, Vedant, Soni, Nimish, Saran, Amitabh
Big Data Analytics (Telematics) for real time testing improvements and integration back to Development of Future Models/EVs2024-26-03521/16/2024
The Auto industry has relied upon traditional testing methodologies for product development and Quality testing since its inception. As technology changed, it brought a shift in customer demand for better vehicles with the highest quality standards. With the advent of EVs, OEMs are looking to reduce the going-to-market time for their products to win the EV race. Traditional testing methodologies have relied upon data received from various stakeholders and based on the same tests are planned. The data used is highly subjective and lacks variety. OEMs across the world are betting big on telematics solutions by pushing more and more vehicles with telematics devices as standard fitment. The data from such vehicles which gets generated in high levels of volume, variety and velocity can aid in the new age of vehicle testing. This live data cannot be simply simulated in test environments. The device generates hundreds of signals, frequently in a fraction of seconds. Multiple such signals can
Sahoo, Priyabrata, Singh, Saurabh, Prasad, Kakaraparti Agam
Electric Vehicle Battery SOH and RUL prediction using Digital Twin Hybrid ML Model considering effect of Driving Behaviour2024-26-01221/16/2024
The adoption of Electric Vehicles (EVs) is primarily limited by their dependence on batteries, which have lesser power density as compared to conventional fossil fuels as well as its ageing deterioration issues over time. Therefore, there is an urgent need to understand the modifications in battery performance characteristics with respect to changes in temperature, charging behaviour and usage pattern, low and high charge states, current variations etc. To resolve such issues, this work proposes the development of a battery digital twin model to accurately reflect battery dynamics during run time. A digital twin is a virtual model replicating a physical system's characteristics. The digital twin is developed using a physics and machine learning model trained with bench-level and vehicle level actual test data. It uses an equivalent circuit model (ECM) to predict the battery's internal resistance and polarization effect due to ionic diffusion process in the cell. ECM model generates
Dongaonkar, Saharsh, Sarkar, Prasanta, Patil, Gaurav, KONDHARE, MANISH, nath, Subhrajyoti
Generation of tire digital twin for virtual MBD simulation of vehicles for durability, NVH and handling evaluation2024-26-03011/16/2024
With the recent development in virtual modelling and vehicle simulation technology, many OEM's worldwide are using digital road profiles in virtual environment for vehicle durability load prediction and virtual design evaluation. For precise simulation results, it is important to have the tire digital twin which is the realistic representation of tire in the virtual environment. The study comprises of discussion about different types of tire models such as empirical, solid model, rigid ring model and flexural ring models such as Pacejka, MF Swift, CD tire, F tire etc. and also the complexity involved in development of these tire models. Generation of virtual tire model requires highly sophisticated test rigs as well as vehicle level testing with Wheel Force transducers and other vehicle dynamics sensors. The large number of data points generated with testing are converted in standard TYDEX format to be further processed in various software tool for virtual model generation. Thus, a
Bakal, Nikhil
Machine learning based fault detection of powertrains in electric vehicles using a digital twin2023-01-12146/26/2023
Vehicles are subject to effects that lead to a more or less rapid degradation of functions. On the one hand, aging effects lead to a gradual reduction in functional capability. On the other hand, unexpected defects in components or modules can lead to functional losses. Therefore faults in the powertrain or vehicle dynamics can cause hazards for the driver as well as uninvolved road participants. They must therefore be detected and intercepted as early as possible in order to maintain vehicle function even in critical situations until a safe operating mode can be established without any risk. This contribution presents an approach that can improve both the product development process and the operation of electrified vehicles by dynamically detecting abnormal situations in the powertrain and vehicle dynamics to infer the fault in the system. For this purpose, the vehicle is connected via 5G to an intelligent digital twin located in the Edge backend. The core component is the fault
Dettinger, Falk, Jazdi, Nasser, Weyrich, Michael, Brandl, Lukas, Reuss, Hans-Christian, Pecha, Urs, Parspour, Nejila, Li, Shiqing, Frey, Michael, Gauterin, Frank, Nägele, Ann-Therese, Lüntzel, Vitus Alexander, Sax, Eric
Condition Monitoring of High-Speed Rotating Machinery using Digital Twins & Machine Learning Techniques2025-01-01285/5/2023
In the era of Industry 4.0, maintenance of factory equipment is changing with new maintenance system using predictive or prescriptive methods. These methods leverage condition monitoring through digital twins, AI, & machine learning techniques to obtain early signs of faults, type of faults, locations of fault etc. Bearings and Gears are one of the most common components and cracking/misalignment/rubbing/bowing are the most common failure modes in high-speed rotating machinery. In the present work, an end-to-end automated Machine Learning based condition monitoring algorithm is developed for predicting and classifying internal gear and bearing faults using external vibration sensors. A digital twin model of the entire rotating system consisting of the gears, bearings, shafts, and housing was developed as a co-simulation between MSC ADAMS and MATLAB. The gears and bearing dynamic models were developed in MATLAB and shaft and housing models were developed in MSC ADAMS flexible body. The
Rastogi, Sarthak, SINGHAL, SRIJAN, Ahirrao, Sachin, Milind, T. R.
The DANA EV NVH Laboratory2025-01-00075/5/2023
A cutting-edge EV powertrain NVH laboratory has been established at DANA Incorporation’s world headquarters in Maumee, enhancing their capabilities in EV powertrain NVH development. This state-of-the-art and industry lead laboratory is designed to meet various NVH requirements for EV powertrain development and sign-off processes. This laboratory supports the use of a live motor for input and is equipped with an input module that allows for detailed examination of NVH characteristics of new powertrain configurations before prototype motors are available, significantly reducing development time for new drivetrain systems. Key features of the laboratory include a hemi-anechoic chamber, two AC asynchronous load motors, an acoustically isolated high-speed input motor, and two battery emulators that cover both low and high voltage requirements. The NVH laboratory enables engineers to assess system performance and correlate it with digital twin models. DANA engineers can develop and optimize
Cheng, Ming-Te, Zugo, Chris
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Mutagaana, Festo
Blockchain based Aircraft Maintenance Records2022-26-11405/26/2022
Currently, Aircraft records and many other archives are in paper format. Paper records like maintenance records included in logbook are cumbersome, inefficient, and insecure compared to the capabilities of modern technology. Blockchain technology is the next step in creating process efficiencies, security, and integrity of maintenance data over the lifetime of an aircraft/engine. Changing paper records to digital records using blockchain is the foundation for a better future towards digital twin and analytics. Blockchain based maintenance can connect a world of data between various groups for extended periods of time by creating a secure digital thread. As regulatory requirements grow and cyber threats loom, blockchain technology can assist in the integrity and security of the captured data. The objective of this paper is to describe the advantages of migrating to digital applications using blockchain and how it can secure the future of the industry.
Jayarajan, Keerthi
Cloud based Architecture to implement Digital Twins2022-26-11475/26/2022
Increased operating cost of aircraft for maintenance and repair is an area that has garnered a lot of attention. Efforts to reduce the cost has not always met with success despite aircraft being built with increased reliability including more durable engines and innovations in approach to maintenance. Most airlines focus on cutting cost of maintenance, repair and overhaul while ensuring operational availability of assets. Means to optimize cost must always be within mandatory bounds of safety of operations. Existing approaches for fleet management and sustainment are largely based on statistical distributions of material properties, physical testing and heuristic design philosophies which are often unable to address extreme requirements. Some of the major challenges include employment of conventional maintenance practices, slower turnaround time, suboptimal supply chain management, poor utilization of resources, below par manufacturing processes, outdated manual processes and inferior
Padmanabhan, Suresh, Sunkara, Bhanu Prakash
Using Digital Twin for faster & accurate performance prediction of Industrial Assets.2022-26-11465/26/2022
Digital Twin is an emerging trend on Industrial use cases. Digital Twin is a Replica of a physical asset in Real time. The traditional way of developing Digital twin is, by using complex transfer functions. However, this requires tremendous amount of expertise with good understanding of the domain & the physics. The accuracy of the Twin model is directly dependent on the transfer functions, which many times result in a disconnect with the physical world. Physics based simulations can result in a better accuracy. However, the time taken by the software to make Real time virtual simulations is a bigger Industrial challenge due to high computational speed. So, we need to strike the right balance between speed & accuracy. There are some software who are attempting to provide solutions to this challenge. One of the techniques is to use Reduced Order Modelling (ROM) to speed up the computation of virtually simulating complex Industrial assets, without too much of approximation. The intent of
Narasimhan, Vijay Sarathy
Digitalization of a climate and altitude simulation test bench for handheld power tools to automate its thermal management system2022-01-07273/29/2022
Mechanical systems accomplish their tasks better when enhanced with cyber technologies. With the rapidly escalating desire for high efficiency, optimization and flexibility, these physical systems ought to be integrated with cyber technologies that enhance exhaustive manipulation of resources and unsurpassed productivity. The gateway for such a synergetic integration can be referred to as digitization. Details regarding the digitization of a High-altitude Simulation chamber are discussed thoroughly in this paper. The simulation chamber was originally designed and developed as a test bench to study the characteristics of alternative fuels used in the engines of handheld tools in different altitudes and thermal conditions. It encompasses all the possible realistic temperature variations with altitude raising to 3500m above sea level. Since the testbed’s effectiveness is highly dependent on achieving a combination of desired temperature and altitude values to maintain them for a defined
Nenninger, Philipp, Hadamek, Christof, Renner, Marius, Derbie, Arsema, Kettner, Maurice, Aslan, Ferhat
Application of a Digital Twin Virtual Engineering Tool for Ground Vehicle Maintenance Forecasting2022-01-04343/29/2022
The integration of sensors, actuators and real time control in transportation systems enables intelligent system operation to minimize energy consumption and maximize occupant safety and vehicle reliability. The operating cycle of military ground vehicles can be on- and off-road in adverse environments which places a demand on continuous subsystem functionality to fulfill missions. On–board diagnostic systems can alert the operator of degraded operation once established fault thresholds are passed. An opportunity exists to estimate vehicle maintenance needs using model-based predicted trends and compiled information from fleet operating databases. A digital twin, created to virtually describe the dynamic behavior of a physical system using computer mathematical models, can estimate the system behavior based on current and future operating scenarios while accounting for past operating effects. In this manner, the collection of real time data of the physical vehicle can be constantly
Eddy, Conner, WAGNER, Adam, Wagner, JOHN, Castanier, Matthew
Executable Digital Twin – Prevent the early failure of a truck anchorage using Smart Virtual Sensors2022-01-09323/29/2022
Executable Digital Twins (xDT) are starting a revolution in the industry, where high fidelity simulation models extend their usage from the design and validation phases to in-operation and service phase. Two critical technology blocks in this revolution are Model Order Reduction and Smart Virtual Sensing. The former allows the high-fidelity models to be represented in compact forms and the latter allows to extend the limits of physical sensors and provide full field data combining simulation models and test data in a real-time estimator framework. The smart virtual sensing technology leverages a state-of-the-art Kalman filtering approach to combine the simulation and physical testing. This allows to virtually measure locations that are not accessible with physical sensors due to e.g. physical constrains or high temperatures. In case of large sensors setups, the instrumentation time, and hence the cost, can be greatly reduced by using a combination of physical and smart virtual sensors
Scurria, Leoluca, Risaliti PhD, Enrico, Buss, Dayane, Kubo, Pablo, Tamarozzi, Tommaso, Cornelis, Bram
The ICON Digital Wind-Tunnel Validation2022-01-10883/29/2022
The discipline of aerodynamics engineering is a cornerstone in the rapid transition of the automotive industry to emission-free technologies. The endless pursuit of maximum energy efficiency, comfort, and safety requires designers to achieve unprecedented aerodynamic characteristics for the cars of tomorrow, which would be impossible to attain using traditional development processes. In recent years, computational testing has reached a maturity level close to that of physical wind-tunnel testing with all of its’ sophistications. Complementing the data from physical testing, well-calibrated computational simulations provide opportunities to interrogate and analyze salient flow phenomena in even greater depth, and explore the design space with more flexibility. Enabling designers to easily and efficiently exploit the increasing power and range of computational simulation to achieve the emission-free objectives of the automotive industry represents a significant challenge. This talk will
Hrncir PhD, Zbynek
Development of Argonne VIL workflow for CAVs to demonstrate the energy-efficient automated driving controls in real vehicles 2022-01-05003/29/2022
Argonne National Laboratory (ANL) has developed automated driving control strategies that aim at maximizing energy-efficiency. Up to 22% energy savings were demonstrated [1] in a large-scale study performed in RoadRunner, a tool for the energy-centric modeling of connected and automated vehicles (CAVs). This paper describes how these controls were implemented and tested in real vehicles on dynamometer setting, ANL vehicle in the loop (VIL) workflow, to demonstrate both the efficacy of the controls themselves, but also to validate the RoadRunner models. The main concept of the ANL VIL workflow is to build a digital twin and connect to a real vehicle on dynamometer as if it can drive on the real road. The ANL VIL workflow mainly consists of three steps, integration, preparation, and experimentation. A digital twin of the vehicle and the testing environment was built by the help of RoadRunner for the integration. The digital twin includes virtual road such as intersection, traffic signals
JEONG, Jongryeol, Kim, Namdoo, Di Russo, Miriam, Grave, Julien, Han, Jihun, Stutenberg, Kevin, Karbowski, Dominik
Inverse Reconstruction of the Spatial Distribution of Dynamic Tire-Road Contact Forces in Time Domain Using Impulse Response Matrix Deconvolution for Different Measurement Types2021-01-10618/31/2021
In tire development, the dynamic tire-road contact forces are an important indicator to assess structure-borne interior cabin noise. This type of noise is the dominant source in the frequency range from 50-450 Hz, especially when rolling with constant angular velocity on a rough road. The spatial force distribution is difficult or sometimes even impossible to simulate or measure in practice. So, the use of an inverse technique is proposed. This technique uses response measurements in combination with a digital twin simulation model to obtain the input forces in an inverse way. The responses and model properties are expressed in the time domain, since it is specifically aimed to trace back the impact locations from road surface texture indents on the tire. In order to do so, the transient responses of the travelling waves as a result of these impacts is used. The framework expresses responses as a convolution product of the unknown loads and impulse response measurements. Tikhonov
Devriendt, Harald, Naets, Frank, Kindt, Peter, Desmet, Wim
Innovation on a Larger Scale: Robotic Fiber Placement and 3D PrintingTBMG-385062/1/2021
Washington, DC
Technology Leaders: Industry Renaissance Transforms Invention, Learning, Production, and TradeTBMG-3819612/1/2020
A profound societal transformation is underway on a scale not seen since the great European Renaissance. Then, Gutenberg’s printing press made books — and the knowledge they contained — available to all. Today, the new book is the experience. Digital technology, which enables virtual experiences, is enabling an Industry Renaissance that is shaking all sectors of society with new ways, both real and virtual, of inventing, learning, producing, and trading.
How Digitalization Reduces Program Risk in Platform DevelopmentTBMG-374598/1/2020
Electronic and electrical (E/E) design demands of the 21st century will no doubt present a tremendous challenge throughout the aerospace and defense industry. Profitability in all phases of platform development will be challenged. Along with staying profitable, there is a need to innovate. But with innovation comes risk. How do you limit risk to stay profitable?
DIGITAL TWINS20AERP06_046/1/2020
How The Digital Replica Concept Is Used By Robotic Systems AV&R's robotic systems are designed to automate surface finishing (profiling, polishing, blending, deburring) on critical parts of aircraft engines, such as blades. These systems operate using dedicated software called BrainWave which orchestrates the interactions between all of the robotic system's components. For several years, AV&R has been developing Brain-Wave software to facilitate the use of the robotic system by its operators. One of the features developed is visualization of the system following the concept of digital twins. What is the concept of digital twins? How does this innovative concept apply in the case of robotic systems for complex industries such as aeronautics? What are its concrete uses? And how do digital twins improve the use and performance of robotic systems?
Digital Twins: How The Digital Replica Concept Is Used By Robotic SystemsTBMG-370966/1/2020
AV&R's robotic systems are designed to automate surface finishing (profiling, polishing, blending, deburring) on critical parts of aircraft engines, such as blades. These systems operate using dedicated software called BrainWave which orchestrates the interactions between all of the robotic system’s components. For several years, AV&R has been developing BrainWave software to facilitate the use of the robotic system by its operators. One of the features developed is visualization of the system following the concept of digital twins.
Unsettled Technology Opportunities for Vehicle Health Management and the Role for Health-Ready ComponentsEPR20200033/17/2020
{"italic":"health-ready\n components","content":["Game-changing opportunities abound for the application of vehicle health\n management (VHM) across multiple transportation-related sectors, but key\n unresolved issues continue to impede progress. VHM technology is based upon the\n broader field of advanced analytics. Much of traditional analytics efforts to\n date have been largely descriptive in nature and offer somewhat limited value\n for large-scale enterprises. Analytics technology becomes increasingly valuable\n when it offers predictive results or, even better, prescriptive results, which\n can be used to identify specific courses of action. It is this focus on action\n which takes analytics to a higher level of impact, and which imbues it with the\n potential to materially impact the success of the enterprise. Artificial\n intelligence (AI), specifically machine learning technology, shows future\n promise in the VHM space, but it is not currently adequate by itself for\n high
Holland, Steve
Collaboration: A Powerful, Effective Strategy for Medtech Companies Moving to Embrace Industry 4.0 and Drive Digital TransformationTBMG-362763/1/2020
The digital revolution under way in medtech manufacturing will continue to transform the industry into a more connected, efficient, and agile ecosystem. However, the challenge for many companies is to plot the best way to embrace various existing and new technologies and move toward smarter manufacturing — their version of the “factory of the future.” Every company is at a different stage of digital transformation. Some companies are wholeheartedly piloting, integrating, and scaling disruptive technologies within their operations in a bid to realize greater supply chain and internal operational benefits. Others are at a different place in their digital journey, identifying incremental opportunity areas where they can demonstrate initial impact and justify further investment.
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