Browse Topic: Digital twin

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Advancing Reliability Centered Maintenance: A Robust Statistical Approach for MTBF Estimation in Aero-Engine Fleets via Subgrouping Strategy and Dual Weibull Modeling2026-99-02137/31/2026
Fleet heterogeneity, from manufacturing variations and diverse operating conditions, complicates reliability analysis by obscuring true failure patterns in aero-engines. This is a critical challenge in an industry as inaccurate Mean Time Between Failures (MTBF) estimates threaten safety and inflate operational costs, by forcing a choice between inefficiently conservative maintenance or the risk of in-service failures. Conventional analysis often fails by pooling all fleet data. To address this, our paper presents an analytical framework that improves predictive accuracy by filtering, rather than aggregating statistical noise. The methodology uses a Randomized Block Design (RBD) and ANOVA hypothesis test to screen a diverse dataset and isolate statistically homogeneous subgroups. This filtration identifies a core fleet with a consistent failure signature, providing a purified dataset for modeling. This refined data is then modeled using both Weibull and the Exponentiated Inverse Weibull distributions to ensure the results are robust and not model-dependent. Applying this framework to a 25-engine dataset that experienced 66 failures, we isolated a stable failure pattern, yielding a primary MTBF of 171.16 hours and a cross-validated MTBF of 176.35 hours. The close 3% convergence between these models validates our approach. By providing a dependable MTBF, this work establishes a stronger foundation for data-driven Reliability Centered Maintenance (RCM). It empowers maintenance planners to move toward evidence-based intervals, safely extending engine time-on-wing, optimizing spare parts inventory, and significantly reducing direct operational costs for airlines.
Jubaid, Mayin Uddin, Bebe, Gibson, Bigyen, Musa Pethuel, Anik, S M Kullul Mehedee, Yasmin, Ashrafi, Sahran, Mohamed Sideek Mohamed
Design of a Digital Twin Interactive System for a 3D Printer2026-99-08597/30/2026
This paper takes a 3D Printer proposed by the project team in the early stage as the research object, constructs a digital twin entity including 3D models and data models in order to develop a digital twin interactive software. By activating the real-time correlation between 3D models and data models, valuable data exchange can be achieved between the digital twin entity and the physical entity, and valuable data can be used to drive both to refresh their operating status.
Li, Qi, Wu, WenKai, Lang, ZhiQi, Jiao, HongCheng, Jing, Tao, Zhao, HanTao, Dong, Shen, Shi, Lei
Efficiency Modeling, Experimental Validation, and Machine Learning-Assisted Model Calibration of a Two-Speed E-Drive Transmission for Construction Equipment02-19-03-00197/27/2026
This study details the development and experimental validation of a high-fidelity one-dimensional (1D) simulation model for a two-speed transmission designed for off-road vehicles, such as tractors and backhoe loaders used in agricultural and civil engineering applications. The model, implemented in the AMESim platform from Siemens, integrates physics-based loss sub-models for all major components, including gears, bearings, seals, and fluid drag (churning) losses. After development, the model was rigorously validated against test bench data, with efficiency measurements taken across various speed, torque, and oil level combinations, demonstrating a strong correlation with experimental results. A detailed analysis enabled the quantification of the contribution of each loss mechanism, identifying the countershaft gears and input shaft bearings as the primary contributors. Furthermore, a Machine Learning (ML)–based calibration framework, employing Bayesian Optimization, was implemented to reduce discrepancies between simulation and experiment and to generate a synthetic dataset for the creation of fast-executing surrogate models. The study concludes that the proposed methodology constitutes an effective tool for efficiency analysis and optimization during early design stages, establishing a foundation for future integration with ML techniques and the development of digital twins.
Ferreira, Tiago Simao, Fallahi, Farzad, Kedziora, Slawomir, Hichri, Bassem, Kiefer, Jean-Daniel
A Virtual-Physical Mapping Model for Aluminum Alloy Pitting Corrosion2026-99-16117/24/2026
Corrosion critically damages structural strength and affects the structural safety, so there is an urgent need for a method that can accurately model and predict corrosion. Digital twin technology offers new methodologies for corrosion research. This study develops a digital twin-enabled virtual-reality mapping model for simulating aluminum alloy pitting corrosion. The model accounts for the effect of temperature on corrosion and establishes temporal correlations between field conditions and simulations through damage factor (DF) analysis coupled with detailed fatigue rating (DFR) methodology. Experimental validation using 7A04 aluminum specimens confirms the model’s reliability, with maturity analysis demonstrating its applicability in aircraft corrosion research. Through numerical simulation methods, this study simulates the evolutionary law of pitting corrosion development, reflecting the level of structural pitting corrosion damage. This investigation establishes a fundamental theoretical framework for condition monitoring and lifetime prediction of aircraft components affected by pitting corrosion.
Lv, Shengli, Liu, Chenglong, Sun, Jingjue
Digital Twin Simulation and Verification System Design and Implementation for Satellite Production Lines2026-99-18047/17/2026
In this paper, we focus on satellite production lines and design and implement a digital twin simulation and verification system for them. This is to improve manual documentation efficiency and provide sufficient process controllability in the small satellites’ batch production and assembly testing. We built a layered architecture. This allows the system to dynamically interact with AIT data management systems, structured process systems, and equipment data by fusing multi-source data. We also develop functional modules that combine lightweight 3D model visualization, dynamic simulation engines, and hybrid scheduling optimization algorithms. These modules can perform twin simulation, execute processes, intelligently schedule production, manage work reporting, conduct intelligent analysis, trigger anomaly alarms, and perform system management. We also dynamically simulate complex workflows like satellite transfer and automated assembly. These workflows are then verified using 3D virtual scene modeling and physical engines. We use time-series analysis to improve scheduling accuracy and multidimensional dynamic monitoring and hierarchical response to enhance production stability. In practice, the system can provide visualized control over the full process of satellite production. This greatly improves assembly efficiency and process controllability. It can also be an extensible digital way for aerospace manufacturing. The use of hierarchical architecture design and multimodal data fusion can be further applied in the complex equipment intelligent manufacturing.
Zhao, Fenghua
Privacy-Preserving UAV-Based Traffic Data Acquisition Platform2026-99-05957/10/2026
This paper puts forward a Privacy-Preserving UAV-Based Traffic Data Acquisition Platform to address 1) privacy leakage, 2) limited scenario coverage, and 3) low traffic data utilization efficiency in urban traffic monitoring environments. Our system integrates three innovations: 1) Dynamic Privacy Masking (DPM) and Dual-Track acquisition (DTC), which hides sensitive information (e.g., faces, license plates or LPL) in real-time while preserving critical traffic data (e.g., vehicle density, speed), 2) traffic data Localization (DL) and Privacy-Enhanced Federated Learning (FEFL), enabling cross-regional collaboration without raw traffic data sharing by perturbing neural network updates with differential privacy (DP), and 3) Ground-Air Collaboration (GAC) and VPF (VPF), combining UAVs with ground sensors and digital twins (DTs) to cover blind spots (e.g., tunnels, extreme weather). Experimented on UA-DETRAC and CitySim traffic data-sets, the platform achieves 92% privacy compliance (GDPR/PIPL), 87.5% mAP accuracy, and 85% road network coverage, outperforming other methods (e.g., FedUAV, static blurring). It supports applications such as traffic flow optimization, accident prevention, and regulatory alignment.
Zhang, Shilin, Yan, Ming
Development of a Digital Twin of an Electrical Powertrain in a Testbench Environment for Efficiency Estimations2026-01-07447/1/2026
The aim of this work is to develop a modular, real-time-capable digital twin of an electric powertrain based on machine learning (ML)-based model structures and a systematic, component-oriented architecture with a focus on efficiency estimation in test bench environments. The further goal here is to enable virtual testing, which can be used for frontloading and thus both prevent errors and increase the speed of product development. Based on a comprehensive set of measured and derived test bench data, a multi-stage procedure is implemented that integrates data acquisition, physically informed feature selection, modeling at the component and subsystem level, and hybrid coupling strategies. The digital twin captures inverter, electric machine, and mechanical transmission stages and generates consistent predictions of key variables such as torque, speed, power factors, and subsystem as well as overall drivetrain efficiency. The methodology enables a systematic comparison of black box, dark grey box, grey box, and bright grey box architectures with respect to prediction accuracy, information content, and real-time capability. The methodology provided uses new model structures that explicitly integrate physical dependencies while also using ML models to map nonlinear effects. The hybrid architectures presented have been shown to significantly reduce the measurement effort while achieving nearly identical model quality and surpassing purely physics-based models in terms of accuracy, robustness, and real-time capability. For the final bright grey-box architecture, average relative efficiency errors below 1 % are achieved while maintaining real-time execution rates. The study shows that bright grey box-models in particular offer a best-case compromise between the requirements of information content, error quality, and synchronization rate, thus representing a methodological advance over conventional digital twins, which are often created at the component level. The shown methodology provides an implementable framework for digital twins of electric powertrains in industrial test environments.
Kopp, Lennart, Proksch, Daniel, Ockert, Niels, Karthaus, Carsten, Kley, Markus
Validation of an Adaptive Order Finite Element Model for Transmission Loss Simulation of Trimmed Automotive Structures2026-01-07076/20/2026
Vehicle electrification and increasing demands for driving comfort present significant challenges for designing effective noise control treatments (NCTs) in modern vehicles. Lightweight, low-emission designs often compromise acoustic efficiency. A popular and efficient way of compensating for this is through the use of multi-layer ‘trim’ material configurations to noise radiating surfaces to mitigate noise across a wider frequency range. Traditional 3D finite element models, while accurate and even needed to capture the full dynamic behaviour, become computationally prohibitive for complex automotive structures like firewalls, which feature intricate shapes, high curvature, and material compression. This computational burden limits design exploration and timely noise performance predictions. To overcome these limitations, this paper presents an innovative adaptive higher-order finite element method to evaluate the sound transmission loss (STL) of automotive, including the effect of poro-elastic and viscoelastic soundproofing materials. To show its capabilities, a digital twin was developed for a STL test setup for a production vehicle firewall with and without NCT. We present simulation results for different firewall configurations, comparing them against experimental data for the panel STL levels and relative improvements due to a NCT modification. The findings demonstrate the method's accuracy, efficiency, and applicability to real-world automotive engineering problems and also shed light on the trade-offs between model idealization and fidelity of the digital twin.
Van Genechten, Bert, Vansant, Koen, Purohit, Bimal, Effinger, Veronika
A Review of Welding Techniques in Metal Additive Manufacturing: Advancements, Challenges, and Future Directions05-19-04-00296/10/2026
The present review evaluates recent advances in the development of Welding-Based Additive Manufacturing (WBAM) technologies using arc, high-energy density, solid-state, and hybrid welding systems by providing an interdisciplinary assessment of technological aspects, sensing, process optimization, and multi-process strategies. It is concluded that, in spite of considerable progress in process optimization and control, there exist numerous paradoxes associated with relationships among process conditions, structure, and properties, especially those related to heat input effects on material microstructure and performance. An important finding is the fragmentation of predictive modeling approaches, where physics-based and data-driven methods remain inadequately integrated, limiting generalizability and accuracy. Another important conclusion is related to the dominance of the effect of thermal history and multi-physical phenomena on the mechanical performance of the material produced by WBAM technologies. Besides, the complexity and contradiction in defect generation mechanisms, monitoring, and evaluation methodologies restrict the development of process standardization and certification. New directions in intelligent fabrication based on artificial intelligence and digital twins are identified.
Santhana Babu, A.V., John Rajan, A., Mishra, Aishwary, Chakravarthy, P., Jayabalakrishnan, D.
An Open-Source Framework to Automate Virtual Optimization of Internal Combustion Engine Calibration Maps through Shape-Based Strategy2026-37-00016/9/2026
The automotive industry is facing increasingly stringent regulatory constraints, driving the need for faster and more efficient powertrain development. This results in higher systems complexity, making internal combustion engine calibration progressively more challenging to meet performance and emissions targets. This, combined with the manual nature of traditional calibration workflows, leads to a time-consuming process that heavily relies on human expertise. Although virtualization can reduce development time and costs, the overall workflow remains largely dependent on manual decision-making and iterative refinement. In this context, this work presents a virtual calibration framework based on a genetic algorithm, aimed at the automated optimization of engine calibration maps to satisfy performance and emissions constraints, while reducing manual effort. Each calibration map is represented through a polynomial parameterization. Specifically, a generic three-dimensional polynomial with map-specific order encodes the shape of each map, ensuring smoothness which directly impact on drivability. Accordingly, the calibration problem is reformulated as the optimization of a compact set of polynomial parameters that uniquely define the full set of calibration maps, rather than individual set-point. Each candidate solution is assessed by generating the corresponding calibration maps and simulating the engine behavior through a neural-network-based digital twin, providing predictions of operating conditions, hardware limits, performance metrics, and emissions. The proposed framework was validated on a passenger-car diesel engine, considering a reduced yet representative set of calibration maps, including main injection start of injection, air mass, boost pressure, and injection rail pressure. The objective of optimization was the minimization of brake mean fuel consumption, subject to an upper bound constraint on nitrogen oxides emissions. The global optimization process explored approximately 106 different calibration candidates within about 36 hours, leveraging parallel computation on a standard laptop. The results indicate that the procedure can deliver multiple near-optimal preliminary calibration solutions, providing an effective starting point for subsequent manual finetuning.
Romano, Gianvito, Aglietti, Filippo, Spedicato, Tonio, Cozza, Ivan Flaminio, Capra, Andrea
Can Large Language Models Enable Sustainable Driving Behavior? A Study on Large Language Model-Based Eco-Driving13-07-02-00066/9/2026
Large language models (LLMs) have shown remarkable capabilities for perceiving driving environments and making interpretable, logical decisions for autonomous driving. However, their potential for more comprehensive driving strategies, especially concerning energy efficiency, remains underexplored. Most existing studies primarily focus on driving safety, which may inadvertently increase energy consumption. To address this issue, this study explores the use of LLMs as high-level controllers to jointly optimize driving safety and energy efficiency. A textual prompt is designed for the LLM, incorporating few-shot examples that describe scenarios, states, and actions. The LLM processes the scenario and state prompts describing the surrounding traffic environment. It generates a high-level control signal, which is then translated into low-level vehicle motion commands in a high-fidelity traffic simulator with realistic physics, vehicle dynamics, road slopes, and network topology. Experiments in campus-scale digital twin car-following scenarios demonstrate that the proposed LLM-based framework achieves an average reduction of 4.16% in energy consumption compared to the reinforcement learning paradigm, while maintaining driving safety and providing interpretable high-level decision-making. This study highlights the potential of LLMs for longitudinal eco-driving applications under the evaluated simulation settings, extending previous LLM-based autonomous driving research that primarily focused on safety to also consider energy efficiency.
Wang, Haoyu, Li, Zhenning, Wang, Siying, Zhou, Zijing, Zhang, Xiang, Yang, Zhifeng, Ou, Shiqi (Shawn), Qi, Hao
Hybrid Digital Twin for Aero-Engine Bearing Prognostics: A Physics-Informed Machine Learning Approach to RUL Prediction2026-26-07186/1/2026
Unscheduled maintenance due to the failure of critical components, such as aero-engine rolling element bearings, is a leading cause of costly Aircraft-on-Ground (AOG) events; consequently, current time-based maintenance practices are inefficient and prone to risk. This paper develops a resource-efficient Hybrid Digital Twin (HDT) model for an engine bearing, focusing on the dynamic prediction of spall growth due to Rolling Contact Fatigue (RCF), thereby enabling a condition-based maintenance paradigm. The HDT architecture integrates two core models: (1) a physics-informed model that uses established life and fatigue theory to define initial degradation thresholds, and (2) a data-driven Recurrent Neural Network (RNN), specifically a Long Short-Term Memory (LSTM) network, for dynamic degradation rate modeling. The methodology utilizes a Monte Carlo simulation coupled with RCF progression equations to generate a large, high-fidelity synthetic run-to-failure dataset under varying operational loads, accurately simulating realistic mission profiles. This approach addresses the critical "data scarcity" challenge in aviation. To ensure operational reliability, the framework incorporates Uncertainty Quantification (UQ) using Monte Carlo Dropout and addresses the "Sim-to-Real" gap through Transfer Learning on the NASA IMS bearing dataset. The HDT demonstrates a significant improvement in prognostic accuracy, achieving a Root Mean Square Error (RMSE) reduction of over 71% compared to baseline models. Furthermore, a cost-benefit analysis suggests a potential fleet savings of $240,000 per 100 engines by avoiding false negatives. This computationally efficient approach supports the Digital Engineering Transformation theme by providing a scalable blueprint for the virtual qualification of critical mechanical components.
Mohamed, Abbas
Digital Twin Modeling of Electro-Hydraulic Asymmetric Actuator System with Parameters Identified under High-Speed Conditions2026-99-07475/15/2026
The electro-hydraulic asymmetric actuator system is widely used in high-precision fields such as aerospace, robotics, and exoskeletons. As application scenarios evolve toward higher speeds and greater precision, the nonlinear characteristics and multi-physics coupling behavior of these systems become increasingly prominent. The accuracy of their modeling and identification directly impacts the effectiveness of system dynamic performance evaluation, control strategy design, and predictive optimization. Therefore, this paper combines the system structure and transmission characteristics to carry out digital twin modeling and parameter identification research under high-speed conditions. First, a coupling model based on motor-load characteristics and flow characteristics is established; then, the least squares method is proposed to identify the frequency domain Bode response of the motor-controlled pump system and the time domain nonlinear parameters of the hydraulic transmission system; finally, the two models are organically combined to establish a Simulink-AMESim co-simulation model, and the accuracy of the constructed nonlinear model is verified through simulation and experimental comparison. The experimental results show that the speed response error is within 0.5%, and the position tracking error is within 0.5mm. This method can accurately model the electro-hydraulic asymmetric actuator system under high-speed conditions.
Wang, Hao, Zhang, Xin, Ma, Tianbing, Wang, Jian, Zhang, Tao, Wang, Lei, Shi, Yongping, Wang, Chao
Automotive Engineering: May 202626AUTD055/14/2026
Forvia Hella ready with ADB, but NHTSA test stands in the way A demonstration ride shows the glare-free, game-changing power of adaptive driving beams, already available in Europe. An approval test from NHTSA is proving difficult for OEMs to pass. Sharper validation without brute force How CERTUS reshapes AV testing. Simulation-driven battery development From material selection to system-level performance. How simulation unlocks efficient and innovative motor design Engineers are still at the heart of the development process as simulation tools become great levelers. Engineering in the second quarter of the 21st Century Building a trusted digital twin and decision-centric simulation ecosystem. Engineering in the second quarter of the 21st Century Building a trusted digital twin and decision-centric simulation ecosystem. Independent materials testing for OEM validation How validated data provides the foundation for approved components. Editorial All the ways: Learning via print in a digital era The Navigator Uber wants a piece of every robotaxi Bosch Shows off its first U.S. electrolyzer in support of hydrogen research Engineering better reusable bulk containers for the industry The dawn of agentic autonomy in factories Some Automakers Retreat from North American EV Market Enabling certified GoogleTest for safety-critical embedded software Toyota expands all-electric bZ 'family' First Drive: 2026 Subaru Outback Wilderness Product Briefs Spotlight: Testing & simulation, semiconductors Q&A TMMK president: Solar and bright, quieter factory floor help production
Simulation-Based Route Analysis and Digital Twin Integration for Electric Aircraft and eVTOL Design & OperationsF-0082-2026-00655/5/2026
The rapid expansion of electric aviation and eVTOL operations introduces tightly coupled challenges related to energy‑constrained aircraft design, battery and thermal management, mission planning, and the generation of certification‑relevant evidence. This paper presents an integrated simulation workflow developed by AVL, Unisphere, and blueflite that combines high‑fidelity electric powertrain and battery models with a guidance‑level, digital‑twin‑based 4‑D trajectory simulation driven by historical weather and operational constraints. At each mission time step, the trajectory layer provides time‑resolved environmental and routing conditions, while the system‑level models compute instantaneous power demand, state‑of‑charge evolution, and thermal response, enabling mission feasibility assessment under realistic wind, temperature, and airspace effects. The workflow is calibrated and validated using flight telemetry from blueflite's active eVTOL cargo aircraft development, ensuring alignment between simulation assumptions and real‑world mission execution. The validated framework is subsequently applied to seasonal route studies and large‑scale virtual flight campaigns spanning multiple regions and years, enabling statistically robust assessment of energy margins, thermal behavior, and mission‑duration variability. The results demonstrate how integrated, traceable simulation can bridge conceptual design and real‑world electric flight operations, supporting informed decision‑making by OEMs and operators in aircraft design, validation, and deployment planning.
Schneider, Jürgen, McClearen, James, Anger, Michael
Improvement and Demonstration of a Machine Learning-Based Digital Twin Framework for Rotorcraft Life Assessment Using In-Service Operational DataF-0082-2026-00605/5/2026
Traditional safe-life methodologies for rotorcraft structural components rely on deterministic safety factors to account for uncertainty in loads, material properties, and operational usage. While effective for ensuring safety, these approaches lead to early retirement lives and reduced aircraft availability. This paper presents an updated digital twin-based probabilistic framework for rotorcraft component fatigue life assessment that integrates a probabilistic stress–life (S-N) material model, machine learning-based load estimation from flight data, and Monte Carlo uncertainty propagation. The approach is demonstrated for a critical location on the CH-146 Griffon main rotor yoke. Compared with earlier work, the present study advances the framework through independent validation of the load-estimation model and application to available in-service flight data from multiple mission categories. A probabilistic sensitivity analysis is used to examine the separate and combined effects of material variability and load-estimation uncertainty on fatigue life, cumulative probability of failure, and hazard rate. For the CH-146 demonstration case, the results indicate that the material fatigue strength uncertainty has a major impact on the lower tail of the life distribution and the corresponding reliability-based life, whereas load-estimation accuracy uncertainty has a secondary influence on risk metrics. The application of the digital twin framework to operational, search and rescue, and training mission data further shows that mission-specific usage variability plays an important role in the evolution of fatigue damage accumulation and structural risk. Overall, the proposed framework provides a more informative basis for risk-based rotorcraft life assessment by explicitly quantifying uncertainty and incorporating aircraft-specific operational data. The study is intended as a step toward validation of the framework rather than a completed operational deployment.
Asaee, Zohreh, Bombardier, Yan, Renaud, Guillaume
Multiphysics Modeling and Performance Assessment of Ultrasonic Welding for Thermoplastic CompositesF-0082-2026-01205/5/2026
Ultrasonic welding (UW) provides a rapid and efficient method for joining composite components by inducing resin flow through thermally driven diffusion and crystallization at the bonded interface. However, in the absence of a multiphysics modeling framework or a digital twin approach, current practice still depends on extensive trial-and-error testing to determine key welding parameters such as vibration amplitude, weld time, weld pressure, hold time, and downspeed. While in-situ thermal cameras can monitor surface temperatures, the internal temperature at the bonded interface is often significantly higher, introducing the risk of thermal degradation and inconsistent bond quality. To overcome these limitations, GEM developed a high-fidelity multiphysics model to establish a quantitative relationship between process parameters and the evolving temperature field within welded thermoplastic parts. The model integrates coupled mechanical, thermal, and acoustic physics to simulate high-frequency vibrations and static pressure, capture the generation and spatial distribution of heat, and represent the temperature-dependent viscoelastic response that governs bond formation. A validation test matrix was designed by systematically varying weld time and vibration amplitude. Through-thickness temperature distributions were measured using infrared thermal imaging, enabling direct comparison with model predictions. Upon validation, the model was applied for process tailoring, allowing precise control of temperature distribution to achieve target bond strength. This integrated modeling and validation approach demonstrated substantial benefits, including reduced design iterations, accelerated process optimization, and improved quality and performance of welded composite structures.
Walthers, Mark, Li, Rui, Wei, Qingxuan, Lua, Jim
Computer-Aided VTOL Design: from Slide Rulers to Artificial IntelligenceF-0082-2026-01985/5/2026
Vertical Take-Off and Landing (VTOL) aircraft represent one of aviation's most complex design challenges, balancing lift, thrust, stability, and control within an inherently unsteady aerodynamic environment. Since the 1940s, computational methods used to design VTOL systems have undergone a profound transformation, progressing from hand-drawn airflow approximations and wind-tunnel testing to today's high-fidelity digital twins, computational fluid dynamics (CFD), and AI-assisted optimization. The evolution of these methods mirrors the broader technological shift from empirical design toward simulation-driven innovation. The greatest transformation in VTOL design of the past 80 years is the shift from material and mechanical innovation to computational and cognitive design. Modern aircraft are as much products of computation and data as of metal and composites. As electric propulsion, autonomy, and digital twin technology converge, the next generation of designs, particularly configurations inspired by power systems such as hybrid-electric, hydrogen, battery only, will extend this century-long trajectory into a new paradigm: sustainable, intelligent, and continuously self-optimizing VTOL flight.
Stanzione, Kaydon
Digital Twin-based Integrated Airspace Management for Urban Air Mobility Services: Architecture, Intent Modeling, and DeploymentF-0082-2026-02155/5/2026
Urban Air Mobility (UAM) represents a paradigm shift in metropolitan transportation, introducing electric vertical takeoff and landing (eVTOL) aircraft into dense urban ecosystems. This transformation is driven by advances in electrification, digital infrastructure, and integrated airspace management. According to the U.S. Department of Transportation's Advanced Air Mobility National Strategy 2025, UAM is expected to become a cornerstone of multimodal urban transport, with commercial operations projected in multiple U.S. cities before 2030 [1].
Namuduri, Kamesh, Sampath, Arunkumar
Engineering in the second quarter of the 21st Century26AUTD05_055/1/2026
Building a trusted digital twin and decision-centric simulation ecosystem The automotive industry has been experiencing significant change and transformation. Electrification, software-defined vehicles, advanced driver assistance systems, and increasing electrical system integration are fundamentally reshaping how vehicles are designed and validated. As integration complexity continues to increase, the expectations for design cycle times are being compressed. Programs that once relied on extended validation timelines are now expected to deliver the same level of confidence in a fraction of the time. Traditional engineering workflows were built around sequential design phases, iterative simulations, and heavy reliance on physical validation. Design concepts were documented, prototypes were constructed, tests were performed, and results were compiled in reports and specifications that informed the next iteration. That approach worked well when systems were less complex and product life cycles were longer. In recent years, the volume of data, the speed of development, and the interconnected nature of modern vehicle architectures demand a different approach.
Patterson, Jeremy
The dawn of agentic autonomy in factories26AUTD05_115/1/2026
The global automotive industry has reached a new era. If 2025 was defined by the cautious exploration of “experimental pilots” and the collection of vast data lakes from connected vehicle fleets, 2026 marks the year that data finally gains a mind of its own within the assembly plant. We are witnessing a transition from passive automation to integrated, agentic autonomy. This is a shift that moves beyond simple programmed robotic arms and toward systems capable of independent reasoning and real-time optimization. This evolution is not just a technical upgrade; it is a fundamental restructuring of how vehicles are built, de-risked, and scaled in an increasingly volatile global economy.
Panigrahi, Dijam
Optimizing Virtual Scenario Testing for Autonomous Vehicles with Large-Scale Scenario Generation2026-01-00504/7/2026
The validation of Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) Systems, especially at higher automation levels such as SAE Level 3 or 4, demands the testing of a vast array of scenario variants far exceeding the scope of standard safety specifications like Euro NCAP (The European New Car Assessment Programme). Autonomous vehicles require thorough real-world testing to ensure automotive safety. However, public road tests are costly and risky. Instead, virtual scenarios - digital twins of real environments - offer a safe, cost-effective testing alternative. Exhaustive simulation across this high-dimensional scenario space, which includes variations in actor behavior, environmental conditions, and event characteristics, is computationally infeasible. We propose a constraint-solving approach to address this challenge that leverages mathematical and geometric techniques to analytically assess the existence and validity of scenario variants prior to simulation. Two primary methods are explored: (1) random or sequential generation of scenario variants with a pre-simulation pruning step to eliminate invalid cases, and (2) direct generation of valid variants by solving constraint systems that ensure the desired events occur under specified conditions. Importantly, maintaining an effective balance between these two approaches is central to our methodology, as the optimal mix depends on the specific testing goals and requirements. This framework implemented using MATLAB®, Simulink®, the Automated Driving ToolboxTM, and the Euro NCAP Support Package®, systematically reduces the scenario space by excluding impossible cases. Our approach aims to significantly reduce reliance on extensive simulation and enable more targeted and efficient validation for safety compliance.
Karve, Omkar, Saurav, Saket, Purwar, Prabhanshu
In-Use Life Digital Twin (Redundant Storage) and Swappable Use Case for Battery Durability2026-01-03974/7/2026
As regulatory frameworks for zero-emission vehicles (ZEVs) and battery electric vehicles (BEVs) continue to evolve, there is growing emphasis on monitoring battery durability and usage throughout the vehicle lifecycle. These regulations increasingly specify the use of data monitors and tracking mechanisms to assess battery health and performance. In addition, regulations require anti tampering mechanisms especially for monitors that have external write access. Historically, regulations focused primarily on vehicle warranty; however, with the introduction of battery durability monitors, clarity is needed for the new battery durability monitors. More specifically if the battery durability monitors track with the lifetime of the vehicle or if they follow the lifetime of the battery. Furthermore, current regulations provide no guidance on high-voltage (HV) traction battery service strategies or methods to protect monitors from tampering by external customers. This paper will classify battery durability tracking parameters (DIDs) according to whether they align to the lifetime of the vehicle or the battery itself. Building on this classification, a service strategy is proposed that considers typical vehicle architectures: when the battery management Electrical Computer Unit (ECU) is fully integrated with or separated from the high voltage traction (HV) battery. The outlined service strategy not only supports regulatory compliance, but also enhances data integrity by mitigating the risk of tampering with monitored parameters through a Digital Twin framework. More specifically, the Digital Twin framework introduces redundant storage of critical information in multiple storage locations such as ECUs and then a mechanism for correlating that critical information to determine a mismatch. This approach anticipates future requirements for tamper-proofing and ensures secure, reliable tracking of battery durability metrics through redundant ECU storage.
Laskowsky, Patricia, Bunnell, Justin, Zettel, Andrew, Albarran, Josue
Vehicle Test Platform with Experimentally Correlated Tire Model for Torque Vectoring Control2026-01-06224/7/2026
This paper presents a testing platform for the development of lateral stability control systems in independent motor electric vehicles (EVs). A 10 degree of freedom (DOF) vehicle simulation and a radio control test vehicle are constructed to enable controls validation scalable to full size vehicles. These vehicle simulations, or ‘digital twins’, have been widely adopted throughout the automotive industry due to their lower operating costs and ease of implementation. Virtual models are not perfect representations of reality, however, and physical testing is still necessary to validate systems for use in the real world. This is especially true when testing safety-critical features such as stability control. As a result, a simulation environment working in conjunction with a test vehicle represents an optimal hybrid approach. In this work, a high fidelity vehicle model is constructed in the Matlab/Simulink environment. To capture the effect of suspension, the digital twin is capable of modeling all angular and linear degrees of freedom of the vehicle body. The vehicle model must also estimate wheel forces during high-sideslip maneuvers. The Pacejka Magic Formula is used for its accurate representation of tire behavior in highly transient driving scenarios. This vehicle model describes the behavior of a physical vehicle. For this purpose, a 1/5 scale radio controlled vehicle with independent rear wheel propulsion is designed and assembled. All physical parameters of the test vehicle required by the vehicle model are estimated through direct measurement or estimation through test maneuvers. Magic formula coefficients are estimated from GPS, inertial, and odometry measurements collected throughout defined test maneuvers. Vehicle model behavior is then benchmarked against the test vehicle. An S-curve maneuver is performed in simulation and experimentation to ensure accuracy and consistency across transient and steady state behavior. In future work, focus will turn to creating an ADAS control system which re-stabilizes a vehicle after a collision using torque vectoring.
Petersen, Nicholas Conner, Robinette, Darrell
Digital Twin Aerodynamics Model Validation for HALO Wind Tunnel2026-01-06104/7/2026
A simulation-based aerodynamics model of the Honda Automotive Laboratories of Ohio (HALO) Wind Tunnel, a three-quarter open-jet (ground plane) configuration opened in 2022 for full-scale automotive testing, was initiated to support data fusion for more accurate surrogate models in vehicle engineering programs. The objective was to demonstrate that a matched set of boundary values between the physical wind tunnel and the three-dimensional numerical model yield correct responses for several key flow field quantities, starting with the baseline empty tunnel case: (1) streamwise static pressure distribution, (2) evolution of the free shear layers downstream of the nozzle exit plane, and (3) ground-plane boundary layer development. Pressure-based measurement probes were deployed in these regions using a four-axis overhead traverse to acquire validation data in the large facility, including instrument verification between a 14-hole probe and Pitot-static rake. Detached eddy simulation (DES) and Reynolds-Averaged Navier Stokes (RANS) turbulence models were evaluated for the numerical approach. This work describes the three-dimensional model setup and presents these data comparisons.
Patel, Sajan, Disotell, Kevin, Eagles, Naethan
An Integrated Methodology for Sustainable Mobility Infrastructure Planning: The Atlanta Aerotropolis Case Study2026-01-00974/7/2026
By the early 2020s, more than 4.5 billion people have been living in urban areas worldwide, compared to just 1 billion in 1960. Rising growth in urban populations present challenges to infrastructure and transportation systems. Higher traffic levels and reliance on conventional vehicles have contributed to heightened greenhouse gas (GHG) emissions, rising global temperatures, and irreversible environmental degradation. In response, emerging transportation solutions—including intelligent ridesharing, autonomous vehicles, zero-tailpipe-emission transport, and urban air mobility—offer opportunities for safer and more sustainable transportation ecosystems. However, their widespread adoption depends not only on technological performance and efficiency, but also on integration with current infrastructure, safety, resilience to unexpected disruptions, and economic viability. A dynamic agent-based System-of-Systems (SoS) transportation model is developed to simulate vehicle traffic and human movement for assessing mobility solutions against different demand scenarios and possible disruptions within a well-defined metropolitan area. The analysis adopts the concept of an airport city—a cluster of residential, commercial, and industrial spaces surrounding major airports—as a representative urban context. Using the Atlanta Aerotropolis as a case study, this work introduces an interactive, parametric decision-support methodology for evaluating the impact and benefits of future mobility options, as part of transportation master planning. Given the multi-objective and multi-stakeholder nature of transportation planning (e.g. local government, urban planners, engineers, and technology providers), the proposed approach leverages simulation-enabled digital twins of mobility solution alternatives to analyze traffic performance across multiple criteria, including energy consumption, emissions, affordability, accessibility, and connectivity within the broader urban infrastructure. The study reveals cost-benefit trade-offs among mobility solutions in the context of disruptive scenarios, such as the 2026 FIFA World Cup hosted by Atlanta, GA. The results highlight the importance of deploying a mix of mobility options over the city’s transportation network to maximize sustainability while maintaining resilient operations.
Rana, Vishva, Balchanos, Michael, Mavris, Dimitri, Valenzuela Del Rio, Jose
AI Model Creation and System Verification of “Telemotion” Infrastructure-Cooperative Autonomous Driving System Using Digital Twin2026-01-02554/7/2026
The concept of the vehicle has changed as a result of many innovations over the last decade in the fields of connected, autonomous/automated, shared, and electric (CASE) technologies. At the same time, labor shortages in Japan are becoming more serious due to a decline in the working population. To help resolve these issues, a remote-controlled autonomous vehicle driving system called Telemotion has been developed that automates the movement of vehicles in production plants. This system is an autonomous driving and transportation system in which the recognition, judgment, and operation functions of driving are handled by a control system outside the vehicle that communicates wirelessly with the vehicle. This system utilizes artificial intelligence (AI) and other advanced technologies to realize safe unmanned autonomous driving, and is already in operation in production plants. Currently, efforts are under way to build a digital twin environment and conduct AI learning using computer graphics (CG) to configure the system and improve the accuracy of the AI models with the aim of expanding its use to other factories. Within this digital twin environment, it is possible to examine previous tasks by reproducing the vehicles, processes, cameras, and vehicle movements present at a production site. Utilizing this digital twin enabled a significant reduction in the labor required to implement the system.
Hatano, Yasuyoshi, Iwazaki, Noritsugu, Nagafuchi, Yuhei, Iwahori, Kento, Tanaka, Atsushi, Uezu, Satoru, Kanou, Takeshi, Inoue, Go, Okamoto, Yuki, Oka, Yuhei, Kakuma, Daisuke, Chiba, Hiroya, Egashira, Kazuki, Ishikuro, Megumi, Sawano, Takuro
A System of System Design Framework for Multi-Domain Vehicles2026-01-02634/7/2026
Traditionally, ground vehicle design is based on identifying engineering solutions that fulfil the requirements and specifications put forth by the stakeholders. Although a vehicle is a single entity, it is composed of many subsystems and thousands of parts that must operate together in unison to meet all design goals. A System of Systems (SoS) design approach enables the consideration of subsystem performance within a framework of overall system operation, which includes possible tradeoffs. This collaborative approach to subsystem and primary system design draws upon modelling, optimization, tradespace analysis and virtual studies. In this paper, a system of system design approach will be investigated for a collection of multi-domain vehicles assembled to undertake coordinated search and rescue operations on land and water. A host ground vehicle, an unmanned aerial drone, an unmanned marine drone and an unmanned tracked vehicle constitute the family of multi-domain vehicles which will be used for the search and rescue mission. A digital twin for this family of vehicles will be created to support numerical design studies. The System of Systems approach will enable tradeoffs in vehicle and family design to be evaluated using optimization tools. To visualize the designs, tradespace analysis tools will be key to identifying the tradeoffs and performance at the system level and the individual vehicle level. A case study is undertaken to simulate the trajectory of an aerial drone for a search and rescue operation and calculate its - ilities for such a scenario. In the future, the same exercise will be performed for three other models highlighted above and incorporate their -ilities to incorporate into the subsequent steps of optimization and tradespace analysis. This paper showcases the System of Systems approach and highlights the advantages and challenges faced in implementing such an approach for the purposes of achieving a specific mission through the collaborative and diverse vehicles used.
Somanchi, Anang, Abeynayake, Chandima, Deshmukh, Mrunal, Suresh, Johir, Ramnath, Satchit, Turner, Cameron, Schmid, Matthias, Castanier, Matthew P., Rapp, Stephen, Jaczkowski, Jeffrey J., Wagner, John
Enhancing Track Vehicle Design Engineering Through Digital Twins: A Data-Driven Approach2026-01-02664/7/2026
Digital Twin technology can significantly improve the engineering product design process, especially when considering ground vehicle applications. Data-driven computer studies can assist engineers and key stakeholders in evaluating performance, durability, and other system design tradeoffs. To enable this process, the availability of relevant, numerically generated, laboratory, and/or field data is required. Proper data use enables the digital exploration of “what-if” scenarios, reducing necessary field testing and allowing for the examination of hard-to-test operating conditions. When considering the Digital Twin toolset, a collection of models and simulations are assembled to supplement virtual testing endeavors. These models include surrogate, CAD/CAE, and others. In this paper, an off-road track vehicle design is reviewed through the fusion of numerical and field data to evaluate future design enhancements. Preliminary results demonstrate that subtle feature upgrades can produce measurable performance gains without compromising listed requirements and specifications. The proposed design framework establishes a methodology for virtual engineering practitioners. In addition, a simulation is able to generate design and solution space visualizations for the assessment of design tradeoffs, optimizing three Key Performance Indices (KPIs) or Key Design Specification (KDS) objectives.
Suber II, Darryl, Bradley, Andrew, Singh, Shubhendra, Turner, Cameron, Castanier, Matthew P., Wagner, John
Standardization of On-Vehicle Measurement of BEV Thermal System Energy Consumption and Range Impacts2026-01-01324/7/2026
This paper presents research and digital twin modeling results to support work on a methodology to properly account for the energy consumed by the thermal system of a BEV, for use within both existing Petroleum-Equivalent Fuel Economy (PEFE) calculations, and the proposed addition of hot and cold weather range values to the consumer-facing Monroney label [1]. Properly accounting for thermal system impacts would incentivize minimizing energy consumption of these systems, since 1) BEV PEFE is a direct input to an OEMs overall CAFE performance, and 2) the values on the Monroney label has some impact on consumer vehicle choice. The impetus for this work was Final Rules issued by the EPA and NHTSA in early 2024 eliminating A/C Efficiency Credits for BEVs from the 2027 MY, thus eliminating regulatory incentives to minimize energy consumption of these systems. Higher energy consumption will produce a number of negative secondary effects, including higher real-world greenhouse gas emissions, reduced vehicle range, greater strain on the nation’s electrical grid, and higher vehicle mass leading to reduced vehicle safety - should OEMs opt to merely install larger batteries to address cold and hot weather range impacts instead of implementing lower energy-consuming technology. The results from the analysis, which ideally would be confirmed with follow-up vehicle tests, show that for a baseline, PTC-heat based system, thermal system energy consumption represents 19.2% of the total energy consumed by a BEV on an annual basis, using an ambient-VMT weighted approach. It seems to be the technical equivalent of “straining at a gnat while swallowing a camel” to focus so much time and energy on identifying incremental improvements in energy consumption from the propulsion-portion of a BEV, while by comparison ignoring the system that according to this analysis can account for nearly 20% of the total on an annual basis.
Taylor, Dwayne
Dassault Systèmes and NVIDIA Partner to Build Industrial AI Platform Powering Virtual Twins26AERD03_013/1/2026
Dassault Systèmes and NVIDIA have announced a long-term strategic partnership to establish a shared industrial architecture for mission-critical artificial intelligence across industries. Combining Dassault Systèmes' Virtual Twin technologies with NVIDIA AI infrastructure, open models and accelerated software libraries will establish science-validated industry World Models, and new ways of working through skilled virtual companions on the agentic 3DEXPERIENCE platform, that empower professionals with new expertise.
PRODUCT BRIEFS26AUTP03_163/1/2026
PRODUCT BRIEFS26AUTP02_172/1/2026
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 and a 15% weight reduction in an EV motor housing through topology optimization. The proposed approach not only accelerates R&D cycles but also supports circular economy principles by facilitating the remanufacturing of legacy components. This work contributes to the ongoing shift toward sustainable mobility by bridging the gap between legacy engineering and next-generation powertrain innovation.
Bernikov, Mark Alexandrovich, Kurmaev, Rinat
Integrated Quality Assurance Frameworks for Testing Advanced Automotive Systems in the Era of ADAS and E-Mobility2026-26-05681/16/2026
Ensuring the safety and functionality of sophisticated vehicle technologies has grown more difficult as the automotive industry quickly shifts to intelligent, electric, and connected mobility. Software-defined architectures, electric powertrains, and advanced driver assistance systems (ADAS) all require strong quality assurance (QA) frameworks that can handle the multi domain nature of contemporary vehicle platforms. In order to thoroughly assess the functionality and dependability of next generation automotive systems, this paper proposes an integrated QA methodology that blends conventional testing procedures with model-based validation, digital twin environments, and real-time system monitoring. The suggested framework, which includes hardware-in-the-loop (HIL), software-in-the-loop (SIL), and over-the-air (OTA) testing techniques, concentrates on end-to-end traceability from specifications to validation. Simulating intricate situations for ADAS, electric vehicle battery temperature management, and dynamic system updates in connected platforms are prioritized. This study also outlines the main obstacles to integrating QA methods with changing regulatory environments and draws attention to discrepancies between operational performance in real-world scenarios and compliance benchmarks. Early fault detection, lifecycle validation, and continuous improvement are made possible by the QA process's transition from reactive to proactive through the integration of digital twins and predictive analytics. A strategic roadmap for QA specialists and test engineers to adjust to changing industry demands is presented in the paper's conclusion. In addition to promoting safety and dependability, the suggested framework speeds up time to market, lowers development costs, and increases consumer confidence in cutting-edge automotive technologies.
Komanduri, Arun Srinivas, Srivastava, Anuj
Real-Time Range Optimization for Selected Destinations by Dynamic Energy Management Strategy (DEMS) in BEVs2026-26-01681/16/2026
The automotive industry has undergone significant transformation with the adoption of electric vehicles (EVs). However, the inadequate driving range is still a major limitation and 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 range anxiety and gaining a competitive edge in the market. This paper introduces Dynamic Energy Management Strategy (DEMS) for electric vehicles (EVs), designed to optimize battery usage and extend the 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 identifying 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 intelligently allocates energy to each component, ensuring optimal performance. DEMS automatically selects the best drive mode and strategically manages energy distribution, prioritizing essential functions such as the drivetrain, followed by comfort features like heating or air conditioning, and luxury features like ambient lighting or seat massagers. This dynamic energy allocation is continuously adjusted to maximize efficiency, making EVs more efficient and extending their range. Simulations demonstrate that this approach can extend battery life and driving range, offering a promising solution for commercial EVs with existing battery technology. This strategy advances the sustainability and functionality of battery electric vehicles in modern transportation ecosystems.
Dey, Supriyo, Venugopal, Karthick Babu, Penta, Amar, Kumar, Rohit, Arya, Harshita
Scalable and Modular Data-Driven Hybrid Chassis Dynamometer Framework for EV Powertrain Evaluation2026-26-03631/16/2026
In its conventional form, dynamometers typically provide a fixed architecture for measuring torque, speed, and power, with their scope primarily centered on these parameters and only limited emphasis on capturing aggregated real-time performance factors such as battery load and energy flow across the diverse range of emerging electric vehicle (EV) powertrain architectures. The objective of this work is to develop a valid, appropriate, scalable modular test framework that combines a real-time virtual twin of a compact physical dynamometer with world leading real-time mechanical and energy parameters/attributes useful for its virtual validation, as well as the evaluation of other unknown parameters that respectively span iterations of hybrid and electric vehicle configurations, ultimately allowing the assessment of multiple chassis without having to modify the physical testing facility's test bench. This integration enables a blended approach, using a live data source for now, providing a point of calibration and validation for the virtual model(s), as well as using the virtual model capability to determine other unknown/measurable characteristics about the physical model. So, this test framework makes that system's capability, representing an enhancement to its ability, with a virtual twin merging them together to enable virtual evaluation of multiple configurations of the chassis without changing the physical test stand. So, this combined real-world and virtual framework offers a scalable and flexible testing and modelling platform for both early performance characterisation, as well as life cycle-based energy evaluations. In responding to the identified gaps, this work introduces an innovative hybrid chassis dynamometer framework that applies to a real-world test bench in tandem with a concurrently simulated virtual model, offering early-stage validation and optimisation potential using the shift-left development proposition. The result is a reusable and forward-thinking platform supporting efficient EV development by forecasting and drawing informed insights into energy flow, battery performance, and lifecycle behaviour from ahead of typical physical testing boundaries.
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 minimal 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 safety of the battery, sensor data, including current, voltage, and temperature, can be continuously monitored through external measurement devices to generate the battery's State of Charge (SoC). Individual battery parameters such as state of charge, power, energy, health, and safety can continuously send data to a real-time monitoring system. An advanced BDT can contribute to enhanced computational capacity, real-time data collection and analysis, visualization, predictive maintenance, life cycle management, failure prediction based on degradation chemistry, and ML algorithms for various OEMs. Recent developments in key technologies have facilitated the development of innovative features within the Digital Twins (DT) system, such as Big Data for real-time fast and accurate analysis, AI/ML for model training and decision-making, the Internet of Things for live communication, cloud for storage and fast computation, and Blockchain for Life Cycle Management (LCM)/Battery Passport. In this review, we have systematically integrated the early adoption of BDT models and their systematic advancement with continuously evolving physical and AI/ML-based models.
Chaturvedi, Vikash, M, Venkatesan, Lanke, Siddhi, Subramaniam, Anand, Karle, Manish, Pandit, Rugved, Gupta, Drishti, Karle, Ujjwala Shailesh
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 Data Lake and associated services, designed for efficient storage and processing of large volumes of data from multiple markets and vehicle types. The research introduces an adaptable procedure for data collection campaigns, applicable to diverse global markets and encompassing a wide range of vehicles, from internal combustion engines to electric and hybrid models. A key feature of this approach is the establishment of advanced data decoding protocols, enabling precise interpretation of CAN network information from vehicles of different manufacturers and models, even when the CAN structure is not previously known. The study defines standardized parameters for data recording, ensuring comparability across different markets and vehicle types, while developing adaptive analysis methodologies to identify specific driving patterns based on vehicle segment, propulsion technology, and demographic characteristics. This comprehensive approach is underpinned by a framework that ensures compliance with data protection regulations globally, facilitating ethical and legal data management across different jurisdictions. The anticipated outcomes include the creation of a highly flexible data-as-a-service platform capable of integrating and analyzing driver data worldwide, and the establishment of a standardized procedure for characterizing driver profiles. The development of advanced vehicular data decoding capabilities allows for the inclusion of a wide variety of brands and models, enabling truly global insights into driver behavior. This study lays the groundwork for a global understanding of driver behavior, providing automotive manufacturers with a powerful tool to adapt their designs to the needs of users worldwide, accelerating innovation in vehicle design and improving the safety of future vehicles.
Arturo, Rubio, Marín Saltó, Anna, Diaz, Francisco, Olivencia, Sergio
Software-Defined Vehicles: Architecting the Future of Intelligent and Connected Mobility2026-26-06911/16/2026
This paper presents a comprehensive technical review of the Software-Defined Vehicle (SDV), a paradigm that is fundamentally reshaping the automotive industry. We analyze the architectural evolution from distributed Electronic Control Units (ECUs) to centralized zonal compute platforms, examining the critical role of Service-Oriented Architectures (SOA), the AUTOSAR standard, and virtualization technologies in enabling this shift. A comparative analysis of leading High-Performance Computing (HPC) platforms, including NVIDIA DRIVE, Tesla FSD, and Qualcomm Snapdragon Ride, is conducted to evaluate the silicon foundation of the SDV. The paper further investigates key enabling technologies such as Over- the-Air (OTA) updates, Digital Twins, and the integration of Artificial Intelligence (AI) for applications ranging from predictive maintenance to software-defined battery management. We scrutinize the competing V2X communication standards (DSRC vs. C-V2X) and address the paramount challenges of functional safety (ISO 26262) and cybersecurity (ISO/SAE 21434) in this new landscape. Finally, we identify open research challenges, ethical considerations, and the future trajectory of SDVs toward fully AI-defined, intelligent, and connected mobility.
Ahmad, Aqueel, Hemanth, Khimavath, Kumar, Om, Kumar, Rajiv, Haregaonkar, Rushikesh Sambhaji
Nexifi11D: A Comprehensive Multidimensional Framework for Futuristic Manufacturing Ecosystems2026-26-02821/16/2026
This paper presents Nexifi11D, a simulation-driven, real-time Digital Twin framework that models and demonstrates eleven critical dimensions of a futuristic manufacturing ecosystem. Developed using Unity for 3D simulation, Python for orchestration and AI inference, Prometheus for real-time metric capture, and Grafana for dynamic visualization, the system functions both as a live testbed and a scalable industrial prototype. To handle the complexity of real-world manufacturing data, the current model uses simulation to emulate dynamic shopfloor scenarios; however, it is architected for direct integration with physical assets via industry-standard edge protocols such as MQTT, OPC UA, and RESTful APIs. This enables seamless bi-directional data flow between the factory floor and the digital environment. Nexifi11D implements 3D spatial modeling of multi-type motor flow across machines and conveyors; 4D machine state transitions (idle, processing, waiting, downtime); 5D operational cost breakdowns covering electricity, tooling, labour, coolant, and depreciation; 6D AI/ML-based failure prediction using temperature and pressure inputs; 7D predictive downtime triggers based on learned thresholds; 8D sustainability analytics measuring CO₂ emissions per motor; 9D workforce optimization via virtual shift scheduling and fatigue simulation; 10D supply chain resilience through simulated part delays and buffer modeling; and 11D risk and quality management using defect simulation and risk scoring. All data are generated live and visualized through Grafana dashboards, enabling real-time monitoring of OEE, energy use, defects, and AI-based alerts. Nexifi11D establishes a unified, cyber-physical platform for intelligent, sustainable, and predictive manufacturing, making multidimensional factory optimization practically demonstrable within one connected environment.
Kumar, Rahul, Singh, Randhir
A Data-Centric Approach to Identify Optimal NVH Simulation Tools and Solvers2026-26-03561/16/2026
The world is moving towards data driven evolution with wide usage tools & techniques like Artificial Intelligence, Machine Learning, Digital Twin, Cloud Computing etc. In automotive sector, the large amount of data being generated through physical and digital test evaluations. Computer-Aided Engineering (CAE) is one of the highest contributors for data generation as physical testing involves high cost due to prototypes & test set-up. The Automotive Noise, Vibration & Harshness (NVH) field is advancing exponentially due to new stringent regulatory norms & customer preferences towards comfort, where digitally advanced techniques are playing a key role in the revolution of NVH. Data generation through CAE tool is a crucial aspect of Engineer’s daily activities and selecting such appropriate CAE software and solvers is critical, as it influences user interface experience, accuracy, solution time, hardware requirements, variability expertise, Design of Experiments ability, and integration with other environments. This study is intended to evaluate and compare these key parameters across leading software and solvers within the automotive NVH CAE domain, using a vehicle finite element model. This paper references the development of a comprehensive matrix which assists engineers in making intended decisions while selecting CAE software and solvers tailored to their specific needs. By using this engineers can improve their proficiency in different analysis with optimized solution time. It also help to identify seamless integration with existing system. This ultimately improves the overall efficiency and effectiveness of their CAE processes. Additionally, the matrix aids software vendors in identifying gaps in existing capabilities and aligning their offerings to meet the needs of CAE engineers.
Hipparge, Vinod, Masurkar, Nikita, Arabale, Vinand, Billade, Dayanand
Optimizing Vehicle Dynamics: A Methodology for Reducing Correlation Gaps2026-26-00911/16/2026
Final design choices are frequently made early in the product development cycle in the fiercely competitive automotive sector. However, because of manufacturing tolerances design tolerances stiffness element fitment and other noise factors physical prototypes might show variations from nominal specifications. Significant performance differences (correlation gaps) between the digital twin representation produced during the design phase and real-world performance may result from these deviations. Measuring every system parameter repeatedly to take these variations into account can be expensive and impractical. The goal of this study is to identify important system parameters from system characteristic data produced by controlled dynamic testing to close the gap between digital and physical models. Dynamic load cases are carried out with a 4-poster test rig where vehicle responses are captured under controlled circumstances at different suspension locations. An ideal set of digital model parameters is found by examining these target responses and comparing them with the output of the digital twin. This procedure makes digital simulations more accurate and guarantees that they more accurately depict the behavior of actual vehicles. By reducing the need for extensive physical validation this method increases the dependability of digital twins and facilitates more effective design refinement. In the end this approach helps to improve overall performance predictions in the early stages of design optimize vehicle dynamics and lower development costs. Across various vehicle architectures the research conclusions may prove useful facilitating more accurate simulations and well-informed engineering choices.
Verma, Rahul Ranjan, Goli, Naga Aswani Kumar, Prasad, Tej Pratap
AI-Driven Digital Twin for Enhanced Suspension Assembly Testing in Automotive Vehicles2026-26-04451/16/2026
This paper presents the development and implementation of a digital twin (DT) for the suspension assembly of automotive vehicles—an essential subsystem for assessing vehicle performance, durability, ride comfort, and safety. The digital twin, a high-fidelity virtual replica of the physical suspension system, is constructed using advanced simulation methodologies, including Finite Element Analysis (FEA), and enriched through continuous integration of empirical test data. Leveraging machine learning techniques, particularly Artificial Neural Networks (ANNs), the DT evolves into a dynamic and predictive model capable of accurately simulating the behaviour of the physical system under diverse operational conditions. The primary aim of this study is to enhance the precision and efficiency of suspension testing by enabling predictive maintenance, real-time system monitoring, and intelligent optimization of test parameters. The digital twin facilitates early detection of potential failures, thereby minimizing downtime and reducing maintenance costs. Furthermore, it enables the exploration of a wide range of test scenarios without the need for extensive physical prototyping, resulting in significant savings in time and resources. Experimental results validate the digital twin’s capability to replicate the physical suspension assembly with high accuracy, offering actionable insights into system reliability and performance. This study underscores the transformative potential of AI-augmented digital twin technology in modernizing traditional testing frameworks, providing a scalable and cost-effective solution for the design, validation, and lifecycle management of automotive components.
Sonavane, Pravinkumar, Patil, Amol
Artificial Intelligence in Passenger Cars: Unlocking Intelligent Mobility and Human-Centric Vehicle Innovation2026-26-06721/16/2026
Artificial Intelligence (AI) is radically transforming the automotive industry, particularly in the domain of passenger vehicles where personalization, safety, diagnostics, and efficiency. This paper presents an exploration of AI/ML applications through quadrant of the key pillars: Customer Experience (CX), Vehicle Diagnostics, Lifecycle Management, and Connected Technologies. Through detailed use cases, including AI-powered active suspension systems, intelligent fault code prioritization, and eco-routing strategies, we demonstrate how AI models such as machine learning, deep learning, and computer vision are reshaping both the user experience and engineering workflow of modern electric vehicles (EVs). This paper combines simulations, pseudo-algorithms and data-centric examples of the combined depth of functionality and deployment readiness of these technologies. In addition to technical effectiveness, the paper also discusses the challenges at field level in adopting AI at scale i.e., data scarcity, regulatory, sensory fusion reliability, and user trust. The set of recommendations on safe, modular, and scalable integration roadmap, including the importance of continual learning, hybrid digital twins, and legacy-system interoperability, is provided. By offering a comprehensive yet application-driven perspective, this work serves as both a technical reference and strategic blueprint for stakeholders aiming to embed intelligent systems across the vehicle lifecycle, from predictive diagnostics to real-time adaptive user interfaces.
Hazra, Sandip, Tangadpalliwar, Sonali, Khan, Arkadip
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 modelling 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, but beyond that, it is able to aid in deciding and prevent faults. This investigation aims to set a stage for an adaptable, data-driven battery control system in the realm of connected and self-driving EVs, by fiercely defending the concepts of modular openness, edge deploy ability, and critical safety insights.
Pardeshi, Rutuja Rahul, Kondhare, Manish, Sasi Kiran, Talabhaktula
Testing and Validation of Future Mobility Vehicles using Realistic Digital Twin of Road Corridors and Tires2026-26-05801/16/2026
The automotive industry is rapidly evolving with technologies such as vehicle electrification, autonomous driving, Advanced Driver Assistance Systems (ADAS), and active suspension systems. Testing and validating these technologies under India’s diverse and complex road conditions is a major challenge. Physical testing alone is often impractical due to variability in road surfaces, traffic patterns, and environmental conditions, as well as safety constraints. Virtual testing using high-fidelity digital twins of road corridors offers an effective solution for replicating real-world conditions in a controlled environment. This paper highlights the representation of Indian road corridors as digital twins in ASAM OpenDRIVE and OpenCRG formats, emphasizing the critical elements required for realistic simulation of vehicle, tire, and ADAS performance. The digital twin incorporates detailed 3D road profiles (X-Y-Z coordinates), capturing the geometry and surface variations of Indian roads. The process of generating the digital twin of road corridor involves mapping road corridors using high-density and high-precision LiDAR scanning, DGPS, and camera sensors. A framework along with mathematical algorithms is developed and tuned specifically for extracting Indian road corridor elements, enabling the creation of accurate and detailed digital representations of roads and associated infrastructure. Proposed digital twin framework provides a robust foundation for evaluating vehicle and tire performance by capturing the unique characteristics of Indian roads and corridors diverse surface types, complex urban layouts along with varying infrastructure elements. It supports accelerated development cycles, improved safety, and optimized comfort, offering automotive OEMs and suppliers a reliable virtual platform for testing and validating next-generation vehicles tailored to Indian driving conditions.
Joshi, Omkar Prakash, Shinde, Vikram, Pawar, Prashant R
Development of 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 validation methodology is of prime importance prior to deployment process. This paper explores the integration of Hardware-in-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 HIL systems with OTA workflows, wherein a virtual vehicle environment is simulated in real time to reproduce a replica of the actual operating conditions for the target ECU under test. The OTA update is injected through a simulated or physical OTA backend and transmitted to the control unit interfaced within the HIL loop. This setup facilitates the emulation of update scenarios including firmware re-flashing, configuration updates, security checks, and rollback mechanisms under controlled, observable conditions. Key advantages include the ability to inject faults, monitor system response, verify communication integrity, and perform automated regression tests without risking physical prototypes or production vehicles. This integration of OTA and HIL not only enhances pre-deployment validation but also lays the foundation for continuous development and in-field update strategies in connected EV platforms. The proposed framework can be scaled to multiple ECUs and integrated with CI/CD pipelines for automated nightly testing. Future work will explore combining this setup with Digital Twin environments and Machine Learning-based anomaly.
Khare, Shivani, Karle, Ujjwala, Subramaniam, Anand
Transforming Manufacturing: Increasing Throughput and Reducing Production Complexity with Digital Twins2026-26-04491/16/2026
This paper presents a bidirectional digital twin developed for the Fischertechnik Smart Factory Kit, enabling real-time simulation and validation of production line modifications prior to actual deployment. The digital twin integrates with a Siemens Programmable Logic Controller (PLC) to mirror real-world operations, capturing live production data and visualizing key factory parameters, such as product, process, and resource metrics within a 3D environment. Engineers can test various optimization scenarios by adjusting robot speed and path, conveyor speeds, part & process sequences, and modifying equipment layout sizes to enhance efficiency. Based on the optimization scenarios, the best-performing configurations are identified using metrics such as throughput, cycle time, and resource utilization. Once validated, these changes are directly deployed to the PLC, ensuring seamless implementation. Beyond capacity optimization, this solution enhances overall production efficiency by minimizing idle time and parts waiting time, balancing workloads, and reducing unplanned disruptions. Additionally, by virtually simulating product variations and process changes, the digital twin helps identify design simplifications, reduce product complexity, and streamline manufacturing workflows. A digital twin of the manufacturing system serves as an integrated solution, unifying capabilities such as predictive maintenance, efficiency monitoring, simulation, and analytics in real time. By bridging technology gaps and offering a comprehensive view of the entire production process, it enhances decision-making, maximizes resource utilization, and facilitates seamless technology adoption across the factory. This approach significantly reduces downtime, accelerates response times, and boosts automation, demonstrating the transformative potential of digital twins in optimizing manufacturing operations [1].
Kumar, Rahul, Singh, Randhir
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 time. 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. Achieving this requires the development of robust digital processes that deliver accurate result predictions rather than merely directional design insights. This paper investigates several factors, including modelling strategies and boundary conditions to enhance the reliability of 3D CFD simulation models for aerodynamic predication. It explores the potential of creating a 'digital twin' a virtual replica of actual wind tunnel to replicate test environments around the vehicle. The study also emphasizes the significance of accurately modelling an empty wind tunnel as a foundational step in digital twin development. Finally, the paper validates the methodology by comparing results for multiple vehicles against physical test data, focusing on aerodynamic drag coefficients, pressure maps, and tomography data across different configurations ultimately enhancing confidence in the digital aerodynamic evaluation process.
Sharma, Sandeep Kumar, Chalipat, Sujit, Maiyya, Sandeep
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