Browse Topic: Product development

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Efficient Multi-Parameter Optimization of a Twist Beam Rear Axle for Electric Vehicles using TOPSIS method for Durability2026-28-0067To be published on 02/12/2026
This study investigates the parameter optimization of a Rear Twist Beam (RTB) for an electric vehicle (EV) during the early stages of product development. Adapting the RTB design from a current Internal Combustion Engine (ICE) vehicle platform presents a complex challenge: accommodating increased rear vehicle load while minimizing cost and maintaining existing rear hard points. To address this, a Computer-Aided Engineering (CAE)-driven optimization using the Taguchi method was employed, avoiding costly physical durability tests. The key design parameters considered were the thickness and material grade of the RTB's child parts: the cross beam, trailing arms, and reinforcements, while preserving their original shapes. An L8 orthogonal array was utilized to efficiently evaluate the influence of these parameters on durability performance, and the optimal combinations for maximizing durability were identified. Furthermore, an Analysis of Variance (ANOVA) was conducted to quantify the
Madaswamy, Arunachalam, Dhanraj, Sudharsun, Govindaraju, Karthik, Lokaiah, Srinivasan
Virtual Paint Shop: Automotive E-coating Process2026-26-0365To be published on 01/16/2026
In automotive vehicle manufacturing, paint shop constitutes one of the highest energy intensive processes. This steers automotive OEMs to continuously improve production efficiency and reduce operational costs of the processes involved in paint shop through digital twin technologies. In addition, the push for shorter time-to-market emphasizes the need for simulation-based manufacturing processes, such as virtual testing and CAE simulations. The simulation-based processes enable faster and data-driven decision-making early in the product development cycle, thereby ultimately reducing cost and development time. Among the various stages in the paint shop, two of the important stages 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 the processes in these stages
Gundavarapu, V S Kumar, P, Vivekaanandan, Garg, Manish, Navelkar, Tanay, BS, Balachandran
Achieving Full ASIL-B Level Functional Safety (FuSa) for Automotive Clusters2026-26-0008To be published on 01/16/2026
Integrating advanced technologies into modern vehicles has led to an increasing focus on Functional Safety (FuSa), especially for the Automotive Integrated Cluster Module (ICM) to ensure the safety of the driver and passengers. This paper highlights the need to bring certain ICM components under an Automotive Safety Integrity Level B (ASIL-B) context using Classic AUTOSAR. This paper discusses the challenges faced and the solutions implemented for achieving compliance with ISO 26262 standards along with the Classic AUTOSAR framework. We are proposing a standardized and structured methodology for the design of the components in compliance with the key safety principles, including Freedom from Interference (FFI), execution under privileged levels, and integrity verification, particularly by adopting Classic AUTOSAR frameworks. This paper also presents the Functional Safety (FuSa) goals for these components and also extend to their configuration management and updating strategies within
Singh, Iqbal, Kumar, Praveen
Virtual Validation of ADAS Algorithms Using Driver-in-the-Loop Simulation2026-26-0050To be published on 01/16/2026
Driver-in-the-Loop (DIL) simulators have become crucial tools across automotive, aerospace, and maritime industries in enabling the evaluation of design concepts, testing of critical scenarios and provision of effective training in virtual environments. With the diverse applications of DIL simulators highlighting their significance in vehicle dynamics assessment, Advanced Driver Assistance Systems (ADAS) and autonomous vehicle development, testing of complex control systems is crucial for vehicle safety. By examining the current landscape of DIL simulator use cases, this paper critically focuses on Virtual Validation of ADAS algorithms by testing of repeatable scenarios and effect on driver response time through virtual stimuli of acoustic and optical warnings generated during simulation. To receive appropriate feedback from the driver, industrial grade actuators were integrated with a real-time controller, a high-performance workstation and simulation software called Virtual Test
Sharma, Chinmaya, Bhagat, Ajinkya, Kale, Jyoti Ganesh, Karle, Ujjwala
Nexifi11D: A Comprehensive Multidimensional Framework for Futuristic Manufacturing Ecosystems2026-26-0282To be published on 01/16/2026
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
Scalable and Modular Data-Driven Hybrid Chassis Dynamometer Framework for EV Powertrain Evaluation2026-26-0363To be published on 01/16/2026
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
Kumar, Akhilesh, V, Yashvati
Optimizing Stick Slip Performance in Automotive Interiors by Material Selection, Additives and Design Strategies2026-26-0325To be published on 01/16/2026
The automotive industry is highly competitive, especially in terms of design and perceived quality. The use of hard plastics with a high gloss finish is driven by styling trends and the push towards zero gaps, making interfaces critical. In-cabin mood lighting is another feature being offered as a theme for interiors. Dashboard or cockpit designs often incorporate a significant amount of polycarbonate-acrylonitrile butadiene styrene (PC-ABS) and polycarbonate (PC). These materials provide strength and design flexibility but have the disadvantage of material incompatibility when used together, leading to stick-slip phenomena. Traditionally, felt tapes were used as interface isolation to solve this problem, but this increased manufacturing costs and assembly complications. The study focuses on the stick-slip phenomenon and material interface modifications. Specifically, it examines selecting the right surface finish on one side of the PC & PC-ABS interface to change adhesion and friction
Mohammed, Riyazuddin, R, Prasath, Rahman, Shafeeq
Simulation-Driven Load Spectrum Prediction: Virtual RLDA for Chassis and Suspension System2026-26-0095To be published on 01/16/2026
In the rapidly evolving and highly competitive automotive industry, manufacturers are under immense pressure to bring products to market quickly while meeting customer expectations. As a result, optimizing the product development timeline has become essential. Structural integrity analysis for chassis and suspension systems lies in the accurate acquisition of operational load spectra, conventionally executed through Road Load Data Acquisition (RLDA) on instrumented vehicles subjected to proving ground excitation. At this point, RLDA is mainly used for final validation and fine-tuning. If any performance shortfalls, such as premature component failure or durability issues, are discovered, they often trigger design revisions, prototype rework, and additional testing. This study proposes a Virtual Road Load Data Acquisition (vRLDA) methodology employing a high-fidelity full-vehicle multibody dynamic (MBD) representation developed in Adams Car. The system is parameterized and uses high
Goli, Naga Aswani Kumar, Prasad, Tej Pratap
Optimizing Vehicle Dynamics: A Methodology for Reducing Correlation Gaps2026-26-0091To be published on 01/16/2026
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
Verma, Rahul Ranjan, Goli, Naga Aswani Kumar, Prasad, Tej Pratap
NN FMU: Deep Learning Models for Next-Gen xiL Vehicle Simulation2026-26-0452To be published on 01/16/2026
Functional Mock-up Units (FMUs) have become a standard for enabling co-simulation and model exchange in vehicle development. However, traditional FMUs derived from physics-based models can be computationally intensive, especially in scenarios requiring real-time performance. This paper presents a Python-based approach for developing a Neural Network (NN) based FMU using deep learning techniques, aimed at accelerating vehicle simulation while ensuring high fidelity. The neural network was trained on vehicle simulation data and trained using Python frameworks such as TensorFlow. The trained model was then exported into FMU, enabling seamless integration with FMI-compliant platforms. The NN FMU replicates the thermal behavior of a vehicle with high accuracy while offering a significant reduction in computational load. Benchmark comparisons with a physical thermal model demonstrate that the proposed solution provides both efficiency and reliability across various driving conditions. The
Srinivasan, Rangarajan, Ashok Bharde, Pooja, Mhetras, Mayur, Chehire, Marc
Real-time range optimization for selected destinations by Dynamic Energy Management Strategy (DEMS) in BEV’s2026-26-0168To be published on 01/16/2026
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
Study of Drive Trace Indices and their correlation with CO2 in Chassis Dynamometer testing2026-26-0517To be published on 01/16/2026
With introduction of CAFÉ norms in India, the manufacturers of all M1 Category vehicles (not exceeding 3,500kg GVW) must ascertain that they comply with Annual Corporate average CO2 target as defined in regulation. Moreover, this target will become stricter in different stages in upcoming years. Hence CO2 emissions are becoming one of the major focus parameters during vehicle development. There are various factors that can impact CO2 emissions during measurement in test cycle such as MIDC/WLTC. One such major factor is driving variations. Although speed and time tolerances are provided during the test (as part of AIS 137/AIS 175) limit the variation, even within these tolerances, drive-related effects can be significant contributors to test results variability. Monitoring and control of such variations is important to understand the true fuel economy potential of the vehicle. Drive Trace indices are standardized metrics that can be used to evaluate the driving variations. The aim of
ER, Shivram, Rawat, Vijaypal, Khandelwal, Vineet, Kumar, Arun, Malhotra, Jitendra
Next Gen Automotive EDS Architecture Design Verification using Real-Time Function Mode Simulation and Failure Condition Analysis2026-26-0454To be published on 01/16/2026
In current automotive electrical design practices, simulation and functional verification of the Electrical Distribution System (EDS) at the design stage are largely absent. Typically, the validation of controller behaviour and EDS wiring is performed only during HIL, SIL, MIL, prototype testing, or physical vehicle trials, often revealing issues late in the development cycle. This delay leads to costly redesigns, prolonged timelines, and increased chances of failure during vehicle integration. Therefore, there is a critical need for an early-stage simulation methodology that ensures robust EDS and controller design, enabling "first-time-right" readiness at the design stage itself. The proposed simulation framework introduces a function mode-based structural approach, where individual vehicle functions are mapped to their corresponding electrical circuits within the vehicle’s wiring system. Resistance values are allocated to specific paths depending on the active function or
Jaisankar, Gokulnath, Warke, Umakant, Chakra, Pipun, Borole, Akash
Facilitating Virtual Testing at an Industrial Level by Simulation Data Management2026-26-0440To be published on 01/16/2026
Automotive OEMs can derive significant cost savings by reducing the quantity of physical crash tests and thereby accelerate product development, when they follow the Euro NCAP Virtual Testing guidelines. It helps in optimizing the overall vehicle development process via more efficient simulations, as well as facilitates in early adoption of new safety regulations. In this pursuit, companies must comply with strict Euro NCAP requirements, which includes transparency and traceability of virtual tests. A major challenge therein is model validation – which requires highly precise detailing and extensive use of data for accurately replicating real physics of the problem. Deploying these workflows into an existing simulation process can be a complicated and time-consuming task, particularly when integrating various simulation and testing methods. A powerful simulation and process data management system (SPDM) can thereby assist companies to automate their entire simulation process, ensures
Thiele, Marko, Sharma, Harsh
Integrated Quality Assurance Frameworks for Testing Advanced Automotive Systems in the Era of ADAS and E-Mobility2026-26-0568To be published on 01/16/2026
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
Multi-Band Frequency Approach to Correlate Proving Ground and Road Simulator for an Electric Two-Wheeler2026-26-0556To be published on 01/16/2026
Real-world usage subjects two-wheelers to complex and varying dynamic loads, necessitating early-stage durability validation to ensure robust product development. Conducting a full life-cycle durability testing on proving grounds is time-consuming, extremely difficult for the riders involved, and costly, which is why accelerated testing using rigs such as the road simulator system have become a preferred approach. The use of road simulators necessitates, accurately measured inputs and precise simulation to ensure proper actuation of the rig, thereby enabling realistic representation of road undulations. Road Load Data Acquisition (RLDA) is used to measure various inputs such as displacement, acceleration, strain, and predicted forces from the road which are then used to simulate real-world conditions in a road simulator. This paper covers two important aspects essential for achieving an accurate and clear representation of road simulation in a 4-DOF road simulator, encompassing both
Ganju, Shubham, V, Vijayamirtharaj, Prasad, Sathish, R S, Mahenthran
Accelerating Product Development Life Cycle through Digitalization of Fatigue Life for Structural Components2026-26-0496To be published on 01/16/2026
In today’s competitive market, accelerating product development without compromising durability is critical. Durability is a key benchmark for assessing the quality and reliability of automotive products. To meet rising customer expectations and regulatory demands, the industry must develop vehicles that offer high performance and reduced weight while ensuring safety. A well-engineered vehicle must retain structural integrity and functionality under normal operating conditions and withstand extreme load events without critical failure. Achieving this requires effective Road Load Data Acquisition (RLDA), integrated with robust design practices and efficient validation processes. However, physical RLDA is time-consuming and costly, as it depends on prototype vehicles that are often available only in the later development stages. Failures identified during these late-stage tests can delay the product launch significantly. To address these challenges, this paper introduces a digital
Kokare, Sanjay, Dwivedi, Sushil, Siddiqui, Arshad, Iqbal, Shoaib
AI-Driven Digital Twin for Enhanced Suspension Assembly Testing in Automotive Vehicles2026-26-0445To be published on 01/16/2026
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
A Quality-Driven Approach to Engineering Sign-Off for Software and Robust Product Development2026-26-0581To be published on 01/16/2026
This abstract outline a paper detailing the development and implementation of a comprehensive framework designed to enhance the quality and governance of the product development process in the automotive industry. As the sector undergoes significant transformation—driven by electrification, autonomous systems, and increasing regulatory and customer expectations—the need for structured development approach and robust oversight has become critical to success. The paper introduces a newly developed framework for Final Data Judgment (FDJ) and Engineering Sign-Off (ESO), representing a next-generation strategy to defect free design, robust engineering quality management and product maturity assurance. This methodology emphasizes customer-centric deliverables, risk-based assessments, and technical rigor to minimize post-SOP issues and ensure alignment with end-user expectations. The approach prioritizes end-user impact by embedding rigorous quality checkpoints that align engineering outputs
Digikar, Ashish, Pathak, Isha, Kothari, Bhushan
A Digital Twin Framework for Real-Time Optimization of Solar-Integrated Energy Systems2026-26-0418To be published on 01/16/2026
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
AI-Driven Predictive Methodology for Bolt Integrity in Vehicle Durability Testing2026-26-0648To be published on 01/16/2026
The application of AI/ML techniques to predict truck tailgate bolt loosening represents a major innovation for the automotive industry, aligning with the principles of Industry 4.0. Traditional physical testing methods are both expensive and time-consuming, often identifying issues late in the development process and necessitating costly design changes and prototype builds. By harnessing AI/ML, manufacturers can now analyze tailgate slam and bolt preload data to accurately forecast potential bolt loosening issues. This predictive capability not only enhances quality and safety standards but also significantly reduces the costs associated with tooling and builds. The AI/ML tool described in this paper can simulate a variety of load conditions and predict bolt loosening with over 90% accuracy, considering factors such as changes in loads, bolt diameters, washer sizes, and unexpected masses added to the tailgate. It provides valuable design insights, such as recommending optimal bolt
Sivakrishna, Masani, Das, Mahat, Singh, Abhinav, Karra, Manasa, Shienh, Gurpreet, Luebke, Amy
Optimizing Tire NVH Performance in Electric Vehicles: A Comprehensive Approach Using Benchmarking, Simulation, and Validation2026-26-0605To be published on 01/16/2026
The electrification of mobility is becoming increasingly vital in meeting global zero carbon footprint targets, with nations setting ambitious goals to achieve by 2030. As part of this transformation, the development of Electric SUVs (E-SUVs) capable of navigating diverse terrains and road conditions presents a significant challenge, often extending vehicle development timelines. To address these demands, many Original Equipment Manufacturers (OEMs) are adopting a ‘Born-Electric’ platform approach, specifically tailored to the unique requirements of electric vehicles. However, this approach introduces several challenges, particularly in maintaining the Noise, Vibration, and Harshness (NVH) levels expected by customers. The elimination of the Internal Combustion Engine (ICE) powertrain has significantly shifted the NVH dynamics of E-SUVs, with tire road rolling noise becoming a dominant factor influencing in-cabin noise perception, Tire-induced noise and vibrations emerge as primary
Singh, Ram Krishnan, Deivasigamani Purushothaman, Balakrishnan, Paua, Ketan, Ahire, Manoj, Adiga, Ganesh N
Early Prediction of CNG Filling Time using Artificial Intelligence for Design Optimization2026-26-0662To be published on 01/16/2026
The CNG fuel system is a critical component in vehicle development and typically includes large-volume gas cylinders (up to 800 liters). As these vehicles often operate over long distances daily, frequent refueling is necessary, making a short gas filling time highly desirable. Currently, CNG filling time is evaluated after prototype development to determine the refueling duration. If the filling time is excessive, design changes to the fuel system are required, leading to significant cost and time penalties and causing delays in the vehicle product development cycle. By integrating AI/ML technology, a mathematical model has been developed based on various influencing parameters. This model will predict CNG gas filling time across all commercial vehicle platforms at the initial Zero design release gateway. This early prediction will enable further optimization to improve gas filling time. The goal of this research work to optimising the filling time for various platform before physical
Choudhary, Aditya Kant, Petale, Mahendra, Dutta, Surabhi, Bagul, Mithilesh
Representative Drive cycle velocity profiles Prediction using ML Algorithms for Energy Efficiency2026-26-0638To be published on 01/16/2026
Maximizing vehicle energy efficiency and performance is a high priority for the automotive industry as customer expectations rise. Engineers constantly face the challenge of balancing the conflicting goals of achieving superior performance and maximizing energy efficiency, all while meeting increasingly tight development timelines. Leveraging digital methods can potentially enable a considerable reduction in development lead times. Driving cycles act as standardized measurement procedures for certifying a vehicle's fuel efficiency and driving range. Representative velocity profiles condense numerous real-life driving cycles to enable quicker energy analysis and driver feedback evaluation. This paper introduces a novel methodology for generating synthetic drive cycles, such as average velocity cycles and ideal consumption velocity cycles, based on real-life driving scenarios. In this study, the importance of creating representative drive cycles to enhance vehicle performance and energy
Kanakannavar, Rohit, Kelkar, Kshitij, Sadalge, Anand
EV Battery Evolution in India: Challenges, Innovation, and Policy Synergy2026-26-0174To be published on 01/16/2026
In the pursuit of sustainable transportation and energy security, India is placing increased focus on addressing the multifaceted challenges associated with electric vehicle (EV) battery technology. As a cornerstone of EV performance and adoption, battery systems in India face critical issues such as high import dependency, limited local manufacturing capabilities, cost inefficiencies, safety concerns, and lack of standardized design frameworks. This paper presents a comprehensive analysis of India's evolving battery ecosystem, emphasizing indigenous design, development, and scalable in-house manufacturing under the "Make in India" initiative. It examines supportive government policies, strategic public-private partnerships, and the pivotal role of research institutions in fostering innovation and overcoming original equipment manufacturers' (OEMs) and end-users' concerns. It also focus on prototype product development challenges. Through detailed case studies, technological reviews
Thorat, Jalindar Sukhadev, Sandhu, Jivraj Singh, Kumar, Dharmendra
Digital Twin-Driven Systems Engineering for Complex Automotive Features2026-26-0379To be published on 01/16/2026
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
Novel Approach for Predicting Steering Rack Load Using Multibody Simulation2026-26-0378To be published on 01/16/2026
In the initial stages of a vehicle development program, the sizing of various components is a critical deliverable. The steering system, in particular, requires a precise estimation of the rack load for the appropriate sizing of the rack and assist units. Accurately predicting the load on the system during the early stages of development is challenging, especially in the absence of benchmark or legacy data. Commonly used processes for estimating parking steering effort often employ simplistic approaches that may fail to account for parameters such as tire size, vertical stiffness, and steering geometry, leading to reduced accuracy. This paper introduces an advanced methodology for predicting steering rack loads, which incorporates considerations such as contact patch size and pressure variation, as well as the tire jacking effect. The methodology involves mathematical modeling of the contact patch using mesh-grids, utilizing common inputs available in the early stages of vehicle
Shirke, Umesh, Dabholkar, Aniruddh, Bardia, Vivek, Srivastava, Harshit, Prasad, Tej Pratap
Predictive Modelling of Battery Behavior for Enhanced Energy Management in Electric Vehicles2026-26-0385To be published on 01/16/2026
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
Developing Advanced CAE Model for Door Glass Regulator Operating Noise Reduction through TEST/CAE Correlation2026-26-0311To be published on 01/16/2026
The importance of performance improvement and weight optimization using CAE in the early design stages of vehicle development has been continuously increasing over the past two decades. And, the accuracy of CAE models plays key role, more efforts are required to improve it. Representation of exact physical model in CAE is still a big challenge for achieving an accurate TEST vs CAE correlation. In this research, the procedure and application of door system model correlation were described to reduce the glass regulator operating noise. The function of a regulator in door system is to open and close the glass. The regulator under improper design creates unwanted noise and discomfort to the passenger. In current door CAE Model, we are unable to identify this real time problem. Therefore, this paper discusses how to correlate door system model according to the assembly stage based on modal correlation and it helped in calculating accurate glass regulator operating noise. As a result, the
Panuganti, Naresh Kumar, Choi, Seungchan
Software-Defined Vehicles: Architecting the Future of Intelligent and Connected Mobility2026-26-0691To be published on 01/16/2026
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
Use Of Digital Wind Tunnel For Improved Aerodynamic Prediction2026-26-0372To be published on 01/16/2026
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
Introducing a Novel System Engineering Framework for the safe deployment of Software Defined-Vehicles2026-26-0690To be published on 01/16/2026
The past decade has seen a systemic shift in the automotive landscape and the constituent parts of a vehicle. The automotive industry has shifted from a primarily hardware components industry to a software heavy industry, with software controlling majority of the vehicle functions. Coupled with the ability to fully update or evolve a vehicle's capabilities or functionalities, post point of sale through software updates, the technical, commercial and service landscape of the automotive industry is rapidly changing. This has brought increasing focus to the concept of Software Defined Vehicle, where the vehicle is not only constantly evolving, but is also becoming more personalised by leveraging data collected through the life of the vehicle. This requires a rethink of the current development and deployment approaches for vehicles, which are software-intensive. In this paper, we introduce a novel four-step system engineering framework for the safe development and deployment of Software
El Badaoui, Halima, Jame-Elizebeth, Mariat, Khastgir, Siddartha, Jennings, Paul
A Data-Centric Approach to Identify Optimal NVH simulation Tools and Solvers2026-26-0356To be published on 01/16/2026
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
Multibody Dynamic Analysis of Belt-Type CVT Systems in Two-Wheelers: A Study on Slip, Misalignment, and Transmission Efficiency2026-26-0079To be published on 01/16/2026
More efficient drivetrain technologies are in greater demand in the two-wheeler market as a result of the introduction of BS6.2 emission standards. In order to satisfy these performance and regulatory requirements, Continuously Variable Transmission (CVT) systems—which are renowned for their stepless gear shifting and increased fuel efficiency—are being given more and more consideration. However, because CVT is nonlinear and multibody dynamic, accurately predicting its behavior is still a difficult task. With an emphasis on variables like belt slip, pulley misalignment, and transmission efficiency, this study provides a thorough multibody dynamic analysis of a belt-type CVT system used in two-wheelers. High-fidelity analysis of the belt-pulley interaction under various load and speed conditions is now possible thanks to the development of a novel modeling methodology using advanced multibody dynamics simulation software – Virtual Dynamics. The method makes early design validation
Shah, Swapnil, Mane, Prashant, Voncken, Antonius, Emran, Ashraf
Standardized Design Principles and Emerging Opportunities for AI-Integrated CAD Systems2026-26-0632To be published on 01/16/2026
Artificial Intelligence (AI) in the automotive industry is growing and transforming in different segments of the industry. Still there is a significant gap persisting in the standardization of design principles and the incorporation of manufacturing constraints in system. However current development in AI CAD systems is isolated and non-parametric way, in contrary the conventional way of CAD methodology is knowledge based and systematic parametric steps which is agile to the iterative improvement. Hence it will be challenging in integration and adoption of these AI CAD systems in the well-established product development cycle. The research focuses on identifying the scope of AI integration which includes generative design, automated error detection, and design pattern-dependent learning systems, but also stresses the importance of standardized policies to address fundamental questions of system coherence, uniformity, and broad applicability. This research paper studies the adoption of
Shaikh, Taha, Harel, Samarth, Kumar, Akarsh, Venkitachalam, Muthukumar, Shah, Bhumika, Chakraborty, Pinka
Conceptual Framework for Real-Time Battery Health Estimation in Automotive Digital Twins Using Reinforcement Learning2026-26-0658To be published on 01/16/2026
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
Master Complexity of Version Management in SDM by AI-Driven Assistance2026-26-0439To be published on 01/16/2026
Simulation-driven product development involves numerous CAE model iterations, where each version represents a critical difference. Usually, these multiple model versions are generated by hundreds of simulation engineers working in teams distributed across the globe, making functional collaboration a key for effective product development. To manage vast amounts of CAE data generated by engineers working simultaneously on a project, it is imperative to have a robust version management system to track changes in the CAE data. A robust version management is the backbone of an effective simulation data management (SDM) system. It involves capturing and documenting model changes at every design iteration. An accurate documentation of the model changes is crucial as it helps in understanding the model evolution and collaboration among engineers. However, documenting is usually considered a boring and tedious task by many engineers. This often leads to bad change documentation, which in turn
Thiele, Marko, Sharma, Harsh
Virtual Reality in Assembly Simulations : Outpacing Tecnomatix Process Simulate for Faster Time-to-Market2026-26-0398To be published on 01/16/2026
Virtual Reality (VR) technology is emerging as a transformative solution in manufacturing industry. It offers significant advantages over traditional tools like Tecnomatix Process Simulate (PS) in assembly and ergonomic simulations. Analysis using PS is time consuming and lacks real time human interaction as it relies on detailed modelling and sequential workflows, which will delay the identification of assembly no-build conditions and ergonomic issues. This paper evaluates the time and cost saving potential of VR in assembly processes and explores its role in minimizing the need for physical prototypes across various stages of vehicle development. VR provides interactive environments, enabling interaction with 3D models and real-time collaboration with various teams across the globe. This leads to faster identification of assembly process flaws, quicker iteration cycles, and a reduced need for physical prototypes in the station development process for the lines. VR allows individuals
Nagendran, Rakesh Kumar
A procedure for validation of full vehicle CAE models with physical tests for passive safety applications2026-26-0411To be published on 01/16/2026
With the fast development of computational analysis tools and capacities during the past ten years, complex and substantial computer-aided engineering (CAE) simulations are now economically possible. While the cost of crash tests has risen steadily, the fidelity and complexity, which numerical simulations could address, has multiplied keeping the cost of computational analysis more stable. The use of CAE simulations complements physical testing, accelerating the product development process, reducing cost, and ensuring safe and more optimized designs. The fundamental goal of CAE is a significant reduction in the number of physical tests conducted during the product development process. However, validating the CAE model with physical tests is essential to ensure accuracy and reliability. Simulations performed using a validated CAE model could be used to make decisions like airbag deployment or high voltage shutdown without an actual physical test. This paper discusses validating an
Upendran, Anoop, Knuth, Joseph, Krishnappa, Giri, Punnaiappan, Arunsankar
Virtual ECU: Development & Validation Platform2026-26-0683To be published on 01/16/2026
Vehicle software is moving towards software-centric architectures and hence software-defined vehicles. With this transition, there is a need to handle various challenges posed during development and validation. Some of the challenges include unavailability of hardware limiting the evaluation of various hardware options, board bring-up and hence leading to delays in software development targeted for the hardware, eventually leading to delayed validation cycles. To overcome the above challenges, we present in this whitepaper a virtual ECU (vECU) framework integrated with a CI/CD pipeline. A Virtual ECU (Electronic Control Unit) is a software-based emulation of a physical ECU. The adoption of virtual ECUs empowers development teams to commence software development prior to the availability of physical hardware. Multiple tools are available to demonstrate virtual ECUs, for example, QEMU, Synopsys, QNX Cabin, etc. vECU setup, when paired with a CI/CD pipeline, allows continuous integration
Singh, Jyotsana, Shaikh, Arshiya, Mane, Rahul, Burangi, Piyush
Tractor Clutch wear simulation by Inhouse development2026-26-0572To be published on 01/16/2026
To develop an accelerated Clutch test simulation method at inhouse laboratory as System level of validation to replicate field phenomena in short time with controlled repeatability and increased test accuracy. Wet clutch is one of the M&M technology project for higher hp(75) tractors. Currently Mahindra having dry clutch system in higher hp tractors & presently Wet clutch development in progress for higher HP tractors for increasing clutch life & to reduce clutch pedal operation as per customer requirement, hence new test rig to be developed for validating new tractor wet clutch system as per RWUP. Previously tractor clutch system is completely evaluated through field testing on tractor level. Thus, there are several drawbacks in field testing like more waiting time to avail seasons, more testing time, evaluation of limited number of samples, non-availability of complete tractor on time, less accuracy, less repeatability of test results, unsafe conditions of rider, higher human fatigue
D, Yashwanth, Raja, R, Udayakumar, S, M, Jeevaharan, Vijayakumar, Narayanan
Transforming Manufacturing: Increasing Throughput and Reducing production Complexity with Digital Twins2026-26-0449To be published on 01/16/2026
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
Multi-Model Analysis of O-Ring Compression and Relaxation Force Predictions Using Machine Learning and CAE Simulations2026-26-0466To be published on 01/16/2026
O-rings play a critical role in ensuring leak-proof seals in a wide range of engineering systems. Accurate prediction of their compression and relaxation behavior under various material and geometric configurations is essential for optimal design and reliability. This study presents an analysis of machine learning techniques to predict two key performance outputs, compression force and relaxation force (after 10 minutes) trained on computer-aided engineering (CAE) simulation data. The experimental setup was represented in CAE simulation and the results were compared with experimental data conducted at ZF test facilities. Simulation results correlated well with the experimental data (deviation was less than the 5%). To create a dataset for training machine learning (ML) models, realistic ranges for the input parameters such as hardness and geometrical parameters were determined, and simulation data were generated using design of experiments (DOE). Multiple ML models were developed and
Kosgi, Durgaprasad, Alva, P Pancham, Dangeti, VenkataKrishna Pavan
Clutch Thermal Adequacy Assessment in Repeat Restart Launch Condition on Gradient2026-26-0434To be published on 01/16/2026
The clutch is a mechanical device that engages and disengages power transmission from the engine to the wheels. It consists of the clutch disc assembly and cover assembly. The clutch disc, with friction material linings, is mounted on the transmission shaft and gets clamped between the flywheel and cover assembly to transmit power as required. However, wear and tear occur due to the differential speeds and slippage between the engine and transmission during operation. Clutch performance is assessed for its thermal behavior under repeat restart conditions on steep gradients, which is essential for the reliability and durability of commercial vehicle clutches. Repeat restart test is a key test to assess clutch performance on a 12% gradient. This test replicates real world scenarios, such as truck launches on hilly terrains under full load, where the clutch experiences significant slippage due to speed differences. This generates heat, and repeated engagements can cause the temperature to
Munisamy, Sathishkumar, Chollangi, Damodar, Mane, Sudhir
Reliability of Rubber Grommets by Stochastic Analysis of Geometric Tolerances2026-26-0501To be published on 01/16/2026
Manufacturing tolerances pose considerable challenges in the production of rubber-based components, often leading to dimensional variations that can compromise overall product reliability. These inconsistencies may result in a range of functional issues, including poor sealing, excessive wear, misfit during assembly, and premature failure. Among these components, grommets are especially sensitive due to the complex, non-linear behavior of rubber materials, making precise dimensional control critical to ensure consistent and dependable performance. Even minor deviations in grommet geometry can trigger substantial assembly difficulties. Issues such as misalignment, buckling, insertion challenges, and potential damage to neighboring parts are commonly encountered. These complications typically require multiple rounds of design revisions and physical testing, which extend development timelines and significantly increase production costs. Real-world observations from various automotive
Beesetti, Siva, Hattarke, Mallikarjun, James Aricatt, John, Pathan, Eram
Digital Twin for Motor & MCU incorporating Deep learning AI-ML algorithms for intelligent prognostics for Electric vehicles2026-26-0479To be published on 01/16/2026
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
Driving Thermal Intelligence – Pioneering Digital Twin-Driven Hardware-In-Loop for Smart Climate Control Modules2026-26-0380To be published on 01/16/2026
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
Simulation Based Approach to Optimize Electric Vehicle Battery Thermal System2026-26-0376To be published on 01/16/2026
The performance and longevity of Li-ion batteries in electric vehicles are significantly influenced by the cell temperature. Hence, efficient thermal management techniques are essential for battery packs. Simulation based optimization approaches can enhance these techniques during early product development and life cycle. In this work, a method to optimize the battery thermal system comprising of cooling plate and external insulation is described. The first part is to optimize the cell heat extraction by the cooling plate. The heat transfer coefficient of the coolant is a parameter which can indicate the thermal efficiency of a cooling plate. At the same time, it's important to keep the coolant pressure drop within a reasonable limit. A 1D simulation methodology is described to optimize the cooling channel height based on pressure drop and heat transfer coefficient targets. This methodology is based on empirical correlations and multi-objective pareto optimization. The advantage of
U, Reghunath, P S, Shebin
Correlation of Power Rating for Battery Electric Vehicles Simulation vs. Test Validation in Accordance with regulatory compliance2026-26-0575To be published on 01/16/2026
Accurate power and energy assessment in Battery Electric Vehicles (BEVs) requires high-fidelity simulation models that reflect real-world performance. This study presents a power rating correlation of the Stellantis Dodge model in compliance with the GTR 21 standard, validating the simulation’s accuracy against physical test data. Simulations conducted using STLAsim, Stellantis’ proprietary tool, estimate EDM power at the inverter input and axle output under standardized conditions. The operating parameters were shared with the testing team to ensure consistency; however, the initial test results deviated by approximately 18 kW from the simulation target. To address this delta, EDM speed and vehicle speed were optimized, achieving a close alignment with the simulation and meeting the GTR 21 power rating criteria. The results demonstrate a strong correlation between the simulation and test data, confirming the model’s accuracy and reliability. This study underscores the effectiveness of
Mahajan, Prasad, Kesarkar, Sidhesh, Ali, Shoaib
Reverse Engineering and Digital Twins in Powertrain Design: Enhancing Performance and Sustainability2026-26-0065To be published on 01/16/2026
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
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