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In autonomous driving and advanced driver-assistance systems (ADAS), radar sensors play a crucial role in detecting and tracking objects under diverse environmental conditions [1, 2, 3]. One persistent challenge is the accurate representation of long vehicles, such as trucks and buses, whose extended radar signatures often result in a wide spatial spread of detections with similar radial velocities, making association difficult due to gating limitations. The GRID component processes raw radar measurements to generate occupancy grids and extract candidate targets, including stationary environmental structures [4, 5]. In the case of stationary long vehicles, multiple stationary objects may overlap with grid-based environment targets, allowing them to be consolidated into a single tracked object. This paper presents a novel approach for radar object merging and retention decision-making using fuzzy logic to distinguish between true moving and stationary objects in the presence of GRID targets and object association mechanisms. The association modules link radar measurements or detections, when available, with existing tracks to maintain temporal consistency, enabling the formation of radar objects represented by track points. The proposed method fuses radar objects based on their radial velocity and spatial overlap, while a fuzzy logic–based decision engine evaluates object persistence. The approach improves object continuity and reduces false positives in driving functions such as Blind Spot Detection (BSD), Emergency Brake Assist (EBA), and Adaptive Cruise Control (ACC). Simulation and real-world validation results demonstrate significant improvements in object tracking accuracy, particularly for long vehicles in urban environments.
Mohanty, Sanket, Sarkar, Sanglap
Modern vehicles increasingly rely on software-intensive functions, which continue to drive the growth and complexity of Electronic Control Units (ECUs). Conventional architectures based on single core or statically partitioned multicore microcontrollers are becoming inadequate for meeting emerging requirements such as deterministic real time performance, high communication throughput, synchronized time bases, and secure message handling. This paper introduces a software driven multicore consolidation approach that enables multiple ECU functions to be integrated on a single high-performance microcontroller while maintaining essential attributes of safety, determinism, and responsiveness. The proposed architecture emphasizes the co-location of dependent software modules to reduce cross core communication overhead and enhance runtime efficiency. By leveraging AUTOSAR compliant multicore Basic Software (BSW) stacks, including Communication, NVM, STBM, Diagnostics, and OTA, the system supports improvements in timing accuracy, data throughput, and task synchronization. In addition, core-specific communication channel assignment, interrupt-based routing, HSM supported secure frame handling, and multicore enabled NVM with a master–satellite design collectively contribute to more efficient CPU utilization and reduced latency under high load communication scenarios. The approach supports key industry relevant capabilities (e.g., runtime remote software updates, sensor fusion) and enables efficient handling of high-volume communication activity, making it adaptable for emerging multicore ECU architectures in next generation vehicles. The theoretical analysis presented in this paper describes how the proposed software driven consolidation can reduce system overheads, improve the organization of multicore workloads, and support more structured parallel execution. The study further highlights that a well-designed multicore software strategy can serve as a practical and scalable direction for future automotive electronics, supporting higher levels of feature integration and communication flexibility, along with lower CPU load compared to traditional multicore ECU implementations. Considerations for future enhancements in software component grouping and communication stack adaptability are also discussed.
Sadhu, Santosh, Toshniwal, Sachin
The introduction of Rear Wheel Steering (RWS) to the existing multi-link suspension system requires careful analysis of kinematics & elasto-kinematics characteristics in order to deliver optimal vehicle dynamics performance. Since the existing multi-link suspension system is not optimized for the use of a steerable wheel, the introduction of RWS requires considerable hard point changes & geometric modifications in order to deliver optimal steerability performance. Steering parameters like kingpin inclination (KPI), caster orientation, scrub radius, caster trail, and lateral and longitudinal offsets of the KPI axis to the wheel center and tire contact patch become deciding parameters that must be given due consideration to ensure the desired steering dynamics. This research aims to identify principal vehicle dynamics K&C parameters important to the effective application of RWS without sacrificing handling and stability of the vehicle. The challenge is to compromise rear axle articulation with prevailing suspension geometry and packaging needs, especially for Sport Utility Vehicle (SUV) platforms with severe packaging conditions. Simulation-based design process with Adams software is utilized to iteratively tune suspension parameters while maintaining fundamental vehicle dynamics attributes. The overall research proposes a step-by-step methodology for the evaluation and optimization of suspension-level performance parameters that have influence on the vehicle dynamics characteristics of the vehicle, ultimately resulting in the synthesis of an efficient, dynamically robust, and packaging-conformant multi-link suspension system with rear wheel steering unit. The conclusions are highly valuable as feedback on vehicle dynamics adaptation techniques for advanced chassis systems with rear axle steering.
Thakur, Pragyesh Singh, Prabhakara Rao, Vageesh, Mani, Arun, J., Ramkumar, Nayak, Bhargav, M., Sudhan
As automotive architectures evolve toward Software Defined Vehicles (SDVs) and high levels of driving automation, traditional safety analysis techniques rooted in hardware reliability require significant adaptation. The Classical Event Tree Analysis (ETA), a cornerstone of ISO 26262 functional safety compliance, relies on static, binary branching logic where each function either fails or succeeds. While highly interpretable and effective for modelling random hardware failures, classical ETA struggles to capture the dynamic, non-linear interactions, temporal degradation, and complex human-machine interface (HMI) dependencies that are characteristic of modern Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. This paper proposes State Dependent Event Tree Analysis (SD-ETA), a methodological extension designed to integrate discrete state vectors and temporal dependencies into the classical ETA framework. SD-ETA assigns probabilities to the branches for the dynamic nonlinear interactions, temporal degradation, and complex human-machine interface (HMI) dependencies instead of binary values, thereby allowing the system state (comprising vehicle physics, environmental conditions, and driver attention metrics) to dynamically update based on the outcomes of the preceding nodes. This approach retains the visual hierarchy and regulatory familiarity of standard event trees while significantly enhancing model fidelity for Safety of the Intended Functionality (SOTIF) scenarios governed by ISO 21448. To demonstrate the necessity and utility of this methodological evolution, this paper presents a comparative case study analysing a system operating under degraded environmental conditions. The quantitative application reveals that while classical ETA predicted a relatively low collision risk by treating sensor performance as a static binary state, the dynamic SD-ETA simulation exposed a critical hidden safety gap, calculating a higher collision probability due to compounded latency and degraded physical operating parameters. Ultimately, SD-ETA equips safety practitioners and automotive regulators with a modernized, time-aware framework to accurately evaluate the complex, interacting risks of autonomous systems without sacrificing the transparency required for rigorous safety certification.
Sharma, Dhiraj Mohan, Mutagi, Megha, Reddy, Kakarla Subba
In recent years, global automotive manufacturers have been steadfastly engaged in the pursuit of advanced technical solutions to address the increasingly stringent noise pollution standards mandated by national and international regulatory authorities for their domestic and export markets respectively. Aligned with this overarching objective, the Engine Design and Development team at Maruti Suzuki India Limited (MSIL) has undertaken focused efforts to reduce powertrain-induced noise and vibration (NV). Recognizing that the engine unit’s principal static subsystems, namely the intake and exhaust assemblies and critical structural components such as the cylinder head (CH) and cylinder block (CB), serve as primary sources of acoustic emissions, special emphasis was placed on their refinement. Additionally, particular attention was directed toward the engine’s rotating and reciprocating components, especially the camshaft and crankshaft assemblies, to minimize overall measured sound pressure levels.
Singh, Amandeep, Singh, Jaspreet, Kumar, Narinder
The transition toward Software-Defined Vehicles (SDVs) [1] is driving a fundamental shift in automotive electrical and electronic architectures [1], where centralized high-performance computing platforms must support mixed-critical workloads under strict requirements for determinism, performance isolation [2], and functional safety. Consolidating safety-critical and non-critical applications onto shared System-on-Chip (SoC) platforms [3] introduces significant challenges in ensuring predictable execution behavior, bounded latency, and compliance with safety standards [4, 15]. Addressing these challenges is essential for enabling scalable and reliable next-generation automotive systems [1] [5]. This paper presents a hardware–software co-design approach to achieve deterministic and safety-certified operation in SDV [1] architectures. Unlike traditional implementations that rely on software hypervisors or hardware isolation independently, the novel core contribution of this work is an integrated, dual-layer hardware-software orchestration model. It dynamically coordinates static Stage-2 translation with hardware Extended Resource Domain Controllers (XRDC) and Memory Partitioning and Monitoring (MPAM) to enforce end-to-end spatial, temporal, and interconnect-level bus isolation. The proposed solution combines hardware-assisted resource isolation mechanisms, including Hardware-enforced resource isolation and Memory Partitioning and Monitoring (MPAM) [6], with a lightweight partitioning hypervisor and Virtual Machine Monitor (VMM). XRDC [7] provides fine-grained access control for memory and peripheral resources, while MPAM [6] enables memory bandwidth allocation and monitoring to minimize resource contention across competing workloads. On the software side, the partitioning hypervisor [8] enforces strong temporal and spatial isolation between Virtual Electronic Control Units (vECUs), allowing mixed critical applications to coexist safely on a shared platform. VMM supports efficient virtualization [9, 10] and lifecycle management of virtual machines while maintaining deterministic execution characteristics. The proposed architecture aligns with the requirements of ISO 26262 [4], enabling the certification of safety critical applications alongside non-critical services. By integrating hardware-enforced isolation with software-level partitioning, the approach ensures predictable system behavior and reduces interference between workloads. The results demonstrate that a coordinated hardware–software strategy is essential to bridge the gap between high-performance computing and functional safety, thereby enabling scalable, deterministic, and safety-compliant SDV [1] platforms for future automotive applications. System-level validation conducted on an NXP S32G3 [7] [10] vehicle network processor platform demonstrates the efficacy of the proposed co-design. Under artificial memory-bus contention generated by background non-critical (QM) Linux traffic, the hardware-enforced MPAM bandwidth regulation reduced maximum worst-case execution time (WCET) jitter for an ASIL real-time control loop from 142.6 μs down to 8.4 μs—achieving a 94.1% reduction in latency variation. Paravirtualized hypercall operations achieved a 40.2% latency reduction compared to standard Trap-and-Emulate approaches.
Khan, Misbah Ullah
The rapid evolution of automotive electronics has pushed traditional vehicle electrical and electronic (E/E) architectures to their limits. Conventional designs, characterized by point-to-point wiring harnesses, distributed domain-specific ECUs, and communication buses such as LIN, CAN, and Ethernet, impose significant drawbacks in terms of vehicle weight, integration complexity, and long-term scalability. As the industry moves toward the Software-Defined Vehicle (SDV) paradigm, there is a compelling need to rethink the foundational principles governing in-vehicle connectivity. This paper proposes a wireless-centric SDV architecture in which non-functional-safety-relevant sensors and actuators communicate over standardized wireless protocols, eliminating the need for their associated physical wiring. By decoupling these peripheral nodes from the wired backbone, the proposed architecture reduces harness bulk, lowers vehicle mass, minimizes redundant compute hardware, and significantly reduces integration cost. Safety-critical systems including those governing powertrain, braking, and steering are explicitly retained on hardwired, deterministic communication channels in compliance with ISO 26262 functional safety requirements. This paper additionally defines explicit assumptions and fallback behavior governing that wired/wireless boundary. The architectural framework is evaluated against key SDV design objectives such as modularity, over-the-air (OTA) updateability, and feature scalability, assessed qualitatively against published engineering precedent, with a structured validation roadmap defined to quantify latency, reliability, and coexistence performance in follow-on work. The analysis demonstrates that a hybrid wired-wireless topology can substantially simplify vehicle E/E architecture while maintaining the reliability and latency guarantees demanded by automotive-grade systems. This work contributes to a structured transition roadmap for OEMs and Tier-1 suppliers seeking to adopt SDV principles without compromising vehicle safety or regulatory compliance, together with an explicit protocol-selection framework and a functional-safety argument for the wired/wireless boundary.
Hebbale Ramkumar, Ramya, Sarkar, Dibas
Modern day vehicles are equipped with multiple features to enhance user experience, increased safety and continuous over the air updates for upgradability and updatability of vehicles over lifetime. The implementation of every embedded automotive software feature consumes microcontroller resources (like RAM and ROM) which are highly critical and need careful handling. In an SDV architecture, where continuous software upgrades and updates are enabled, optimal feature handling becomes necessary. The microcontroller resources chosen during the development phase often reaches the threshold limit even before series production. In such scenarios, software feature enhancement/updates are challenging. When the memory is limited and becomes a constraint for installing newer features / upgrades, OEM has minimal provision to enhance or introduce new features. The paper presents framework to OEM to get a summary of how to manage functionality in a vehicle variant depending on specific demographic location and user preferences. The summary of the functionality usage is reported to end user in the vehicle system.
Chandrashekar, Sandhya, Hubli, Ashwini
Temperature management in both residential environments and commercial vehicle cabins are rapidly evolving toward intelligent, interconnected climate-control ecosystems. However, current systems operate in isolation, with no mechanism for synchronizing thermal comfort preferences between a user’s smart home and the cabin environment of a truck used for either personal mobility, last-mile delivery, or commercial logistics. This disjointed control results in inconsistent comfort experience, unnecessary HVAC energy consumption, and the inability to anticipate user needs based on transition patterns (e.g. leaving a cool home for a hot truck cabin) between home and vehicle. To address this technical challenge, this research paper provides bidirectional communication temperature-synchronization framework in which a smart-home HVAC app communicates seamlessly with the truck’s cabin climate-control system, integrating user-specific comfort profiles(such as temperature preferences, real-time cabin and home temperature, humidity levels, HVAC operating states, outside weather forecasts, trip start times, thermal-soak estimates etc.) and environmental prediction models to maintain consistent thermal comfort across both environments. Based on user’s comfort profile, our framework derives the Comfort Deviation Index (CDI), Transient Temperature Gradient (TTG) and applies predictive algorithms to precondition the cabin or home environment accordingly. The AI technique Long Short-Term Memory (LSTM) network is used for forecasting temperature transitions across home-to-vehicle. Our technique becomes even more pronounced in small-haul trucks that operate under frequent stop-and-go conditions, where rapid cabin temperature fluctuations are common. There is currently no existing research or framework that enables seamless temperature synchronization between a user’s home environment and the truck cabin. A smart-home–to-cabin synchronization approach would fill this gap by intelligently accounting for outside weather, trip duration, thermal soak, and energy-efficiency constraints. Additional challenges include ensuring secure communication over cloud and edge networks, managing latency constraints for real-time actuation, and preventing conflicts between home HVAC automation and automotive HVAC control logic
Prajapati, Saurav, Sagar, Kshitij Singh, B. S., Sanchitha
Automotive radar systems do not authenticate the signals they receive, allowing an external transmitter to create phantom objects that can trigger automatic emergency braking (AEB). This paper quantifies the threat using real radar measurements from the public RadarScenes dataset and evaluates a software countermeasure that requires no hardware modification. Computer-generated phantom radar returns were added to 200 motorway sequences and adjusted so the OS-CFAR detector accepted them as real objects. In the undefended system, radar detections were tracked as obstacles and braking was initiated when an object appeared within 30 m, resulting in unnecessary braking in 87.0 percent of attacked sequences. The proposed two-layer filter combines kinematic plausibility gating with camera-radar cross-validation. Layer 1 checks track history and physical movement over three consecutive frames, while Layer 2 reduces confidence when a clear-weather radar detection lacks camera confirmation. With both layers active, false braking fell to 3.5 percent, with no genuine detections missed across 200 genuine sequences. SHAP analysis of a Random Forest classifier identified kinematic implausibility as responsible for 62.3 percent of correct suppressions and produced a machine-readable evidence record to support cybersecurity documentation under ISO/SAE 21434 and safety documentation under ISO 21448. These results apply to an offline replay experiment using synthetic injections. Validation with real attack hardware, synchronized camera data, and vehicle testing remains future work.
Sehgal, Vishal, Sekaran, Nikhil
Automotive industry is currently experiencing a major transition towards Software-Defined Vehicles (SDV) to facilitate seamless new feature integration via over the air (OTA) update. SDVs integrate more than 50 + ECUs with more than 400+ software functions, leading to significant software complexity and a substantial need for comprehensive bench level testing to prevent software issues at vehicle level. To address these challenges, a robust and edge case focused test specification is essential, ensuring maximum requirement coverage across all scenarios while incorporating insights from past bugs and field issues. Currently, the automotive industry faces challenges in producing high-quality test specifications mainly focusing on functional testing (both positive and negative scenario), fault injection testing, ISO standards testing, network management testing which often exceed 2,000 + test cases for SDV features in parallel with maintaining consistency over time, ensuring maximum system-requirement coverage, and meeting project milestones. This paper introduces a new method to overcome above said problems. We provide a comparative study involving our proposed method with other methods to show the efficacy of the proposed method.
Arumugam, Jayalakshmi
Automotive E/E architecture is getting transition from distributed ECUs to high performing Zonal architectures. Traditional diagnostic methodologies based on Unified Diagnostic Services (UDS/ISO 14229) is being followed for providing failure analysis. It is difficult when debugging complex software and to find issues where lies exactly at modular level called “stack trace”. This could provide solutions for software bugs, failures or even container-level crashes in Software-Defined Vehicle (SDV). This paper provides a remote diagnostic framework that makes use of the ISO 17978 (SOVD) standard to visualize a “Vehicle-wide Crashlytics” as analogous to modern mobile application analytics with Firebase SDK. Also, the comparison of failure management with traditional diagnostic method to handling fault management in remote framework with both ISO 17978 (SOVD) and ISO 20077/20078 (Remote Diagnostic) is made. It also speaks how methodology can be formed for software logs capturing and traces execution triggered by function software level anomalies. Also, the constrains such as V2X bandwidth and data is monitored and controlled with Diagnostic Saliency Algorithm that helps to determine only relevant telemetry is transmitted via 5G or V2N. This framework proposal is checked with a simple application failure scenario, which leads to the reduction in “Mean Time to Repair” (MTTR) for software bugs. The reduction in MTTR is achieved since actionable root cause of software issue is shared directly from vehicle’s run-time environment. Future development of vehicle functions (SaaS) is expected to be integrated with SOVD and cloud-native observability patterns. This is much needed towards the scalable and quick development and deployment of applications into Vehicle as Digital Hub.
Kalirajan, Vignesh, Annamalai, Viswachandar, Balakrishnan, Arun Shankar
The Software-Defined Vehicle (SDV) paradigm is reshaping automotive electronic architectures by enabling continuous software rollout & artificial intelligence (AI) evolution on platforms over the total service life of the hardware. This creates a fundamental challenge for electronic control unit (ECU) design: to enable sustained deployment, adoption & maintenance of AI models over vehicle’s lifetime. This paper presents a technology independent framework for integrating AI lifecycle management into SDV ECU architectures, while decoupling AI & software innovation from hardware constraints. It further discusses the role of scalable computing platforms based on high-performance CPUs, GPUs & SoCs, in supporting increasing AI workloads over time. In addition, the evolution of AUTOSAR-based hardware abstraction mechanisms is examined as an enabler for software portability & hardware independence. The discussed framework addresses key lifecycle aspects, including AI model deployment, versioning, rollout strategies & long-term maintainability within established automotive safety, cybersecurity frameworks. The presented work contributes to future-ready SDV systems capable of sustaining continuous AI innovation over extended operational lifetime.
Ghosh, Sayantan, Anudeepika K., Durga, Nagaraju, Yamuna
The brake pedal assembly is a critical load bearing automotive component that must perform reliably under severe service conditions, especially during emergency braking events. Structural assessment is commonly carried out using computationally intensive non-linear finite element analysis, which can limit rapid design iterations during early product development. This study presents a physics informed surrogate modelling framework for predicting displacement and von Mises stress responses of a brake pedal directly from unstructured three dimensional mesh geometry. A structured dataset was created through parametric morphing and a Design of Experiments approach by varying pedal arm angle, pedal moment length, flange/web thickness, and combined geometric conditions. The simulation database was developed using conventional solvers and the generated cases were used to train Physics Artificial Intelligence based on the Transformer Neural Simulator architecture. The trained model showed close agreement with conventional solver results for both peak response values and contour distributions. For representative testing cases, predicted displacement and stress responses remained within acceptable engineering tolerance compared with the finite element baseline. After training, the PhysicsAI workflow reduced the prediction turnaround time compared with the conventional solver workflow. The results suggest that Design of Experiments driven datasets combined with geometric deep learning can provide an efficient workflow for rapid brake pedal structural assessment during concept development and iterative design studies.
N., Rengarajan, Praveen, Kannan, Philip Thomas, Febin
Off-highway vehicles (OHV) increasingly rely on electronically controlled, safety-critical systems such as braking, steering, powertrain, and implement control electronic control unit (ECUs). The integration of connectivity, telematics, precision-agriculture features, and semi-autonomous operation has transformed these machines into complex cyber-physical systems. While these capabilities improve productivity and operational efficiency, they also expand the attack surface, creating cybersecurity risks that can directly impact vehicle control, operator safety, and field operations. Off-highway platforms often operate in remote environments, use legacy in-vehicle networks, and have long service lifecycles, further amplifying exposure to cyber threats. Recent research and real-world demonstrations highlight that many attacks on safety-critical controllers exploit fundamental architectural weaknesses rather than advanced exploits. Common scenarios include unauthenticated CAN-based communication enabling message injection, replay, or spoofing of steering and braking commands; compromised telematics or wireless interfaces pivoting into safety-critical networks; unsecure over-the-air update mechanisms permitting unauthorized firmware installation; and misuse of diagnostic or maintenance access paths to invoke privileged ECU functions. Network-level attacks such as denial-of-service and sensor-level manipulation, including GPS or perception spoofing, further threaten the integrity and availability of control functions relied upon by modern agricultural machines. This paper examines these attack scenarios in the context of off-highway safety-critical controllers and discusses how Hardware Security Modules (HSMs) provide a practical trust anchor for mitigating such risks. HSMs onboard hardware-isolated execution, tamper-resistant key storage, and cryptographic enforcement that strengthen ECU security beyond secure boot. HSM-backed mechanisms enable authenticated and integrity-protected software updates, prevent execution of unauthorized firmware, support message authentication and freshness on in-vehicle networks, and harden diagnostic access through secure challenge-response mechanisms. By grounding cybersecurity risks in realistic off-highway use cases and mapping them to deployable HSM-backed controls, the paper illustrates how hardware-anchored security enhances runtime trust, supports safety objectives, and aligns off-highway vehicle architectures with emerging vehicle cybersecurity expectations, including the EU Cyber Resilience Act (CRA). In addition, a PoC implementation based on an automotive-grade embedded controller is presented to demonstrate secure boot, authenticated CAN communication, diagnostic access control, and runtime integrity monitoring, together with a validation methodology that demonstrates their practical effectiveness.
Maheshwaram, Chetan, Dasari, Manoj Sham
Accurate representation of strain-rate dependent stress–strain behavior is essential for reliable structural analysis and crashworthiness assessment in automotive engineering as vehicle structures and safety-critical components experience a wide range of loading conditions, from quasi-static deformation during forming and durability events to high strain-rate loading during crash and impact scenarios. Conventional material characterization approaches rely on extensive experimental testing to generate strain-rate dependent constitutive data, including specialized high strain-rate techniques that are costly, time intensive, and require dedicated infrastructure, which often limits the availability of comprehensive material data during early vehicle development phases and supplier evaluation. This paper presents a data-driven methodology for predicting strain-rate dependent stress–strain behavior of automotive steels using limited experimental input data. The proposed approach integrates physically meaningful material properties with a machine-learning framework to construct a generalized stress prediction model that remains consistent with classical material mechanics principles. Fundamental parameters governing material behavior, including strain, strain rate, yield stress, density, Poisson’s ratio, and elongation limit, are used as inputs to capture the combined influence of deformation state, loading rate, and intrinsic material characteristics. A Random Forest regression algorithm is employed to model nonlinear relationships without relying on predefined analytical hardening or rate-sensitivity formulations. The methodology is developed and validated using experimentally obtained stress–strain data from multiple automotive steel grades under quasi-static and dynamic loading conditions, and model performance is evaluated through correlation analysis between predicted and experimental stress responses across the full strain range. The results demonstrate strong agreement with experimental observations and robust generalization capability across different materials and strain-rate regimes, with minor deviations observed at very high strain levels attributed to limited data density near fracture conditions. The proposed framework significantly reduces dependence on extensive physical testing while maintaining accurate representation of material behavior, providing a practical and efficient alternative for material characterization that supports faster material selection, reduced testing effort, and improved robustness of automotive computer-aided engineering simulations.
Shedolkar, Pravinkumar, Dudhewar, Swapnil, Shukla, Anurag, Mhaskar, Dnyaneshwar, Chavan, Avinash, Kakade, Ajit
Oil canning, characterized by localized buckling and surface waviness in thin metal panels, is a critical quality parameter in the automotive industry. Since outer panels serve both structural and aesthetic functions, even minor surface distortions can adversely affect perceived vehicle quality. Traditionally, evaluation of oil canning relies on extensive nonlinear Finite Element simulations. For example, assessing a single hood design variant may requires thousands of nonlinear Finite Element analyses. Considering multiple design iterations across major vehicle panels including hood, roof, doors, fenders, and trunk, the total number of simulations can exceed tens of thousands, leading to substantial computational cost and development time. Consequently, this conventional approach is both time intensive and resource demanding. To address these limitations, advanced Machine Learning techniques are employed to significantly reduce simulation effort as well as accelerate design exploration. Initially, a deep learning based one-dimensional Reduced Order Model is developed to predict load–displacement response and oil canning propensity for a given panel. Indentation points on the hood panel are selected using a space filling Latin Hypercube sampling method. The Machine Learning based one-dimensional Reduced Order Model is trained by using 300-500 indentation points and validated using approximately 40-50 unseen indentation points. The verification is performed on multiple hood geometries. The predicted results demonstrate strong correlation with Finite Element generated load–displacement curves with that of ML based 1-dimensional reduced order model. Furthermore, to enable rapid design exploration across different panel geometries, a dataset of 40 different hood designs is utilized. A geometric deep learning approach is applied to cluster panel geometries, and the resulting representations are used within a 3-dimensional deep neural network tool Ansys SimAI™ to predict oil canning propensity and quantify it using the canning ratio. Validation study is carried out on unseen panel geometries, and the Machine Learning predicted oil canning zones show good agreement with Finite Element identified critical regions. The proposed Machine Learning framework reduces prediction time from hours to minutes, offering a scalable and cost-effective solution for early-stage design evaluation and mitigation of oil canning in automotive panels.
Sarnobat, Rajvardhan Vilasrao, Kumar, Dinesh, Jawale, Akshay, Balaji, Ananya, Kulkarni, Shreya
The automotive industry is leaning heavily into virtual testing to speed up vehicle design and cut physical prototyping costs. However, checking if a simulation matches a real car still relies on older, rigid math formulae and manual checks. This makes virtual testing less reliable for highly dynamic driving maneuvers, required in vehicle design. To address this, following paper introduces an automated software framework built to accurately measure the differences between physical test data and virtual simulations. The methodology was validated using a lightweight off-road prototype at the National Automotive Test Tracks (NATRAX) on a custom-engineered track inducing high-fidelity roll, pitch, and yaw excitations. To guarantee accuracy, an identical digital twin of this complex topography was constructed within a high-fidelity vehicle-dynamics simulation environment. The system features an algorithmic engine that automatically reads, cleans, and synchronizes distinct physical and virtual telemetry streams. Initial validation using standard metrics like NRMSE, RMSE, and S&G proved insufficient, failing to quantify errors in data exhibiting non-linear temporal shifts caused by stochastic human driver latencies. To overcome these limitations this study applies Windowed Dynamic Time Warping (DTW) to operationalize the ISO/TS 18571:2024 standard. This approach leverages the optimal warping path to extract continuous, vector-based error profiles, isolating phase and amplitude inconsistencies across the entire event. This automated framework significantly reduces correlation time by transitioning from subjective scalar scores to high-resolution diagnostic error vectors. By quantifying dynamic fidelity through this automated, high-precision DTW computational tool, the methodology validates virtual models as robust predictive tools, allowing simulation data to yield results comparable to physical benchmarks. This capability enables faster vehicle development by allowing rapid evaluation of multiple parameters in a virtual environment. It supports performance fine-tuning with reduced physical testing and can be integrated into automated workflows for repeatable data correlation, improving validation efficiency and reducing time.
Umorya, Divyansh, Pithadia, Chirag, S. J., Srihari, Jaiswal, Manish, Hardiya, Karan, Marmat, Hemant
In truck manufacturing, each vehicle requires thousands of small parts across models and variants. Each truck requires 4,000 to 15,000 standard components, with 500 to 1,500 part types that vary slightly. During the design phase, engineers select components from extensive part libraries without complete visibility into assembly complexity, procurement impact, or total lifecycle cost. This results in high part variability on multi-model production lines, increasing operator confusion, assembly effort, rework, tooling requirements, and supply chain inefficiencies. To streamline this, the authors developed a consolidated small-parts catalogue using a data-driven approach. The existing standards, specifications, usage volumes, and cost data were analyzed, and parts were clustered based on their features. These clusters were mapped to assembly stations using a visualization based digital tool that displays all parts used at each station and highlights functionally similar components that can be used alternatively. The developed tool for optimization quantifies consolidation opportunities, provide before and after part counts, with estimated cost-saving potential to reduce complexity in the design and assembly stations. For length-based optimization, manual validation of screw lengths across variants was replaced by an automated evaluation. The automated system replaces the proposed parts at existing assembly location across variants, and evaluates collision, interference, thread engagement, and clearance with nearby components. It increases the quality and reduces significant time for each proposal. Overall, the approach identified analytical consolidation opportunities of 24% in total small-part variant count and delivered a directly measured reduction exceeding 75% per proposal length validation time. Engineering effort reduction (∼50%) and direct procurement cost savings (up to 3%) are indicative projections pending production implementation. This approach improves manufacturing robustness and standardization repeatability for future vehicle programs.
Mani, Dharmaraj, Vijayasekaran, Vinothkumar, Jyoti, Nagaraj, Fasge, Abhishek
In welding fixture design, ensuring accessibility for manual Metal Active Gas (MAG) welding is essential for achieving required weld quality, proper welding angles, operator efficiency, and overall manufacturability. Manual welding operations depend on handheld torches, where limitations in reachability, torch orientation, and confined working spaces can significantly impact production feasibility. Automotive body structures are typically composed of numerous small and complex components within a largely monolithic assembly. For welding such smaller parts, hand jigs are used as independent tools to ensure proper positioning and stability. Accessibility and usability of these hand jigs play a crucial role, particularly in tight and restricted areas, directly affecting operator ergonomics and weld consistency. Traditionally, validation of welding torch accessibility and hand jig usability is performed during the physical try-out stage, often leading to late identification of design issues, fixture modifications, increased lead time, and higher development costs. This study proposes a Virtual Reality (VR)-based validation methodology to evaluate manual MAG welding accessibility and hand jig usability during the fixture design phase. CAD models of fixtures and components are integrated into an immersive VR environment, enabling real-time simulation of operator interaction. Critical parameters such as reachability, collision detection, welding angle, torch orientation, operator posture, and hand jig accessibility are assessed. The VR-based approach enables realistic evaluation of human interaction and ergonomic conditions, which are not effectively captured using conventional simulation tools. Early validation through VR significantly reduces design iterations, minimizes rework, and improves fixture design robustness. Overall, the proposed methodology enhances productivity, reduces costs, and accelerates project execution in welding fixture development. To provide a quantitative ergonomic assessment, the study introduces an Ergonomic Accessibility Index based on Green and Red Zone weld classification. In the Bus Middle UB fixture case study, the EAI improved from 70.8% to 95.8% after the product geometry was repositioned upward, demonstrating improved welding accessibility and operator ergonomics.
Hanumanthappa, Subramanya, Rajagopal, Vinoth, Vakkalad, Ramesh, Nagendran, Rakesh Kumar, Soni, Sanjay
Electric vehicle charging infrastructure depends on two authentication paradigms with significant weaknesses. Autocharge, defined under DIN SPEC 70121, identifies vehicles by their unprotected Media Access Control (MAC) address with no cryptographic verification. Plug and Charge under ISO 15118 adds cryptographic authentication but relies on a centralized Vehicle-to-Grid Public Key Infrastructure (V2G-PKI) that creates single points of failure and multi-hour certificate revocation windows. These weaknesses are not theoretical: CVE-2025-12357 demonstrated man-in-the-middle interception of Signal Level Attenuation Characterization (SLAC) exchanges, and field studies have documented Transport Layer Security (TLS) silent downgrades in over 30 commercial vehicles. This paper describes a blockchain-anchored framework that brings both sets of existing solutions under a single decentralized trust layer built on a permissioned Hyperledger Fabric consortium of Original Equipment Manufacturers, Charge Point Operators, and e-Mobility Service Providers. A compact Elliptic Curve Digital Signature Algorithm (ECDSA) token with rotating pseudonyms handles vehicle authentication, charger side caching enables sub-millisecond verification for known vehicles and credential revocation propagates through blockchain event subscriptions rather than periodic certificate list refreshes. A calibrated Python simulation with real ECDSA operations yields a median cache-hit latency of 81 microseconds on a warm cache (76 microseconds in the city case). Under realistic city deployment conditions with 85 percent local traffic, that median holds while outperforming Autocharge by 375 times and PnC with Online Certificate Status Protocol by 3,700 times. Highway and rural chargers starting with empty caches see median latency around 10 milliseconds, still 3 times faster than Autocharge and 30 times faster than PnC with OCSP. Revocation reaches every consortium peer within one block-commit cycle, measured at 2.04 s worst-case on a live multi-node Hyperledger Fabric network. All tested scenarios remain within ISO 15118 Session Discovery Protocol timing budgets.
Tumram, Sayali Ashok
The increasing adoption of structural adhesives in electric vehicle battery packs introduces challenges in predicting joint performance under complex loading and operating conditions. While advanced material models can capture detailed failure mechanisms, their application in early-stage design is often limited by extensive material characterization requirements and high computational cost and time. This study presents a computationally efficient finite element methodology for screening-level assessment of adhesive joint stress distribution and utilization using readily available material data from standard datasheets. The approach is specifically developed for early design stages, where rapid assessment, limited input data, and multiple design iterations are critical. The method is compared against adhesive manufacturer material datasheet strengths using strength-normalized loading to assess consistency of load transfer representation, hotspot locations, and relative stress-utilization trends. It is further extended to evaluate multiple joint configurations, including lap joints, T-joints, and hybrid joints, enabling a broader assessment of adhesive behavior across different loading conditions. A systematic design sensitivity analysis is performed to investigate the influence of key parameters such as adhesive bonding area, and geometric variations on joint stress utilization and hotspot sensitivity. The results highlight the capability of the approach to identify key design sensitivity parameters within the studied joint configurations and support informed decision-making during the concept development phase. The study demonstrates that the proposed methodology enables efficient screening of design concepts, can reduce reliance on high-fidelity material models during early-stage concept screening, and supports reduced evaluation turnaround time. This work establishes a practical right-level modeling strategy that complements advanced simulation techniques by enabling faster iterations, reduced computational effort, and compatibility with existing automotive CAE workflows.
Sugumar, Mohanraj, Bhat, Sadashiv
High-voltage (HV) cables and connector terminals in electric vehicles (EVs) are susceptible to high current loads. Due to high current, there is excessive temperature rise near the terminals and crimping region inside the connectors. Excessive temperature beyond critical limits may pose risks to connected components and compromise passenger safety. Conventional cooling approaches are often energy-intensive and rely on active thermal management systems, which may not always be suitable for high power applications. Therefore, there is a need for a localized, rapid, and reliable passive thermal management strategy that can be retrofitted into existing connector designs with some structural modifications that would provide significant safety enhancements. This study proposes a novel approach by incorporating phase-change material along with solid fins inside the connector. The PCM has been placed co-axially surrounding the terminals of the connectors and enclosed inside a casing to ensure no direct contact with the connector components. The fins are also enclosed inside the same casing with projections going into the PCM. The PCM acts as heat storage unit, delaying the time for the peak temperature to rise and the fins distribute the heat rapidly inside the PCM. The objective is to maintain the terminal temperature below 100°C with 125 A current rating. A 3-D transient thermal analysis was performed on a CAD model encompassing PCM with solid fins inside the female connector. A comparative analysis between a traditional connector to a fin enhanced PCM connector showcased a drastic delay in temperature rise (acting time) by 115 seconds. Furthermore, the slope of rise in temperature decreased significantly beyond 100°C, slowing the rapid heating of the terminals. Thus, a passive sustainable system is designed for localized thermal management of HV Connectors.
Neogi, Angshuman, Singh, Jitendra, Kumar, Ashutosh
As the global automotive industry pivots toward sustainable mobility, reducing vehicle curb weight remains a paramount objective for enhancing energy efficiency and range. This paper presents a robust and comprehensive methodology for the light weighting of a two-wheeler chassis through the integration of Design of Experiments based optimization and Power Spectral Density durability analysis. The study begins by establishing a baseline durability profile for the vehicle using frequency-domain durability simulations under random vibrational loading, ensuring a benchmark for structural integrity. To explore the design space effectively, frame tube thicknesses are parameterized, and a Design of Experiments design matrix is constructed using Modified Extensible Lattice Sequence technique. This design matrix is solved, and a response matrix is built. These two matrices are combined, and appropriate curve-fitting techniques are applied to construct a Reduced Order Model. This mathematical surrogate model enables the rapid evaluation of the frame’s response under varying frame thickness combinations, thereby avoiding computationally expensive traditional Finite Element Analysis iterations. The optimum combination of frame tube thicknesses is identified using the Global Response Surface Method. This configuration minimizes the overall mass of the vehicle while strictly maintaining predefined durability and safety targets. The final optimized frame is evaluated by a secondary Power Spectral Density durability analysis to validate the accuracy of the surrogate model’s predictions. The integration of this metamodel-based design approach results in a significant reduction in structural weight. Ultimately, this framework accelerates the design cycle by developing a lightweight, cost-effective, and durable structure, thereby minimizing the overall time-to-market for sustainable automotive products.
Chettiar, Alfred Selvamani, V., Vinothkumar, S., Deepak, Praveen, Kannan
As the global automotive industry increasingly prioritizes electrification and long-term sustainability, the demand for aggressive lightweighting in automotive chassis engineering has intensified. This study presents a process-driven, multi-stage structural optimization workflow designed to maximize total mass reduction while maintaining rigorous structural performance. To mitigate the high computational costs and software license overhead of nonlinear Finite Element (FE) analysis, the methodology transforms complex models into efficient linear representations. This strategic shift preserves critical structural behaviors while significantly enhancing license efficiency and total computational simulation throughput. The novelty of this workflow lies in an integrated optimization loop that begins with Topology Optimization to define primary load paths, followed by Free Shape Optimization to mitigate stress concentrations. By embedding manufacturing constraints such as draft angles and minimum thickness directly into the optimization process, the methodology effectively eliminates traditional redesign cycles. Optimized designs are then seamlessly translated into CAD via advanced ANSA tools for validation of von Mises stress, plastic strain, and buckling. This synchronized transition from analysis to realization reduced the development cycle by 50%, enabling a design freeze significantly faster than conventional methods. The integrated approach achieved a 15% mass reduction while meeting all functional durability and crash requirements. Given that a 1% reduction in mass correlates directly to a 1% reduction in CO2 emissions, the resulting design substantially minimizes the vehicle's total carbon footprint. By lowering material consumption and maximizing computational efficiency, this study offers a repeatable solution for balancing simulation fidelity with the urgent demands of modern sustainable automotive design.
Venkataraman, Nageswaran, Rajendran, Hariprasad, Karimbedu, Saikumar, Yadav, Balwan