Browse Topic: Electrical, Electronics, and Avionics

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Analysis Techniques for Racecar Data Acquisition [mms update 07.03.25]R-367To be published on 03/22/2099
Data acquisition has become an invaluable tool for establishing racecar - and car/driver - performance. Now that the ability exists to analyze each and every performance parameter for car and driver, accurate use of this data can provide a key advantage on the racetrack. This book provides a thorough overview of the varied methods for analyzing racecar data acquisition system outputs, with a focus on vehicle dynamics.
Segers, Jorge
Requirements Extract for AS50881H Wiring Aerospace VehicleSA-DIG-000035/1/2026
This digital standard is a requirements extract of AS50881H Wiring Aerospace Vehicle. This file contains a general requirements extraction as well as files that are optimized for use with Doors Classic, Siemens Polarian, and PTC. <img src="https://wcm14-tst.cld.sae.org/site/binaries/content/gallery/mobilus-brx/digital-supplements/as7140-data-model.png/as7140-data-model.png/sae%3Amedium" alt="AS7140 Data Model" />
testsofware-test-123jms4/1/2026
test
Digital Standards TEST w/Standards/SupplementSAE-SUP-TEST12 (Current)3/27/2026
Chose Standards and Supplement instead of Media/Software.
SAE TOMORROW TODAY - Building an End-to-End Autonomous Driving System135572/27/2026
Join us as we sit down with Richard Chelminski, Senior Vice President, Head of SDV Platform, at 42dot, an autonomous driving software and mobility platform development startup from Hyundai Motor Group. We discuss how the company is pushing boundaries with its end-to-end autonomous driving system, Atria AI, which is focused on Level 4 autonomy and designed for seamless integration into consumer vehicles -- no LIDAR required! You'll also learn why camera-first and radar technologies are reshaping the industry, how software is now at the heart of vehicle development, and what the future holds for Transportation as a Service (TaaS). If you're curious about the next wave of smart, software-defined vehicles and autonomous driving innovation, this episode is a must-listen. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging
Patterson, Lori
A Dynamic Cybersecurity Benchmarking Framework for Automotive Cyber-Physical Systems2026-26-06121/16/2026
As the automotive industry accelerates towards greater connectivity and automation, ensuring robust cybersecurity has become a critical priority, especially in emerging markets like India. This paper introduces a comprehensive framework designed to assess and enhance the cybersecurity of production-ready vehicles, with a particular focus on addressing the unique challenges posed by both domestic and international markets. The proposed system combines advanced threat modeling, vulnerability assessment, and dynamic evaluation techniques to continuously monitor and quantify a vehicle’s cybersecurity posture throughout its lifecycle. In the Indian context, where the automotive market is rapidly evolving and the adoption of connected and automated vehicles is on the rise, the need for tailored cybersecurity measures is particularly pressing. By evaluating the impact of cyber threats on safety, data privacy, financial risks, and vehicle functionality, the system generates a detailed
Shah, Ravindra, Awasthi, Vibhu Vaibhav, Karle, Ujjwala
Securing the Future of Electric Vehicle Charging: Identifying and Mitigating Cybersecurity Vulnerabilities2026-26-06141/16/2026
This paper examines the key security vulnerabilities present in modern EV charging infrastructure, with a focus on the potential threats to payment systems, communication protocols, physical security, and data privacy. In particular, it looks at the vulnerabilities associated with unencrypted communication, the risks of unauthorized access to charging stations, and the potential for denial-of-service (DoS) attacks. As the global adoption of electric vehicles (EVs) accelerates, the importance of a secure and reliable EV charging infrastructure becomes paramount. However, the expansion of charging stations and the growing integration of smart technologies introduce significant cybersecurity risks that must be addressed to ensure the safety, privacy, and reliability of the network. In addition to discussing emerging risks in relation to new technologies, such as Vehicle-to-Grid (V2G) systems, and outlining the necessity of industry-wide collaboration and the development of standardized
Aggarwal, Akshit, Gupta, Saurabh, Sirohi, Kapil, Arisetty, Venkatesh, Chatterjee lng, Avik
Considerations for Vulnerability Management in the Automotive Industry2026-26-06261/16/2026
With the emergence of Software-Defined Vehicles (SDVs), more complex software and connectivity technologies are introduced to support new advanced use cases. These use cases include, e.g., phone as a key, smart parking, summon vehicle, remote diagnostics and vehicle management. However, complex software functionality and external connectivity also increase the attack surface of vehicles and its ecosystem. As such we have seen various cybersecurity attacks targeting vehicles and OEM backends in the past few years. In this paper, we first perform a classification of recent automotive cybersecurity attacks. We further perform an analysis of these attacks and associated vulnerabilities considering the application of best practices of vulnerability management approaches including Common Vulnerability Scoring System (CVSS), Exploit Prediction Scoring System (EPSS), and Stakeholder-Specific Vulnerability Categorization (SSVC). CVSS is a standardized framework used to assign severity scores to
Oka, Dennis Kengo, Vadamalu, Raja Sangili
RAKSH: A Blockchain Derived In-Vehicle Key Management System2026-26-06201/16/2026
Modern cars have advanced significantly with the rapid growth of connectivity and communication technologies. In the wake of rising cyberattacks and enforcement of regulations, implementation of cybersecurity is imperative to safeguard vehicles. The cybersecurity controls such as secure boot, secure updates, secure communication require cryptographic primitives (keys/certificates). These security features are largely dependent on robust Key Management System (KMS), as keys are the sensitive assets that must be protected throughout the lifecycle of vehicle. Several security critical applications like over-the-air and car-to-car interaction essentially needs robust KMS to protect the vehicle assets from expanding attack vector. Traditionally KMS is established centrally in a backend server. The cloud based KMS is becoming complex due to increased number of keys/certificates required to provision in a vehicle. We propose a self-governing in-vehicle key management system for a gateway
Goyal, Yogendra, Sutar, Swapnil, Jaisingh, Sanjay
MOSAIC-TARA: A Comprehensive TARA Methodology for Automotive Cybersecurity2026-26-06091/16/2026
Threat Analysis and Risk Assessment (TARA) is a continuous activity, acting as a foundation of cybersecurity analysis for electrical and electronics automotive products. Existing TARA methodologies in the automotive domain exhibits challenges due to redundant and manual processes, particularly in handling recurring common assets across Electronic Control Units (ECUs) and functional domains. Two primary approaches observed for performing TARA are Manual-Asset-Centric TARA and Catalogue-Driven TARA. Manual-Asset Centric TARA is constructed from scratch by manually identifying the assets, calculating risks by likelihood, and impact determination. Catalogue-Driven TARA utilizes the precompiled likelihood and impact against identified assets. Both approaches lack standardized and modular mechanisms for abstraction and reuse. This results in poor scalability, increased efforts, and difficulty in maintaining consistency across vehicle platforms. The proposed method in this research overcomes
Goyal, Yogendra, Sinha, Swati, Sutar, Swapnil, Jaisingh, Sanjay
Artificial Intelligence in Passenger Cars: Unlocking Intelligent Mobility and Human-Centric Vehicle Innovation2026-26-06721/16/2026
Artificial Intelligence (AI) is fundamentally reshaping the landscape of automotive engineering, particularly in passenger vehicles where comfort, safety, and customization are paramount. This paper presents a comprehensive review of current and emerging AI-driven applications in passenger cars, including intelligent vehicle insurance, real-time traffic optimization, personalized driving experiences, emotion recognition, and advanced EV battery management. The integration of AI technologies—ranging from machine learning and computer vision to natural language processing and deep neural networks—enables predictive maintenance, adaptive driver-assist systems, and next-generation human-machine interfaces. AI’s role in vehicle insurance is also explored through behaviour-based risk scoring and claims automation. Furthermore, emotion-aware cabin systems are discussed, which aim to detect driver fatigue, stress, or distraction to improve road safety and mental wellness. The paper also
Hazra, Sandip, Tangadpalliwar, Sonali, Khan, Arkadip
Enhancing Engine Reliability Testing with IoT, AI and ML: Real-Time ML-based Limit Monitoring and Predictive Maintenance with IoT sensor2026-26-06471/16/2026
The integration of Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) has transformed various industries, offering substantial benefits. The application of these technologies in engine reliability testing has immense potential as they offer real-time monitoring and analysis of engine performance parameters. Engine reliability testing is vital for ensuring the safety, efficiency, and longevity of engines. Traditional methods are time consuming, expensive, and rely heavily on manual inspection and data analysis. This paper shows how IoT, AI and ML technologies can greatly enhance the efficiency of engine reliability testing. The paper includes the following case studies: 1. ML-based Limit Monitoring for test data: With the help of machine learning models, we monitor the Engine health by comparing predicted and measured values during real-time testing. Intelligent algorithms predict and compare values, detect anomalies, and identify root causes in real-time
Yadav, Sanjay Kumar, kumar, prabhakar, R, Dinesh, Joon, Sushant, Rai, Ayush, Tripathi, Vinay Mani
Next-Gen Automotive: Enablers and Monetization for Automotive SaaS and Software Defined Vehicles2026-26-06871/16/2026
The automotive industry is undergoing a paradigm shift, changing its model from hardware-centric products to software-driven platforms. It is driven by two underlying paradigms: the emergence of Software-Defined Vehicles (SDVs) and the increasing adoption of Automotive Software-as-a-Service (SaaS) models. Together, these trends are transforming the way the industry generates value, engages with customers, and builds new revenue models. This research examines the most important technical pillars underpinning next-generation automotive ecosystem creation. Of these pillars, centralized computing infrastructures, embedded cloud integration, efficient over-the-air (OTA) update engines, and enhanced cybersecurity models designed to protect larger numbers of connected vehicles are observed. It also evaluates the imperative significance of digital identity management to provide improved personalized experience, the critical role of edge computing to ensure real-time processing, and the promise
Saini, Gourav, Jahagirdar, Shweta, Khandekar, Dhiraj Baburao
Implementation of Automotive Cybersecurity Assurance Level2026-26-06241/16/2026
This paper explores the implementation of ISO-21434 Automotive Cybersecurity Assurance Levels (CAL), focusing on enhancing component level cybersecurity for a vehicle. CAL values, which range from 1 to 4, provide a metric for ensuring that assets are protected against relevant threats at various phases of the Software Development Life Cycle (SDLC). By identifying potential attack vectors and their severity early in the SDLC, specifically during the Concept phase of ISO 21434, organizations can determine the CAL values. The CAL value serves as a benchmark to determine the level of severity required during the design, development, and verification phases of the SDLC. This paper outlines a method to establish CAL values as per ISO-21434 guidelines. The proposed methodology includes a detailed analysis of threat modeling, which is crucial for identifying and mitigating potential cybersecurity risks. By conducting threat modeling, organizations can systematically assess vulnerabilities and
Ghosh, Subham, Khader Batcha, Jashic
Automotive Cybersecurity Regulatory Framework2026-26-06251/16/2026
During the last few years, cybersecurity has become one of the key aspects in the automotive industry to ensure the safety and security of vehicles throughout their lifecycle. The implementation of new technologies and the goal of deploying fully automated vehicles in the not-so-distant future have created new needs regarding vehicle safety. As the automotive industry shifts toward automated and connected vehicles, the possibility of cyberattacks emerges as a significant concern. To address these challenges, different regulatory bodies and governments worldwide have developed or are developing a new set of regulations and standards with varying approaches to legislate or standardize the minimum criteria that manufacturers must follow to ensure security against potential attacks, adding requirements to ensure the cybersecurity of the vehicles at all levels. At the United Nations level, UN Regulation 155 was introduced in 2021, establishing requirements for manufacturers' cybersecurity
Flix, Oriol, Lujan Tutusaus, Carlos, Hidalgo, Justin
Navigating the Field of Automotive Cybersecurity Relevant Regulations2026-26-06231/16/2026
In recent years many automotive cybersecurity relevant regulations have been released and some have already started to come into effect. Moreover, some other regulations will come into effect in the next few years. These regulations provide requirements and guidance to automotive organizations with different degree of specifics. In this paper, we review a number of different cybersecurity relevant regulations such as UNR 155, UNR 156, AIS 189, AIS 190, GB 44495, GB 44496, EU Cyber Resilience Act, 15 CFR Part 791. We break down and categorize these regulations based on their scope and highlight key areas relevant to different teams within the organizations. These key areas include cybersecurity management system (CSMS), software update management system (SUMS), secure software development and software supply chain security, continuous cybersecurity activities (monitoring, incident response), and vulnerability disclosure and management. We then map responsibilities from the regulations
Oka, Dennis Kengo, Vadamalu, Raja Sangili
Cybersecurity in Automotive OTA Update Systems and Software Store2026-26-06211/16/2026
Automotive Over-the-Air (OTA) software updating has become a cornerstone of the modern connected vehicle, enabling manufacturers to remotely deploy bug fixes, security patches, and new features. However, this convenience comes with significant cybersecurity challenges. This paper provides a detailed examination of automotive OTA update security and the associated software store (software Applications & services store) mechanisms. I discuss the current industry standards and regulations - notably ISO/SAE 21434 and the United Nations Economic Commission for Europe (UNECE) regulations UN R155 (cybersecurity) and UN R156 (software updates) - and explain their relevance to secure OTA and software update management. I then explore the Uptane framework, an open and widely-adopted architecture specifically designed to secure automotive OTA updates. Next, OTA-specific threat models are analyzed, detailing potential attack vectors (such as malicious update injection, man-in-the-middle attacks
Kurumbudel, Prashanth Ram
"Automotive Cybersecurity: Regulatory Landscape, Type Approval, and the Road Ahead"2026-26-02461/16/2026
Effective communication is the key for bringing harmony - be it the communication between humans and humans, or communication between machine and machine. Today’s car is a sophisticated gadget, equipped with the best of technologies running using millions of lines of codes of software. The effective use of these technologies involve communication between car to car and car to infrastructure using Dedicated Short Range Communication (DSRC), C-V2X (Cellular Vehicle-to-Everything). It is pertinent that any communication using the internet needs to be digitally secure and that the systems are designed to mitigate the perceived threats. The methods used for ensuring cyber safety of automobiles need to be verified before the end product is put to use. Automotive Industry Standards AIS-189 and AIS-190 have been formulated to provide a harmonized verification framework. Both the vehicle manufacturer and the test agency need to equip themselves with necessary skills and tools to ensure
Nayak, Pratik, Tandon, Vikram, Badusha, Akbar, Desai, Manoj, Sathianesan, Rejin
Vehicle usage Digital Twins - Mobility analysis test case2026-26-03941/16/2026
In the evolving landscape of the automotive industry, this study presents an innovative approach to developing digital twins for driver profiles, establishing a standardized and scalable procedure for collecting and analyzing driving data on a global scale. The proposed methodology centers on the development of a robust cloud infrastructure, including a Data Lake and associated services, designed for efficient storage and processing of large volumes of data from multiple markets and vehicle types. The research introduces a adaptable procedure for data collection campaigns, applicable to diverse global markets and encompassing a wide range of vehicles, from internal combustion engines to electric and hybrid models. A key feature of this approach is the establishment of advanced data decoding protocols, enabling precise interpretation of CAN network information from vehicles of different manufacturers and models, even when the CAN structure is not previously known. The study defines
Arturo, Rubio, Marín Saltó, Anna, Diaz, Francisco, Olivencia, Sergio, Medina, Alba
Cybersecurity threats for connected passenger EV Buses of state transport units in India.2026-26-06101/16/2026
There are several chances to enhance fleet management, passenger safety, and operational efficiency by integrating connected technology into electric passenger buses, especially in Indian state transport agencies. But this connectivity also brings with it a fresh set of cybersecurity threats that might jeopardize these cars' dependability and security. With an emphasis on India's state-run bus fleets, this study examines the cybersecurity risks unique to connected electric vehicles (EVs) utilized for public transportation. Vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and fleet management communication systems are examined for key vulnerabilities. Potential attack vectors are highlighted, including data interception, GPS spoofing, unauthorized access, and cyberattacks on charging stations. We also look at how these cybersecurity threats affect public safety, operational disruptions, and passenger privacy. This paper also assesses the difficulties Indian state transport
Mokhare, Devendra Ashok
Durability Validation of Full Vehicle Structures Using Strain Correlation and RLDA-Based Simulations2026-26-05231/16/2026
Durability validation of full vehicle structures is crucial to ensure long-term performance and structural integrity under real-world loading conditions. Accurate strain correlation between physical testing and finite element (FE) simulations plays a key role in the validation, ensuring reliable predictions of vehicle durability. In this paper, a comprehensive methodology for strain correlation on a full vehicle subjected to torture track testing is implemented. Strategic placement of strain gauges is determined through a combination of static analysis, modal analysis, and identification of critical stress regions in the FE model. Strain gauges were installed on key structural locations of a prototype to capture strain responses under severe loading environments. Simultaneously, vehicle wheel loads were recorded on the torture track to build a detailed Road Load Data Acquisition (RLDA) dataset at critical hard points, Loads measured in terms of wheel loads (WFT) are fed to MBD model to
JAJU, MAYUR, Dokhale, Sandeep, Gadre, Nilesh, Patil, Sanjay
Next-Gen Driver Health and Wellness Platform for Fatigue Detection and Emotional Wellbeing Monitoring2026-26-06391/16/2026
Driver fatigue and emotional distress are major contributors to traffic accidents, especially among long-haul and professional drivers, making their detection a critical area in road safety and public health. While existing research has explored various sensor-based and wearable solutions to monitor driver states, these approaches often suffer from high cost, intrusiveness, or limited scalability. Camera-based systems have recently gained attention for being less invasive and more practical for real-world deployment; however, many still depend on multiple sensors or cloud-based processing, which raise concerns around energy consumption, privacy, and integration complexity. To address these gaps, we present a sustainable, camera-only Driver Health and Wellness Platform for real-time fatigue detection and emotional wellbeing monitoring. The system leverages a single in-cabin camera and applies lightweight, edge-optimized computer vision models to analyze facial features, eye dynamics
Iqbal, Shoaib, Imteyaz, Shahma
A Large Concept Model Approach for Summarizing Automotive Documents and Enhancing Vehicle Data Query2026-26-06761/16/2026
Technological advancement in automobile production, regulations, sales, and services leads to extensive technical documents and manuals. However, lengthy documentation, language barriers make it challenging to find quickly, highlighting the importance of summarization to bridge this knowledge gap. The Large Language Models (LLMs) have demonstrated impressive capabilities in tasks like Q&A, coding, drafting, and scientific acumen, and are commonly used to condense many complex documents. Despite LLMs being widely used in various sectors like research and journalism, LLMs face challenges in reliably summarizing content. Their word-by-word tokenization can sometimes fail to capture the true meaning and context, especially for abstractive summaries. This paper explores Large Concept Models (LCMs), which operate on principles of semantic reasoning, cross-modality integration, and hierarchical structuring at a higher conceptual level. This makes LCMs language and modality agnostic
Singh, Samagra, Ravi, Utkarsh, Vikram, Prateek, Shenoy, Lakshmi, Awasthi PhD, Anshuman
Machine Learning Approach to ISO Road Class Prediction Using Multiple Linear Regression2026-26-06671/16/2026
The inertial profiler methodology is traditionally employed in RLDA (Road Load Data Acquisition) to measure road profiles and classify test routes into ISO road classes. However, this approach demands significant time and effort during instrumentation. Also, during data acquisition, laser height sensor data is affected especially during adverse conditions such as rainy seasons or on surfaces with improper reflectivity. Additionally, substantial resources are required for data processing to convert raw measurements into road classifications. To address these challenges, an initial attempt was made to establish a relationship between axle acceleration responses and road profiles, enabling axle acceleration measurements during RLDA to predict ISO road classes. However, this approach relied on a simple linear model that considered only axle acceleration responses, rendering the predictions susceptible to inaccuracies due to varying parameters such as vehicle speed. To overcome these
P, Praveen Kumar, P, Dayalan, Sriramulu, Yoganandam
Unlocking Edge AI in Software-Defined Vehicles: Balancing model complexity, compute resources, and data access2026-26-06861/16/2026
The potential of Edge AI in automotive goes beyond ADAS and automated driving. AI-driven optimizations in driving efficiency, in-cabin personalization, predictive maintenance, and enhanced safety require intelligent processing at the vehicle edge. However, integrating AI into current vehicle electrical/electronic architectures (EEAs) presents significant challenges. This paper examines how AI models can run on general-purpose ECUs, gateways, and domain controllers while balancing model complexity, compute resources, and data availability. We explore trade-offs, architectural choices, and the need for software configurability to ensure AI models evolve with new data and vehicle parameters. We conclude by providing a glimpse into Sonatus’ latest AI product that encapsulates a holistic approach optimized for vehicle platforms—spanning cloud-based model development, optimization, and validation to in-vehicle inference—that unlocks the potential of Edge AI and SDVs for the automotive
Sah, Mohamadali, Khatri, Sanjay
Innovative Preconditioning Solutions for Efficient High-Current Battery Charging2026-26-01671/16/2026
In high-performance charging systems, managing higher currents is crucial for efficient battery charging. However, elevated battery and charging gun pin temperatures pose significant challenges, limiting the duration and effectiveness of high-current charging. Our advanced control system addresses these barriers by optimizing charging time while maintaining optimal temperature ranges for both the battery and charging gun. This is achieved through innovative preconditioning solutions that incorporate both active and passive cooling configurations. Our system features a unique preconditioning approach with dedicated cooling circuits for the charging socket and battery. The passive liquid cooling system ensures effective temperature management without additional energy consumption, while the active liquid cooling system provides enhanced cooling capabilities for more demanding scenarios. By integrating these cooling configurations, our control system significantly reduces charging time
Badgujar, Pankaj Ravindra, Bhosale, Subhash, Dave, Rajeev
ALLOY WHEEL RIM LINEAR CRASH VALIDATION WITH BENCH LEVEL TEST PROCEDURE2026-26-05741/16/2026
Crash test plays a very crucial role in determining the passenger safety along with driver safety in most modern vehicles. This has become a prominent factor for many buyers to choose a safe car. During crash test, many components tend to fail. Amongst them, the major safety critical component which hampers the drivability of a vehicle is Wheel and Tyre Assembly. With the introduction of low aspect tyres, the failure rate of these assemblies has increased. A very high importance is given to ensure these parts withstand the subjection load as it is directly related to function of vehicle. Many methods are available to test the Wheel and Tyre assembly to ensure they pass the crash criteria. We have developed a novel test method which can simulate the crash pattern in the rig/bench level. The method employs a mechanical actuator which can be operated at designated load application to ensure the assembly undergoes the anticipated failure. The process is repeated with different types of
Medaboyina, HarshaVardhan, Singh, Ram Krishnan, SUNDARAM, RAGHUPATHI, Jithendhar, Ashokan
Emerging Trends in Emission Optimization2026-26-02291/16/2026
The exhaust emission control is a vital aspect of automotive development, aimed at ensuring effective control of pollutants such as NOx, CO, and HC. The traditional method of calibrating emission control strategies, that requires extensive vehicle testing with both the fresh and aged catalysts under a variety of operating conditions, is a time-consuming process. The frequent changes in emission legislation creates new challenges for achieving a faster time to market and effective utilization of resources. So, the use of automation and machine learning (ML) in the domain of emission control is the need of the hour to proactively tackle these challenges. This paper attempts to explore emerging trends in emission optimization, such as the automated measurement process and ML/AI based data analysis, that can improve the overall process efficiency in the calibration of emission control strategies. The automated measurement process is achieved using the automation software. The data
Dhayanidhi, Hukumdeen, Balasubramanian, Karthick, A, Akash
Addressing Seat Belt Discomfort Among Indian Female Driving Population Through Anthropometric Data Collection and Simulation2026-26-00161/16/2026
The rate of female drivers in India is increasing alongside the rapid growth of the Indian automotive industry. A driving comfort survey conducted among female drivers revealed that many experiences dis-comfort when wearing seat belts—whether while driving or as front-seat passengers. This discomfort is primarily due to a phenomenon referred to as “neck cutting.” The root cause of neck cutting is likely related to vehicle design, which is traditionally based on Anthropometric Test Devices (ATD’s) representing the 50th percentile of the global population. However, a literature review indicated that the anthropometric dimensions of the Indian populations are generally smaller than those of the global for the respective candidate. This highlighted the need to collect region-specific anthropometric data for India. During this research work, Indian anthropometric data was collected through the Size India 2.0 project, covering a wide demographic region across India. This study was in
Kulkarni, Nachiket, Chitodkar, Vivek V, Eknath Chopade, Santosh, Yamgar, Babasaheb S, Mahajan, Rahul
Fuel Cell Electric Vehicles: Method for Characterization of Onboard Power Management System and Defining Vehicle Level Attributes2026-26-05701/16/2026
Fuel Cell and Battery Pack/Capacitors are two power sources on-board a Fuel Cell Electric Vehicle. Each having it's own set of boundary conditions and ideal operating conditions in order to deliver expected performance over the intended useful life. Such a dual power source architecture poses a challenge of optimally balancing power delivery from Fuel Cell and Battery Pack/Capacitor in order to ensure health of both these important aggregates of vehicle over it's intended useful life. Therefore, a method and tool for characterizing the Power Management System of Fuel Cell Electric Vehicle is required during development and validation. "Aggregate Sizing" and "Calibration of Power Control Strategy" are significant factors for optimum life of Fuel Cell powered vehicle without compromising performance. Although the former is mainly a design decision, the latter is crucial during testing and validation to achieve the design objective and avoid unwanted failures. Distribution of load catered
Deshmukh, Naval, Ganguly, Sutanu, Sindgikar, Pavan, Jain, Sarika
A Comprehensive Analysis of Sealing Methods for EV Battery Packs: Trends and Insights.2026-26-01801/16/2026
The widespread adoption of electric vehicles (EVs) has brought about unique engineering challenges, particularly in the design of battery packs, which are central to vehicle performance, safety, and longevity. One critical requirement is maintaining ingress protection (IP) ratings of IP67 or higher to safeguard against water and dust exposure. These ratings are essential to ensure compliance with homologation standards and to meet the demands of diverse terrains and operating conditions. Achieving effective sealing of EV battery packs is thus a key aspect of their design and engineering. This study presents an in-depth analysis of sealing technologies employed in EV battery packs, focusing on four primary types: Adhesive-based sealants, Cure-In-Place Gaskets (CIPG), Foam Cut seals, and Rubber Gaskets. Benchmarking data gathered from more than 90 vehicle models across 20+ brands provide insights into adoption trends, historical shifts, evolution and interface requirements of different
Varambally, Vishakha, Sithick basha, Abubakker, Chalumuru, Madhu, Yaser, K U Syed, Sasikumar, K
Assessing the impact of hole piercing edge quality on fatigue samples for S550MC steel sheet.2026-26-03061/16/2026
The exceptional strength, formability, and weldability of S550MC steel sheets make them a cornerstone material in the automotive industry. These properties translate into the creation of high-performance automotive components like chassis parts, structural reinforcements, and safety cages, ultimately contributing to enhanced vehicle safety and overall performance. Furthermore, S550MC steel boasts excellent fatigue resistance, a critical factor for ensuring long-term reliability in demanding automotive applications that experience repeated stress cycles. However, beyond its inherent properties, optimizing the performance of S550MC components hinges on a nuanced understanding of the critical relationship between hole edge quality and fatigue failure. This meticulous study delves into the impact of hole-piercing clearance on the quality of the hole edges in modified fatigue samples manufactured from S550MC steel, and its effect on the fatigue life. The surface morphology was assessed
Nahalde, Sujay, Hingalje, Abhijeet, Ughade, Vikas, Singh, Udita, More, Hemant
Reverse Engineering of CAN (Controller Area network) of an Electric vehicle2026-26-01501/16/2026
The communication between different vehicle electronic controller units (ECU) for various applications (I.e. To propel the vehicle or for In-vehicle Infotainment) CAN (Controller Area Network) is most frequently used for In-Vehicle communication. Given the proprietary nature and lack of standardization in CAN (Controller Area Network) configurations, which are often not disclosed by manufacturers, the process of CAN reverse engineering becomes highly complex and cumbersome. Additionally, the scarcity of publicly accessible data on electric vehicles, coupled with the rapid technological advancements in this domain, has resulted in the absence of a standardized and automated methodology for reverse engineering the Controller Area Network (CAN). This process is further complicated by the diverse CAN configurations implemented by various Original Equipment Manufacturers (OEMs), which are often not disclosed to the public. In this paper the manual approach is taken for reverse engineering
KUMAR, ROHIT, Sahu, Hemant, Penta, Amar, Bhatt, Purvish
Transforming Gear Manufacturing_ A Deep Learning Approach to Quality Prediction2026-26-06461/16/2026
The automotive industry is rapidly transitioning towards Industry 4.0, transforming vehicle manufacturing. To achieve a lower carbon footprint, it is crucial to minimize raw material wastage and energy consumption. Reducing component wastage, lead time, and automating gear manufacturing are key areas. Gear micro-geometry inspection is vital, as variations affect service life and NVH (Noise, Vibration, Harshness). Despite standards for permissible errors, manual evaluation is often needed.This subjective evaluation approach will have a possibility that a gear with undesired variations gets assembled into the product. These issues can be detected during NVH testing, leading to replacement of part and re-assembly thus increasing lead time. This generates a need for an automated system which could reduce the human intervention and perform the activity. This paper discusses methodologies to meet these requirements and achieve desired results. The research aims to develop a deep learning
Ramakrishnan, Gowtham Raj, Baheti, Palash, PR, Vaidyanathan, Durgude, Ranjit, Bathla, Archana, R, Greeshmita, V, Rangarajan
Leveraging Telematics Big Data Analytics for Real-Time Monitoring of Catalytic Converter Failures and Root Cause Identification for Emission Compliance2026-26-05911/16/2026
The automotive industry is continuously evolving at high pace to meet rising customer expectations, reliability, reduced maintenance, and most relevant, compliance with stringent emission norms. Traditionally, the analysis of vehicle emissions relies heavily on periodic inspections and manual checks. These conventional methods are often time-consuming, prone to human error, and lack the ability to provide real-time insights. Also, identifying Catalytic converter failures due to non-manufacturing issues require meticulous physical inspections and historical data reviews, which are not always accurate or timely. Telematics or Connected cars technology being one of the major technological innovations in recent times revolutionizes these processes by enabling real-time data exchange between vehicles and external systems. The current study presents an innovative approach to utilizing telematics data for real-time monitoring of vehicle emissions and pinpointing Catalytic converter failures
DEV, Triyambak, Prasad, Kakaraparti Agam, Kalkur, Varun, Modak, Saikat, AGARWAL, Shashank, Chandra, Animesh, Paul, Varsha, Garg, Amit, Sundararaman, Venkataraman, Bose, Sushant
Regenerative braking control strategy for electric commercial bus under Indian road condition using artificial neural network2026-26-06451/16/2026
The main objective of this research is to create a more efficient regenerative braking control strategy for electric commercial buses operating under Indian road conditions. The strategy uses Artificial Neural Networks (ANNs) to optimize the regenerative braking process. Regenerative braking helps to recover energy that would otherwise be lost during braking and convert it back into usable power for the vehicle. The challenge is to design a system that works effectively on the diverse and often challenging road conditions found in India, such as varying gradients, traffic patterns, and road surface types. This study begins by collecting data (which includes vehicle speed, road gradient, traffic condition, etc.) from real-world driving conditions and aims to train an Artificial Neural Network (ANN) using a large set of driving data collected under various conditions to predict the most efficient regenerative braking settings for different driving scenarios. While the results are
Saurabh, Saurabh, Bhardwaj, Rohit, Patil, Nikhil, Gadve, Dhananjay, Amancharla, Naga Chaithanya
Development of a High-Fidelity Vehicle Dynamics Model with Rear-Wheel Active Roll Control for Improved Stability2026-26-00931/16/2026
This work introduces a unified vehicle dynamics framework that combines detailed modeling and active control to enhance ride comfort and handling stability. A full 12-degree-of-freedom vehicle model is developed to simulate dynamic responses across a range of driving conditions. Unlike conventional approaches, this model incorporates both linear and nonlinear tire behavior, enabling more accurate prediction of vehicle motion. To further improve performance, an active roll control mechanism is proposed, utilizing rear-wheel suspension actuators to counteract undesired body roll and yaw instabilities. A novel co-simulation environment is established by coupling MATLAB/Simulink-based control strategies with high-fidelity multibody dynamics from ADAMS Car. The model is calibrated and validated using experimental data from an instrumented test vehicle. Results show that the integrated control system significantly reduces critical parameters such as roll angle, yaw rate deviation, and
Duraikannu, Dinesh, DUMPALA, GANGI REDDY
A Centralized Framework for Sustaining Safety-Critical Vehicle Systems Through Periodic Servicing2026-26-00031/16/2026
As vehicles are becoming more complex, maintaining the effectiveness of safety critical systems like adaptive cruise control, lane keep assist, electronic breaking and airbag deployment extends far beyond the initial design and manufacturing. In the automotive industry these safety systems must perform reliably over the years under varying environmental conditions. This paper examines the critical role of periodic maintenance in sustaining the long-term safety and functional integrity of these systems throughout the lifecycle. As per the latest data from the Ministry of Road Transport and Highways (MoRTH), in 2022, India reported a total of 4.61 lakh road accidents, resulting in 1.68 lakh fatalities and 4.43 lakh injuries. The number of fatalities could have been reduced by the intervention of periodic services and monitoring the health of safety critical systems. While periodic maintenance has contributed to long term safety of the vehicles, there are a lot of vehicles on the road
HN, Sufiyan Ahmed, Khan, Furqan, Srinivas, Dheeraj
Advanced Driver Assistance for Sunroof Misuse: Alarm Trigger and Speed Limiting System2026-26-00411/16/2026
Sunroof-equipped vehicles are gaining rapid popularity in India, especially among young and urban users. However, unsafe practices like occupants protruding through the sunroof during driving have led to increasing injuries and fatalities, particularly in sudden braking or collisions. This behavior, prohibited under the Motor Vehicles Act, remains an overlooked safety risk in today’s vehicles. This paper presents an industry-first innovation: an Automated Safety Alarm and Speed Control System designed to detect and prevent sunroof misuse. Using integrated photoelectric and infrared beam sensors, the system detects human extension beyond the sunroof boundary while the vehicle is in motion. Upon detection, it triggers a tiered safety response: an immediate dashboard warning, an audible alert if vehicle speed exceeds 15 km/h, and an active speed limiter that restricts vehicle speed to 20 km/h until safe conditions are restored. This marks a shift from passive warnings to active vehicle
Padmanapan, Gopi, Yadav, Sanjeev
Real-Time In-Cabin Vital Sensing and Emergency Response Using Ultra-Wideband Radar in Automotive Environments2026-26-00121/16/2026
This study introduces a novel in-cabin health monitoring system leveraging Ultra-Wideband (UWB) radar technology for real-time, contactless detection of occupants' vital signs within automotive environments. By capturing micro-movements associated with cardiac and respiratory activities, the system enables continuous monitoring without physical contact, addressing the need for unobtrusive vehicle health assessment. The system architecture integrates edge computing capabilities within the vehicle's head unit, facilitating immediate data processing and reducing latency. Processed data is securely transmitted via HTTPS to a cloud-based backend through an API Gateway, which orchestrates data validation and routing to a machine learning pipeline. This pipeline employs supervised classifiers—Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF)—to analyze features such as temporal heartbeat variability, respiration rate stability, and energy distribution patterns
Singh, Samagra, Pandya, Kavita, Jituri, Keerti
Reducing Risk in Safety Critical Systems through Early Safety Case Development and Dialectic Arguments2026-26-00441/16/2026
The rapid evolution of modern automotive systems—powered by advancements in autonomous driving, and connected vehicle technologies— pose fundamental challenges to design and integration. Ensuring the safety of these highly interconnected, software-driven systems, with the widespread reuse of components across platforms. This growing complexity calls for a more structured and rigorous approach to safety assurance than traditional methods. Traditional safety cases tend to take a linear, justification-focused approach that mainly focuses on positive assertions —statements of compliance or success—while giving limited attention to potential weaknesses, or gaps in supporting evidence. This practice may lead to criticism that such arguments are “too positive,” portraying an overly optimistic view of system safety without sufficiently acknowledging areas of unresolved risk. As a result, conventional approaches for developing safety case may overlook complex interactions, assumptions, and
Kumar, Amrendra, Bagalwadi, Saurabh, McMurran, Ross
An Innovative Crash Sensing System Architecture for Robust functionality of Safety restraints in different Challenging Real World Accident scenarios2026-26-00171/16/2026
Title: An Innovative Crash Sensing System Architecture for Robust functionality of Safety restraints in different Challenging Real World Accident scenarios. Authors: Sunil Kovalam, Harihara Sudhan, Praveen Bansode and Sanjay Gaddamwar Mahindra & Mahindra Ltd Abstract: In the emerging markets especially like India and other similar countries, growing traffic density on the roads leading to different kind of accident types including head on collision, rear end collision, side impact collision and accidents involving with pedestrians or cyclists or two wheelers etc. These accidents could be due to over-speeding, distracted driving and inadequate road infrastructure. Providing necessary safety restraints system (Airbags and Seat belts) and its robust functionality in different real world accident scenarios will be a challenging to vehicle manufactures. It is a high time to redefine the traditional crash sensing architecture strategies with logical approach based on the thorough study of
KOVALAM, SUNIL KUMAR
Artificial Neural Network for predicting thermal cool-down behavior of a School Bus Cabin2026-26-06531/16/2026
Passenger vehicles, such as buses, exhibit significant heat absorption when parked under direct sunlight, leading to elevated interior temperatures during daytime and a consequent reduction in thermal comfort. In our previous study, we developed and validated a transient numerical method to predict the thermal behavior of the cool-down process in a school bus cabin. Despite its accuracy, modeling such complex simulations using Computational Fluid Dynamics (CFD) remains time-intensive, with pre-processing taking up to a week and simulations requiring an additional 8-10 hours. While physics-based simulations are known for their accuracy, they are inherently slower. Conversely, data-based meta models offer faster results, but their outputs must be carefully correlated with test data or physics-based model data. This study aims to leverage existing CFD data to train and develop an Artificial Neural Network (ANN) based meta model, which is then benchmarked against test results for
Suryavanshi, Apoorva, sharma, shantanu
Reverse Engineering and Digital Twins in Powertrain Design: Enhancing Performance and Sustainability2026-26-00651/16/2026
This study explores the application of reverse engineering (RE) and digital twin (DT) technology in the design and optimization of advanced powertrain systems. Traditional approaches to powertrain development often rely on legacy designs with limited adaptability to modern efficiency and emission standards. In this work, we present a methodology combining 3D scanning, computational modeling, and machine learning to reconstruct, analyze, and enhance internal combustion engines (ICEs) and electric vehicle (EV) drivetrains. By digitizing physical components through RE, we generate high-fidelity DT models that enable virtual testing, performance prediction, and iterative improvement without costly physical prototyping. Key innovations include a novel mesh refinement technique for scanned geometries and a hybrid simulation framework integrating finite element analysis (FEA) and multi-body dynamics (MBD). Our case study demonstrates a 12% increase in thermal efficiency for a retrofitted ICE
Bernikov, Mark Alexandrovich, Kurmaev, Rinat
Lightweight AI Deployment for Legacy Automotive ECUs: A Practical Optimization Approach2026-26-06631/16/2026
Automotive systems are increasing adopting data-driven and intelligent functionality in the areas of predictive maintenance, virtual sensors and diagnostics. This has led to a need for the AI models to be directly run on vehicle ECUs. However, most of these ECUs – especially those in cost-sensitive or legacy platforms lack the computational capacity and parallel processing support required for standard AI implementations. Given the stringent real-time and reliability requirements in automotive environments, deploying such models presents a unique challenge. This paper proposes a practical methodology to optimize both the training and deployment phases of AI models for low-computation ECUs that operate without parallelism. Designing lightweight model architectures, using pruning and quantization techniques to minimize resource utilization, and putting in place a strategy appropriate for single-threaded execution are the three main objectives of the developed approach. The goal is to
Sharma, Sahil, Mathew, Melvin
Real-Time Lane Change Risk Estimation Using Extended Lane Change Risk Index (ELCRI) and Fault Tree Analysis2026-26-00401/16/2026
Accidents during lane changes are increasingly becoming a problem due to various human based and environment-based factors. Reckless driving, fatigue, bad weather are just some of these factors. This research introduces an innovative algorithm for estimating crash risk during lane changes, including the Extended Lane Change Risk Index (ELCRI). Unlike existing studies and algorithms that mainly address rear-end collisions, this algorithm incorporates exposure time risk and anticipated crash severity risk using fault tree analysis (FTA). The risks are merged to find the ELCRI and used in real time applications for lane change assist to predict if lane change is safe or not. The algorithm defines zones of interest within the current and target lanes, monitored by sensors attached to the vehicle. These sensors dynamically detect relevant objects based on their trajectories, continuously and dynamically calculating the ELCRI to assess collision risk during lane changes. Additionally
Dharmadhikari, Mithil, S, Mrudula, Nair, Nikhil, Malagi, Gangadhar, Paun, Cristin, Brown, Lowell, Korsness, Thomas
Remaining Charging Time Accuracy Improvement through Preconditioning Prediction in Electric Vehicles2026-26-06781/16/2026
Remaining Charging Time (RCT) is an essential metric that is displayed to the customer/driver while charging the vehicle. It tells the driver how much time is remaining for the vehicle to be charged to 100% Display State of Charge (SOC) or the target SOC set by the driver. This metric helps drivers plan their time and decide when to unplug their device. For large battery systems like electric vehicles, the understanding of how much longer the battery needs to charge can help optimize energy use and avoid overcharging. The avoidance of un-necessary prolonged charging affects the long-term health of the battery. The RCT is calculated using a basic formula as the ratio of total energy needed to charge the battery to target SOC and the power pushed into the battery for charging. In this RCT formula numerator for a battery to be charged to a specific target SOC is fixed, but the denominator depends on lot of factors during charging and Battery Preconditioning is one of those factors that
Jawed, Shahid, Srikantaiah, Sudarshan
Experimental Correlation of Virtual and Physical Testing for Regulatory ADAS Features: A Roadmap towards Fully Virtual ADAS Validation”2026-26-00481/16/2026
Validation of Advanced Driver Assistance Systems (ADAS), under global regulations is primarily anchored to vehicle tests. While possessing accuracy, validation through physical means is costly, slow, and not scalable in the variety of situations. The development of high-fidelity simulation environments and advanced sensor modelling presents the possibility of shifting regulatory testing for ADAS to virtual platforms. This paper describes an experimental analysis conducted with the aim of full virtual homologation by correlating real world scenarios, test scenarios and virtual vehicle simulated results for the most important ADAS regulatory features. A calibrated vehicle simulator with embedded sensor and actuator models was used to create standard ADAS test scenarios, which were then validated against data collected from a proving ground. Comparison benchmarks included response time for brake application, inter-vehicle spacing, lateral travel deviation, activation of the system, and
Saraswat, Shubham, Vishe, Prashant, Singh, Jitender
Advancing Sustainable Mobility: Transitioning to Zero-Emission Electric Vehicles with BEVs and FCVs2026-26-01641/16/2026
The transportation and mobility sector is undergoing a profound transformation, with a growing emphasis on sustainability and minimizing the environmental impact of transportation. Among the most significant trends is the transition to electric vehicles (EVs) in the form of Battery and Fuel cell, which produce zero emissions without any harmful gases release in nature. This review highlights several infrastructure-related issues and critical factors that could drive India's transportation sector toward adopting electric vehicles. It also delves into the fundamental understanding of e-mobility, shedding light on the daily challenges and barriers it faces. Furthermore, the study explores research aspects, including the strategies, methods, and tools used for electric vehicles to complete the research on Battery electric vehicles (BEV) and also comparative analysis with Fuel cell vehicles (FCVs). The shift BEVs has been driven by decreasing battery costs and advancements in charging
Kumar, Dr. Vijay Bhooshan
A procedure for validation of full vehicle CAE models with physical tests for passive safety applications2026-26-04111/16/2026
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
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