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This SAE Aerospace Recommended Practice (ARP) discusses design philosophy, system and equipment requirements, environmental conditions, and design considerations for rotorcraft environmental control systems (ECS). The rotorcraft ECS comprises that arrangement of equipment, controls, and indicators which supply and distribute dehumidified conditioned air for ventilation, cooling and heating of the occupied compartments, and cooling of the avionics. The principal features of the system are: a A controlled fresh air supply b A means for cooling (air or vapor cycle units and heat exchangers) c A means for removing excess moisture from the air supply d A means for heating e A temperature control system f A conditioned air distribution system The ARP is applicable to both civil and military rotorcraft where an ECS is specified; however, certain requirements peculiar to military applications—such as nuclear, biological, and chemical (NBC) protection—are not covered. The integration of NBC
AC-9 Aircraft Environmental Systems Committee
The intent of this report is to encourage that the thermal management system architecture be designed from a global platform perspective. Separate procurements for air vehicle, propulsion system, and avionics have contributed to the development of aircraft that are sub-optimized from a thermal management viewpoint. In order to maximize the capabilities of the aircraft for mission performance and desired growth capability, overall system efficiency and effectiveness should be considered. This document provides general information about aircraft Thermal Management System Engineering (TMSE). The document also discusses approaches to processes and methodologies for validation and verification of thermal management system engineering. Thermal integration between the air vehicle, propulsion system, and avionics can be particularly important from a thermal management standpoint. Due to these factors, this report is written to encourage the development of a more comprehensive system
AC-9 Aircraft Environmental Systems Committee
This SAE Aerospace Recommended Practice (ARP) defines recommended analysis and test procedures for qualification of pneumatically, electrically, manually, and hydraulically actuated air valves. They may be further defined as valves that function in response to externally applied forces or in response to variations in upstream and/or downstream duct air conditions in order to maintain a calibrated duct air condition (e.g., air flow, air pressure, air temperature, air pressure ratio, or air shutoff). Qualification testing performed on the airplane to verify compatibility of the valve function and stability as part of a complete system is outside the scope of this document. Refer to ARP1270 for design and certification requirements for cabin pressurization control system components. As this document is only a guide, it does not supersede or relieve any requirements contained in detailed Customer specifications.
AC-9 Aircraft Environmental Systems Committee
This specification covers a corrosion-resistant steel in the form of investment castings homogenized and solution and precipitation heat treated to 180 ksi (1241 MPa) tensile strength.
AMS F Corrosion and Heat Resistant Alloys Committee
This study discusses the generalized workflow and design techniques for detecting radiated emissions from vehicle electronic systems to ensure an electromagnetic compatible (EMC) vehicle specified by radiated emission standards such as CISPR-12 and CISPR-25. In this work, CST studio suite software is used to examine the vertical polarization in an E vehicle. The results of the radiated emission are plotted as dBμV/m vs Hz to understand the radiation effects generated by different electronic devices across different frequencies. The discussed method serves as a guide for forming a virtual electromagnetic environment where a real vehicle is simulated to study the interference effects and design a suitable filter to reduce the effect of EMI.
Manuelraj, MasilamaniPrasad, SuryanarayanaNarayanan, Siva Suriya
The automotive industry produces a vast amount of multilingual textual data ranging from technical manuals to diagnostic reports that demand efficient summarization and reliable semantic reasoning. At present, the traditional large language models (LLMs) operating at the token level struggle not only with cross-lingual understanding and domain-specific reasoning but also are prone to hallucinations, leading to inaccurate insights and responses [2, 5]. This paper introduces a Unified Concept Model (UCM) architecture for the automotive domain that processes language at the concept level using multilingual, modality-agnostic embeddings, enabling coherent cross-lingual summarization and reasoning. The UCM encodes entire sentences as semantic vectors by leveraging the SONAR embedding space, a multilingual, modality-agnostic sentence representation that supports over 200 languages. This approach to encoding facilitates a deeper understanding across language boundaries and complex technical
Singh, SamagraRavi, UtkarshVikram, PrateekShenoy, LakshmiAwasthi PhD, Anshuman
Software Defined Vehicles (SDV), Software Defined Networks (SDN), Software Defined (Power) Grids (SDG) are just a few examples of how the Software Defined Transformation is unfolding across many industries today (collectively being referred to as Software Defined X – SDX). This paper defines a maturity model for Software Defined Transformation and evaluates different industries including Automotive on their evolution so far. This cross-industry view of SDX helps in analyzing where SDV’s could be headed. A 2020 paper [1] lays out the complexity of the automotive software, with companies pursuing several directions in this transformation. The automotive industry has not yet reached a consensus on the direction it is taking on SDV. While companies like Tesla are already making software centric cars, traditional OEMs like General Motors, Toyota, Ford etc. are making huge investments and redefining their business models, tech stacks and operations to leverage the power of software. There is
Mathur, Akshay RajMisra, AmitMakam, Sandeep
Edge Artificial Intelligence (AI) is poised to usher in a new era of innovations in automotive and mobility. In concert with the transition towards software-defined vehicle (SDV) architectures, the application of in-vehicle edge AI has the potential to extend well beyond ADAS and AV. Applications such as adaptive energy management, real-time powertrain calibration, predictive diagnostics, and tailored user experiences. By moving AI model execution right into edge, i.e. the vehicle, automakers can significantly reduce data transmission and processing costs, ensure privacy of user data, and ensure timely decision-making, even when connectivity is limited. However, achieving such use of edge AI will require essential cloud and in-vehicle infrastructure, such as automotive-specific MLOps toolchains, along with the proper SDV infrastructure. Elements such as flexible compute environments, deterministic and high-speed networks, seamless access to vehicle-wide data and control functions. This
Khatri, SanjaySah, Mohamadali
High energy impact testing using free fall mass is a crucial method for evaluating the structural integrity, and safety performance of automotive components subjected to sudden impact forces. This study focuses on assessing critical parts such as wheel rims, suspension knuckles, commonly exposed to unintentional impacts during vehicle operation, maintenance, or collisions. The test involves dropping a standardized mass from predetermined heights onto the component to simulate real-world impact scenarios. Key performance indicators include deformation, crack propagation, fracture resistance, and energy absorption capacity. Wheel rims and knuckles are evaluated for their ability to maintain structural integrity under localized impact without compromising vehicle handling or safety. Seats and related interior structures are tested to ensure occupant protection during crash-like events. Other components, such as brackets, mounts, or housings, are included based on functional criticality
Roham, PrasadBagade, MohanSinnarkar, NitinPawar, Prashant RShinde, Vikram
Artificial Intelligence (AI) is radically transforming the automotive industry, particularly in the domain of passenger vehicles where personalization, safety, diagnostics, and efficiency. This paper presents an exploration of AI/ML applications through quadrant of the key pillars: Customer Experience (CX), Vehicle Diagnostics, Lifecycle Management, and Connected Technologies. Through detailed use cases, including AI-powered active suspension systems, intelligent fault code prioritization, and eco-routing strategies, we demonstrate how AI models such as machine learning, deep learning, and computer vision are reshaping both the user experience and engineering workflow of modern electric vehicles (EVs). This paper combines simulations, pseudo-algorithms and data-centric examples of the combined depth of functionality and deployment readiness of these technologies. In addition to technical effectiveness, the paper also discusses the challenges at field level in adopting AI at scale i.e
Hazra, SandipTangadpalliwar, SonaliKhan, Arkadip
The explosive growth of electric vehicles (EVs) calls forth the need for smart battery management systems that can perform health monitoring and predictive diagnostics in real-time. The conventional battery modelling methods mostly do not cover the complicated, dynamic behaviors coming from different usage patterns. The study outlines a structure that would use Reinforcement Learning (RL)-based AI agent as a part of the Battery Electrical Analogy (BEA) simulation platform. With the help of the AI agent, different health parameters such as State of Health (SOH), State of Charge (SOC), and the signs of early thermal runaway can be predicted in real-time. The suggested design takes advantage of the simulation-based approach to have the agent learn and utilizes a decentralized cloud architecture suitable for scaling and reducing the response time. The RL agent performs an essential role in the process by tagging along with the continuous learning and the adjustment of the battery
Pardeshi, Rutuja RahulKondhare, ManishSasi Kiran, Talabhaktula
As automotive headlamp serves Active Safety functions, it must comply the functional and performance requirements as per regulatory standards across various geographies like AIS (Automotive India Standards), FMVSS (Federal Motor Vehicle Safety Standards), ECE (Economic Commission of Europe) etc. The process of validating headlamp levelling compliance as per regulatory standards involves physical testing with various vehicle loading conditions. This traditional method is labor-intensive, time-consuming, and consumes significant resources. There is a need for a predictive solution that can simulate and validate headlamp levelling tests virtually, thereby reducing dependency on physical trials. Headlamp levelling compliance is a critical regulatory requirement to ensure optimal visibility and safety under varying vehicle loading conditions. This paper presents an Artificial Intelligence and machine learning-based (AI/ML) solution to simulate headlamp levelling tests virtually/digitally by
Mandloi, PrinceJoshi, Vivek S.GHANWAT, HEMANTUgale, AnandMunda, RohitGHAN, PRAVIN
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 and ML technologies can enhance the efficiency of engine reliability testing. The paper includes the following case studies:
Yadav, Sanjay KumarKumar, PrabhakarR, DineshJoon, SushantRai, AyushTripathi, Vinay Mani
This paper presents a comprehensive numerical methodology for simulating the coupled process-structure behavior of short glass fiber-reinforced, injection-molded thermoplastics. The approach integrates elastoplastic and anisotropic material characteristics using three engineering tools: Moldflow, Digimat, and ABAQUS. It accounts for fiber orientation and injection molding defects, linking to thermo-mechanical performance. This method enables accurate virtual modeling of real-time injection-molded components by transferring anisotropic data from Moldflow to ABAQUS. In this study, short fiber orientation and potential injection molding defects such as weld lines and residual stresses are discussed using Moldflow simulation. Besides, Digimat is employed as an interface tool to facilitate the transfer of Moldflow simulation results, namely fiber orientation and material behavior in the allied configurations directly into ABAQUS. This integration enables the evaluation of thermo-mechanical
T, KalingaYanamadala, Dharma TejaMattupalli, VenkataChirravuri, BhaskaraMiller, Ronald
The Vehicle software is moving towards software-centric architectures and hence software-defined vehicles. With this transition, there is a need to handle various challenges posed during development and validation. Some of the challenges include unavailability of hardware limiting the evaluation of various hardware options, board bring-up and hence leading to delays in software development targeted for the hardware, eventually leading to delayed validation cycles. To overcome the above challenges, we present in this whitepaper a virtual ECU (vECU) framework integrated with a CI/CD pipeline. A Virtual ECU (Electronic Control Unit) is a software-based emulation of a physical ECU. The adoption of virtual ECUs empowers development teams to commence software development prior to the availability of physical hardware. Multiple tools are available to demonstrate virtual ECUs, for example, QEMU, Synopsys, QNX Cabin, etc. vECU setup, when paired with a CI/CD pipeline, allows continuous
Singh, JyotsanaShaikh, ArshiyaMane, RahulBurangi, Piyush
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, GopiYadav, Sanjeev
The Objective is to develop a testing load case which can assess vehicle electric parking brake (EPB) performance and durability at vehicle level in different project development phases. In current scenario the EPB become one of a primary feature available in many passenger vehicles helps customers to apply this secondary braking system to hold the vehicle when parked. So, it is particularly important to evaluate this feature close to RWUP for the vehicle service life and studying the result before vehicle launch. The test method should be capable of capturing failures related to physical concerns, electrical characteristics, actuation time, gradient vehicle hold, effectiveness during vehicle running and durability. The most important challenge in this test method development is it should simulate the actual sequence followed by user in field on vehicle. A completely automated test set up integrating PLC and COBOT with closed loop feedback developed and discussed in this paper. During
Dhanapal, M RVijayakumar, NarayananMahesh, BB, VenkatasubramanianArthanathan, Sankaranarayanan
Bogie suspension systems are becoming increasingly popular in tipper vehicles to enhance their performance and durability, especially in demanding environments like construction and mining areas [1]. Bolsters contribute significantly to the overall performance and durability of the bogie suspension systems of tipper vehicles by evenly distributing the loads across the whole suspension system. They act as shock absorbers and negate the impact caused by the rough terrains and heavy loads, thereby reducing stress on individual components and maintaining the structural integrity of the vehicle. Bolsters also help in improving the ride comfort and to maintain the position of the suspension system [2]. This study focuses on the comprehensive testing and evaluation of bolsters to understand their modes and displacement data derived from field data. The primary objective is to analyse the performance and behaviour of bolsters under various operational conditions. Critical manners of
V Dhage, YogeshKolage, Vikas
The distribution of mobility equipped with electrified power units is advancing towards carbon-neutral society. The electrified power units require an integration of numerous hardware components and large-scale software to optimize high-performance system. Additionally, a value-enhancement cycle of mobility needs to be accelerated more than ever. The challenge is to achieve high-quality performance and high-efficient development using Model-Based Development (MBD). The development process based on V-model has been applied to electrified power units in passenger vehicle. Traditionally, MBD has been primarily utilized in the left bank (performance design phase) of the V-model for power unit development. MBD in performance design phase has been widely implemented in research and development because it refines prototype performance and reduces the number of prototypes. However, applying the MBD to an entire power unit development process from performance design phase to performance
Ogata, KenichiroKatsuura, AkihiroTsuji, MinakoMatsumoto, TakumiIwase, HiromuNakasako, SeiyaTakahata, Motoki
In the pursuit of environmental sustainability and cleaner transportation, the global automotive industry is expediting transformation. This paper utilized multi-decade data spanning from 1975 to 2024, for the development of predictive models for fuel economy and CO₂ emissions across a wide range of vehicle technologies from 2026 - 2050. This is done with the help of advanced machine learning algorithms like Linear and Random Forest Regression in Python and integrating insights through Power BI visualizations, the project identifies key correlations between vehicle attributes such as weight, powertrain, and footprint and their environmental performance. Results highlight the increasing impact of electric vehicle adoption, hybridization, and light weighting on overall emissions reduction. These insights help forecast the direction of fuel economy standards, emission patterns, and technology shifts across manufacturers and vehicle types. Beyond technical predictions, the study offers a
Hazra, SandipTangadpalliwar, SonaliHazra, Sanjana