Automotive Research Association of India (ARAI) is co-operative industrial research association by the automotive industry with the Ministry of Industries, Government Of India The objectives of the Association are Research and Development in automotive engineering for industry, product design and development, evaluation of automotive equipment and ancillaries, standardisation, technical information services, execution of advanced courses on the application of modern technology and conduct of specific tests.The Automotive Research Association of India, (ARAI) is located in the western part of Pune, Maharashtra. The 105 hectares (260 acres) of land houses various laboratories, test facilities spread over 8343 m2 of area. It is well connected by air, rail and road about 25 km from Pune Airport and 12 km from the railway station. The Institute has been set up by the Indian Vehicle and automotive ancillary manufacturers and the (Government of India), Ministry of Industry, as a co-operative industrial research body to provide services to the industry in the fields of applied research and product development in automotive engineering.It is also responsible for car mileage figure for every car sold in India. ARAI claims to be the first Indian institute to develop HCNG fuel engine.
This study reports the fabrication of multifunctional, fully bio-based shape memory polymer (SMP) composites reinforced with cellulose nanocrystals (CNCs) and lignin nanofibers for sustainable energy harvesting applications. Polycaprolactone (PCL) and polylactic acid (PLA) matrices were reinforced with 1–10 wt.
The escalating dependence of Autonomous Vehicles on Intelligent Transportation Systems (ITS) has highlighted the imperative for comprehensive security protocols to safeguard such vehicles against cyber threats. Intrusion Detection Systems (IDS’s) are pivotal in ensuring the protection of these systems by detecting and alleviating unauthorized access and nefarious activities. The German Traffic Sign Recognition Benchmark (GTSRB) database, which encompasses an extensive compilation of traffic sign imagery, functions as a vital asset for the advancement of machine learning-based IDS. This research elucidates an intrusion detection system (IDS) that employs machine learning algorithms to scrutinize the GTSRB database. The proposed IDS emphasize the preprocessing of the GTSRB dataset to extricate pertinent features that can be employed for the training of machine learning models. Research also focuses on model development with machine learning algorithms to classify traffic signs and discern anomalies suggestive of potential intrusions. The efficacy of the models is evaluated utilizing accuracy thereby ensuring that the IDS can consistently differentiate between benign and malicious activities. This inquiry contributes to the domain of intelligent transportation systems by establishing a resilient framework in autonomous vehicles for intrusion detection, thus bolstering the security of automated traffic management systems against prospective cyber threats. The results underscore the criticality of incorporating machine learning methodologies in real-time systems to proactively mitigate security vulnerabilities and preserve the integrity of traffic data.
Biodiesel (BD) blends are emerging as vital renewable substitutes for conventional diesel worldwide, with Indonesia pioneering B35 adoption, followed by the United States (B20), Brazil (B13), and numerous countries implementing B5–B10 blends. In India, dependence on imported fossil fuels and the environmental hazards caused by stringent emissions underscore urgent sustainability needs. This research evaluates soybean-derived BD blends, specifically SBD20D80 and SBD30D70, in a 250 kVA rating power-generating set (genset) diesel engine to optimise performance metrics and emission profiles compared to baseline diesel. Experiments were performed as per ISO 8178; D2 5-Mode cycle at a fixed 1500 rpm speed, incorporating engine performance, emissions, and endurance assessments. The engine out emissions and the emissions downstream of the aftertreatment system are measured and compiled. Specific experimental and statistical procedures, as described in existing literature, have been addressed to ensure robustness across studies. Key findings show that NOX, HC, CO, PM, smoke, CO2, and NH3 emissions all complied with stringent CPCB IV+ regulations. Notably, SBD20D80 and SBD30D70 blends exhibited pronounced reductions in HC, CO and smoke relative to diesel, with a marginal increase in NOX and NH3. Furthermore, the engine sustained consistent performance output and reliability throughout a rigorous 500-hour durability test using SBD30D70 fuel. The outcomes highlight soybean biodiesel's potential to contribute to sustainable energy solutions while promoting cleaner genset engine applications.
Curtain airbags are the most effective protective systems to prevent severe/fatal head injuries in side collisions with narrow objects such as poles or trees. One of the important parameters of curtain airbags is the inflated zone i.e. the coverage area of the airbag, which decides the extent of head protection for occupants with different anthropometries in different seating rows. EuroNCAP first introduced the concept of Head Protection Device Assessment (HPDA) in 2015., In addition to the performance requirements in the dynamic test, EuroNCAP started assessing the deployed curtain airbag/s for its area coverage and verification of inflated zones for various anthropometries over occupant rows. In India, there is now a near total adoption of curtain airbags as standard fitment by the OEMs. Further, introduction of Bharat NCAP (BNCAP), a Perpendicular Pole Side Impact test is conducted for assessing the effectiveness of curtain airbags in a dynamic test, but currently, does not perform the HPD assessment. The paper studies the Head Protection Device/Curtain Airbags offered in Passenger Vehicles in India w.r.t. the Head Protection Device Geometric Assessment (HPDA). This assessment analyses the effectiveness of curtain airbags present in Indian passenger vehicles for protection of occupants of different anthropometries. The study is conducted on the vehicles that have been tested at ARAI and are also currently under sale in the Indian market.
This paper presents an innovative in-lab accelerated testing approach for chassis-mounted components, with a particular focus on the cooling module of commercial vehicles. The proposed method simulates real-time data acquired from field operations and replicates all critical chassis modes, including torsion. Additionally, real-time coolant circulation at specified pressure and temperature maintenance are feasible during durability testing, enhancing the realism of the test environment. The cooling modules, comprising the radiator, intercooler, and charge air cooler (CAC), often experience failures due to various multi-axial inputs and chassis modes. This paper introduces an innovative methodology for replicating field conditions in the lab, utilizing seven servo-hydraulic actuators to simulate multi-axial inputs. The accuracy of in-lab simulation for the acceleration levels at input and response locations of the cooling module exceeds 90%. This makes it a preferred choice for test engineers to simulate field failures or validate designs well in advance of final production, thereby avoiding issues at later stages of vehicle launch. This innovative approach offers flexibility to accelerate the test duration while ensuring the retention of over 90% of the damage observed in real-world conditions. By utilizing the same chassis frame and mounting locations, the test maintains consistent boundary conditions, providing reliable and accurate results. This method significantly enhances the efficiency and effectiveness of testing processes for commercial vehicle components, ensuring robust and reliable performance. [4]
