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This project was undertaken with an objective to develop methodology by formulating set of procedures that would help in achieving the end goal. Once methodology is established, it paves way to optimize the end results more effectively which results in reduced lead time during product development. Methodology can either be based on pure experimental investigations or by simulations. Combination of mathematical and empirical approach is inherently followed in simulations, which helps in reducing the testing time and overall cost. Commercial vehicles (CV) have seen paradigm shift in the fuel consumption (FC) certification approaches, with an intention to align with 2016 Paris climate agreement. Use of simulation tool like VECTO for commercial vehicle FC certification has gained momentum in Europe. Overall experience gained in commercial vehicle FC simulation has motivated us to leverage the learnings for off-road applications like agricultural tractors.

The Indian government has been promoting electric vehicles through various policy initiatives, such as offering incentives and subsidies to EV manufacturers and consumers, establishing charging infrastructure across the country, and setting ambitious targets for EV adoption. These measures aim to reduce the dependence on imported fossil fuels, mitigate air pollution, and promote sustainable mobility. As a result, the demand for electric vehicles across India is steadily increasing, and the country is emerging as a lucrative market for EVs globally. An electronically commuted brushless DC (BLDC) motor usually functions for traction in electric two-wheelers. Electric vehicles need to comply with electromagnetic compatibility (EMC) requirements. During the EMC compliance of electric two-wheelers, it is certain that the BLDC motor and its controller play an important role.

The Light commercial vehicle (LCV) is primarily used for the last mile delivery and it hold the volume share of around 61% in the commercial vehicle segment. The last mile delivery services have seen a massive surge after the CoVID 19 pandemic resulting is the increase sale of LCV in last few years and is expected to grow further by 8-11% in the coming years. However, city logistic is also responsible for most pollution and noise in the city. Hence, policymakers are aiming to reduce carbon footprint by promoting the use of Electric vehicle by providing incentive to automakers though schemes like FAME I and FAME II. In order to effectively reduce the carbon footprint within city it is important to increase the use of new electric vehicle and convert the old polluting vehicles to electric. Hence, a retro fitment solution for converting used LCV to electric can help in reducing emission as well as noise pollution. Later the same solution can be offered as OE fitment solution.

Due to the transformation of the automotive industry from conventional vehicles to electric vehicles, new challenges have emerged concerning Electromagnetic Compatibility. Though the Radiated Emission limits in global regulation are the same for both types of powertrains of vehicles, however, due to the phenomena of conversion of high voltage to low voltage, rapid charging/discharging, and different components involved in electric powertrain, the Radiated Emission from electric vehicles give a strikingly different trend which is challenging to combat. When compared with the conventional Spark Ignition vehicle, many other electronic components of the electric vehicle stay in the mode of Power ON while in the “Ignition ON” steady state. This resulted in us observing a significant shift in the amplitude and frequency throughout the frequency band of Radiated Emission measurement.

Researchers are under pressure to investigate and discover ways to improve the efficacy and reduce emissions from ICE due to the depletion of energy resources and the growing concern over global warming. Hydrogen is viewed as a promising fuel and has been investigated as a potential fuel in combustion because to several desirable qualities like carbon-less content and strong flammability limitations. When equated to other alternative fuels like LPG, CNG, LNG, etc., hydrogen has inimitable qualities because it lacks carbon, making it one of the promising alternatives fuels. In order to achieve zero CO2 emissions for traffic applications in the near future, hydrogen being an automotive fuel in ICE is a solution. The ICE powered by hydrogen is prepared for that. The actual drawbacks of using hydrogen in ICE generally are manufacturing, storage, and development of the requisite infrastructure. Hydrogen can be produced in its many forms.

India is one of the largest markets for the automobile sector and considering the trends of road fatalities and injuries related to road accidents, it is pertinent to continuously review the safety regulations and introduce standards which promise enhanced safety. With this objective, various Advanced Driver Assistance Systems (ADAS) regulations are proposed to be introduced in the Indian market. ADAS such as, Anti-lock Braking Systems, Advanced Emergency Braking systems, Lane Departure Warning Systems, Auto Lane Correction Systems, Driver Drowsiness Monitoring Systems, etc., assist the driver during driving. They tend to reduce road accidents and related fatalities by their advanced and artificial intelligent fed programs. This paper will share an insight on the past, recent trends and the upcoming developments in the regulation domain with respect to safety.

Autonomous Emergency Braking (AEB) systems play a critical role in ensuring vehicle safety by detecting potential rear-end collisions and automatically applying brakes to mitigate or prevent accidents. This paper focuses on establishing a framework for the Verification & Validation (V&V) of Advanced Driver Assistance Systems (ADAS) by testing & verifying the functionality of a RADAR-based AEB ECU. A comprehensive V&V approach was adopted, incorporating both virtual and physical testing. For virtual testing, closed-loop Hardware-in-Loop (HIL) simulation technique was employed. The AEB ECU was interfaced with the real-time hardware via CAN. Data for the relevant target such as the target position, velocity etc. was calculated using an ideal RADAR sensor model running on the real-time hardware. The methodology involved conducting a series of test scenarios, including various driving speeds, obstacle types, and braking distances.

The paper talks about Quantification of Alertness for vision based Driver Drowsiness and Alertness Warning System (DDAWS). The quantification of alertness, as per Karolinska Sleepiness Scale (KSS), reads the basic input of facial features & behaviour recognition of driver in a standard manner. Although quantification of alertness is inconclusive with respect to the true value, the paper emphasised on systematic validation process of the system covering various scenarios in order to evaluate the system’s functionality very close to the reality. The methodology depends on definition of threshold values of blink and head pose. The facial features are defined by number of blinks with classification of heavy blink and light blink and head pose in (x, y, z) directions. The Human Machine Interface (HMI) warnings are selected in the form of visual and acoustic signals. Frequency, Amplitude and Illumination of HMI alerts are specified.

Advanced Driver Assistance Systems (ADAS) play a crucial role in enhancing road safety by providing intelligent assistance to drivers. To ensure the reliability and effectiveness of ADAS functions, rigorous verification and validation processes are necessary. One critical aspect of this process is scenario generation, which involves creating diverse and representative driving scenarios for testing and evaluating ADAS functions. This paper proposes a novel approach for synthetic scenario generation specifically tailored for Indian road conditions. The approach leverages real-time road data collected from various sources, including camera sensors, Lidar sensor, GPS devices, and traffic monitoring systems. The collected data is processed and analyzed to extract relevant information, such as road geometries, traffic patterns, and environmental conditions.

One of the primary reasons for road accidents is driving while distracted or drowsy. Often, long and monotonous road journeys lead to distracted or drowsy driving. Therefore, there is a need for a system which alerts a distracted or drowsy driver. Moreover, as the levels of autonomy move beyond SAE Level 2, the system assumes a larger share of the dynamic driving task. Under challenging circumstances, the system might ask the driver to take back vehicle control. To guarantee safety, it’s crucial to monitor the driver’s condition in order to assess their readiness to regain control of the vehicle. An advanced safety feature known as a driver monitoring system (DMS), sometimes referred to as a driver state sensing (DSS) system, is designed to monitor a driver’s attentiveness and alertness, providing warnings or alerts to refocus their attention on driving when drowsiness or distraction is detected.

The low running cost of electric two wheelers is resulting in high popularity and growing market share. The electric two wheelers are generally considered silent. However, the tonal nature of radiated noise is annoying for both driver and pedestrians in electric vehicles. This study focuses on electric two-wheeler with annoyance issue. The customer complained about non-linear noise with respect to speed and also whining during loading and coasting. The detailed Noise Source Identification (NSI) revealed that the noise is due to motor and transmission whine and structural resonances of chassis excited by this motor excitation during runup and gear whine orders during coasting when excitation by motor is absent. The motor mounting and chassis are structurally modified with suitable stiffeners and damping based on the results of modal analysis. The transmission gear geometry and contact pattern are optimized for transmission error reduction.

Battery is one of the safety critical systems in EV. As the number of EVs increases, battery safety becomes an important task to avoid any mishap during its use, as even small accidents may slow down the adaptation of EVs. Automotive environment being one of the harshest operating environments, it is important to ensure both mechanical and electrical safety of the battery pack. Li-Ion batteries are most popular among traction batteries, due to their high energy density, long life, and fast charging capabilities. But mechanical damage, over temperature, short-circuit, etc. may lead to battery thermal runaway, causing a major accident. Mechanical abuse of battery can be one of the reasons that may lead to the damages mentioned above, eventually causing thermal runaway in batteries. That’s why all major battery safety standards have requirements for vibration and mechanical shock tests.

A bus is integral part of public transportation in both rural and urban areas. It is also used for scheduled transport, tourism, and school transport. Buses are the common mode of transport all over the world. The growth in economy, the electrification of public transport, demand in shared transport, etc., is leading to a surge in the demand for buses and accelerating the overall growth of the bus industry. With the increased number of buses, the issue of safety of passengers and the crew assumes special importance. The comfort of driver and passenger in the vehicle involves the vibration performance and therefore, the structural integrity of buses is critically important. Bus safety act depicts the safety and comfort of bus operations, management of safety risks, continuous improvement in bus safety management, public confidence in the safety of bus transport, appropriate stakeholder involvement and the existence of a safety culture among bus service providers.

Computer-aided engineering (CAE) is a routinely used technology for the design and testing of road vehicles, including the simulation of their response to an impact. To increase automotive industry competitiveness by reducing physical test-based type approval and to improve road safety, recent initiatives have been taken by both industry and public authorities to promote the use of virtual testing through numerical simulation as an alternative way to check regulatory compliance. [1] To ensure acceptance of this alternative method, the accuracy of the simulation models and procedures needs to be assured and rated independently of the modelling process, software tools, and computing platform. Similarly, it is also imperative to understand the uncertainties emerging out of different component design parameters and analyze their sensitivity towards producing deviations in the reported results as per the requirements of the regulatory standard.

The Indian automotive industry is striving towards more safe and durable vehicles. A need was felt to study the effect of changes in axle static loads on fatigue life of the axle components. Also, there was a need to develop generic test method, as there are no test standards or generic methods available in public domain for fatigue testing of commercial vehicle axles. The study was carried out to check direct effect of change in axle loads on various connections on axle, effect of suspension configuration and force distribution, Vehicle dynamics, etc. In this paper, an India specific generic load spectra was evaluated for accelerated laboratory validation. Paper discusses the methodology as; study of heavy commercial vehicle systems, road load data collection on identified test vehicles w.r.t. test matrix finalized, India specific test loads and load spectra development, normalization of axle load spectra w.r.t to static axle weights and arriving at test guidelines.

The term Industry 4.0 is well known in contemporary automotive landscape. It encompasses a smart integrated framework of IIoT (Internet of Things) and industrial automation with machine learning, artificial intelligence and big data analytics to arrive at optimal solutions to running the processes in a streamlined, efficient and effective manner. Industry 4.0 has assumed critical significance in the contemporary era of people working from remote locations to operate processes in order to build products, thereby ensuring business continuity. Consequently, it follows that if industry 4.0 is applied to automotive homologation activity, it will lead to a standardized evaluation, consistent fidelity of testing, accurate judgement of the product under test with regards to its certification, and most importantly, timed delivery to release in the market. The author hereby elucidates a unified Industry 4.0 Framework for Automotive homologation in India which is the need of the hour.

Wheel rims and wheel hub bearings are critical components of Heavy Commercial Vehicle (HCV) suspension systems and are subjected to extensive fatigue loading throughout their operational life. Actual loading conditions on wheels are a combination of radial loads (vertical loads) and cornering loads (lateral loads) acting simultaneously and are directly influenced by payload and road conditions. Currently for Indian usage, there are test guidelines [1] only for separate uniaxial Radial Fatigue Test (RFT) and Cornering Fatigue Test (CFT) for wheel rims which might not represent realistic combined loading conditions, and no generic guidelines are available for testing of wheel hub bearings.

The Construction & Mining field is continuously upgrading, reshaping under the stimulus of technical enhancement. India is considered one of fastest growing country in the word. Requirement for Construction Equipment Vehicles in India is continuously growing due increased rate infrastructure development. To promote development of the Construction Equipment Vehicles (CEV’s) manufacturing sector it was also necessary to build a new governance architecture. Every vehicle plying on road has to comply with Central Motor Vehicle Regulatory requirements as per CMVR act 1989. Earlier 2021 CEV’s were required to go through performance trials like brake, steering effort, turning circle measurement, speedometer calibration as dynamic tests as per regulations.

With the recent development in virtual modelling and vehicle simulation technology, many OEM’s worldwide are using digital road profiles in virtual environment for vehicle durability load prediction and virtual design evaluation. For precise simulation results, it is important to have the tire digital twin which is the realistic representation of tire in the virtual environment. The study comprises of discussion about different types of tire models such as empirical, solid model, rigid ring model and flexural ring models such as Pacejka, MF Swift, CD tire, F tire etc. and also the complexity involved in development of these tire models. Generation of virtual tire model requires highly sophisticated test rigs as well as vehicle level testing with Wheel Force transducers and other vehicle dynamics sensors. The large number of data points generated with testing are converted in standard TYDEX format to be further processed in various software tool for virtual model generation.

The Indian market for battery-powered electric vehicles (xEV) is growing exponentially in the coming years, fueled by tumbling lithium-ion battery prices and favorable government policies. Lithium-ion battery is leading in clean mobility ecosystem for electric vehicles. LiBs efficient and safe performance for tropical climatic conditions is one of the primary requirements for xEV to succeed in India. The performance of LiBs, however, is impacted due to ambient temperature as well as the heat generated within cell due to the load cycle electrochemical reaction. The acceptable operating temperature region for LiBs normally is between 20 °C to 45 °C and anything outside of this region will lead to degradation of performance and irreversible damages. Therefore, understanding the thermal behavior is very crucial for an efficient battery thermal management.