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Leaf Springs are commonly used as a suspension in heavy commercial vehicles for higher load carrying capacity. The leaf springs connect the vehicle body with road profile through the axle & tire assembly. It provides the relative motion between the vehicle body and road profile to improve the ride & handling performance. The leaf springs are designed to provide linear stiffness and uniform strength characteristics throughout its travel. Leaf springs are generally subjected to dynamic loads which are induced due to different road profiles & driving patterns. Leaf spring design should be robust as any failure in leaf springs will put vehicle safety at risk and cost the vehicle manufacturer their reputation. The design of a leaf spring based on conventional methods predicts the higher stress levels at the leaf spring center clamp location and stress levels gradually reduce from the center to free ends of the leaf spring.

The automobile industry is going through one of the most challenging times, with increased competition in the market which is enforcing competitive prices of the products along with meeting the stringent emission norms. One such requirement for BS6 phase 2 emission norms is monitoring for partial failure of the component if the tailpipe emissions are higher than the OBD limits. Recently PM (soot) sensor is employed for partial failure monitoring of DPF in diesel passenger cars.. PM sensor detects soot leakage in case of DPF substrate failure. There is a cost factor along with extensive calibration efforts which are needed to ensure sensor works flawlessly. This paper deals with the development of an algorithm with which robust detection of DPF substrate failure is achieved without addition of any sensor in the aftertreatment system.

Range anxiety is one of the major factors to be dealt with for increasing penetration of EVs in current Automotive market. The major reasons for range anxiety for customers are sparse charging infrastructure availability, limited range of Electric vehicles and range uncertainty due to diverse real-world usage conditions. The uncertainty in real world range can be reduced by increasing the correlation between the testing condition during vehicle development and real-world customer usage condition. This paper illustrates a more accurate test methodology development to derive the real-world range in electric vehicles with experimental validation and system level analysis. A test matrix is developed considering several variables influencing vehicle range like different routes, drive modes, Regeneration levels, customer drive behavior, time of drive, locations, ambient conditions etc.

As a car OEM, we continuously strive to set the bar for competitors with every product. Consumer travel experiences are enhanced by increasing passenger cabin silence. There is only one steering system opening in the firewall panel, which is used for allowing intermediate shaft's fitment on the pinion shaft of the steering gear. The steering grommet is the sole component that covers the firewall cut-out without disrupting steering operations, which has a substantial impact on the NVH performance of the vehicle. It is typically used in cars to eliminate engine noise and dust entering to passenger compartment. The part is assembled inside the vehicle where the steering intermediate shaft passing through BIW firewall panel. We use a bearing, plastic bush, or direct rubber interference design in the steering grommet to accommodate the rotational input the driver provides to turn the automobile.

As we all know, automotive headliners are an essential component of any car’s interior as they cover all the internal components and provide a clean and finished look. Headliners not only increase the aesthetic appeal of a car’s interior, but also acts as an insulation and sound absorption source. As per the latest Government norms, Curtain Airbag (henceforth called as CAB) has been made mandatory and this change calls for the corresponding changes in the Headliner packaging of all passenger vehicles. In general, curtain air-bag deployment calls for a twist open of Headliner at lateral sides (a portion below Hinge-line) during the deployment. This enables the inflated airbag to flow inside the passenger cabin to protect the passenger from any injury. Conventionally no components are packaged below the hinge-line area of headliner to avoid obstruction for CAB deployment and any part fly-off concerns.

Restraint systems in automotives are inevitable for the safety of passengers. Seat belts are one such restraint system in automotives that prevent drivers and passengers from being injured during a crash by restraining them back. Seatbelt on automotives has interface with Body-in-white (henceforth called as BIW) and Trim parts in-order to serve its purpose at vehicle level. One such interface part of seat belt is the web guide, which assists and ensures the nylon web’s smooth motion at different seat track positions. Web-guides on automotives ensure the flawless motion of seat belt web at pillar trim areas. In this paper, we are discussing alternate ways of assisting the seat belt web without the web-guide as a separate part. In-order to assist and ensure the motion of nylon web in its trajectory, we have extended the flange of the pillar trim involved.

Mechanical control cables or Bowden cables are widely used in various applications for push-pull actions of mechanical systems. In mid-segment tractors, the linkage systems are designed along with control cables to actuate controls such as throttle, braking, transmission shift, position control, etc. due to its design flexibility. Output force and travel efficiency are two major performance parameters that depend on the routing, cable design composition, friction material, load transfer, etc. Virtual simulations can be used to predict cable performance and efficiency. There are different methodologies currently used to model the cable. These available methods can accurately predict either performance or travel efficiency. There is no method available in-house to predict both these parameters. In this paper, a new cable modeling method is proposed by authors using multi-body simulation (MBS) software MSC ADAMS.

Attaining better acoustic performance and back-pressure is a continuous research area in the design and development of passenger vehicle exhaust system. Design parameters such as tail pipe, resonator, internal pipes and baffles, muffler dimensions, number of flow reversals, perforated holes size and number etc. govern the muffler design. However, the analysis on the flow directivity from tail pipe is limited. A case study is demonstrated in this work on the development of automotive muffler with due consideration of back pressure and flow directivity from tail pipe. CFD methodology is engaged to evaluate the back pressure of different muffler configurations. The experimental and numerical results of backpressure have been validated. The numerical results are in close agreement with experimental results.

Internal combustion engine vehicles are major contributors to many environmental and health hazardous emissions and sometimes consume more fuel. New regulations like Corporate Average Fuel Efficiency (CAFÉ) norms are coming up and demand lower emissions. Original Equipment Manufacturers (OEMs) are committed to bringing various technological advancements in Internal Combustion Engine (ICE)powered vehicles to maximize their efficiency. Hence it is important to reduce the loss and improve the fuel economy. This paper explains a new approach methodology used for reducing the gearbox drag by 5- 10 %. This improvement can significantly contribute to the overall efficiency improvement thus carbon footprints of vehicle getting reduced.

The global automotive industry is growing rapidly in recent years and the market competition has increased drastically. There is a high demand for passenger car segment vehicles with high torque delivery and fuel economy for a pleasant drivability experience. Also, to meet the more stringent emission requirements, automakers are trying very hard to reduce the overall vehicle gross weight. In lowering both fuel consumption and CO2 generation, serious efforts have been made to reduce the overall engine weight. An engine cylinder block is generally considered to be the heaviest part within a complete engine and block alone accounts for 3-4% of the total weight of the average vehicle, thus playing a key role in weight reduction consideration. Aluminum casting alloys as a substitute for the traditional cast iron can mean a reduction in engine block weight between 40 and 55% [9], even if the lower strength of aluminum compared to grey cast iron is considered.

Original Equipment Manufacturers (OEMs) should innovate ways to delight customers by creating affordable products with improved drive experience and occupant comfort. Vehicle refinement is an important initiative that is often take-up by the project teams to ensure that the product meets the customer’s expectations. A few important aspects of vehicle refinement include improving the Noise Vibration Harshness (NVH), ride and handling performance pertaining to the Functional Image (FI) of the product. Optimizing the suspension design variables to meet both ride and handling performance is often challenging as improving the ride will have a deteriorating effect on handling and vice-versa. The present work involves Multi-Objective Design Optimization (MDO) of the suspension system of an automotive Sports Utility Vehicle (SUV) platform considering both ride and handling requirements, simultaneously.

Modern day automotive market demands shorter time to market. Traditional product development involves design, virtual simulation, testing and launch. Considerable amount of time being spent on virtual validation phase of product development cycle can be saved by implementing machine learning based predictive models for key performance predictions instead of traditional CAE. Durability oil canning loadcase for vehicle hood which impacts outer styling and involves time consuming CAE workflow takes around 11 days to complete analysis at all locations. Historical oil canning CAE results can be used to build ML model and predict key oil canning performances. This enables faster decision making and first-time right design. In this paper, prediction of buckling behaviour and maximum displacement of vehicle hood using ML based predictive model are presented. Key results from past CAE analysis are used for training and validating the predictive model.

Computer Aided Engineering (CAE) simulations are an integral part of the product development process in an automotive industry. The conventional approach involving pre-processing, solving and post-processing is highly time-consuming. Emerging digital technologies such as Machine Learning (ML) can be implemented in early stage of product development cycle to predict key performances without need of traditional CAE. Oil Canning loadcase simulates the displacement and buckling behavior of vehicle outer styling panels. A ML model trained using historical oil canning simulation results can be used to predict the maximum displacement and classify buckling locations. This enables product development team in faster decision making and reduces overall turnaround time. Oil canning FE model features such as stiffness, distance from constraints, etc., are extracted for training database of the ML model. Initially, 32 model features were extracted from the FE model.

In order to sustain in automobile industry, fuel economy and robustness are playing vital role in vehicle. Every gram of weight will have an impact on fuel economy, thus burning a hole in consumers pocket and contributing heavily to the carbon footprint. Composite material development plays important role in meeting the stringent self-imposed targets of the automotive manufacturers and light weighting is becoming a prime option for improving Fuel Economy. The main objective of this paper is to optimize the weight of the luggage lid floor and reduce its cost without compromising on the strength by changing the raw material and manufacturing process. This part is in trunk compartment of the vehicle. Main function of this part is to withstand the luggage load under various user loading patterns at varied temperature and while driving on different road conditions.

Accurate ride and handling prediction is an important requirement in today's automobile industry. To achieve the same, it is imperative to have a good estimation of damper model. Conventional methods used for modelling complex vehicle components (like bushings and dampers) are often inadequate to represent behaviour over wide frequency ranges and/or different amplitudes. This is difficult in the part of OEMs to model the physics-based model as the damper’s geometry, material and characteristics property is proprietary to part manufacturer. This is also usually difficult to obtain as a typical data acquisition exercise takes lots of time, cost, and effort. This paper aims to address this problem by predicting the damper force accurately at different velocity/ frequency and amplitude of measured data using Artificial Neural Networks (ANN).

Suspension plays a crucial role in stabilizing, comfort and performance of a vehicle. During vehicle braking operation, load transfer happens from rear axle to front axle resulting in shifting of vehicle’s center of gravity towards vehicle front for a momentarily duration which is called diving. This phenomenon leads to dropping of traction at rear wheel end resulting in lifting of rear axle with front wheel as pivot. This causes increase in front to rear weight ratio of vehicle system and compromising driver safety due to skidding and locking of rear wheel-end. To minimize this phenomenon’s affect, optimum anti-dive suspension geometry is used to have better rear wheel end traction resulting in improved braking stability.

In the automotive industry the requirement for low emissions has led to the demand for lightweight vehicle structures. Light weighting can be achieved through different iterative approaches but is usually time consuming. Current paper highlights deployment of the multi-loadcase optimization approach for light weighting. This work involves developing a process for multiple loadcase optimization for automotive hood. The main goal is to minimize the weight of a hood assembly by meeting strength and stiffness targets. The design variables considered in this study are thickness of the panels. Design constraints were set for stress and stiffness based on DVP (Design Verification Plan) requirement. Optimization workflow is setup in mode-frontier with design objective of minimizing weight of hood.

Model based calibration is extensively used by the automotive OEMs (Original Equipment manufacturers) because of its correlation accuracy with test data and freezing the operating parameters such as injection timings, EGR rates, fuel quantity etc. The prediction of Brake specific Fuel consumption (BSFC), Exhaust and intake temperatures are very close to test data. The prediction of Brake specific NOx is directionally reliable with acceptable tolerance.

The increasing demand for higher specific power and the need for weight reduction and decrease of emissions have become the driving factors of product development in the automotive market today. Substitution of high-density materials and more precise adjustment of material parameters help in significant weight decrease, but it is accompanied by undesirable cost increase and manufacturing complexity. One of the approaches to optimize the design is through the process of integration which involves integrating the functional elements of two or more components into one and achieving a reduction in weight and cost without impacting required performance. This paper explains a similar approach followed as a part of the Design and Development of 1.5 L, 3 Cylinder CRDI Diesel Engine for a new vehicle platform, developed for automotive passenger car application.

The emission norms around the world are continuously changing and getting stringent with every revision. India is on its way to make its emission norms at par with that prevailing in the developed nations. The cold-start condition is an important factor affecting vehicle emissions from gasoline direct injection (GDI) and port fuel injection (PFI) vehicles. In this paper, the effects of change in torque converter losses on emissions are experimentally investigated in a TGDI AT vehicle. The instant engagement of the torque converter puts a sudden load on the engine and thus affects its stability. Thus, to overcome the stability issue, Engine Torque has to be simultaneously increased for smooth engagement. As a result, the likelihood of the slightly leaner air-fuel mixture in the cylinder, which results in higher NOx formation, is much greater in an AT vehicle than that of a similar MT vehicle.