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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 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.

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.

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.

Bolted joints are the most used joints in automotive suspension assemblies. They are expected to retain the strength over the course of useful life of the vehicle and contribute to durability in a big way through reduction of stress amplitudes. Any sort of loosening or slip or breakage in these joints can lead to noise or catastrophic failures. In the past, such issues were addressed through thumb rules and design guidelines. However, with the focus on first-time right tests with reduced validation time it has become important to upfront predict the suspension joint integrity through simulation. Toward this objective, a novel approach was developed to simulate the suspension joint integrity for bolted joints. This approach considers various parameters like bolt preload, tolerance stackup of the parts in the joint, coefficients of friction of various interfaces, quality of contact and effect of deformation at the thread interface on joint integrity.

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 SAE J1100 based standard cargo volume index methods and predefined luggage objects are very specific to United States population. The European luggage volume calculation and standard luggage calculations are primarily based on DIN and ISO standards. Luggage volume declaration by manufacturers are based on any of these methods. The calculations are complicated and there is a possibility of declaring different values for similar luggage compartments. The major purchase decision of vehicle is based on its luggage capacity and current methods are very limited to make an intelligent decision by a customer. Market specific customer usage patterns for luggage requirements and protecting them in vehicle architecture upfront in concept stage is important to retain the market position and buying preference of customers. The usage patterns is collected from customer clinics and marketing inputs.

Being a safety critical aggregate, every aspect of brake system is considered significant in vehicles operations. Along with optimum performance of brake system in terms of deceleration generation, brake pedal feel or brake feel is considered as one of the key elements while evaluating brake system of vehicles. There are many factors such as liner and drum condition, road surface, friction between linkages which impress the pedal feel. Out of these, in this paper we will be discussing the factors which influence the brake pedal feel in relation to the driver comfort and confidence building. Under optimum braking condition, brake operation must be completed with pedal effort not very less or not very high, brake pedal feel must be firm throughout the operation, in such a way that it will not create fatigue and at the same time it will give enough confidence to the driver while operating with acceptable travel.

Fuel efficiency is critical aspect for commercial vehicles as fuel is major part of operational costs. To complicate scenario further, fuel efficiency testing, unlike in passenger cars is more time consuming and laborious. Thus, to save on development cost and save time in actual testing, simulations plays crucial role. Typically, actual vehicle speed and gear usage is captured using reference vehicle in desired route and used it for simulation of target vehicle. Limitation to this approach is captured duty cycle is specific to powertrain and driver behavior of reference vehicle. Any change in powertrain or vehicle resistance or driver of target vehicle will alter duty cycle and hence duty cycle of reference vehicle is no more valid for simulation assessment. This paper demonstrates approach which uses combination of tools to address this challenge. Simulation approach proposed here have three parts.

The present work discusses the developed simulation model aimed to predict the heat rejection (HR) performance and external pressure drop characteristics of automotive charge air cooler (CAC). Heat rejection and airside pressure drop characteristics of CAC were predicted for the conditions of different charge air mass flow rates and different cooling air velocities. The lack of detailed research on CAC performance prediction has motivated the development of the proposed simulation model. The present 1-D simulation has been developed based on the signal library of AMESIM application tool. Input parameters for this simulation such as core size, tube pitch, tube height, number of tubes, fin density, louver angle, louver pitch, charge air mass flow rate, cooling air velocity, charge air inlet temperature, and ambient temperature. Heat rejection curve and airside pressure drop of CAC were the output of the present simulation.

There is a growing need for the accurate Computer Aided Engineering (CAE) models for vehicle performance evaluation. The reduced product development time and complexity of the vehicle evaluation demands accurate prediction with CAE models. Vehicle dynamics performance evaluation is very critical in vehicle development process, which require very accurate vehicle and tire models. The tire characteristics are represented as mathematical, physics based and empirical models. There are different types of tire models exist like Fiala, PAC, SWIFT and FTire etc, which can be used for vehicle handling, ride and steering performance evaluation. There is a need to study and understand these tire models before applying to specific vehicle dynamic performance. There is a challenge to get the tire models as tire modeling require lot of tests and time consuming.

One of the critical challenges for transmission design is to predict the gear shift dynamics accurately and to ensure smooth gear shift quality for different driver behaviors while shifting. This calls for detailed understanding of the RWUPs. Through prototype testing, understanding the influence of different parameters is costly and time consuming. Also, the testing does not provide necessary visualization of exact physics and the identification of issues is difficult. One of such typical concerns is shift shock while shifting the gear. Sudden gear engagement or disengagement leads to impact torque in drivetrain during shifting of gears, which in turn results in winding and unwinding of powertrain due to vehicle Inertia. This induces noise and vibration that affects driver comfort. The paper presents, the methodology to frontload prediction of dynamics of gear shifting that leads to shift shock behavior.

It is imperative that all automobile manufacturers conduct vehicle level benchmarking at the initial stage of any new project. From the benchmark information, the manufacturers can set relevant targets for their own vehicles under development. In this regard, an accurate prediction of the engine operating points can improve the correlation of the measured fuel economy of the benchmark vehicle. The present work describes a novel method that can be used for the accurate prediction of the engine operating points of any benchmark vehicle. Since the idea of instrumenting the crankshaft/driveshaft with torque transducers is a costlier and time-consuming process, the proposed method can be effective in reducing the benchmarking. Hence, the objective of this work is to develop a mathematical model to calculate the real-time engine operating points (engine speed and torque) using parameters like vehicle speed, accelerator pedal map, driveline inertia, vehicle coastdown force and gradient.

Dual mass flywheel (DMF) is an excellent solution to improve the noise, vibration and harshness (NVH) characteristic of any vehicle by isolating the driveline from the engine torsional vibrations. For the same reason, DMFs are widely used in high power-density diesel and gasoline engines. However, the real-world usage conditions pose a lot of challenges to the structural robustness of the DMF. In the present work, a new methodology is developed to evaluate the robustness of a DMF fitted in a compact sports utility vehicle (SUV) with rear-wheel drive architecture. The abuse conditions (mis-gear, sudden braking, etc) in the real-world usage could lead to a sudden engine stall leading to an abnormally high angular deceleration of the driveline components. The higher rate of deceleration coupled with the higher rotational moment of inertia of the systems might end up in introducing a significantly high impact torque on the DMF.

As the FTP-75 drive cycle does not have a prescribed gear shift pattern, automotive OEMs have the flexibility to design. Conventionally, gear shift pattern was formulated based on trial and error method, typically with 10 to 12 iterations on chassis dynamometer. It was a time consuming (i.e. ~ 3 to 4 months) and expensive process. This approach led to declaring poor fuel economy (FE). A simulation procedure was required to generate a gear shift pattern that gives optimal trade-off amongst conflicting objectives (FE, performance and emissions). As a result, a simulation tool was developed in MATLAB to generate an optimum gear shift pattern. Three different SUV/UV models were used as test vehicles in this study. Chassis dyno testing was conducted, and data was collected using the base and optimized gear shift patterns. Dyno test results with optimized gear shift pattern showed FE improvement of ~ 4 to 5% while retaining the NOx margin well above engineering targets.