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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.

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.

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.

The front air-dam diverts the airflow flowing through the underbody, thereby reducing aerodynamic drag. The height, shape and position of air-dam must be optimized to get improved drag. Extensive iterations are carried out to finalize the front air-dam size and position until the target is achieved. Researchers used to study the effect of air-dam height, then with fixed height will work to finalize position. Studies with interactive effect of front air-dam height and position are scanty. The existing process is time consuming as the front air-dam size and position is adjusted manually and simulation is being performed for each design and requires detailed analysis for all design iterations. The objective of this study is to couple CFD solver with design optimization software to reduce overall manual design iterations to choose the effective front air-dam geometry.

Climate change is primary driver in the current discussions on CO2 reduction in the automotive industry. Current Type approval emissions tests (BS III, BS IV) covers only tailpipe emissions, however the emissions produced in upstream and downstream processes (e.g. raw material sourcing, manufacturing, transportation, vehicle usage, recycle phases) are not considered in the evaluation. The objective of this project is to assess the environmental impact of the product considering all stages of the life cycle, understand the real opportunities to reduce environmental impact across the product life cycle. As a part of environmental sustainability journey in business value chain, lifecycle assessment (LCA) technique helps to understand the environmental impact categories. To measure overall impact, a cradle to grave approach helps to assess entire life cycle impact throughout various stages.

The air velocity achieved at driver and passenger aim point is one of the key parameters to evaluate the automotive air-conditioning system performance. The design of duct, vent and vanes has a major contribution in the cabin air flow directivity. However, visual appearance of vent and vane receives higher priority in design because of market demand than their performance. More iterations are carried out to finalize the HVAC duct assembly until the target velocity is achieved. The objective of this study is to develop an automated process for vane articulation study along with predicting the optimized velocity at driver and passengers. The automated simulation of vane articulation study is carried out using STAR-CCM+ and SHERPA optimization algorithm which is available in HEEDS tool. The minimum and maximum vane angle are defined as parameters and face level velocity is defined as response.

The complexity of Urban driving conditions and the human behavior introduces undesired variabilities while establishing Fuel economy for a vehicle. These variabilities pose a great challenge while trying to determine that single figure for assessment of vehicle’s fuel efficiency on an urban driving cycle. This becomes even more challenging when two or more vehicles are simultaneously evaluated with respect to a reference vehicle. The attempt to fit a generalized linear model, between Fuel Economy as predicted variable and components of a driving cycle as predictor variables produced oxymoronic and counter-institutive results. This is primarily due to existence of multi-collinearity among the predictor variables. The context of the study is to consider the event of driving on a cycle as a random sampling experiment. The outcome of a driving cycle is summarized into a list of predictor variables or components.

Condition Monitoring is the process of identifying any significant change in operating parameters of a machine, which can be indicative of a failure in future. This paper discuss a non-invasive condition monitoring methodology for sensing and investigating the problems which could be identified by noise and vibrations. This could be an easy solution for predicting failures in tractors which are operational in the field. An example of engine tappet is used to demonstrate the methodology. A disturbed setting causes a distinguishable noise, referred to as “tappet rattle”. Android smartphones (with inbuilt sensors - accelerometer, gyroscope and microphone) are used to record noise and vibration from tractors in good condition as well as in disturbed condition. Time series data analysis is done to extract relevant features and then Fourier Transform is applied to the signals for extracting frequency domain signatures.

Several studies in the field of hedonics using subjective responses to gauge the nature and influence of odors have attempted to explain the complex psychological and chemical processes. Work on the effect of odors in alleviating driver fatigue is limited. The potential to improve road safety through non-pharmacological means such as stimulating odors is the impetus behind this paper. This is especially relevant in developing countries today with burgeoning economies such as India. Longer road trips by commercial transport vehicles with increasingly fatigued drivers and risk of accidents are being fuelled by distant producer - consumer connections. This work describes a two stage comparative study on the effects of different odors typically obtainable in India. The stages involve administration of odorants orthonsally and retronasally after the onset of circadian fatigue in test subjects. This is followed by a small cognitive exercise to evaluate hand-eye coordination.

In the event of a frontal car crash, occupant sitting in a car meets various types of injuries like Head injury, Chest compression, Neck injury etc. These injuries may lead to the death of an occupant if exceeded beyond biomechanical limits. Seat belt is a primary restraint system, which when worn controls the motion of occupant sitting inside the car during the event of a car crash. An Anchorage location of three point seat belt system has significant effect on occupant injuries during the crash event. By changing the mount locations of a seat belt anchor points i.e. D-ring, Anchor & Buckle, performance of seatbelt system can be enhanced further thereby reducing occupant injuries to certain extent. As per regulation AIS015, locations of safety belt anchorage points should be within prescribed zone.

This paper is an attempt to compile a systematic approach which can be easily incorporated in the product development system used in the design and development of parking brake systems for passenger cars having rear drum brakes, which in turn can effectively reduce the lead time and give improved performance. The vehicle GVW, percentage gradient and maximum effort limits (as per IS 11852 - Part 3), tire and drum brake specifications were taken as front loading. This data is used for target setting of functional and engineering parameters, such as lever pull effort, lever ratio and angular travel of lever. Design calculations were performed to obtain theoretical values of critical parameters like lever effort and travel. The comparison between target and theoretical values give the initial confidence to the system engineer. Further, the outcome was taken to conceptualize the hard points of lever on vehicle for ergonomics.

In the present scenario, when the vehicle is maneuvering in the gradient, more clutch and accelerator pedal modulations is needed during stop and go condition. These kinds of pedal modulations are not desirable for many customers as it requires more skilled driving. Failure of doing such actions will even result in engine stalling which becomes an annoyance to such customers. In order to overcome this problem, the low idle speed of the engine can be increased only during the drive-off condition. In this paper, we proposed the development and real-time testing of the control algorithm to increase the engine low idle speed during drive-off. This proposed algorithm detects the drive-off condition and then an offset value is added to increase the low idle speed. Various input conditions are considered to enable or disable the increment of engine low idle speed. The control algorithm has been developed using MATLAB/Simulink tool and tested using ETAS EHOOKS tool.