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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.

This paper discusses the methodology setup for grill opening area prediction at the early development phase of the product development lifecycle, using a commercially available 1D simulation tool- AMESIM. Representative under hood has been modeled using Grill, Condenser, Radiator, intercooler, fan, and engine components. Vehicle velocity is used as an input to derive the airflow passing through the grill and other under-hood components based on ram air coefficient, pressure drop through different components (Grill, Heat exchanger, Fan & Engine). This airflow is used to predict the top tank temperature of the radiator. Derived airflow is correlated with airflow obtained from CFD simulation. A balance has been achieved between cooling drag & fan power consumption at different grill opening areas for target top tank temperature. Top tank temperature has been predicted at two different extreme engine heat rejection operating points.

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.

Major focus was given on the Comfort, Fuel efficiency & Safety during the development of the passenger cars, which certainly drives the vehicle business of Original Equipment Manufacturer (OEM’s). The air Conditioning in a car, plays an important role in the area of comfort of the passengers and fuel efficiency point of view. Especially Heat Exchanger plays a pivotal role in the air conditioning system. So, it’s a challenge for the OEM’s to select and design the optimal heat exchanger from the supplier, which meets the performance and packaging requirements during the design phase of the product development cycle. The objective of this paper to focus on analytical calculation or framework was developed using an excel tool considering the effect of variable geometry of fin which includes louver pitch, louver angle and louver length in a multi-pass condenser. Further, this theoretical calculation was validated using experimental data and CFD simulation.

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.

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.

Aerodynamics of on-road vehicles has come to the limelight in the recent years. Better aerodynamic design of vehicle would improve vehicle fuel efficiency with increased acceleration performance. To obtain best aerodynamic body, the series of design modifications and different testing methodologies must be involved in vehicle design and validation phase. Wind tunnel aerodynamic force measurement, road load determination and computational fluid dynamics were the common methods used to evaluate the aerodynamic behavior of the vehicle body. As a novel approach, the present work discusses about the on-road (Real time) testing methodology that is aimed to evaluate the aerodynamic performance of vehicle body using surface pressure mapping. A 64-Channel digital pressure scanner has been utilized in this work for mapping the pressure at different locations of the typical vehicle body.

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.

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 the past few decades, improvement on fuel efficient technologies have progressed rapidly, whereas little emphasis is being made on how the vehicle should be driven. Driving habits significantly influences fuel consumption and poor driving habits leads to increased fuel consumption. In this paper a new system called “Green Drive” is being presented wherein driving habits are closely monitored, evaluated and details are systematically presented to the user. Green Drive system monitors key driving parameters like speed, gear selection, acceleration, unwanted engine idling periods, aggressive braking and clutch override and presents an ecoscore on the infotainment system which is reflection of users driving behavior. The system also offers guidance on the scope for improving driving habits to achieve better ecoscore and hence reduced fuel consumption.

Canopy design is governed by CPCB regulations. The regulations explicitly tells about noise levels. It’s very important to have the proper ventilation of canopy to ensure the proper working at all climatic conditions. Mostly it is installed at commercial locations & hence the ownership cost matters. Reducing the footprint without affecting the power output is challenging. It implies the need of the CFD simulation to predict the cooling performance of the canopy. The baseline canopy is tested to estimate the performance parameters. It is modelled in CFD with all the minute details. All the parts including engine, alternator, fan, fuel tank are modelled. MRF(Moving Reference Frame) model used to simulate fan performance. The cooling systems like radiator & oil cooler is modelled as porous region. The total flow across canopy & the air velocity across critical regions is used to define the performance.

Gear rattle is an annoying noise phenomena of the automotive transmission, which is mainly induced by torsional fluctuation of engine. In this study, torsional vibration of 3 cylinder powertrain is analyzed and improved for reducing the gear rattle from transmission by using parametric optimization. One dimensional Multi-body mathematical model for the torsional vibrations of front wheel drive automotive drivetrain is developed and utilized for the optimization of sensitive parameters of the driveline. Second order differential equations of the mathematical model are solved by using MATLAB and the output response is validated with the test data. Parametric optimization is conducted by using design of experiment method. The updated model is further utilized for optimizing the flywheel inertia, driveshaft stiffness and clutch stiffness. Mathematical modelling and optimization process has helped to achieve NVH targets for driveline.

Melatonin, otherwise popularly known as the “sleep hormone” is known to govern the human circadian rhythms. Current studies indicate that the generation of melatonin is impacted by the ambient light. The natural sleep inducing behavior during night and in darkness, is also due to the same phenomenon. Studies have shown that light of particular wavelengths in the visible spectrum have a higher effect on the amount of melatonin secreted by the human body. Blue light in the wavelengths of around 468 nm is known to inhibit the melatonin secretion, the most. This branch of science known as photobiology is in its nascent stage and is a matter of research pursued by neurologists, endocrinologists and other lighting researchers. Photobiology has several potential applications in the automotive industry, the principal one being driver drowsiness prevention.

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.

Automotive OEMs are aggressively using different materials for interiors due to value proposition and variety of options available for customers in market. Excessive usage of different grade plastics with zero gap philosophy can cause stick slip effect leading to squeak noise. Even though systems and subsystems are designed using best practices of structural design and manufacturing tolerances, extreme environmental conditions can induce contacts leading to squeak noise. Appropriate selection of interface material pairs can minimize the possibilities of squeak conditions. Stick-slip behavior of different plastics is discussed in the present study, along with critical parameters during material compatibility testing in a tribological test stand. Friction coefficient of different material pairs for a defined normal load and sliding velocity are analyzed for patterns to recognize squeaks versus time.

The concept definition phase of typical vehicle development focuses on the architecture definition and optimization based on different constraints/requirements. With the focus on Sustainability, the architecture optimization process must include “Light-weighting” as an optimization criterion. With only concept vehicle architecture available, the vehicle weight estimation becomes judgmental & inaccurate. This paper aims to address this deficiency with a new analytical approach for vehicle weight estimation. The new approach for vehicle weight estimation is a “bottom-up” approach using parametric models for each system weight with the inputs being the relevant vehicle specifications driving the system engineering. For size/shape-driven (rather than functional) systems, the models are content-based & segment-based. The parametric models are then iterated for multiple architecture concepts & specifications and the optimum concept (meeting all functional & business constraints) is chosen.