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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.

In automotive door engineering, fitting the side door glass surface from styling into the cylinder or torus is the basic requirement. Optimization is required to do this, which requires a solver which could be efficacious for best surface fitting. This paper propounds a methodology which could be used for fitting a side door glass surface from styling into the cylinder or torus. The method will significantly help in developing the required surface and can successfully eliminate the cumbersome manual calibrations. The mathematical model mentioned is a novel approach based on “Particle Swarm Optimization” (“PSO” will be used to represent in the paper) towards surface optimization technique. VB script is used to make it applicable in CATIA but could be easily applied in any other programming language like python, java etc. Usually the surface fitting problems deals with the initial guess of the required surface and then its further optimization.

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.

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.

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.

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.

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.

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.

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.

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.

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 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 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.

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.

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.

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.

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.

Door shut-line definition is the first vital step in car body door engineering and depends on the hinge position, hinge shape, manufacturing capabilities and other parameters. In the design process, once the hinge axis definition is finalized door shut-line is defined which should satisfy two major requirements. The requirements are clearance between the door outer surface with its surrounding components (like hinges, fender, other door etc.) and assembly feasibility. Another one is the manufacturability of the proposed design. The above conditions must be checked on different locations of the door as well as w.r.t different openings of the door. The paper presents a mathematical model to determine the door shut-line position with great computational efficiency. This method propounds closure engineer with parameters to define the shut line rather than going for cumbersome manual iterative process.