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In recent years, the push for reduced product development timelines has been more than ever with significant changes in the automotive market. High electrification, intelligent vehicle systems and increased number for car manufacturers are a few key drivers to the same. The front loading of development activities is now a key focus area for achieving faster product development. From vehicle dynamics point of view availability of subjective evaluation feedback plays a key role in optimization various system specifications. This paper discusses an approach for front loading through parallel development of the tire and vehicle chassis system, using advanced simulation and driving simulator technology. The proposed methodology uses virtual tire models which in combination with real-time vehicle model enables subjective evaluation of vehicle performance in driver-in-loop simulators.

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.

The increasing demand for higher specific power, fuel economy, Operating Costs as well as meeting global emission norms have become the driving factors of today’s product development in the automotive market. 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. This becomes a challenge for every automotive engineer to balance the above parameters to make a highly competitive design. This work is a part of the Design and Development of 2.2 L, 4 Cylinder TCIC Diesel Engine for a whole new vehicle platform, concentrated on automotive passenger car operation. This paper explains the selection of a suitable cylinder head gasket technology for a lightweight engine that acts as a sealing interface between the cylinder block and cylinder head.

As customers are inching towards adoption of electric vehicles as an alternative to internal combustion engines, automotive OEM’s will have to embrace this change and equip with new product development process. When it comes to Electric Vehicle (EV) in comparison with Internal Combustion Engine (ICE), NVH plays a major differentiator for vehicle refinement. Squeak and rattles will account for 20-25% of overall in-cabin noise source in an electric vehicle, most of which is observed from interior trims. Trims are mounted using small plastic clips which function as attachments and play a significant role in part retention and part integrity during normal operation and in case of any transient events. The engineering specifications for selecting a clip is force in newtons and it is mostly driven by ease of assembly, serviceability, and durability. A single DOF system with a specimen mass is developed and stiffness and damping are calculated based on transmissibility.

Highly competitive automotive market demands shorter product development cycle while maintaining higher standards of performance in terms of durability and Noise Vibration & Harness (NVH). Engine cranktrain system is one of the major vibration sources in engine and first torsional mode frequency is a key parameter which influences vibration characteristics. Current CAE (Computer Aided Engineering) workflow for evaluating cranktrain system performance is time-consuming and takes around 55 Hrs. It involves crankshaft geometry cleanup, stiffness calculation, 1D model building and post processing. Over the time, significant historical data has been created while performing this virtual simulation during the product development cycle. Having a trained Machine Learning (ML) model based on this historical data, which can predict first torsional mode frequency accelerates the virtual validation. In this paper, prediction of first torsional frequency of cranktrain system using ML is presented.

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.

For decades, customer complaints on Squeak & Rattle issues have come as a question of quality for the automotive industry. Squeak and rattle sounds are customer irritants due to their non-patterned and transient nature. Squeak is a friction induced noise that generally occurs because of rubbing of the two materials that are incompatible with each other. While rattle is a phenomenon that occurs due to the impact between the two parts having unintended gap. They are no more secondary noises and avoiding or elimination of these become significant for brand building and warranty cost reduction. Chucking is a form of squeak noise that occurs due to the interaction between uncoated seal to seal. In Swing gate, this phenomenon is seen when seal bulb inner layers are completely compressed. Swing gate have fore-aft modes that are excited due to dynamic responses from different road profiles.

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 automotive product development, design and development of the chassis plays an important role since all the internal and external loads pass through the vehicle chassis. Durability, NVH, Dynamics as well as overall vehicle performance is dependent on the chassis structure. Even though passenger vehicle chassis has a ladder frame or a monocoque construction, small commercial vehicle chassis is a hybrid chassis with the cabin welded to the ladder frame. As mileage is critical for sale of SCVs, making a light-weight chassis is also important. This creates a trade-off between the performance and weight which needs to be optimized. In this study, a parametric beam model of the ladder frame & the cabin of the vehicle is created in COMSOL Multiphysics. The structure has been parameterized into the long member & crossmember geometry & sections. The model calculates the first 12 natural frequencies, global stiffness, and weight.

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.

There is a higher demand for quieter tractors in the agri-industry, as the continued exposure to noise levels have disastrous effects on operator’s health. To meet the world-wide regulatory norms and to be the global market leader, its mandatory to develop the comfortable tractor which meets homologation requirements and customer expectations. Typically, Operator Ear Level (OEL) noise has been evaluated in the test, after First Proto has been made. This approach increases cost associated with product development due to late changes of modifications and testing trails causing delay in time-to-market aspect. Hence, there is a need to develop the methodology for Predicting tractor OEL noise in virtual environment and propose changes at early stage of product development. At first, full vehicle comprising of skid, sheet metals and Intake-exhaust systems modelled has been built using Finite Element (FE) Preprocessor.

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 automobile industry strives to develop high-quality vehicles quickly that fulfill the buyer’s needs and stand out within the competition. Full utilization of simulation and Computer-Aided Engineering (CAE) tools can empower quick assessment of different vehicle concepts and setups without building physical models. Vehicle execution assessment is critical in the vehicle development process, requiring exact vehicle steering system models. The effect of steering system stiffness is vital for vehicle handling, stability, and steering performance studies. The overall steering stiffness is usually not modeled accurately. Usually, torsion bar stiffness alone is considered in the modeling. The modeling of overall steering stiffness along with torsion bar stiffness is studied in this paper. Another major contributing factor to steering performance is steering friction. The steering friction is also often not considered properly.

Currently the Automotive industry demands highly competitive product to survive in the global tough competition. Even if there is a slight reduction in product cost and time has a high significant impact on business. Engineers are under tremendous pressure to develop competitive and give better product concern resolution at the earliest. To arrest the failure of this thermostat vent, an innovative approach was used to relocate de-aeration restrictor on the hose to the thermostat root. Thus, resolving the product concern by increasing the strength of the vent at root and providing good business impact on cost savings. Physical testing has provided an effective way to smoothen product development for concern resolution. This Paper highlights approach on an attempt to field failure simulation with existing and modified design with lab test results.

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.

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.