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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.

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.

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. Curtain airbag is one such restraint system in automotives that reduces the risk of injury to passengers during crash, without which head injury is inevitable during side crash of a vehicle. So successful deployment of curtain airbag (henceforth called as CAB) is very important in automotive safety during crash. This paper dwells about the optimization done in ramp bracket angle with successful deployment of curtain airbag. This optimization has paved the way for increasing the head-roominess by ~15% and to respect the safety and styling intent in the vehicle successfully. Providing a ramp bracket at the lower bottom side of CAB guides the airbag successfully during deployment. Ramp bracket angle plays a vital role in guiding the airbag inside the passenger’s cabin without any obstruction.

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.

Mechanical control cables or Bowden cables are widely used in various applications for push-pull actions of mechanical systems. In mid-segment tractors, the linkage systems are designed along with control cables to actuate controls such as throttle, braking, transmission shift, position control, etc. due to its design flexibility. Output force and travel efficiency are two major performance parameters that depend on the routing, cable design composition, friction material, load transfer, etc. Virtual simulations can be used to predict cable performance and efficiency. There are different methodologies currently used to model the cable. These available methods can accurately predict either performance or travel efficiency. There is no method available in-house to predict both these parameters. In this paper, a new cable modeling method is proposed by authors using multi-body simulation (MBS) software MSC ADAMS.

In automotive manual transmission gearboxes, the synchronizer rings play a vital role in gear shift operations. The efficiency of the synchronizer ring depends upon the frictional surface geometry. The critical parameter is the synchronizer ring frictional surface circularity. The circularity deviation causes higher synchronizer ring wear and poor cone torque generation. With the current manufacturing methods and the thickness of the synchronizer ring, circularity improvement is a challenge. The synchronizer ring thread turned part is lapped to improve the circularity. Reduction in circularity can be improved by optimizing the lapping operation. In this work, an optimal lapping condition was developed using statistical methods. Taguchi DOE was used to analyze the different parameter combinations along with the noise parameter – different ranges of circularity variation in turning operation. This helps to find the best lapping parameter settings to improve the reduction in circularity.

Manual transmissions are the preferred transmission for drivers who love sporty gear shifts. Manual transmission vehicles are cheaper, very efficient, and offer quick gear shifts. Worldwide manual transmission contributes to 36.15% and in India it contributes overall 80% of today's market share. The customers expect a very smooth gearshift which is a challenge to achieve in all ambient temperatures. In a gear shift event, the synchronizers synchronize the speed of the gears. The force applied at the gear shift knob, generates the cone torque and stops the rotating input shaft for the Neutral (N) to 1 gear shifting. The early morning gear shifts have high gear shift effort. This effort is getting reduced with the increase in temperature. This is due to the drag in the gearbox which is inevitable. This work focuses on improving the very first gear shift event of N to 1 after the engine crank from cold (8°) to hot (80°) condition.

Gears are one of the vital components to transmit torque efficiently. Helical gears are chosen as they transmit higher torque with lesser noise compared to spur gears of same size. All new age gearboxes require to transmit maximum torque with minimum packaging space available to improve torque density. Ways of reducing weight are using lesser density material, decreasing centre distance, and thereby reducing pitch circle diameter of all gears, etc. However, they will also affect torque carrying capacity of gearbox which can lead to gear failure in conventional transmission architecture gearboxes with input reduction method. In input reduction method, torque gets multiplied from input shaft to countershaft. Countershaft torque is multiplied to output shaft gears requiring higher torque capacity gears on output shaft. In this research, output shaft reduction architecture is proposed to avoid torque multiplication from input shaft to countershaft gears.

Attaining better acoustic performance and back-pressure is a continuous research area in the design and development of passenger vehicle exhaust system. Design parameters such as tail pipe, resonator, internal pipes and baffles, muffler dimensions, number of flow reversals, perforated holes size and number etc. govern the muffler design. However, the analysis on the flow directivity from tail pipe is limited. A case study is demonstrated in this work on the development of automotive muffler with due consideration of back pressure and flow directivity from tail pipe. CFD methodology is engaged to evaluate the back pressure of different muffler configurations. The experimental and numerical results of backpressure have been validated. The numerical results are in close agreement with experimental results.

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.

The bolted joints in suspension systems are subjected to severe external service loads during vehicle operation. To prevent the loaded joint from loosening and allowing it to retain its potential energy stored during assembly, a holistic design approach is needed. This paper explains the methodology to design and optimize bolted joints for the suspension systems of a modern 7-seater sports utility vehicle.

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 order to sustain in automobile industry, fuel economy and robustness are playing vital role in vehicle. Every gram of weight will have an impact on fuel economy, thus burning a hole in consumers pocket and contributing heavily to the carbon footprint. Composite material development plays important role in meeting the stringent self-imposed targets of the automotive manufacturers and light weighting is becoming a prime option for improving Fuel Economy. The main objective of this paper is to optimize the weight of the luggage lid floor and reduce its cost without compromising on the strength by changing the raw material and manufacturing process. This part is in trunk compartment of the vehicle. Main function of this part is to withstand the luggage load under various user loading patterns at varied temperature and while driving on different road conditions.

Accurate ride and handling prediction is an important requirement in today's automobile industry. To achieve the same, it is imperative to have a good estimation of damper model. Conventional methods used for modelling complex vehicle components (like bushings and dampers) are often inadequate to represent behaviour over wide frequency ranges and/or different amplitudes. This is difficult in the part of OEMs to model the physics-based model as the damper’s geometry, material and characteristics property is proprietary to part manufacturer. This is also usually difficult to obtain as a typical data acquisition exercise takes lots of time, cost, and effort. This paper aims to address this problem by predicting the damper force accurately at different velocity/ frequency and amplitude of measured data using Artificial Neural Networks (ANN).

Vehicle pull during braking can be defined as the deviation of vehicle travel from intended path of the vehicle by a margin of half a wheel track or more. It is a dynamic phenomenon with very complex inter-dependencies among the combined functioning of various aggregates such as steering system, suspension system, axles, and brakes. The problem is aggravated with shorter wheelbase & higher CG (Centre of Gravity) height, where the instantaneous load transfers are sudden and of relatively high magnitude which can lead to a combination of forces that are responsible for vehicle drifting or pulling to anyone side of centre-line travel. Vehicle with shorter wheelbases, high GVW and high CG heights are more prone to this unstable behaviour due to sudden change in dynamic forces acting on the tires while turning and braking.

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 today’s scenario, internal combustion engines have conflicting requirements of high power density and best in class weight. High power density leads to higher loads on engine components and calls for a material addition to meet the durability targets. Lightweight design not only helps to improve fuel economy but also reduces the overall cost of the engine. Material change from cast iron to aluminium has a huge potential for weight reduction as aluminium has 62% lesser mass density. But this light-weighting impacts the stiffness of the parts as elastic modulus drops by around 50%. Hence, this calls for revisiting the design and usage of optimization tools for load-bearing members on the engine to arrive at optimized sections and ribbing profiles. This paper discusses the optimization approach for one of the engine components i.e., the FEAD (front end accessory drive) bracket.