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Durability is an important indicator to measure the automobile quality and reliability. Automotive industry is striving to develop products having excellent performance to weight ratios and along with high safety standards. A successful product should have adequate robustness during normal customer operation and the ability to withstand high impact events without impairment of function or safety relevant damage. Road Load Data Acquisition (RLDA) along with efficient design and validation processes are, among others, critical factors for success in the automotive industry. Physical RLDA is expensive and time consuming, the prototype vehicles being costly and only available at a later stage in the vehicle development cycle. Component failures occurring on the proto test vehicles can prove to be a major setback, delaying the product launch by months. In order to overcome above challenge, this paper presents an innovative methodology to carry out Digital RLDA (dRLDA).

With the introduction of Connected Vehicles, it is possible to extend the limited horizon of vehicles on the road by collective perceptions, where vehicles periodically share their information with other vehicles and servers using cloud. Nevertheless, by the time the connected vehicle spread expands, it is critical to understand the validation techniques which can be used to ensure a flawless transfer of data and connectivity. Connected vehicles are mainly characterized by the smartphone application which is provided to the end customers to access the connectivity features in the vehicle. The end result which is delivered to the customer is through the integrated telematics unit in the vehicle which communicates through a communication layer with the cloud platform. The cloud server in turn interacts with the final application layer of the mobile application given to the customer.

The automotive application places very special demands on the air conditioning system. As is the case with any other process, system efficiency is very important and the automotive air-conditioning application is no exception. While the characteristics of all the major components in the air conditioning system like compressor, condenser, evaporator and blower contribute to overall system efficiency, localized inefficiencies do play a part and so must be kept to a minimum, especially in this day and age when extra emphasis is being laid on sustainability. One such phenomenon that contributes to the system inefficiency is heat pick-up in suction line. Since the temperature at the evaporator-outlet is quite lower than ambient and also its surroundings (steering system pipes and hoses, engine, air intake pipes and so on), the refrigerant picks up heat as it moves along the suction line up to the compressor inlet. This heat pick-up is detrimental to the overall system performance.

With the advent of BS VI regulations, automotive manufacturers are required to innovate the powertrains, fuel systems, exhaust and its after treatment systems to meet the regulatory requirements. The exhaust regulations can be met either by reducing the exhaust gases being generated by the engine (attacking the source) or by treating the exhaust gases in after treatment devices. The choice of the opted system varies with the manufacturer. The after-treatment devices such as catalytic converters are generally mounted in the engine compartment to take advantage of high temperature of exhaust gases to yield the reactions. Such an arrangement imposes a lot of thermal load on the peripheral components such as gearshift cables, bearings, oil seals, driveshafts etc. Thermal shields or thermal sleeve are used to address thermal issue and to protect transmission components.

For sustainability in automobile manufacturing, recycle, reuse, and repair of used up cutting tools is now an established process. Although many types of tools were designed for one time use and then throw, an increasing awareness of the impact on the natural resources have made manufacturers to put some of these back to use or sell it back to suppliers who have put up a mechanism to extract the elements e.g. Tungsten and use it for manufacturing of new tools. There are many ways in which cutting tools can be recycled. Be it by reshaping a used up throwaway type tool [1], by redesigning of a tool holder for the use of unused cutting edges [2] or reusing short length drills that are used in making of long oil holes in crank case, cylinder head, cam shaft or connecting rods [3]. This paper demonstrates successful use of used up crankshaft grinding wheels.

In case of new generation of commercial vehicles, three shaft transmissions are designed with press fitted gears on counter shaft. It allows user to save the cost of transmission manufacturing by considerable amount. In case of heavy commercial vehicles, which are being used in abusive conditions such as mining and off-road applications, it becomes absolutely necessary to ensure that the gears press fit should withstand the continuous loads and impact loads. There are design guidelines available to ensure proper fit and torque carrying capacity between the mating parts. Still, there are gear slippage, shaft and gear breakage failures in the field. In this scenario, there is a need to develop bench test procedure which will capture such failures in the prototype stage. Looking at the failures in the field, it is necessary to capture all above hidden failures in design validation phase.

Twist-beam suspensions are an example of design solution presenting acceptable performance when applied to passenger cars & light vehicles and it can provide an optimal between cost & performance in the automotive market. For these reasons, twist beam is quite popular in use in rear suspension of light vehicles. In contrary to other types of suspension, the twist-beam has a flexible torsion beam connecting the swing arms. The study of the deformation of this flexible element becomes important to understand its performance and durability behavior. As the name signifies, twist beam major performance attribute is control of twist or opposite wheel travel arising from vehicle roll or road input. Current approach for the study this deformation is through FEA & Multi-body dynamics software tools.

The fuel economy of heavy commercial vehicles can be significantly improved by reducing the rolling resistance of tires. To reduce the rolling resistance of 6×4 tractor, the super single tires instead of rear dual wheel tires are tried. Though the field trials showed a significant increase in fuel economy by using super single tires, it posed a concern of road safety when these tires blowout during operation. Physical testing of tire blowout on vehicle is very unsafe, time consuming and expensive. Hence, a full vehicle simulation of super single tire blowout is carried out. The mechanical properties of tires such as cornering stiffness, radial stiffness and rolling resistance changes during the tire blowout; this change is incorporated in simulation using series of events that apply different gains to these mechanical properties.

The interior components of a passenger vehicle are designed to provide comfort and safety to its occupants. In the event of accident, vehicle interiors are primary source of injuries when occupants interact with them. Vehicle interiors consists of Instrument panel (IP), center console, seats and controls in front of seating position etc. Severity of the injuries depends on the energy dissipating characteristics, profiles, projections of different interior components. These are assessed by ECE R21 and IS12553 head form impact tests. To evaluate the Head form impact performance on Interior components, Computer Aided Engineering (CAE) simulations are extensively used during the vehicle development. In order to predict failure of plastic components and snap joints which might lead to expose sharp edges, it is critical to model plastic material and snap joint.

Production variations of a heavy duty truck for its vibrations were measured and then analyzed through an Ishikawa diagram. Noise and Control factors of the truck idle shake were indentified. The major cause was found to be piece to piece variations of its power-train (PT) rubber mounts. To overcome the same, a new nominal level of the mount stiffness was sought based on minimization of a cost function related to vibration transmissibility and fatigue damage of the mounts under dynamic loadings. Physical prototypes of such mounts were proved to minimize the variations of the driver's seat shake at idling among various trucks of the same design. These learning's are useful for design of various subsystems or components to refine the full vehicle-Noise Vibration Harshness (NVH) at the robust design level.

Due to the recent trend in auto industry to opt for higher power engines, causes increase in vibrations levels in the passenger’s compartment. This requires a better and comprehensive model to analyze vibrations from engine to seat and steering wheel much before the proto stage of development in the design stage itself. For this purpose, modelling is done in ADAMS multi dynamics and assuming the 16 degrees of freedom of the vehicle. Further, a crank drive model is developed to simulate engine excitation forces comprising unbalanced inertia forces and torque fluctuations and their effects seat rail and steering wheel vibration is derived. This tool is an attempt to predict such vibrations caused and assist in design enhancement and streamline the procedure.

Various vehicle manufacturers are launching electric vehicles, which are more sustainable and environmentally friendly. The major component in electric vehicles is the battery, and its performance plays a vital role. Usually, the end of life of a battery in the automobile sector is when the battery capacity reaches 80% of its maximum rated capacity. The capacity of a lithium-ion cell declines with the number of cycles. So, a semi-empirical model is developed for estimating the maximum stored capacity at the end of each cycle. The parameters considered in the model explain the changes in battery internal structure, like capacity losses at different conditions. The capacity estimated using the semi-empirical model is further taken as the inputs for estimating capacity using the Artificial Neural Network (ANN) and Machine Learning (ML) techniques i.e., Linear Regression (LR), Gaussian Process Regression (GPR), Support Vector Machine methods (SVM).

Driving dynamics performance is one of the key customer attributes to be developed during product development. In the vehicle development process, freezing the hardware of the chassis aggregates is one of the major priorities to kick off the other vehicle development activities. The current work involves the development of a multilink suspension for an SUV class vehicle. Typically, each OEM performs several product development loops for maturing the vehicle design. The driving dynamics performance evaluation and tuning happens on a physical vehicle with the driver in Loop. Tuning of suspension parameter on the physical vehicle entails actual replacement of parts/components. This encompasses multiple tuning cycles in product development associated with increased cost and test time. To reduce the product development time and cost while delivering first time right chassis configuration, we took an approach of getting driver-in-loop through driving simulator in the concept phase.

In the context of vehicular safety and performance, brake pads represent a critical component, ensuring controlled driving and accident prevention. These pads consist of friction materials that naturally degrade with usage, potentially leading to safety issues like delayed braking response and NVH disturbances. Unfortunately, assessing brake pad wear remains challenging for vehicle owners, as these components are typically inaccessible from the outside. Moreover, Indian OEMs have not yet integrated brake pad life estimation features. This research introduces a hybrid machine learning approach for predicting brake pad remaining useful life, comprising three modules: a weight module, utilizing mathematical formulations based on longitudinal vehicle dynamics to estimate vehicle weight necessary for calculating braking kinetic energy dissipation; and temperature and wear modules, employing deep neural networks for predictive modeling.

The need for effective control systems is exacerbated by tighter pollution regulations and consumer demands for highly efficiently vehicles especially in the passenger segment. The air flow estimation of engine and accordingly controlling the fuel removes the lacuna of modern gasoline engines. The hot wire type mass air flow sensor is commonly used for air flow measurement, and it generally mounted in clean side piping to prevent damage to air mass flow sensor. The right estimation of air flow is possible by getting uniform flow over the different engine operating speed and load conditions. The placement of air flow sensor becomes critical considering the engine layout and packaging constraints and meeting the sensor mounting requirements. The deviation in mounting of air flow sensor will lead to consequently impact of engine performance and emissions.