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In automotive Body-In-White (BIW) structures, stiffness and the fatigue behavior is greatly influenced by the properties of its joints. Spot welding is one of the most widely used process for joining of sheet metals in BIW. Spot weld fatigue life under Accelerated Durability Test (ADT) is crucial for durability performance of BIW structures. Experience of BIW validations highlighted more number of spot weld failures in CAE when compared to actual tests. Hence, lot of iterations in the form of design modifications are required to be carried out to make these spot welds meet the targets which increases design & development time as well as cost. Current practice uses force-based approach for predicting spot weld fatigue life in CAE. To improve the spot weld fatigue life correlation, extensive study has been carried out on the approaches used for calculating spot weld fatigue life, namely force & stress-based approaches.

As automobiles become increasingly quieter, the wiper operation noise becomes more noticeable by the customer. This paper deals with the experimental approach and the methodology to investigate the Friction induced wiping noise. Role of design in a wiper system plays a very imperative task in meeting the performance of wipers but at the same time it does not cater to the NVH issues. Some of the important design parameters which affect the NVH properties of the wiper system are highlighted in this paper. For better understanding of the system some of the best in class vehicles for SUV category were tested and compared with our test vehicle. In this study more importance given to analytical part which is more important to investigate and in depth study of the friction induced noise. For analytical study some techniques such as time frequency domain i.e. Wavelet transforms, frequency domain and time domain where extensively used.

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.

Customer perceived quality (CPQ) of the car is the impression of excellence that a customer experiences the brand through sight, sound, touch, and scent. Molded-in-color (MIC) bumper’s aesthetic appeal contributes significantly to the CPQ of the car. Typical parameters used to define CPQ are color, gloss, grain definition, grain depth, geometry and draft. In this work the durability of the color and gloss post ageing is understood by using analytical and characterization tools. Using the results of ageing characterization, an attempt has been made to understand the retained newness of MIC bumper.

This article delineates the importance of infrastructure and its related aspects on sustainability of transportation on global and local context. Almost 7% of the GDP in India is spent on transportation and 6% of the CO2 emissions in the world is due to transportation. In countries like India, the road transport has significantly grown over other forms of mobility. This articles introduces different forms of transportations that exists today and the importance of sustainability in transportation sector. Sustainable transportation depends on development of infrastructure to enable smart transport solutions involving intelligent transport system, electric mobility, information management, vehicle health monitoring, advanced traffic management system and driver assistance system in a vehicle. The challenges includes existing transport operations, environmental impact and complexity of existing transport network.

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.