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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.

Body-over-Frame is the primary type of construction used in SUVs, pick-ups, and other commercial vehicles in India. In this type of construction, the body, engine, suspensions etc. are mounted on the ladder frame. Since the frame acts as the skeleton of the vehicle, optimal design of frame at the concept stage of the vehicle program is critical for meeting all structural performance targets. Frontloading of these targets aids in architecture development and reduces future design modifications. The natural frequency response from the frame directly affects the NVH performance of the vehicle. This paper focuses on frontloading the natural frequency targets by performing concept-level simulations on the ladder frame even before creation of 3D concept data. A parametric beam model is created based on the reference vehicles. The beam model has been validated with correlation of more than 85% compared with CAE and physical testing outputs of existing vehicles.

In the conceptualization phase of vehicle development, for achieving reasonable dynamics performance, proper selection of steering system meeting all the requirements is necessary. This requires accurate prediction of major steering performance attributes like steering effort, steering torque, Turning Circle Diameter (TCD), %Ackerman and steering returnability. However, currently available models majorly depend on Computer Aided Engineering (CAE)-analysis or physical trials which requires system detailing and the same cannot be used for early prediction of the steering performances in the concept phase. This paper aims to address this deficiency with the help of a new steering performance calculator. In the calculator, performance attributes namely steering effort, steering torque, TCD and %-Ackerman has been modelled with engineering calculations and other attributes namely steering returnability&precision has been modelled through machine learning techniques.

Transportation system is at the brink of revolution and many new ways of mobility are arising in the market to ease the pressure on the established transportation infrastructure. Many companies and governments around the world are exploring innovative options in the space of shared mobility to reduce the overall carbon footprint. To expedite the adoption of shared mobility in India, it is necessary to make such options comfortable and cost-effective. One of the most effective way to make shared mobility options cost effective is to comfortably increase occupancy per vehicle footprint. This paper aims to evaluate a novel method of occupant seating to identify the maximum number of passengers a vehicle can accommodate without significant impact on occupant comfort. It is assumed that shared mobility options are used for a short duration of commute, and hence the comfort of the seat can be marginally compromised to increase the total number of occupants.