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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.

The automotive industries around the world is undergoing massive transformation towards identifying technological capabilities to improve vehicle performance. In this regard, the engine dynamic torque plays a crucial role in defining the transient performance and drivability of a vehicle. Moreover, the dynamic torque is used as a visualization parameter in performance prediction of a vehicle to set the right engineering targets and to assess the engine potential. Hence, an accurate measurement and prediction of the engine dynamic torque is required. However, there are very few methodologies available to measure the engine dynamic torque with reasonable accuracy and minimum efforts. The measurement of engine brake torque using a torque transducer is one of the potential methods. However, it requires a lot of effort and time to instrument the vehicle. It is also possible to back-calculate the engine torque based on fuel injection quantity and other known engine parameters.

Ride is essentially the outcome of coupled dynamics of various involved sub-systems which make it too complex to deal analytically. Tires, amongst these, are known to be highly nonlinear compliant systems. Selection of tires specifications such as rated tyre pressure, etc. are generally decided through subjective assessment. While experts agree that tyre pressure affects the attributes such as ride to a noticeable degree, the quantification of the change often remains missing. In the current work, vibration levels of various sub-systems relevant to ride in an SUV are measured for three different tyre pressures at different speeds over the three randomly generated roads. For the purpose, artificial road profiles of classes A, B and C are synthesized from the spectrum of road classes defined in ISO 8608:2016 and reproduced on a four-poster test rig.

Climate change is primary driver in the current discussions on CO2 reduction in the automotive industry. Current Type approval emissions tests (BS III, BS IV) covers only tailpipe emissions, however the emissions produced in upstream and downstream processes (e.g. raw material sourcing, manufacturing, transportation, vehicle usage, recycle phases) are not considered in the evaluation. The objective of this project is to assess the environmental impact of the product considering all stages of the life cycle, understand the real opportunities to reduce environmental impact across the product life cycle. As a part of environmental sustainability journey in business value chain, lifecycle assessment (LCA) technique helps to understand the environmental impact categories. To measure overall impact, a cradle to grave approach helps to assess entire life cycle impact throughout various stages.

Fuel economy, performance and drivability are the three important parameters for evaluating the vehicle performance. Powertrain matching plays a major role in meeting the above targets. Fuel economy is measured based on city, highway and some user defined driving cycles which can be considered as real world usage profiles. Performance and Drivability is evaluated based on the in-gear, thru-gear (acceleration performance) and grade-ability performance. The load collective points of the engine greatly influence the engines performance, fuel economy and emissions, which in-turn depends on the N/V ratio of the vehicle. The optimal selection of gear and final drive ratios plays a key role in the optimization of the Powertrain for a particular vehicle. The current study involves dynamic simulation of the vehicle performance and fuel economy at transient engine test-bed for different gear and final drive ratio combinations using AVL DynoExcat-dynamometer.

This paper presents a comprehensive model of a hydraulic power steering system for predicting the transient responses under various steering inputs. The hydraulic system model, which integrates together all fluid line elements and hydraulic components, is formulated using the MSC Easy5 software. A full vehicle model is developed in ADAMS/Car. Functional Mock up Interface (FMI), a tool independent standard is used for co-simulation of ADAMS and Easy5 Dynamic models. This paper describes a co-simulation methodology developed using FMI interface for full vehicle Simulations using hydraulic power steering. A virtual simulation scheme is developed to obtain the system transient responses and the results are compared with those measured from the tests. In general, the simulation results agree with those obtained from the tests under the same steering inputs and operating 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).