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Automotive industry is inclined towards connected, comfortable, environment friendly, efficient and smarter systems. Advanced Driver Assist System (ADAS) technology assist drivers to achieve a safer as well as better ride by automation and improvisation of the vehicular systems. With the advent of ADAS system, there is a significant focus not only in the development of Electronic Control Units (ECUs) and its features to cater to the emerging market but also on the information that could be displayed to meet the functional as well as safety requirements. This ADAS information display ensures timely notification to the driver with unique alerts that can be acoustic or visual. These systems should be tested thoroughly to ensure reliability as failures may impose severe risk on the OEM. Hardware in the loop testing has been largely adopted by industry against manual testing in lieu of the testing constraints imposed by the latter.

Automotive industry is witnessing a significant growth in the number of Electronic Control Units (ECUs) and its features owing to the focused inclination towards customer preference, comfort, safety, environmental friendliness and governmental regulations. The software components are booming as the pivotal to cater to the technology-driven trends such as diverse mobility, autonomous driving, electrification, and connectivity. This necessitates exhaustive testing to ensure quality of the system as any unpredictable failures may impose severe financial and market risk on the OEM. The industry has largely supplemented Hardware-in-the-loop (HIL) testing to manual testing considering the testing constraints posed by the latter. Automation trends complement the demand for quick yet exhaustive testing prior to the market launch.

Model based computer-aided processes offer an economical and accelerated alternative to traditional build-and-test "Edisonian" approaches in engineering design. Typically, a CAE based design problem is formulated in two parts, viz. (1) the inverse design problem which involves identification of the appropriate geometry with desired properties, and (2) the forward problem which is the prediction of performance from the product geometry. Solution to the forward problem requires development of an accurate model correlated to physical data. This validated model could then be used for Virtual Verification of engineering systems efficiently and for solving the inverse problem. This paper demonstrates the rigorous process of model development, calibration, validation/verification, and use of the calibrated model in the design process with practical examples from automotive chassis and powertrain systems.

Virtual Verification (VV) of engineering designs is a critical enabler in the Product Development (PD) process to reduce the time-to-market in a cost efficient manner. Reliance on cost effective VV methods have significantly increased with increased pressure to meet customer expectations for new products at reduced PD budgets. Computer Aided Engineering (CAE) is one such VV method that affords an engineer to make decisions about the ability of the designs to meet the design criteria even before a prototype is built. The first step of the CAE process is meshing which is a time consuming, manual and laborious process. Also mesh development time and accuracy significantly varies with the (1) component (trim body, engine, suspension, brakes, etc.), (2) features predominantly occurring in the component (welds, ribs, fillets, etc.), meshing guidelines based on which the model needs to be developed (durability, safety, NVH, etc.), and the expertise of the meshing engineer involved.