Electrification of vehicles has become an inevitable technological advancement in the coming years as the world sees a big push to reduce emissions in an attempt to slow global warming and climate change. Vehicle electrification focuses on replacing the power produced by internal combustion engines, with systems that alternatively drive the powertrain with electricity produced by high power battery packs, hydrogen fuel cells, etc. Vehicle electrification can also be extended to other areas such as power-assisted steering, traction control, intelligent light system, electromagnetic suspension systems, all-wheel drive, active and passive restraints, etc. Battery packs, considered the heart of an electric vehicle, are majorly used in the EV industry today to replace the internal combustion engines. Battery pack systems require a balancing of performance characteristics to achieve critical product requirements. Vehicle range, driving performance, and passenger comfort are three such examples of electric vehicle requirements that can sometimes act against one another. Integrating the battery pack into the electric vehicle body (chassis) is often a challenging task for engineers. For example, integrating a battery pack might cause too much vibration during operation, affecting passenger comfort and potentially the safety of the passenger. An engineer might think to add some mass and make the battery pack heavier to reduce vibrations. However, this might negatively affect the range (mileage) of the vehicle, the acceleration characteristics, etc. Hence balancing these competing criteria is vital in the development of a battery powered electric vehicles. To achieve the desired NVH performance while also not sacrificing range or comfort, engineers test various virtual model configurations which are built in standard Finite Element Analysis software packages. This aids them in simulating the NVH characteristics to study what and how different characteristics of the battery pack in a Body-in-White (BIW) model affect performance. Manually simulating the required number of scenarios is almost an impossible task as it takes a burdensome number of man-hours. The engineer has to simulate a scenario, wait for the results, study the results and make decisions on the changes he needs to make. This is an iterative and time-consuming process which the product development teams in an EV company cannot always afford. This presentation highlights how modern simulation process automation and multi-disciplinary design space exploration (optimization) can be used in a managed environment to consistently deliver high-performance, low-cost designs within tight timelines. Specifically, this presentation reviews an example of a FEA model of a BIW electric vehicle and the NVH performance of the BIW when a battery pack is integrated. A key performance attribute of automotive bodies is their first torsion mode frequency. When the system operates at a higher torsional frequency, it indicates greater passenger comfort. Typically, NVH analysts will study attributes of a system to increase stiffness and reduce mass. But the mass and frequency of an electric vehicle system are competing characteristics, so, a fine balance needs to be achieved to satisfy all requirements necessary for product development. For this type of simulation, verification is traditionally done manually by correlating the mode shapes (dynamic deflection patterns of a system) with the required reference mode shapes, in order to ensure necessary vibration characteristics. This again is challenging as it requires a lot of time and expertise to do the correlation. This presentation also demonstrates an automated mode shape correlation technique between two analysis models, incorporated within the design space exploration. A well-qualified analysis model acts as the reference to the current model under development. Then a design space exploration (optimization) is setup that exercises a set of parameters that influence overall system stiffness and mass. These include connections (bolts, glues etc.), choice of materials of the battery pack, panel thickness assignments, as well as the inclusion/exclusion of certain components. A manufacturing cost function is applied to the exploration as well to avoid an optimal analysis solution, but an infeasible solution for manufacturing. The objective of the design space exploration (optimization) will be to minimize the mass of the system while maintaining thresholds on cost and the first torsion modal frequency of the system. This presentation will also walk through how intelligent design space exploration and data mining of the results will help you understand your design space and your product better. This will help you in making well-informed design decisions faster and aid in rapid product development.