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Electric vehicles are becoming a rapid growing part of the automotive scene. Batteries are considered as one of the most important and challenging components in the development of electric vehicles. The mechanical performance of the battery module is of great interest and the crashworthiness analysis of the battery module is always a critical design aspect. In crash and other severe events, the battery module is subject to impact loads from different directions. The module is designed with a capability to be deformed and collapsed in a controlled manner to mitigate safety critical damage to battery cells inside the module. In the design process, it is necessary to consider the distribution of the impact loads during the crash to minimize the local damage. In this paper, a finite element model is developed and used as an efficient simulation tool to analyze the dynamic behavior of a generic battery module upon crushing and shocking.

One of the major concerns in the vehicle trim part design is the acoustic targets, which are generally defined by absorption area or coefficients, and sound transmission loss (STL) or sound insertion loss (SIL). The breaking down of acoustic targets in vehicle design, which is generally referred to as cascading, is the process of determining the trim part acoustic targets so as to satisfy full vehicle acoustic performance. In many cases, these targets are determined by experience or by subjective evaluation. Simulation based transfer path analysis (TPA), which traces the energy flow from source, through a set of paths to a given receiver, provides a systematic solution of this problem. Guided by TPA, this paper proposes a component level target setting approach that is based on the statistical energy analysis (SEA), an efficient method for vehicle NVH analysis in mid and high frequencies.

Electric vehicle makers have largely relied on aluminum to make their cars lighter in hopes of offsetting the weight of the battery pack and reducing overall weight. Distortion of Aluminum welding is a big issue due to Aluminum’s high coefficient of expansion ratios. This paper presents an effective numerical approach to minimize weld-induced distortion in Electrical Vehicle Aluminum assembly structures using welding sequence optimization. A numerical optimization framework based on genetic algorithms and Finite Element Analysis (FEA) is developed and implemented. The shrinkage method calibrated using transient approach, is used for the weld sequence optimization to reduce the computation time. The optimization results show that the proposed calibration approach can contribute substantially to reduce distortion by optimizing weld sequences. It enhances final aluminum assembly quality while facilitating and accelerating design and development.