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To meet vehicle interior noise targets and expectations, components including those related to electric vehicles (EVs) can effectively be treated at the source with an encapsulation approach, preventing acoustic and vibration sources from propagating through multiple paths into the vehicle interior. Encapsulation can be especially useful when dealing with tonal noise sources in EVs which are common for electrical components. These treatments involve materials that block noise and vibration at its source but add weight and cost to vehicles – optimization and ensuring the material used is minimized but efficient in reducing noise everywhere where it is applied is critically important. Testing is important to confirm source levels and verify performance of some proposed configurations, but ideal encapsulation treatments are complex and cannot be efficiently achieved by trial-and-error testing.

Electric and Hybrid vehicles have standards for emitting enough noise to reduce danger and risk to pedestrians when operating at low speeds. Simulation can help to support development and deployment of these systems while avoiding a time-consuming, test-based approach to design these AVAS (Acoustic Vehicle Alerting System) warning systems. Traditionally, deterministic simulation methods such as Finite Element Method (FEM) and Boundary Element Method (BEM) are used at low frequencies and statistical, energy-based methods such as Statistical Energy Analysis (SEA) are used at high frequencies. The deterministic methods are accurate, but computationally inefficient, particularly when the frequency increases. SEA is computationally efficient but does not capture well the physics of exterior acoustic propagation. An alternative method commonly used in room acoustics, based on geometrical or ray acoustics, is “Ray Tracing” and can be used for sound field prediction.

Statistical Energy Analysis (SEA) is widely used for modeling the vibro-acoustic response of large and complex structures. SEA makes simulations practical thanks to its intrinsic statistical approach and the lower computational cost compared to FE-based techniques. However, SEA still requires underlying models for subsystems and junctions to compute the SEA coefficients which appear in the power balance equations of the coupled system. Classically, such models are based on simplified descriptions of the structures to allow analytical or semi-analytical developments. To overcome this limitation, the authors have proposed a general approach to SEA which only requires the knowledge of impedances of the structures to compute SEA coefficients. Such impedances can always be computed from an accurate FE model of each component of a coupled system.

Quality and refinement are of paramount importance for luxury vehicles. The rapid electrification of the automotive industry has increased the contribution of aeroacoustics to the consumer perception of sound quality. The ability to predict whole vehicle aeroacoustic interior noise is essential in the development of vehicles with an extraordinary acoustic environment. This publication summarises the development of a process to combine lattice Boltzmann computational fluid dynamics simulations, with a whole vehicle statistical energy analysis model, to predict the aeroacoustic contribution from all relevant sources and paths. The ability to quantify the relative contribution of glazing panels and path modifications was also investigated. The whole vehicle aeroacoustic interior noise predictions developed, were found to be within 2dB(A) of comparable test vehicle wind tunnel measurements, across a broad frequency range (250-5000 Hz).

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.

A new artificial intelligence (model order reduction) / finite element coupled approach will be presented for the risk assessment of battery fire during a car crash event. This approach combines standard crash finite element for the main car body with a reduced order model for the battery. Simulation is today used by automotive engineering teams to design lightweight vehicle bodies fulfilling vehicle safety regulations. Legislation is rapidly evolving to accommodate the growing electrical vehicle market share and is considering additional battery safety requirements. The focus is on avoiding internal short circuit due to internal damage within a cell which may result in a fire hazard. Assessing short circuit risk in CAE at the vehicle level is complex as there involves phenomena at different scales. The vehicle deforms on a macroscale level during the impact event.

The present contribution will present how local micromechanical properties can be used in an industrial way to assess the crash performance of parts made of short fiber reinforced polymers. To this end, local information about the material structure, predicted by a Manufacturing Process Simulation (MPS), is transferred and mapped automatically on the performance composite part model. The homogenization and mapping techniques will be presented for elastic and nonlinear application fields. Short fiber reinforced injected thermoplastics are widely used in the automotive industry in mass production. Reliable prediction of the performance of short fiber reinforced thermoplastics by simulation for statics and crash simulation can be achieved only by accounting for the full manufacturing process coming from process simulation software.

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.

Recent developments in the prediction of the contribution of wind noise to the interior SPL have opened a realm of new possibilities. The main physical mechanisms related to noise generation within a turbulent flow and the vibro-acoustic transmission through the vehicle greenhouse is nowadays better understood. Several simulation methods such as CFD, FEM, BEM, FE/SEA Coupled and SEA can be coupled together to represent the physical phenomena involved. The main objective being to properly represent the convective and acoustic component within the turbulent flow to ensure proper computation of the wind noise contribution to the interior SPL of a vehicle.

Recent developments in the prediction of the contribution of wind noise to the interior SPL have opened a realm of new possibilities in terms of i) how the convective and acoustic sources terms can be identified, ii) how the interaction between the source terms and the side glass can be described and finally iii) how the transfer path from the sources to the interior of the vehicle can be modelled. This paper discusses in detail these three aspects of wind noise simulation and recommends appropriate methods to deliver required results at the right time based on i) simulation and experimental data availability, ii) design stage and iii) time available to deliver these results. Several simulation methods are used to represent the physical phenomena involved such as CFD, FEM, BEM, FE/SEA Coupled and SEA.

Industrial requirements imply optimizing the development cycle, reducing manufacturing costs and reaching marketable product maturity as fast as possible. The design stage often involves multiple sites and various partners. In this context, the use of computer simulation becomes absolutely necessary to meet industrial needs. Nevertheless, this activity can be effective only if it is integrated correctly in the industrial organization. In the aeronautical and space systems industry, mechanical specifications often require the use of composites reinforced by continuous carbon fibers. The goal of this article is to describe how, on a time frame of nearly twenty years, a series of scientific and technical tasks were carried out in partnership in order to develop, validate and implement Resin Transfer Molding (RTM) flow simulation and cure analysis for high performance composites. The research stage started at the university in 1991.

This paper discusses the development of a computationally efficient numerical method for predicting the acoustics of rattle events upfront in the design cycle. The method combines Finite Elements, Boundary Elements and SEA and enables the loudness of a large number of rattle events to be efficiently predicted across a broad frequency range. A low frequency random vibro-acoustic model is used in conjunction with various closed form analytical expressions in order to quickly predict impact probabilities and locations. An existing method has been extended to estimate the statistics of the contact forces across a broad frequency range. Finally, broadband acoustic radiation is predicted using standard low, mid and high frequency vibro-acoustic methods and used to estimate impact loudness. The approach is discussed and a number of validation examples are presented.

Two alternative finite element formulations are described which consider the influence of normal stress components on sheet deformations in Quick Plastic Forming [1]. The new formulations, single field bricks and multi-field shells, were implemented in the forming simulation program PAM-STAMP [2] using a non-linear viscoelastic constitutive relation [3,4]. Simulations of two industrial components indicate that both new elements simulate local necking more accurately than the standard shells which ignore normal stresses. The multi-field shells require slightly more calculation time than the standard shells and significantly less than equivalent brick models.

The Finite Element Method (FEM) and the Statistical Energy Analysis (SEA) are standard methods in the automotive industry for the prediction of vibrational and acoustical response of vehicles. However, both methods are not capable of handling the so called “mid frequency problem”, where both short and long wavelength components are present in the same system. A Hybrid method has been recently proposed that rigorously couples SEA and FEM. In this work, the Hybrid FE-SEA method is used to predict interior noise levels in a trimmed full vehicle due to broadband structure-borne excitation from 200Hz to 1000Hz. The process includes the partitioning of the full vehicle into stiff components described with FE and modally dense components described with SEA. It is also demonstrated how detailed local FE models can be used to improve SEA descriptions of car panels and couplings.