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In the field of automobile development, sufficient structure strength is the most basic objective to be accomplished. Typically, method of strength analysis could be divided into static strength and dynamic strength. Analysis of static strength constitutes the major part of the development, but the supplement of dynamic strength is also dispensable to assure structural integrity. This paper presents a methodology about analyzing the impact strength of body structure based on a Multi-body Dynamics (MBD) and Finite Element Analysis (FEA) combined method. Firstly, the full vehicle MBD model consists of Curved Regular Grid (CRG) road model, Flexible Ring Tire (FTire) model and dynamic deflection-force bump stop model was built in Adams/Car. Next, Damage Initiation and Evolution Model (DIEM) failure criteria was adopted to describe material failure behavior.

A special spot weld element (SWE) is presented for simplified representation of spot joints in complex structures for structural durability evaluation using the mesh-insensitive structural stress method. The SWE is formulated using rigorous linear four-node Mindlin shell elements with consideration of weld region kinematic constraints and force/moments equilibrium conditions. The SWEs are capable of capturing all major deformation modes around weld region such that rather coarse finite element mesh can be used in durability modeling of complex vehicle structures without losing any accuracy. With the SWEs, all relevant traction structural stress components around a spot weld nugget can be fully captured in a mesh-insensitive manner for evaluation of multiaxial fatigue failure.

In the automotive industry, the electric vehicle is the new era, and companies are committed to reducing carbon emissions by electrification of their vehicles. In the development of electric vehicles, the battery is the central power source for all the parts of the vehicle. Usually, it is placed under the body because of its size and mass. So, it is important to protect battery cells from leakage and damage from obstacles. For on-road electric vehicles, speed bumps are one of the crucial obstacles. This paper investigates and analyses the protection of battery pack systems in electric vehicles while encountering speed bump profiles at different speeds. During the physical test on a speed bump, there is a possibility of bump hit on the battery pack system and it is necessary to ensure the structural safety of the battery pack systems. In this study, CAE method has been developed to validate the battery pack system in the event of a speed bump crossing.

Finite element (FE) analyses of macroscopic stress-strain relations and failure modes for tensile tests of additively manufactured (AM) AlSi10Mg in different loading directions with respect to the building direction are conducted with consideration of melt pool (MP) microstructures and pores. The material constitutive relations in different orientations of AM AlSi10Mg are first obtained from fitting the experimental tensile engineering stress-strain curves by conducting axisymmetric FE analyses of round bar tensile specimens. Four representative volume elements (RVEs) with MP microstructures with and without pores are identified and selected based on the micrographs of the longitudinal cross-sections of the vertical and horizontal tensile specimens. Two-dimensional plane stress elastic-plastic FE analyses of the RVEs subjected to uniaxial tension are then conducted.

Many chassis and powertrain components in the transportation and automotive industry experience multi-axial cyclic service loading. A thorough load-history leading to durability damage should be considered in the early vehicle production steps. The key feature of rubber fatigue analysis discussed in this study is how to define local critical location strain time history based on nominal and complex load time histories. Material coupon characterization used here is the crack growth approach, based on fracture mechanics parameters. This methodology was utilized and presented for a truck engine mount. Temperature effects are not considered since proving ground (PG) loads are generated under isothermal high temperature and low frequency conditions without high amounts of self-heating.

Road load data is an essential input to evaluate vehicle durability and strength performances. Typically, load case of pothole impact constitutes the major part in the development of structural durability. Meanwhile, misuse conditions like driving over a curb are also indispensable scenarios to complement impact strength of vehicle structures. This paper presents a methodology of establishing Multi-body Dynamics (MBD) full vehicle model in Adams/Car to acquire the road load data for use in durability and strength analysis. Furthermore, load level between durability and misuse conditions of the same Impact road was also investigated to explore the impact due to different driving maneuvers.

The Los Angeles City Traffic (LACT) brake test is well known acclaimed procedure used by many vehicle manufacturers to assess the brake pad wear behavior and to investigate the Noise, Vibration and Harness (NVH) performance of the brake system. The LACT driving route consists of a set of real-world driving conditions, which has been considered representative of the US passenger vehicle market. The scope of this study is to mimic the LACT test using finite element analysis (FEA) to calculate the wear displacement based on Rhee’s theory. The Leading-edge and trailing edge of the brake pad’s wear tendency is also predicted from the simulation. The finite element model for wear simulation consists of brake system viz., Rotor, Knuckle, Pads, Anchor bracket, Piston, and Caliper.

Reducing emissions and the carbon footprint of our society have become imperatives requiring the automotive industry to adapt and develop technologies to strive for a cleaner sustainable transport system and for sustainable economic prosperity. Electrified hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV) and range extender powertrains provide potential solutions for reducing emissions, but they present challenges in terms of thermal management. A key requirement for meeting these challenges is accurately to predict the thermal loading and temperatures of an internal combustion engine (ICE) quickly under multiple full-load and part-load conditions. Computational Fluid Dynamics (CFD) and thermal survey database methods are used to derive thermal loading of the engine structure and are well understood but typically only used at full-load conditions.

Cargo box is one of the indispensable structures of a pickup truck which makes it capable of transporting heavy cargo weights. This heavy cargo weight plays an important role in durability performance of the box structure when subjected to road load inputs. Finite element representation for huge cargo weight is always challenging, especially in a linear model under dynamic proving ground road load durability analysis using a superposition approach. Any gap in virtual modeling technique can lead to absurd cargo box modes and hence durability results. With the existing computer aided engineering (CAE) approach, durability results could not correlate much with physical testing results. It was crucial to have the right and robust CAE modeling technique to represent the heavy cargo weight to provide the right torsional and cargo modes of the box structure and in turn good durability results.

Accurate prediction of in-vehicle powertrain bounce mode is necessary to ensure optimum responses are achieved at driver’s touch points during 4post shake or rough road shake events. But, during the early stages of vehicle development, building a detailed vehicle finite element (FE) model is not possible and often powertrain bounce modes are computed assuming the powertrain to be a stand-alone unit. Studies conducted on FE models of a large SUV with body on frame architecture showed that the stand-alone approach overestimates the powertrain bounce mode. Consequently, there is a need for a simplified version of vehicle model which can be built early on to compute powertrain modes. Previously, representing all the major components as rigid entities, simplified unibody vehicle models have been built to compute powertrain modes. But such an approach would be inaccurate here, for a vehicle with body on frame architecture due to the flexible nature of the frame (even at low frequencies).

Powertrain wiring cable is a backbone of the electrical architecture in any vehicle electrical system design. The weight of a wiring cable is increasing year by year because of the recent development on high-voltage wiring systems, hybrid electric vehicles (HEVs) and electric vehicles (EVs). Clip failure, loosening clip and terminal breakage under engine roll condition is a common issue in powertrain electric cable (or body harness routing) development cycle in automotive industry. Usage of more number of clips in cable routing results in the powertrain design being more complex and it increases manufacturing cost. The standard procedure practiced to develop any dynamic envelope is by using CAD software tools and performing rigid body movements with the help of the motion file.

Structural durability of the vehicle components is one of the key factors in design and development. This helps in understanding the capability of structures or components to withstand the loads encountered in service over a specified period of use. Durability assessment for vehicle structures requires load inputs. These load inputs can be in form of force, acceleration and displacement and typically generated from road load profiles in the testing lab or by the load groups. But if a program is in its early stage when design data is immature or lab facility is limited then acquiring these load inputs takes time and sometimes not feasible also. In this scenario, we can predict the durability load inputs from road load profiles virtually using dynamic transient simulation. The objective of this work is to predict the durability input signals from road profiles using finite element model by modal transient approach.

Roof is one of the major subsystems of the Body-In-White Structure, which significantly affects the vehicle strength and durability performance criteria. The roof structure should meet the functional targets under the standard operating conditions. Roof design considering various parameters in the initial phase is beneficial in reducing the product timeline for the OEM. The first-time right approach provides an opportunity for Optimization and Cost benefits in the longer term. This paper provides the use of Design for Six Sigma techniques to arrive at a robust and optimum design for the standard roof structure. The roof structure is designed to meet the operating conditions for durability. Roof finite element models are developed with control factors that affect the structure design. Virtual Analysis is performed on the Standard roof structure models.

In the automotive industry, the structural evaluation of suspension systems under customer abusive loads (misuse loads) is essential. The vehicle may experience the abusive loads in fore-aft, lateral and vertical directions. From a design perspective, it is crucial that the suspension should be robust enough to withstand the abusive loading due to maneuvers on various terrains. The main challenge during virtual simulation of these load cases is the time required to complete the assessment. The current method involves a detailed suspension finite element (FE) model which takes considerable computational and post processing time. Any kind of manual mistakes will further increase assessment time. The time becomes even more crucial when the product is in the final stages of the development process where quick turnaround is required.

Brakes are the critical component, plays a significant role regards to performance of vehicle. Vehicle safety is also strongly influenced by proper braking operation, which depends on pad to disc contact interface. Pad and disc surfaces are worn out due to continuous braking events, which in turn affects the life of the brake assembly and its performance. This paper presents the brake pad wear prediction of a disc brake assembly. A new and unworn pair of brake pads are considered for the study and tested under different braking scenarios. Wear simulation procedure is formulated based on Rhee’s wear formula and wear calculation model is established based on friction and wear mechanism. The correlation between the wear behavior of a friction material tested under controlled laboratory conditions and finite element method is investigated. Based on the calculated wear, lifespan of the brake pad is also calculated.

Brake moan noise is a friction induced phenomenon occurring at very low brake pressure, speeds and in the frequency range of 100-500 Hz. Moan noise is induced due to stick-slip phenomenon between brake rotor and pad. In this paper, prediction of moan noise with complex eigenvalue method along with other static steps is described. The complex eigenvalue is performed in multiple steps of analysis. Pretension analysis followed by nonlinear static analysis (For brake pressure and rotor velocity) is performed to provide a sliding motion between the pads and the disk. From the pre-stressed condition at the end of the static analysis, the tangent stiffness matrix is found out by linear perturbation method and followed by that complex eigenvalue analysis also performed. From the eigenvalue analysis, instability modes and the corresponding mode shapes of the brake assembly are calculated by considering the nonlinear effect of loading and contacts.

Artificial intelligence (AI) is dramatically changing multiple industries. AI’s potential to transform Computer-Aided Engineering (CAE) cannot be overlooked. Conventionally, Finite Element Analysis (FEA) is the simulation of any given physical phenomenon to obtain an approximate solution to a group of problems governed by Partial Differential Equations (PDE). Implementation of AI methods in this area combines human intelligence with numerical solutions to make them more efficient. This paper attempts to develop a Deep Neural Network (DNN) model to solve an elasto-plastic impact problem of a symmetric short crush tube made of three materials impacted by a moving wall. A structured learning database was established to train and validate the model using finite element simulations. Tube size, gauge and elasto-plastic material properties were used as input attributes or features. The maximum axial displacement of the tube is the target label to predict.

Accuracy of efficiency maps for permanent magnet synchronous machines depends on loss and torque calculations. Losses that occur in the stator or rotor core material as well as copper loss that occurs in the winding at high speeds are non-linear, and can be predicted by Finite Element Analysis (FEA) precisely. Though prediction is precise with FEA, the technique introduces substantial computational overhead. Replacing FEA with analytical estimates of the losses expedites loss modeling, but reduces precision of loss estimates, making efficiency calculations less precise as well. The prediction method proposed in this paper potentially offers accuracy similar to that attained using FEA while reducing the computational overhead typically required.

Multiphase machines continue to increase in popularity in high power applications due to their proven benefits compared to their three-phase counterparts. However, with the increased phase number and, therefore, the increased number of degrees of freedom, the complexity of both modelling and control strategies significantly increases. This paper proposes a dynamic modelling method for six-phase interior permanent magnet machines using the vector space decomposition transformation, which can be extended to machines with any number of phases. The proposed technique considers the nonlinear characteristics of the machine, such as spatial harmonics, magnetic saturation, and cross-coupling, which are based on flux linkage look-up tables from finite element analysis. The main contribution of this paper is the consideration of the effect of harmonic components and asymmetries within the machine windings on losses.

Strain-rate sensitivity has been neglected in the simulation of the traditional stamping process because the strain rate typically does not significantly impact the forming behavior of sheet metals in such a quasi-static process, and traditional crank or link mechanical presses lack the flexibility of slide motion. However, the recent application of servo drive presses in stamping manifests improvement in formability and reduction of springback, besides increased productivity and energy savings. An accurate simulation of servo stamping entails constitutive models with strain-rate sensitivity. This study evaluated a few strain rate-sensitive models including the power-law model, the linear power-law model, the Johnson-Cook model, and the Cowper-Symonds model through the exercise of fitting these models to the experimental data of a deep draw quality (DDQ) steel.