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The rise in demand for electric and hybrid vehicles, the issue of bearing currents in electric motors has become increasingly relevant. These vehicles use inverters with high frequency switch that generates the common mode voltage and current, the main factor responsible for bearing issues. In the machine structure, there are some parasitic capacitances that exist inherently. They provide a low impedance path for the generated current, which flows through the machine bearing. Investigating this problem in practical scenarios during the design stage is costly and requires great effort to measure these currents. For this reason, a strategy of analysis aided by electromagnetic simulation software can achieve desired results in terms of complexity and performance. This work proposes a methodology using Ansys Maxwell software to simulate two-dimensional (2D) and three-dimensional (3D) model of a three-phase permanent magnet motor with eight poles.

Metal cutting/machining is a widely used manufacturing process for producing high-precision parts at a low cost and with high throughput. In the automotive industry, engine components such as cylinder heads or engine blocks are all manufactured using such processes. Despite its cost benefits, manufacturers often face the problem of machining chips and cutting oil residue remaining on the finished surface or falling into the internal cavities after machining operations, and these wastes can be very difficult to clean. While part cleaning/washing equipment suppliers often claim that their washers have superior performance, determining the washing efficiency is challenging without means to visualize the water flow. In this paper, a virtual engineering methodology using particle-based CFD is developed to address the issue of metal chip cleanliness resulting from engine component machining operations. This methodology comprises two simulation methods.

The process of assembling the bearing and crimp ring to the steering pinion shaft is intricate. The bearing is pressed into its position via the crimp ring, which is tipped inward and fully fitted into a groove on the pinion shaft. Only when the bearing is pressed to a low surface on the pinion shaft, the caulking force for the crimp ring is achieved. The final caulking distance for the crimp ring confirms the proper bearing position. Simulating this transient fitting process using CAE is a challenging topic. Key factors include controlling applied force, defining contact between bearing and pinion surface, and defining contact between crimp ring and bearing surface from full close to half open transition. The overall CAE process is validated through correlation with testing.

This work aims to develop a PA6 nanocomposite with glass fiber (GF) and graphene nanoplatelets (GNPs) focusing on automotive parts application. Polyamide 6 is a semi-crystalline polymer that exhibits high fatigue and flexural strength, making it viable for rigorous applications. Along with the improved electrical, mechanical, thermal, and optical performance achieved in PA6 and GF-based nanocomposites, they can fill complex geometries, have great durability, and are widely utilized due to their capacity of reducing the weight of the vehicle besides a cost reduction potential. The glass fiber is a filamentary composite, usually aggregated in polymeric matrices, which aims to amplify the mechanical properties of polymers, mainly the tensile strength in the case of PA6.

Manufacturing processes impact many factors on a product. Depending on the selected method, development time, part performance and cost are affected. In the automotive sector, there is a growing demand for weight reduction due to the advent of electrification and the greenhouse gas emission regulations. In addition, geometric complexity is a challenging factor for the feasibility of mass production of parts. In this scenario, plastic materials are a very interesting option for application in various vehicle parts, since these materials can be molded by injection, vacuum forming, among others, while maintaining good mechanical properties. Almost a third of a vehicle’s parts are polymeric, making the development of these materials strategic for car manufacturers. This article investigates the impact of the presence of fiberglass in a thermoplastic automotive body part.

Polyurethane (PU) foams are versatile in automotive applications for sound absorption, due to their superior acoustic-absorbing properties, vibration damping and robustness, and seat cushioning products due to their easiness of manufacturing process and cost-effectiveness. In recent studies, micro- and nano-particles were used to improve sound absorption efficiency, these fillers help to form interconnected pore structures in the foam matrix, and this interconnection of pores is advantageous in dissipating heat generated from wave friction with the air. Some of the micro- and nano-particles used are natural fibers (like cellulose, fir, palm), silica, clay, graphene and derivatives, zeolite, and others. This review is an overview of recent advances in the incorporation of fillers in PU foams and the influence they have on the sound absorption capacity of the foams.

In a recent time, which new vehicle lines comes with a huge number of sensors, control units, embedded technologies, and the complexity of these systems (electronics, electrical and electromechanical parts) increases in an exponential way. Considering these events, the expressive generated data amount grows in the same pace, so, consume, transform, and analyze all these data to better understand the modern customer, their needs and how they use the car features becomes necessary. Through that scenario, connected vehicles developed by Ford Motor Company has been generating opportunities to feature’s improvement and cost reduction based on data analysis. This growing quantity of data might be used to optimize feature systems and help engineering teams to understand how the features have been used and enhance the systems engineering design for new or existing features.

Steel represents more than 50% of weight in vehicles, being more susceptible to corrosion processes. Corrosion studies in these components are of great industrial and economic interest, and anticorrosive coatings with efficiency of superior protection is still a relevant area in materials research. Paintings from inorganic and organic hybrid compounds have been used to produce more effective and efficient coatings. Among polymeric coatings, epoxy resin is considered one of the most used anticorrosion coatings, mainly due its excellent protective properties. High barrier level is reached by reinforcing the coatings with inorganic fillers such heavy metal, nanoparticles, silica, and now more recently, carbon-based materials, like graphene and its derivatives.

Rubber is one of the most used materials currently selected to produce automotive parts, but, for specific applications, some improvement is required in its properties through the addition of some components to the rubber compound formulation. Because of that, mechanical, thermal, and chemical properties are enhanced in order to meet strict requirements of the vast range of application of the rubber compounds. In addition to improving material properties, the combination of different substances, also aims to improve processability and reduce the costs of the final product. Recently, the use of nanofillers has been very explored because of their distinctive properties and characteristics. Among the nanofillers under study, graphene is known for its high-barrier property, thermal and electrical conductivities, and good mechanical properties.

A sophisticated finite element analysis (FEA) method for modeling interior permanent magnet (IPM) electric motors is presented. Based on this method, a coupled structural-acoustic analysis procedure was developed to simulate the motor dyno vibroacoustic responses with improved accuracy and reliability for NVH (noise, vibration, and harshness) behavior prediction over a wide range of torques and frequencies under the operational electromagnetic forces. The proposed motor modeling and analysis method is detail-oriented with high fidelity in modeling the structure and complex material representation. To effectively deal with the motor stator core constructed with large numbers of electromagnetic laminae, the unit-cell approach was employed to derive the core material properties by homogenizing the laminated core as an equivalent orthotropic material. Meanwhile, the windings were modeled by capturing the precise geometry for accuracy improvement.

Fretting fatigue was observed in standard cylindrical fatigue samples at the regions in contact with the grips of the test frames during fatigue testing for AlSi10Mg aluminum alloy produced by laser powder bed fusion process (L-PBF). The failure of the fatigue sample grips occurs much earlier than the failure of the gauge section. This results in a damaged sample and the sample cannot be reused to continue the test. This type of failure is rarely seen in materials produced by traditional manufacturing processes. In this study, X-ray residual stress analysis was performed to understand the cause of failure for L-PBF AlSi10Mg with the as-built surface condition. The result indicates that the fretting fatigue failure was caused by the strong tensile residual stress in the as-built state combining with the fretting wear between the sample and the grip. A few potential solutions to avoid the fretting fatigue failure were investigated.

Driver oblique far-side sled impacts were simulated with three surrogates. The EuroSID side impact dummy with rib extension (ES2re), the WorldSID side impact 50th percentile male dummy (WS50M), and the Global Human Body Modeling Consortium’s 50th percentile male human body (GHBM) models. The versions of the surrogates’ models were 7.0, 7.5.1, and 5.0, respectively. Surrogates were seated in the front left driver seat in a virtual generic crossover sled environment. The Finite Element (FE) based environment consisted of a driver seat, a center console, and a passenger seat. Two restraint systems were considered for each surrogate: belt only (BO) and belt plus a generic seat-mounted far-side impact airbag (BB). Surrogates were restrained using a 3-point belt that has a digressive shoulder force load limiter, and retractor, and anchor pretensioners. The far-side airbag used was a 37-liter in volume and has two chambers.

Lithium-ion batteries (LIBs) have been used as the main power source for Electric vehicles (EVs) in recent years. The mechanical behavior of LIBs subject to crush loading is crucial in assessing and improving the impact safety of battery systems and EVs. In this work, a detailed 3D finite element model for a commercial vehicle battery was built, in order to better understand battery failure behavior under various loading conditions. The model included the major components of a prismatic battery jellyroll, i.e., cathodes, anodes, and separators. The models for these components were validated against the corresponding material coupon tests (e.g., tension and compression). Then the components were integrated into the cell level model for simulation of jellyroll loading and damage behavior under three types of compressive indenter loading: (1) Flat-end punch, (2) Hemispherical punch and (3) Round-edge wedge. The comparisons showed reasonable agreement between modeling and experiments.

Currently the automotive industry has been under extremely important technological changes. Part of these changes are related to the way that users interact with the vehicle and fundamental components are the new digital cluster and screens. These devices have created a disruption in the way information is transmitted to the user, being essential for vehicle operation, including safety. Due to new operating conditions, multiple evaluations need to be performed, one of them is the solar temperature Load to ensure correct operation without compromising user safety. This test is required to identify the thermal performance on the screens mounted on the instrument panel. The performance identification is performed on both sides, analytical and physical. In regards finite element simulation it represents the solar chamber as the main source of heat and being the main mechanism of transmission the radiation.

Several factors stimulate the development of new materials in the industry. From specific physical-chemical characteristics to strategic market advantages, technology companies seek to diversify their raw materials. In the automotive sector, the current trend of electrification in vehicles and the increase of government and market demand for reducing the emission of greenhouse gases makes lighter materials more and more necessary. As electric vehicles use heavy batteries, the vehicle weight is directly related to its power demand and level of autonomy. The same applies to internal combustion vehicles where the vehicle weight directly impacts fuel consumption and emissions. In this context, there is a lot of research on special alloys and composites to replace traditional materials. Aluminum is a good alternative to steel due to its density which is almost five times smaller while that material still has good mechanical properties and has better impact absorption capability.

In modern automobiles, many new complex features are enabled by software and sensors. When combined with the variability of real-world environments and scenarios, validation of this ever-increasing amount of software becomes complex, costly, and takes a lot of time. This challenges automakers ability to quickly and reliably develop and deploy new features and experiences that their customers want in the marketplace. While traditional validation methods and modern virtual validation environments can cover most new feature testing, it is challenging to cover certain real-world scenarios. These scenarios include variation in weather conditions, roadway environments, driver usage, and complex vehicle interactions. The current approach to covering these scenarios often relies on data collected from long vehicle test trips that try to capture as many of these unique situations as possible. These test trips contribute significantly to the validation cost and time of new features.

During the design conception of an automobile, typically low-fidelity physics-based simulations are coupled with engineering judgement to define key architectural components and subsystems which limits the capability to identify NVH issues arising from systems interaction. This translates to non-optimal designs because of unexplored design opportunities and therefore, lost business efficiencies. The sparse design information available during the design conception phase limits the development of representative higher fidelity physics-based simulations. To address that restriction on design optimization opportunities, this paper introduces an alternate approach to develop reduced order predictive models using regression techniques by harnessing historical measurement and simulation data. The concept is illustrated using two driveline NVH phenomenon: axle whine and take-off shudder.

Centrifugal pumps are widely used in different thermal fluid systems in automobile industries. Computational fluid dynamics (CFD) analysis of such a thermal fluid system depends on the accurate component modeling of the system components. This paper presents CFD analysis of a centrifugal pump with two different approaches: Transient (moving grid) and the steady state - Multiple Reference Frame (MRF) methods using a commercial CFD solver Simerics MP+®. In addition, flow and pressure drop data obtained using CFD simulations of a vehicle coolant hydraulic system was compared to results from rig test data. The Transient method incorporates the real motion of the pump blades geometry and temporal flow solutions are obtained for instantaneous positions of the blade geometry. In MRF approach, the flow governing equations for the stationary zone are solved in the absolute/inertial reference frame, whereas flow in the moving zone is solved in the relative/non-inertial reference frame.

Designing an efficient vehicle coolant system depends on meeting target coolant flow rate to different components with minimum energy consumption by coolant pump. The flow resistance across different components and hoses dictates the flow supplied to that branch which can affect the effectiveness of the coolant system. Hydraulic tests are conducted to understand the system design for component flow delivery and pressure drops and assess necessary changes to better distribute the coolant flow from the pump. The current study highlights the ability of a complete 3D Computational Fluid Dynamics (CFD) simulation to effectively mimic a hydraulic test. The coolant circuit modeled in this simulation consists of an engine water-jacket, a thermostat valve, bypass valve, a coolant pump, a radiator, and flow path to certain auxiliary components like turbo charger, rear transmission oil cooler etc.

Metal binderjetting (MBJ) and bound metal deposition (BMD) are high throughput additive manufacturing process that have the potential to meet the needs of automotive volume production. In many cases, these processes require a sintering post-process to meet final dimensions. Because the sintering stage is performed free standing (i.e. without the use of tooling) and can involve up to a 20% dimensional change from green part to the final part shape, part distortion can be a concern. In this study, the sintering stage of a bridge geometry was simulated under different parameter settings using a Finite Element Analysis. The sensitivity of the simulation to various process parameter inputs was examined. Physical parts were then produced in 316L using a bound metal deposition and sintering process and compared to prediction. The sintering simulation indicated good agreement with experiment for some dimensions but highlighted the need for additional analysis.