Search Results

The automotive industry is currently experiencing a massive transformation, one like it has not quite seen in the past. With the advent of highly software-driven, always on, connected vehicles, the automotive industry is experiencing itself at a crossroads. While the traditional component-driven design approach to vehicle development worked in the favor of the industry for decades due to vehicles being mostly mechanical in nature, the industry now finds itself struggling to develop well-integrated vehicle solutions with the large dependency on software systems. The fast-paced nature of the software world makes it imperative to approach the development of automobiles from a Systems Engineering perspective. A function-based approach to the development of vehicle architectures can ensure cohesive systems development and a well-integrated vehicle.

This paper compares the results from three human factors studies conducted in a motion-based simulator in 2008, 2014 and 2023, to highlight the trends in driver's response to Forward Collision Warning (FCW). The studies were motivated by the goal to develop an effective HMI (Human-Machine Interface) strategy that enables the required driver's response to FCW while minimizing the level of annoyance of the feature. All three studies evaluated driver response to a baseline-FCW and no-FCW conditions. Additionally, the 2023 study included two modified FCW chime variants: a softer FCW chime and a fading FCW chime. Sixteen (16) participants, balanced for gender and age, were tested for each group in all iterations of the studies. The participants drove in a high-fidelity simulator with a visual distraction task (number reading). After driving 15 minutes in a nighttime rural highway environment, a surprise forward collision threat arose during the distraction task.

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.

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.

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.

Disc brake squeal is always a challenging multidisciplinary problem in vehicle noise, vibration, and harshness (NVH) that has been extensively researched. Theoretical analysis has been done to understand the mechanism of disc brake squeal due to small disturbances. Most studies have used linear modal approaches for the harmonic vibration of large models. However, time-domain approaches have been limited, as they are restricted to specific friction models and vibration patterns and are computationally expensive. This research aims to use a time-domain approach to improve the modeling of brake squeal, as it is a dynamic instability issue with a time-dependent friction force. The time-domain approach has been successfully demonstrated through examples and data.

A DMS (Driver Monitoring System) is one of the most important safety features that assist in the monitoring functions and alert drivers when distraction or drowsiness is detected. The system is based in a DSMC (Driver Status Monitoring Camera) mounted in the vehicle's dash, which has a predefined set of operational requirements that must be fulfilled to guarantee the correct operation of the system. These conditions represent a trade space analysis challenge for each vehicle since both the DSMC and the underlying vehicle’s requirements must be satisfied. Relying upon the camera’s manufacturer evaluation for every iteration of the vehicle’s design has proven to be time-consuming, resources-intensive, and ineffective from the decision-making standpoint.

Auto industry has faced constant challenges in the economic, technology and global trend in the recent years. This is changing the corporative mindset to find creative and innovative processes and methods to evolve the product development system to adjust and deliver competitive products that satisfy customers expectations. Integrating the work from different teams in an organization has been moving from simple roles and responsibilities definition with effective communication channels to a new vision where teamwork progresses in harmony and embraces change to satisfy customers as part of the process. The path to evolve work in engineering that relies on several computational tools continues. In this article, it is presented an integration of different tools to manage vehicle program changes using model-based systems engineering, the present work improves the reaction capabilities of the teams and enables to adjust to changes in the development of a vehicle.

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.

There are a multitude of dynamics faced by any industry. There is also a consistent search and development of technological platforms and services to address these changes. This necessitates a shared work philosophy which involves multiple stakeholders. Verification and validation are integral part of any development irrespective of product, process, or services. Also, every industry has a regulatory compliance to adhere too. But the extent of complexity and the level of dependencies or interactions between modules as well as stakeholders involved, creates slippage at some or other level. Nowadays the industries are also driven by reuse for cost effectiveness. Though it marks the significant improvement in the capability to compete, compatibility is a key measure to a successful product or service launch and sustainability.

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.

Electric vehicles (EVs) are a promising and proven technology for achieving sustainable mobility with zero carbon emissions, very low noise pollution, and reducing the dependency on fossil fuels. Global EV sales have been increasing by ~110 % since 2015, with a significant rise in 2021 (~6.75 mils EV registered) mainly led by China, the US, and Europe, amplifying the EV market share to 8.3% compared to 4.2% in 2020. Future developments aimed at designing better batteries and charging technologies that reduce charging time, reduce initial battery cost, and increased flexibility. In India, EVs are emerging significantly due to stringent Carbon di Oxide (CO2) reduction drives, increasing crude oil prices, and the availability of cheaper renewable energy. Leveraging government promotional policies, evolving the entire ecosystem, globally advantageous manufacturing costs, and competitive engineering skills form the perfect blend for India.

In this paper, a novel mixed-integer programming model is developed to optimally assign the die sets to candidate plants to minimize the total costs. The total costs include freight shipping stamped parts to assembly plants, die set movement, outsourcing, and utilization. Therefore, the objective function is weighted multi-criteria and it takes into consideration some of the key constraints in the real-world condition including “must-move die sets”. An optimization tool has been developed that takes several inputs and feeds them as the input to the mathematical model and generates the optimal assignments with the directional costs as the output. The tool has been tested for several plants at Ford and has proved its robustness by saving millions of dollars. The developed tool can easily be applied to other manufacturing systems and original equipment manufacturers (OEMs).

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.

The Composite Hybrid Automotive Suspension System Innovative Structures (CHASSIS) is a project that developed structural commercial vehicle suspension components in high volume utilising hybrid materials and joining techniques to offer a viable lightweight production alternative to steel. Three components were selected for the project:- Front Subframe Front Lower Control Arm (FLCA) Rear Deadbeam Axle