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Connectivity in ground vehicles allows vehicles to share crucial vehicle data, such as vehicle acceleration and speed, with each other. Using sensors such as radars and lidars, on the other hand, the intravehicular distance between a leader vehicle and a host vehicle can be detected. Cooperative Adaptive Cruise Control (CACC) builds upon ground vehicle connectivity and sensor information to form convoys with automated car following. CACC can also be used to improve fuel economy and mobility performance of vehicles in the said convoy. In this paper, a CACC system is presented, where the acceleration of the lead vehicle is used in the calculation of desired vehicle speed. In addition to the smooth car following abilities, the proposed CACC also has the capability to calculate a speed profile for the ego vehicle that is fuel efficient, making it an Ecological CACC (Eco-CACC) model.

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.

In recent years, the automotive industry has seen an exponential increase in the replacement of mechanical components with electronic-controlled components or systems. engine, transmission, brake, exhaust gas recirculation (EGR), lighting, driver-assist technologies, etc. are all monitored and/or controlled electronically. Connected vehicles are increasingly being used by Original Equipment Manufacturers (OEMs) to collect and transmit vehicle data in real-time via the use of various sensors, actuators, and communication technologies. Vehicle telematics devices can collect and transmit data about the vehicle location, speed, fuel efficiency, State Of Charge (SOC), auxiliary battery voltage, emissions, performance, and more. This data is sent over to the cloud via cellular networks, where it can be processed and analyzed to improve their products and services by automotive companies and/or fleet management.

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.

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.

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.

This study focused on occupant responses in very large pickup trucks in rollovers and was conducted in three phases. Phase 1 - Field data analysis: In a prior study [9], 1998 to 2020 FARS data were analyzed; Pickup truck drivers with fatality were 7.4 kg heavier and 4.6 cm taller than passenger car drivers. Most pickup truck drivers were males. Phase 1 extended the study by focusing on the drivers of very large pickup trucks. The size of 1999-2016 Ford F-250 and F-350 drivers involved in fatal crashes was analyzed by age and sex. More than 90% of drivers were males. The average male driver was 179.5 ± 7.5 cm tall and weighed 89.6 ± 18.4 kg. Phase 2 – Surrogate study: Twenty-nine male surrogates were selected to represent the average size of male drivers of F-250 and F-350s involved in fatal crashes. On average, the volunteers weighed 88.6 ± 5.2 kg and were 180.0 ± 3.2 cm tall with a 95.2 ± 2.2 cm seated height.

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).

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.

The DrivAer realistic generic car model is now established as one of the benchmark geometries to assess the aerodynamic flow field characteristics associated with passenger vehicles. Since its introduction in 2012, the database of experimental studies has grown and provides excellent validation opportunities for analytical methods. This paper compares Computational Fluid Dynamics (CFD) simulations for integral forces, surface pressure distribution and velocity flow fields for the DrivAer model in the notchback configuration. Transient CFD data are obtained by employing hybrid Reynolds Averaged Navier-Stokes (RANS) and Large Eddy Simulation methods (Detached Eddy Simulation - DES) using the finite volume solvers Simcenter Star-CCM+ and the openFOAM based flow solver IconCFD. Computational results are calculated using Wall Resolved Meshes (WRM), where y+ < 1, and Wall Function Meshes (WFM), where 30 < y+ < 100.

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.

By utilizing the vehicle to infrastructure communication, the conventional Green Light Optimized Speed Advisory (GLOSA) applications give speed advisory range for drivers to travel to pass at the green light. However, these systems do not consider the traffic between the ego vehicle and the traffic light location, resulting in inaccurate speed advisories. Therefore, the driver needs to intuitively adjust the vehicle's speed to pass at the green light and avoid traffic in these scenarios. Furthermore, inaccurate speed advisories may result in unnecessary acceleration and deceleration, resulting in poor fuel efficiency and comfort. To address these shortcomings of conventional GLOSA, in this study, we proposed the utilization of collaborative perception messages shared by smart infrastructures to create an enhanced speed advisory for the connected vehicle drivers and automated vehicles.