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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.

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.

This is an extension of simple fuel consumption modeling toward HEV. Previous work showed that in urban driving the overhead of running an ICEV engine can use as much fuel as the traction work. The bidirectional character and high efficiency of electric motors enables HEVs to run as a BEV at negative and low traction powers, with no net input from the small battery. The ICE provides the net work at higher traction powers where it is most efficient. Whereas the network reduction is the total negative work times the system round-trip efficiency, the reduction in engine running time requires knowledge of the distribution of traction power levels. The traction power histogram, and the work histogram derived from it, provide the required drive cycle description. The traction power is normalized by vehicle mass, so that the drive trace component becomes invariant, and the road load component nearly invariant to vehicle mass.

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.

The requirements of the automotive industry move along due to product competitiveness and this contributes to increase complexity in the requirements for evaluation. Simulation tools play a key role thanks to their versatility and multiple physical phenomena that can be represented. The axis of analysis for this paper is the problem of the interaction of airflow and water flow in the cowl/plenum/leaf screen components. Airflow is represented by HVAC system operating and water flow by the vehicle in torrential rain. Initially, one simulation is evaluated at a time, in one side, the airflow entering the HVAC system in which the amount of air entering is monitored and pressure drop, on the other, the water simulation on the vehicle, both using a Lagrangian CFD model (using with tools such as STAR CCM+® or Ansys Fluent®) Due to this, a CFD methodology was developed to evaluate the interaction of air and water flow.

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 paper, written in collaboration with Ford, evaluates the effectiveness of higher cell density combined with higher porosity, lower thermal mass substrates for emission control capability on a customized, RDE (Real Driving Emissions)-type of test cycle run on a chassis dynamometer using a gasoline passenger car fitted with a three-way catalyst (TWC) system. Cold-start emissions contribute most of the emissions control challenge, especially in the case of a very rigorous cold-start. The majority of tailpipe emissions occur during the first 30 seconds of the drive cycle. For the early engine startup phase, higher porosity substrates are developed as one part of the solution. In addition, further emission improvement is expected by increasing the specific surface area (GSA) of the substrate. This test was designed specifically to stress the cold start performance of the catalyst by using a short, 5 second idle time preceding an aggressive, high exhaust mass flowrate drive cycle.

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.

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 order to decarbonize and lower the overall emissions of the transport sector, immediate and cost-effective powertrain solutions are needed. Natural gas offers the advantage of a direct reduction of carbon dioxide (CO2) emissions due to its better Carbon to Hydrogen ratio (C/H) compared to common fossil fuels, e.g. gasoline or diesel. Moreover, an optimized engine design suiting the advantages of natural gas in knock resistance and lean mixtures keeping in mind the challenges of power density, efficiency and cold start manoeuvres. In the public funded project MethMag (Methane lean combustion engine) a gasoline fired three-cylinder-engine is redesigned based on this change of requirements and benchmarked against the previous gasoline engine.

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.

Advanced features in automotive systems often necessitate the management of complex interactions between subsystems. Existing control strategies are designed for certain levels of robustness, however their performance can unexpectedly deteriorate in the presence of significant uncertainties, resulting in undesirable system behaviors. This limitation is further amplified in systems with complex nonlinear dynamics. Hydro-mechanical clutch actuators are among those systems whose behaviors are highly sensitive to variations in subsystem characteristics and operating environments. In a P2 hybrid propulsion system, a wet clutch is utilized for cranking the engine during an EV-HEV mode switching event. It is critical that the hydro-mechanical clutch actuator is stroked as quickly and as consistently as possible despite the existence of uncertainties. Thus, the quantification of uncertainties on clutch actuator behaviors is important for enabling smooth EV-HEV transitions.

Traditionally, the controls system in production vehicles with automatic transmission interprets the driver’s accelerator pedal position as a demand for transmission input torque. However, with the advent of electrified vehicles, where actuators are located at different positions in the drivetrain, and of autonomous vehicles, which are self-driving, it is more convenient to interpret the demand (either human or virtual) in vehicle acceleration or wheel torque domain. To this end, a Wheel Torque-based longitudinal Control (WTC) framework was developed, wherein demands can be converted accurately between the vehicle acceleration or wheel torque domain and the transmission assembly input torque domain.

Development of propulsion control systems frequently involves large-scale transient simulations, e.g. Monte Carlo simulations or drive-cycle optimizations, which require fast dynamic plant models. Models of the air-path—for internal combustion engines or fuel cells—can exhibit stiff behavior, though, causing slow numerical simulations due to either using an implicit solver or sampling much faster than the bandwidth of interest to maintain stability. This paper proposes a method to reduce air-path model stiffness by adding an impedance in series with potentially stiff components, e.g. throttles, valves, compressors, and turbines, thereby allowing the use of a fast-explicit solver. An impedance, by electrical analogy, is a frequency-dependent resistance to flow, which is shaped to suppress the high-frequency dynamics causing air-path stiffness, while maintaining model accuracy in the bandwidth of interest.

The previously developed power-based fuel consumption theory for Internal Combustion Engine Vehicles (ICEV) is extended to Battery Electric Vehicles (BEV). The main difference between the BEV model structure and the ICEV is the bi-directional character of traction motors and batteries. A traction motor model was developed as a bi-linear function of positive and negative traction power. Another difference is that the accessories and cabin heating are powered directly from the battery, and not from the powertrain. The resulting unified model for ICEV and BEV energy consumption has linear terms proportional to positive and negative traction power, accessory power, and overhead, in varying proportions. Compared to the ICEV, the BEV powertrain has a high marginal efficiency and low overhead. As a result, BEV energy consumption data under a wide range of driving conditions are mainly proportional to net traction power, with only a small offset.

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.

Laser welding of the magnesium-bearing AA5xxx aluminum alloys is often beset by keyhole instability, especially in the lap through joint configuration. This phenomenon is characterized by periodic collapse of the keyhole leaving large voids in the weld zone. In addition, the top surface can exhibit undercut and roughness. In full penetration welds, keyhole instability can also produce a spikey root and severe top surface concavity. These discontinuities could prevent a weld from achieving engineering specification compliance, pose a craftsmanship concern, or reduce the strength and fatigue performance of the weld. In the case of a full penetration weld, a spikey root could compromise part fit-up and corrosion protection, or damage adjacent sheet metal, wiring, interior components, or trim.

This paper presents a method of using CAE to determine the pre-load and torque applied to a U-Bolt rear Spring Seat. In this paper it is review two U-bolt design and the stresses generated by the pre-load torque applied, based in this study a process to determine the minimal preload and the torque is discussed. By this process it is possible to determine the minimum Torque and the correct pre-load in the U-Bolt element and assuring the correct fastening of the components avoiding over stress in the Bar elements.

As electric vehicles are becoming increasingly popular and necessary for the future mobility needs of civilization, further effort is continually made to improve the efficiency, cost, and safety of the lithium-ion battery packs that power these vehicles. To facilitate these goals, this paper introduces a data driven model to predict a distribution of surface temperatures for a lithium-ion battery pack: a long short-term memory (LSTM) neural network. The LSTM model is trained and validated with lithium-ion cells electrically connected to form a battery pack. Voltage, current, state of charge (SOC), and cell surface temperature from two arrays are used as inputs from a wide range of high and low temperature drive cycles. Additionally, ambient temperature is added as an input to the LSTM model.