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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.

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.

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.

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.

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.

This study investigated the potential of generating reactive chemical species (including radicals) through pilot fuel injection in negative valve overlap for improving the combustion and emissions performances of spark ignition gasoline engines under low load and low speed operating conditions. Several Ford sub-models were used for simulating the physics and chemistry processes of injecting a small amount of fuel in NVO (negative valve overlap). Effects of different NVO degrees and different pilot injection timings, factors for fuel conversion were simulated and investigated. CO and H2 conversions during NVO, CO and H2 amounts before spark timing were used for comparing different schemes.

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.

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.

The automotive industry is passing for a big transformation, due to technologies advance. The electrical technologies are also on a good rising curve, calling the attention of the Original Equipment Manufacturer (OEMs). This scenario generates the demand for a faster method to train their new hired engineers, when compared with usual on the job training. Model in the Loop (MiL) consists in one of the real-time embedded systems test phases, which is developed in a computational environment, performing a mathematical modeling of the system, presenting an interface that allows the visualization of its dynamics and the signals involved. Two powerful software in industry that apply MiL are the Matlab and Simulink. A project involving these applications was proposed for a team of new hired engineers, developing models of several vehicle Electronic Control Units (ECUs), with some scope reduction as an example the functional requirements reduction.

This article aims to analyze the potential economic effects for consumers with the implementation of the Vehicle-to-Grid (V2G) and the Vehicle-to-Home (V2H) networks in Brazil. Nowdays, the usage of both technologies in Brazil are at a regulatory vacuum for both Battery Electric Vehicles (BEVs), Plug-in Hybrid Electric Vehicles (PHEVs) and Hybrid Electric Vehicles (HEVs). Usually, when a legislation lack occurs, the local OEMs adopt IECs (International Electrotechnical Commissions) and/or SAEs (Society of Automotive Engineers) reference international standards, such as SAE J1634 for range test.

The work presented here is part of the research done in the field of voice biometrics. This paper helps to understand the state-of-the-art in speaker recognition technology potentially capable of solving challenges related to speaker identification (to identify a speaker among multiple speakers) and speaker verification/authentication (to recognize the current speaking person at a pre-defined access level and authenticate accordingly). The research was focused on performing an unbiased evaluation of two individual voice biometric services. The level of accuracy in identifying and authenticating individuals using these services provides an insight into the current state of technology and the state of what other dual authentication methods could be used to achieve a desired True Acceptance Rate (TAR) and False Acceptance Rates (FAR).

Electrochemical models of lithium ion batteries are today a standard tool in the automotive industry for activities related to the computer-aided engineering design, analysis, and optimization of energy storage systems for electrified vehicles. One of the challenges in the development or use of such models is the need of detailed information on the cell and electrode geometry or properties of the electrode and electrolyte materials, which are typically unavailable or difficult to retrieve by end-users. This forces engineers to resort to “hand-tuning” of many physical and geometrical parameters, using standard cell-level characterization tests. This paper proposes a method to provide information and data on individual electrode performance that can be used to simplify the calibration process for electrochemical models.

Vehicle fire investigators often use the existence of burn patterns, along with the amount and location of fire damage, to determine the fire origin and its cause. The purpose of this paper is to study the effects of ventilation location on the interior burn patterns and burn damage of passenger compartment fires. Four similar Ford Fusion vehicles were burned. The fire origin and first material ignited were the same for all four vehicles. In each test, a different door window was down for the duration of the burn test. Each vehicle was allowed to burn until the windshield, back glass, or another window, other than the window used for ventilation, failed, thus changing the ventilation pattern. At that point, the fire was extinguished. Temperatures were measured at various locations in the passenger compartment. Video recordings and still photography were collected at all phases of the study.

The System Usability Scale (SUS) is used across industries, to evaluate a product’s ease of use. As the automotive industry increases its digital footprint, the SUS has found its application as a simple and reliable assessment of various in-vehicle human machine interfaces. These evaluations cover a broad scope and it is important to design studies with participant fatigue, study time, and study cost in mind. Reducing the number of items in the SUS questionnaire could save researchers time and resources. The SUS is a ten-item questionnaire that can measure usability and learnability, depending on the system. These ten questions are highly correlated to each other suggesting the SUS score can be determined with fewer items. Thus, the focus of this paper is two-fold: using principal component analysis (PCA) to determine the dimensionality of SUS and using this finding to reduce variables and build a regression equation for SUS scores for in-vehicle human machine interfaces.

The fatigue analysis of a full car body requires the sheet metal (sheet fatigue), spot welds (spot weld fatigue) and seam welds (seam weld fatigue) to be thoroughly evaluated for durability. Traditionally this has always been done in the time domain, but recently new frequency domain techniques are able to perform these tasks with numerous advantages. This paper will summarize the frequency domain process and then compare the results and performance against the more usual time domain process.

Recently, graphene has attracted both academic and industrial interest because it can produce a dramatic improvement in properties at very low filler content. This review will focus on the latest studies and recent progress in the swelling resistance of rubber compounds due to the addition of graphene and its derivatives. This work will present the state-of-the-art in this subject area and will highlight the advantages and current limitations of the use of graphene for potential future researches.

The long-term weathering behavior of three different 3D printable, non-stabilized, UV cure resin formulations (A and B with thiol-ene base, and C with acrylate chemistry) was studied using tensile testing, nano-indentation, and photoacoustic infrared (FTIR-PAS) spectroscopy. To this end, type I tensile bars were printed from each resin system using a 3D printer, and were post UV-cured under a broad spectrum source. Systems A and C showed a similar trend after weathering. They first experienced an increase in modulus and tensile strength, due to additional crosslinking of the residual unreacted species. This increase in mechanical properties was followed by a drop in modulus, tensile strength, and percent elongation, due to the over-crosslinking and consequent embrittlement. System B, however, showed remarkable retention of the mechanical properties before/after weathering.

Given continued industry focus on reducing parasitic losses, the ability to accurately measure the magnitude of losses on all driveline components is required. A standardized test procedure enables manufacturers and suppliers to measure component losses consistently, in addition to offering a reliable process to assess enablers for efficiency improvements. This paper reviews the development of SAE draft standard J3218, which is a comprehensive test procedure to break-in and characterize the efficiency of beam axles. Focus areas of the study included ensuring the axle’s efficiency does not change as it is being characterized, building a detailed map of efficiency at a wide range of operating points, and minimizing test time. The resulting break-in procedure uses an asymptotic regression approach to predict fully broken in efficiency of the axle and determine how much the efficiency of the axle changes during the characterization phase.