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In order to sustain in automobile industry, fuel economy and robustness are playing vital role in vehicle. Every gram of weight will have an impact on fuel economy, thus burning a hole in consumers pocket and contributing heavily to the carbon footprint. Composite material development plays important role in meeting the stringent self-imposed targets of the automotive manufacturers and light weighting is becoming a prime option for improving Fuel Economy. The main objective of this paper is to optimize the weight of the luggage lid floor and reduce its cost without compromising on the strength by changing the raw material and manufacturing process. This part is in trunk compartment of the vehicle. Main function of this part is to withstand the luggage load under various user loading patterns at varied temperature and while driving on different road conditions.

FEA based simulations are extensively used in automotive industry for improving the product design and reducing the time taken for design and prototyping. FEA based simulations require material data as an input in form of material models. Most commonly used material models for simulation of metallic materials are elastic models and elasto-plastic models, which provide very good correlation till ultimate tensile strength (UTS). For simulation beyond UTS value, elasto-plastic material model has to be used along with material model considering the damage accumulation post UTS. For crash like event in automotive crash, required material models should consider the effect of various stress state conditions (Triaxiality) and strain rate sensitivity of materials along with damage accumulation. In LS Dyna solver, MAT_ADD_EROSION material model (GISSMO) along with MAT_024 is widely used for these applications.This paper will focus on development of GISSMO material model card for advanced high strength steel. Various stress state conditions will be considered along with strain rate sensitive properties. Paper will also cover the MAT_024 material model card which is used in association with MAT_ADD_EROSION for elasto-plastic region. Simulated data of coupons and component will be validated with experimental results.

In most cases, the properties of a metal are evaluated in their as rolled condition, prior to any work hardening or bake hardening. But in the Automotive World, these steels get work hardened during the forming process and bake hardened in the paint shop. The goal of this paper is to evaluate the variations in the performance of Dual Phase (DP) steels and understand the most optimized method of testing and property generation. This method can then be used to extrapolate to real automotive components. Dual Phase Steels or DP Steels contain a mixture of Ferrite & Martensite from which they derive their name. They are a part of the advanced high strength and ultra-high strength steels steel family according to World Auto Steels. The Ferrite phase, with its iron content contributes to the material displaying an increased level of ductility whilst, the martensitic phase provides the steel with increased mechanical strength. These two properties together enable the steel to be highly desirable in the automotive industry with varying uses from being used as Body Panels to Crash critical components. These two critical part sets of the vehicle are often heavily simulated and tested. Over the course of this paper, we will try to understand the effects of work hardening, also referred to as Pre-Straining and bake hardening on two popularly used DP Steels. DP grade steels, ISC590Y and ISC780Y were used for the study. The aim of the study is to understand the effect of bake hardening, effect varying levels of work hardening and bake hardening on these steels and correlate it to the base data which is used in simulations. It is also important to try and understand the optimum degree of work hardening that might be required to gain the most out of these steels.

Energy dispersive X-ray fluorescence (EDXRF) analysis have made it possible to conduct elemental analysis on a variety of fields, including those with environmental, automotive, geological, chemical, pharmaceutical, archaeology, and biological origins. The ability of EDXRF to deliver quick, non-destructive, and multi-elemental analytical findings with increased sensitivity is of great importance. It is a vital tool for quality control and quality assurance applications. Thus, EDXRF plays an important role to compare batch-to-batch products for meeting quality standards.This paper presents application of EDXRF as an effective tool for quick qualitative and quantitative evaluation of given samples. A few simple case studies demonstrating application of EDXRF are presented, which includes identification of the filler contained in the polymer, coating thickness, elemental composition of the particulate matter collected on filter paper, multi-element analysis of printed circuit boards (PCB) and a delamination case. Such type of rapid and non-destructive technique provides an effective means for analysis of variety of materials used in automobiles.

Helmholtz resonator is a very common anechoic measure, and it is widely used in pipe acoustic fields. Based on the enlightenment of the classic Helmholtz resonator, this paper proposes a headrest resonator model and extends it to the acoustic field of the passenger cabin to improve the road noise in the car.Firstly, through the theoretical model of Helmholtz resonator, the relationship between its resonance frequency and the geometric size of the resonator is clarified. Secondly, the influence of the headrest resonator on the acoustic field characteristics of the car is studied through finite element simulation analysis. It is demonstrated that the headrest resonator is placed in the car, and the sound pressure distribution characteristics of the passenger's inner ear near the resonance frequency change significantly. At the same time, through 3D printing, a sample of the headrest resonator is made. In addition, the acoustic test of the passenger cabin-headrest resonator coupling system is designed, and the test results are compared with the simulation results, which proves the effectiveness of the headrest resonator in improving the acoustic field characteristics in the car. And the test further verifies that the headrest resonator reduces the peak of the vehicle road noise of 120Hz by 3-5dB.It is hoped that the research in this paper can provide a low-cost-effective solution and enlightenment for the improvement of low-frequency noise in the car.

The paper discusses the process of developing an SAE damping measurement test method that is suitable for testing bars that are not made of steel or are difficult to measure with the traditional Oberst bar method. The method is based on measuring mechanical impedance (force over velocity) of a vibrating bar. The bar is excited at the center using a shaker and hence it is also called a CenterPoint method. The paper discusses the round robin tests that have been conducted so far and discusses the test results that will help develop the standard. The paper discusses the variability of the round robin test results within a laboratory, between laboratories, as well as the coefficient of variation for these measurements. The paper also discusses various parameters that should be carefully monitored in this study, that otherwise could affect the precision of the test procedure.

The automotive acoustics arena is rich with application opportunities for carbon neutral or climate positive parts. The design of potential “green” NVH solutions however cannot compromise the intended acoustical performance of the vehicles. This paper investigates the acoustical needs of OEM vehicles with an emphasis to apply green solutions. The acoustic solutions proposal in this paper compares and contrasts barrier densities, sealing characteristics between traditional and carbon neutral acoustical barriers. It also compares important material properties. Furthermore, these comparisons demonstrate that vehicular acoustic performance need not be compromised as the industry moves towards more climate friendly initiatives. Lastly, the paper identifies an ideal application for planet friendly, carbon neutral NVH solutions. This is accomplished by using acoustical barrier applications that are production ready and commercially available today. None of these carbon neutral NVH solutions alter the anticipated acoustical performance of the target vehicle in a negative manner.

Following Part 1 of the previous study, this paper reports the structure’s exciting force and summarize the overall research results. An experimental study was conducted to clarify the relationship between engine combustion and vibration, and to establish technology to suppress it. This study focused on the vehicle interior noise caused by combustion in which vibration transmission is the main component at high speed and high load region. A phenomenon in which both the combustion’s exciting force and the structure’s exciting force are combined is defined as vehicle interior noise caused by combustion. Conventionally, combustion and vibration are often discussed in terms of the average cycle, but considering the nonstationary property of vibration, in this paper analyzed the structure’s exciting force characteristics for vibration in cycle-by-cycle. Analysis was conducted using the combustion indicators clarified in the previous study. The engine vibration is affected by piston specifications even with the same heat release characteristics. Based on the analysis results, the piston rotation angle and translational displacement are defined as indicators of the structure’s exciting force. In this paper, after discussing the piston motion characteristics as the structure’s exciting force and the indicators of vibration suppression, the results clarified by the research combined with the previous study are summarized.

This paper describes idle vibration reduction methods using a Stellantis vehicle as a case study. The causes of idle vibration are investigated using the NVH source, path, and receiver method. The torque transfer path into a vehicle has shown to be very important in determining vehicle idle vibration response. New electronic control enablers that affect idle vibration are tested and discussed, including Neutral Idle Control (NIC), Transfer-case Idle Control (TIC,®), and Switchable Engine Mounts (SEM). The Design For Six Sigma (DFSS) analysis method is used to arrive at an optimized result for vehicle idle vibration. This paper also discusses the results confirming TIC’s capability of reducing idle vibration on all-wheel drive vehicles.Transfer-case Idle Control is a new idle vibration control enabler developed by Stellantis and a patent was awarded by the United State Patent and Trademark Office.

When people evaluate the engine NVH, they are not only mentioning whether the engine is loud or not, they say that is noisy or quiet. The difference between these two comments is the way they quantify the engine NVH, from the noise level or the sound quality. Lots of engineers used objective tools to quantify engine sound quality performance for years. In this paper, we would like to propose a novel sound quality matrix for the diesel engine which includes loudness, sharpness, mid-frequency, and high-frequency knocking parameters to perform the sound quality evaluation. In addition, an application case is described in this paper to demonstrate the usage of the new matrix.

Simulation and testing are often done by different engineers in different departments of a company. This can lead to disconnects and unrealistic predictions, especially if the person doing simulations does not have an experimental background. On the other hand, experimental results can also include errors that result in misleading answers. It is important for the engineer doing either testing or simulation to have a good understanding for what results are plausible and what results might be suspect. This paper will provide examples where error crept into testing or simulation that could have been caught and corrected early if a good feel for “reasonable” results had been in place. The importance of understanding how a software package is analyzing the data will be explained, since settings buried deep within a menu structure can drive misleading results. The paper will also make the case for simulation engineers to be provided with some experimental background, so that they have a better physical understanding of the structures or components being simulated. The ideal case is where the same person is responsible for both simulation and experimental validation.

HVAC system design has an accountability towards acoustic comfort of passengers of a vehicle. Owing to larger cabin volume of a bus, multiple air blowers have to be installed to ensure comfort of passengers. Such multiple blowers produce significant flow induced noise inside the cabin. For commercial success, it becomes essential to predict intensity of such a flow induced noise at very early stages in product development. Conventionally sliding mesh based CFD approach is deployed to predict flow and turbulence noise around each blower. However due to complexity, this method becomes computationally intensive resulting in cost and time inefficiency. Hence it is desirable to innovate around an alternative rapid, reliable prediction method, which ensures quick turnaround of prediction. This paper describes a unique innovative approach developed around a multiscale method where flow induced noise generated by a single blower in motion is predicted using commercial Lattice Boltzmann CFD software with a digitally scaled down HVAC system in an anechoic digital wind tunnel. These CFD predictions are used to replace all blowers with virtual stationary speakers inside digital cabin to emulate noise emanated by a large HVAC unit. Authors named this method as total Noise Multiscale Approach, in the paper. With the total multiscale approach, overall sound pressure level predicted inside the bus cabin at rear passenger ear levels are comparable with the physical test measurements and has shown fair correlation. Using this multiscale total noise method, computational cost and turnaround time has significantly reduced compared to the flow conventional resolving approach for cabin with all the blowers. This predictive total noise method found useful in designing and planning countermeasures during product development.

This paper describes a psychoacoustic test in the Exterior Effects Room (EER) at the NASA Langley Research Center. The test investigated the degree to which sound quality metrics (sharpness, tonality, etc.) are predictive of annoyance to notional sounds of Urban Air Mobility (UAM) vehicles (e.g., air taxis). A suite of 136 unique (4.6 second duration) UAM rotor noise stimuli was generated. These stimuli were based on aeroacoustic predictions of a NASA reference UAM quadrotor aircraft under two flight conditions. The synthesizer changed rotor noise parameters such as the blade passage frequency, the relative level of broadband self-noise, and the relative level of tonal motor noise. With loudness constant, the synthesis parameters impacted sound quality in a way that created a spread of predictors both in synthesizer parameters and in sound quality metrics. Forty subjects listened to the suite of UAM noise stimuli in the EER and judged each sound individually on a standard scale of annoyance. Additionally, a subset of the UAM noise stimuli were compared to a reference sound that varied in loudness. From these responses, the relative effect of changes in loudness or changes in other sound quality metrics on annoyance was evaluated. This paper covers background and motivation for the test, details of how the sound stimuli were generated, and details of the test design and execution. Test results investigate how sound quality may affect perceived annoyance to UAM vehicle noise, indicating the importance of sharpness, tonality, impulsiveness, and roughness on annoyance to UAM noise.

NVH (Noise, Vibration and Harshness) of the electric drive axle (EDA) is a key attribute in electric-vehicle development. The NVH attributes of the EDA directly determines the driving comfort and customer feeling of the vehicle. Especially in pure electric working condition, the EDA noise is more perceptible by people without the engine noise masking. This paper investigates the abnormal noise in the vehicle caused by EDA. First, the filtered playback method is used to identify abnormal noise frequency between 330Hz and 430Hz.Adopted modal analysis, MASTA simulation, modulation noise analysis to identify problematic critical parts. The validity of the results is verified using the DOE method by part exchange, and finally locked to the source of gear parameters Rs and Fr. By adjusting the production process of gear and the second shaft, the assembly process error was avoided, and the gear parameter targets are formulated. The verification results of the whole vehicle assembly of small batch EDA showed that the optimized production process reduced the vibration and the interior noise of the EDA, and effectively optimized the abnormal noise problem of the EDA of commercial vehicles. The diagnostic ideas, test methods and optimization schemes for the abnormal noise of the EDA in this paper have important reference significance and value for the design of electric drive systems for passenger cars and commercial vehicles.

The automotive industry is evolving towards Electrified Vehicles (EV) in the recent years. Compared to the traditional ICE vehicles, tire noise induced by the tire-road interaction, is no longer masked by the internal combustion engine, and therefore becomes one of the most dominant sources of noise within the cabin and acoustic emission perceived by by-standers.Robust source characterization is one of the most important tasks for road noise prediction. The receiver-independent tire blocked forces are often used as ire-road source characteristics, which can be applied to any test-based or FE-based vehicle model to obtain the interior noise. They can be inversely identified from measurements on a tire test rig or on an in-situ vehicle. However, this inverse process needs to be repeated for different tires, roads and rolling speeds, which can become time-consuming and expensive.In this paper, an alternative solution for the blocked force calculation is proposed: a direct simulation of blocked forces from measured road profile data. This approach consists of two major steps. The first step is a road envelopment module, which allows the user to reprocess a once measured road profile at any desired speed. This is done using a CAE based approach to get in an objective manner to the right envelopment settings for the given tire, thus avoiding dependency on experience-based tuning of envelopment functions. In a second step, the enveloped profile is applied to a test-driven, concept CAE tire model which enables to predict the blocked forces and moments. As the whole process is implemented in the frequency domain, this allows efficient integration with full vehicle NVH calculations for design optimization.This paper gives a detailed description of the proposed direct or forward approach to get to the blocked forces, with a validation case to demonstrate the obtained results.

Highly accelerated life testing (HALT) identifies potential failure modes and operating limits in a brief timeframe. Generally, engineers control the vibration portion of a HALT test using overall acceleration (gRMS) as the sole metric. gRMS is a broad-spectrum intensity measurement that allows engineers to make decisions with a simplified version of their product’s response. A more comprehensive and accurate metric that considers relative fatigue is needed to improve confidence when setting HALT test durations and determining when to remove a test item from the table or stop the test. This paper presents an approach that improves the quantification of the vibration portion of a HALT test using fatigue damage spectrum (FDS) analysis. The FDS allows engineers to analyze the vibratory environment from a HALT machine and compare it to a desired accrued fatigue level. Applications of the new approach include 1) analyzing variations in relative fatigue due to a change in mass, position on the HALT table, or orientation, and 2) comparing the accrued fatigue for multiple samples while monitoring critical components and known or suspected failure locations. This paper provides example applications that support the proposed method over the standard gRMS metric.

From the early 1980’s through 2015, the engineering community has witnessed dramatic changes in the technology for mitigating sound in vehicles. Throughout these years, vehicle noise, vibration, and harshness (NVH) engineers were skilled and talented in testing vehicles, and at conducting laboratory studies of materials and trim-insulator components. This paper surveys a wide variety of processes that NVH automotive engineers use to develop trim-insulator packages which are effective in reducing cab-interior noise while being esthetically appealing. Analytical models are used to cascade overall cab-interior sound pressure level (SPL) targets to trim-insulator performance targets. This paper also reviews how sound absorption is employed to reduce cab-interior SPL. Acoustical material laboratory tests are briefly described along with the role they play in selecting trim-insulator products for vehicles. And finally, body shell acoustical isolation is examined, and a mathematical relationship is derived between cab air-leakage and acoustical degradation.

The sound produced by Unmanned Aerial Systems (known as UAS or Drones) is often considered to be one of the main barriers (alongside privacy and safety concerns) preventing the widespread use of these vehicles in environments where they may be in close proximity to the general public. To better understand the potential environmental noise impact of commercial UAS operations, work undertaken by the University of Salford has focused on two key areas. Firstly, how to characterise and measure the sound produced by UAS during outdoor flight conditions and secondly, better understanding of the dose response of UAS noise when the listener is in either an indoor or outdoor environment. The paper describes a field measurement campaign undertaken to measure several UAS performing flyovers at different speeds and take-off weights. The methodology of the measurement campaign was strongly influenced by emerging guidance and has been used to calculate the directivity of sound propagation which may be of significant benefit when modelling environmental noise impacts. This paper also presents details of a listening experiment designed to investigate the subjective response to a number of UAS operations when the listener is simulated to be either in an indoor or outdoor position. The results of the listening experiment have been analysed using linear regression analysis to understand which ‘loudness’ metric either conventional (LAeq, LASmax or LAE) or more specialised loudness metrics such as Loudness (N5), Perceived Noise Level (PNL) or Effective Perceived Noise Level (EPNL) are most appropriate for estimating perceived ‘loudness’ and ‘annoyance’. The results of this experiment indicate that both LAeq, LASmax were equally good at predicting the perceived loudness and annoyance with an Adjusted R Squared value of 0.90 and 0.93 for loudness and annoyance respectively. Loudness metric performed marginally better with adjusted R Squared values of 0.96 and 0.90 for annoyance and loudness respectively.

With a view to promote mobility electrification, improved comfort and handling with lower cost are crucial factors in next generation of EV and HEV design. In contrast to ICE platform, electrified counterparts displays distinct NVH characteristics that present challenges in terms of weight transfer, steering, motor vibrations, etc. From a holistic perspective, this paper proposes a compounded suspension system serving dual purpose of dynamic damping and power rejuvenation utilizing electric motor as part of the tuned mass damper inertia system. A variable inertance mechanism is developed in form of geartrain while motor vibration itself receives calculated harness through tuned mass damping. Furthermore, suspension deformation undergoes desirable mitigation as a result of effective simulated annealing optimization focused on shifting objective value according to input tradeoff prediction. Nonlinear system dynamics are considered as a means to broaden the damping bandwidth. Besides power regeneration through storage medium, this paper proposes an alternative method of direct energy transition from collection port to shaft, which provides robust torque assist and response. Utilizing tuned motor suspension, vehicle body movements are counteracted by collaborative motor damping and adjustable in-situ shock absorbers, in return improving maneuver stability. The suspension system is validated through numerical simulation in a full car model. The simulation results indicate that the studied model is capable of vertical acceleration attenuation of 27% and harvesting efficiency of 58% in regeneration mode while producing lower wheel chassis oscillation by 31%. The results show that, owing to the presence of coupled motor activation, the proposed suspension outperforms the existing control methods in terms of both passenger comfort and tire grip.

This paper investigates different regenerative braking strategies applied to Battery Electric Vehicles, such as series and parallel brake blends. The comparison includes energy efficiency assessment using homologation and real-world drive cycle and objective and subjective drivability evaluation. Multiple simulations are performed using a one-dimensional (1D) vehicle model developed in Simulink and a static driving simulator. The driving simulator provides a fair comparison of real-world driving since it creates repeatable highway and urban traffic conditions. These simulations compare the system energy efficiency by looking at the battery's state of charge (SOC). The drivability is assessed on top of consumption by using the static driving simulator. It is objectively measured by calculating the longitudinal acceleration change ratio over time, which occurs during the regeneration ramp-in and ramp-out, for different pedal positions and pedal gradients. The drivability is also subjectively evaluated by assessing the system's smoothness and absence of shakes during braking maneuvers and the deceleration feels while “freely” coasting at high speeds. This study clarifies the utilization of a driving simulator integrated with a model-based design apporoach to develop regenertive braking controls and braking system architectures for electrified vehicles. In the study case presented in this paper, the Series regenerative braking shows better efficiency and better drivability, especially for conditions of low accelerations lower than 0.3g.

Electric vehicles and autonomous vehicles are two major innovations that are currently available to the general public or under development. While they can be separate products, it is also likely that many autonomous vehicles, if not all, will be electric vehicles as well. This is seen in the SAE/GM AutoDrive Challenge and its successor, the SAE/GM AutoDrive Challenge II; in both competitions, an electric vehicle, the Chevy Bolt, is provided to the collegiate teams, which they then work to turn into an autonomous vehicle. The combination of electric vehicles and autonomous vehicles creates certain challenges, among them the issue of powering sensors and the consequent impact on the vehicle’s electric powertrain, and in particular on the vehicle’s range. The various sensors required to provide data to the autonomous vehicle will deliver different types of information and draw varying amounts of power, and this needs to be carefully considered in the vehicle’s overall design, with sensors chosen to provide all needed information to design a vehicle that can operate safely, but also minimize the range reduction that would result from the sensors’ power requirements. This is addressed in the competition; in Year 1 of the AutoDrive Challenge II, one of the Mobility Innovation challenge tasks, the 0-0-0 Challenge, asked teams to analyze the sensor suite and its power draw. The work presented in this paper draws on that analysis conducted by Kettering University’s Bulldog Bolt team, leading to a sensor suite that is optimized for the scenario presented in the competition. The sensor suite is presented along with the specific reasoning leading to its selection, and a discussion of some of the tradeoffs that take place in that selection. The specific sensor suite presented in this paper would be of value for similar scenarios in which autonomous vehicles may be expected to operate, and the methodology presented could be useful in other scenarios to formulate appropriate sensor suites.

The desired yaw rate is a vital target parameter for vehicle stability control, which is currently determined as a steady-state yaw rate by the linear single-track vehicle model. Tire nonlinearity deteriorates the effect of vehicle stability control at larger lateral acceleration. This paper proposes a new calculation method of the steady-state yaw rate considering the tire nonlinearity based on the brush tire model. To validate and verify the proposed method, step steering tests of the target vehicle under different lateral accelerations are carried out on a real proving ground. The results show that when the lateral acceleration is relatively small, the difference between the calculation results of the proposed method and the traditional one is not apparent, and both methods can provide a good estimation for the steady-state yaw rate; however, when the lateral acceleration is relatively large, the difference becomes apparent. It can be shown that the linear tire model cannot be able to calculate the steady-state yaw rate accurately, while the brush tire model agrees well with the observation. This difference shows the importance of considering tire nonlinearity in vehicle stability control, especially in extreme driving conditions. By analyzing the existence of the solution of the yaw rate equation, this paper finds that the vehicle speed upper limit under which the vehicle can be able to achieve steady-state steering is also affected by the tire nonlinearity. A Simulink vehicle model is built to verify the steady-state vehicle speed upper limit for vehicles with different steering characteristics. The results of the modeling and tests provide certain guidelines for developing a vehicle stability control system.

This study focused on the vehicle interior noise caused by combustion in which vibration transmission is the main component at high speed and high load region. A phenomenon in which both the combustion’s exciting force and the structure’s exciting force are combined is defined as vehicle interior noise caused by combustion. Conventionally, combustion and vibration are often discussed in terms of the average cycle, but considering the nonstationary property of vibration, in this paper analyzed the combustion characteristics for cycle-by-cycle and investigated indicators for the combustion’s exciting force. The engine vibration is affected by heat release characteristics even with the same engine structure specifications. The heat release characteristics were determined as indicators for the combustion’s exciting force. Transfer Path Analysis (TPA) revealed that there is piston transmission in the target frequency band. Through eigenvalue analysis, it was found that there was no resonance with the eigenvalue of the structures in the target frequency band. Even with the same combustion characteristics, the engine vibration is affected by piston specifications. Piston motion characteristics were determined as indicators for the structure’s exciting force. In this paper, after overview the impact of each exciting force on vibration, the heat release characteristics of the combustion’s exciting force and the indicators for vibration suppression are mainly discussed.

The world is moving towards E-mobility solutions and Battery Electric Vehicles (BEVs) are the main enabler towards it. Li-ion cells are the fundamental building block of any BEVs. There are three common types of Li-ion cell design i.e., cylindrical cells, Prismatic Cells and Pouch cells. Ensuring safety of BEVs are critical to gain customer trust and acceptance over Internal Combustion Engine (ICE) vehicles. EV fire is found to be one of the major concerns related to using higher energy batteries. During a crash event, Post-Crash Electrical Integrity of the BEV is to be ensured and hence primary focus is on mitigation of Li-ion cell internal short circuit. It has been seen in prior published articles that cell internal short circuit can be triggered by physical intrusion of cell. This paper primarily focusses on simulating the mechanical behavior of cylindrical cell under various crush conditions. In this paper, we will discuss about the trade-off between Computer Aided Engineering (CAE) model fidelity of cylindrical Li-ion cell & usage of such models in full vehicle crash simulations in LS-DYNA®. Proposed method for Cell CAE model is validated against 3 different crush tests conducted. This approach has shown acceptable accuracy for crushability in all 3 test conditions with reduced modeling complexity when compared with prior published work. Proposed Cell CAE model is found to be compatible in use with full vehicle level crash simulations, thus helps in predicting Li-ion cell intrusion accurately during crash simulations of BEVs.

When a turbocharger exhaust-driven turbine wheel spins fast enough to produce the desired level of boost, a wastegate is typically used to allow the excess exhaust pressure to divert around the turbine wheel. By opening the wastegate (typically boost-pressure referenced), exhaust pressure bypasses the turbo’s turbine wheel to prevent the turbo from reaching an unsafe speed. To actuate wastegate, different actuating mechanisms like pneumatic, vacuum or electric are available, which regulates poppet valve positions e.g. full close, open or partially open.In electronic wastegate valve, exhaust pressure pass through the bypass hole collides with the face of valve causing vibration. Such vibration is transmitted to the wastegate components causing rattle noise. It is challenging to design a wastegate mechanism which can sustain wastegate loads at high temperature and give quiet and robust performance within the full operating range of the engine. In order to improve the rattle noise, the mass damper for frequency generation for canceling the vibration is additionally applied used to reduce the clearance between parts. Valve spring is one of the critical components of the wastegate system, which absorbs impactive energy coming from wastegate loads exerts on the valve and reduces the vibrations of wastegate linkages transmitting towards the actuator side.This paper describes methodology to design a functionally optimized wastegate linkage system and valve spring at an early stage. Multi-body dynamic (MBD) along with nonlinear transient dynamic FEA approach was developed for analyzing wastegate washer/cup spring system. The nonlinear FEA tools was used to assess component(s) design robustness due to the MBD generated dynamic loading in balance to geometry, thermal loading and material capabilities. Advanced valve spring and creep modeling are used to quantitatively study the spring relaxation after long time exposure to high temperature and stress. The simulation methodology outlined in this paper is used to investigate turbo wastegate linkage undergoing dynamically varying forces from the pulsation of the exhaust gas in transient engine operation, which results in identifying NVH issues and durability-relevant weak spots in the early-design phase and providing the development tool of robust design.Testing is done to better understand wear and NVH of all components within the wastegate subsystem caused by engine induced mechanical vibrations. Data from this testing is used early in the turbocharger design and development process to validate analysis models and to size wastegate components

Prior research has tested the validity of Cycles Engine render in Blender for the creation of physically correct lighting models; however, a research gap still exists in examining the use of Arnold render engine in 3DS Max for accident reconstruction and other forensic settings [1]. Specifically, the process presented in this paper utilizes the Arnold render engine within 3DS-Max to analyze the lighting models. Arnold is a physically-based render (PBR) engine and can be used to recreate an accident scene geometry and lighting conditions. The goal is to create light sources within Arnold that represent the real-world light sources. The light sources in Arnold are quantified by several variables, including intensity, color, and size. The intensity and size variables determine the self-emitted radiance of the light source and require further explanation to determine the relationship between these variables in Arnold and real-world lighting quantities. In this paper, we compare and validate the relationship between intensity units in Arnold and photometric quantities and illustrate that the lighting produced is based on the physics of light and matches Cornetto and Suway’s results [1].

As the automotive industry is quickly changing towards electric vehicles, we can highlight the importance of aerodynamics and its critical role in reaching extended battery ranges for electric cars. With all new smooth underbodies, a lot of attention has turned into the effects of rim designs and tires brands and the management of these tire wakes with the vehicle. Tires are one of the most challenging areas for aerodynamic drag prediction due to its unsteady behavior and rubber deformation.With the simulation technologies evolving fast regarding modeling spinning tires for aerodynamics, this paper takes the prior work and data completed by the authors and investigates the impact on the flow fields and aerodynamic forces using the most recent developments of an Immerse Boundary Method (IBM). IBM allows us to mimic realistically a rotating and deformed tire using Lattice Boltzmann methods.This is the third manuscript in a collection of spinning wheel investigations with a full vehicle. The work presented in this paper follows up on the next steps identified in prior publications, taking full advantage of the detailed and accurate experimental data captured at a full width single belt wind tunnel.Different levels of complexity in wheel modeling are explored and compared to the wind tunnel data allowing to explore the effects of modeling refinement on the computed flow field and aerodynamics forces.

An automotive wiring harness is the backbone of the electrical architecture, and it runs throughout the vehicle to transmit electric power. In a virtual simulation, the mechanical properties of individual strands cannot be considered for the harness bundle (or) cable. Predicting the mechanical properties of electrical cables is a challenging task, and it has major setbacks in virtual simulation. This paper proposes an approach to find out the mechanical properties of an electrical cable and explains how the values are used in virtual simulation.Cable modelling is represented as a lumped mass (or) modelled with a 1D element in the conventional FE modelling approach. In the first part of the study, finite element modelling and material modelling procedures of high and low-voltage electrical cables routed through brackets and troughs are discussed. Mechanical properties are developed using an inverse stiffness characterization method from bench level physical testing in static and dynamic conditions. The physical setup is replicated in a virtual simulation. The material properties used in simulation are iterated until the results match the physical testing results.Material properties derived from the inverse stiffness approach are tested with various applications, and it gives promising agreement in correlation and prediction with physical test results. A Test Vs CAE correlation exercise has been performed for various problems like random vibration analysis, mechanical shock test, and engine roll simulation. The main objective of the paper is to present a suitable material calculation method for electric cables that encounter structural problems in static and dynamic conditions. The test-based inverse stiffness characterization method is observed as an efficient method for the finite element material modelling of cables. Adopting the proposed method, high manual effort and computation time involved in micro-level modelling of cables can be avoided.

Unsuitable shift control strategies may increase the vehicle jerk and clutch wear. In order to improve the shift quality of electric vehicles (EVs) equipped with dual clutch transmission, this paper proposes an optimal shift control strategy based on linear quadratic regulator, in which weighting matrices are selected by using genetic algorithm (GA). The dynamics of the shift process of the dual clutch transmission is analyzed to establish the dynamic model of the driving system. In addition to the vehicle jerk, the friction work of clutch is also considered as one of the performance criteria and a new linear quadratic objective function is formulated. The optimal weighting matrices for obtaining a globally optimal solution are selected benefit from the global search capacity of genetic algorithm. The optimal target trajectories of the torque of the two clutches and motor are obtained by simulating the linear quadratic regulator (LQR). The dynamic model of the driving system including dry clutch is built and a controller is introduced to track the optimal target trajectories. The simulation results indicate that the optimal control strategy proposed in this paper can effectively reduce the vehicle jerk and friction work of clutch.

Preventing skidding is essential for studying the safety of driving in curves. However, the adhesion of the vehicle during the driving process on the wet and slippery road will be significantly reduced, resulting in lateral slippage due to the low adhesion coefficient of the road surface, the speed exceeding the turning critical, and the turning radius being too small when passing through the corner. Therefore, to reduce the incidence of traffic accidents of passenger cars driving in curves on rainy and snowy days and achieve the purpose of planning safe driving speed, this paper proposes a curve active safety system based on a deep learning algorithm and vehicle dynamics model. First,we a convolutional neural network (CNN) model is constructed to extract and judge the characteristics of snow and ice adhesion on roads. By training the residual network, the road surface can be identified and classified under 7 different weather conditions, and the adhesion coefficient of the road surface at this time can be obtained. In addition, the magic formula is used to establish a tire curve driving dynamics model and combined with the curve radius and other parameters to solve the safety speed threshold in the curve driving process. Finally, MATLAB and CarSim software are used to build a simulation platform for verification, and real vehicle experiments verify that the system has strong reliability and robustness. The research shows that the prediction accuracy of the training set and verification set of the system reaches 93.7% and 85.93% respectively. Compared with the traditional back propagation (BP) neural network method, the recognition accuracy of the road adhesion coefficient is improved by 4.53%. Therefore, the recognition algorithm combined with road surface parameter information in this paper has higher prediction accuracy and robustness, which can significantly improve the safety of vehicle driving on curves on rainy and snowy days.

As the United States Army strives for electrification and hybridization of tactical and combat vehicles in alignment with its Climate Strategy, it is necessary to capture all aspects of the drive cycle. One key area for consideration is the amount of time that the vehicles spend idling. Indeed, military vehicles can idle for a considerable amount of time, especially given that soldiers must keep their vehicles running to power critical electronic subsystems. Current, standardized drive cycles do not fully capture the degree that military vehicles idle. This study begins to address this gap by analyzing geo-location data collected from the National Training Center (NTC) for several different tactical vehicles including the High Mobility Multipurpose Wheeled Vehicle (HMMWV), the Bradley Fighting Vehicle, and the Abrams Main Battle Tank. This paper details the extraction, cleaning, and analysis of the geo-location data. The analysis provides insight to how often each vehicle type was stationary over the course of a multi-week training exercise. When coupled with common tactics required in this operating environment, the total amount of time that each vehicle was idling may be approximated. The analysis found that over a given training rotation at NTC, the vehicles spent a considerable amount of time idling, leading to a significant amount of fuel consumption. The paper concludes by evaluating opportunities to mitigate vehicle idle fuel consumption including anti-idle kits and auxiliary power units.

Precise measurement of Air-fuel ratio (AFR) or Lambda value plays a substantial role in controlling exhaust emission from an internal combustion engine. Estimation of AFR is a significant factor to determine the engine performance and to optimize the catalyst conversion efficiency which has direct impact on increase or decrease of emissions. Most of the production two-wheeler engine determines AFR by using non-linear lambda sensor (Narrow band oxygen sensor) but it limits the AFR control due to restrictions in its performance and operating time. A wideband lambda sensor is more accurate and faster but may not be economical to place on low-cost vehicles. A time varying ion current signal can be easily captured on vehicle with minimal additional requirements. AFR has direct correlation with various engine parameters such as Engine speed, Throttle position sensor (TPS), Manifold air pressure (MAP), Fuel injection pulse width (FPW), etc. These signals can be captured with the pre installed vehicle sensors. Neural network-based model can be designed and trained to estimate AFR from different vehicle parameters. Results of neural network model can be improved by considering narrowband sensor as an additional input along with others inputs.The work presented in this paper is implemented for a production vehicle ignition system for two wheeled vehicles. Multiple engine parameters as an input and Universal exhaust gas oxygen sensor (UEGO) as an output are acquired using a data acquisition system. The data is used to train and validate the neural network model to determine the possibility of using it for accurate AFR estimation. A wide range of lambda sensor is considered by performing experiment at different engine operating conditions. The model is validated for steady state as well as dynamic operating condition by running engine on dynamometer and by riding vehicle on day traffic scenario respectively. A detailed study is presented to compare the various models of artificial neural network (ANN) and recurrent neural network (RNN). The paper discusses the results by considering the effect of variation in different neural network model parameters along with engine parameters.

In India, , as per mandate of hon'ble Supreme Court of India for reduction of emission due to vehicles, compressed natural gas (CNG) powered city buses and passengers cars are in use since 2000. Their usage is limited to metropolitan cities like Delhi, Mumbai, Bangalore etc. due to limitation of CNG storage and dispensing infrastructure along with low energy density storage. High energy density liquid form of natural gas storage (LNG) can overcome these difficulties and promising in near future. Simultaneously, there is a need for development of efficient fuel storage system, fuel supply system, engine optimization & calibration, engine lubricant etc. suitable for implementation of LNG for automotive application. In this background, the present work is aimed at the framework of engine testing facility, development of dedicated lubricant and performance of the engine for LNG application.This paper describes LNG engine test lab standard operating procedure developed during the LNG internal combustion engine dynamometer testing programme. Due to safety requirement, it is neither advisable nor permissible to connect large LNG cryogenic tankers to engine test bench. Hence, state of the art small & portable cryogenic LNG tank (450 Lit water capacity (WC)) comprised of vacuum and super insulated layered configuration manufactured as per International Organization for Standardization (ISO) 21029-1 and type tested according to ISO-12991 was designed as horizontal tank to transport LNG from large LNG cryogenic tanker to engine testing site. Bharat Stage (BS) VI emission standard complied LNG engine testing facility was developed and forced convection heat transfer methodology applied for avoiding icing at re-gasified liquefied natural gas (RLNG) fuel line.This paper presents the development of dedicated lubricant for LNG fuelled heavy duty (HD) engine and establishment of oil drain interval and evaluation was done on 6-cylinder HD engine using LNG fuel. The engine was optimized for using LNG fuel. Initial performance of the engine using LNG was compared vis-à-vis CNG and, thereafter, the engine was subjected to endurance test of 1500 hours as per engine simulated driving cycle that closely represents road drive conditions to validate the technology and to establish the drain interval of lubricant. Further, to access the actual performance, limited field trials of 30,000 Km with LNG fuelled busses have been completed. It has been observed that LNG shows reduction of CO, THC and CH4 emissions and NOx emissions increased as compared to CNG. However, these values meet the BS VI emission norms. Oil sampling and analysis were undertaken after completion of every 100 hrs along with performance of the engine i.e. Power (kW), Torque (Nm) and brake specific fuel consumption (BSFC) (g/kWh) and emission characteristics. Engine performance was found satisfactory during endurance test. Developed engine oil demonstrated excellent behavior with LNG fuel and used engine oil physicochemical properties i.e. Kinematic viscosity@100°C, Total Base number (TBN), Total acid number (TAN), oxidation, nitration and wear elements (Iron (Fe), Copper (Cu), Aluminum (AL), Lead (Pb)) were reported well within the permissible limit. The study shows that the lubricant can be used for LNG application.

The paper describes the integration of a high-speed data acquisition and diagnostics controller used in an advanced engine platform. The controller enables ultra-low emissions and new benchmarks of engine efficiency while running a Gasoline Compression Ignition (GCI) cycle on a 2.2L, 4-cylinder engine. The system enables real-time combustion feedback and vibration analysis in engines. The paper focuses on: (1) the development of an interpolative sampling algorithm for transposition of time acquired data to the crank angle domain using a production crank sensor (60-2 tooth wheel); (2) the control unit, high-speed data acquisition, communication rates between the dedicated data acquisition and base controller to ensure cycle-to-cycle feedback; and, (3) validation exercises using cylinder pressure measurements. The study shows how the algorithm resolves cylinder pressure information over an engine cycle, validating its robustness across acquisition rates of 50 and 200 kHz, with crank angle resolutions of 0.5° and 0.1°. Results show that the 50 kHz, 0.5° hardware yield combustion phasing and torque estimates within 0.1° and 1% torque of those attained with the 200 kHz, 0.1° hardware. Finally, study shows the dedicated controller and communication speeds allow for next-cycle fuel injection correction for speeds above 5000 rpm.

Vehicles equipped with Level 4 and 5 autonomy will need to be tested according to regulatory standards (or future revisions thereof) that vehicles with lower levels of autonomy are currently subject to. Today, dynamic Federal Motor Vehicle Safety Standards (FMVSS) tests are performed with human drivers and driving robots controlling the test vehicle’s steering wheel, throttle pedal, and brake pedal. However, many Level 4 and 5 vehicles will lack these traditional driver controls, so it will be impossible to control these vehicles using human drivers or traditional driving robots. Therefore, there is a need for an electronic interface that will allow engineers to send dynamic steering, speed, and brake commands to a vehicle. This paper describes the design and implementation of a market-ready Automated Driving Systems (ADS) Test Data Interface (TDI), a secure electronic control interface which aims to solve the challenges outlined above. The interface consists of a communication port integrated into automobiles which lack traditional manual controls. Via this interface dynamic test scenarios can be executed by plugging in a Vehicle Control Unit (VCU), which is an element of the TDI hardware/firmware intended to be available only to authorized users. Physically, the VCU can interface with the On-board Diagnostics (OBD) OBD-II connector that is present on essentially all modern automobiles. This paper also describes a demonstration of the complete VCU with the secure TDI using a vehicle equipped with a Dataspeed drive-by-wire kit, with its steering, throttle, and brake control systems mimicking the behavior of an autonomous subject vehicle.

The paper presents an approach used to generate a customer-oriented drive cycle using the MATLAB-based drive cycle generation tool for EVs developed by Isuzu Technical Center of America. The drive cycle generation tool extracts important features from the customer vehicle data and compares it with the globally used pre-existing candidate cycle to generate a representative drive cycle. The tool can read multiple file formats of preprocessed data or raw data from the vehicle telematics systems. This data is then processed using a unique and efficient algorithm developed by the Isuzu engineers, calculating seven important vehicle dynamic parameters. These selected parameters are compared with the pre-existing candidate cycles used across the globe in multiple iterations to generate a custom representative drive cycle that best fits real-world customer driving behavior. The generated drive cycles are then validated using the 1D vehicle model in the GT-POWER tool, resulting in a fidelity of more than 95% [1] concerning the governing criteria and seven key parameters. The tool provides high flexibility and control over the process of generating a drive cycle. The seven key parameters selected in this tool are prominent parameters used globally to evaluate vehicle performance. By altering one of these parameters, one can simulate vehicle operation under various use scenarios. The tool gives users authority over the parameter weightage for drive cycle development. The drive cycle that is ultimately created using it will be a combination of varied proportions of the candidate cycles employed in the automotive sector [1]. This paper focuses on the usage of the tool, and the development of the tool has been explained. In this paper, the tool is utilized to analyze the 7 key parameters from customer driving patterns and these 7 parameters are used to develop a real-world drive cycle(routes) with the help of GT-RealDrive. The key parameters of these driving routes are then compared and judged based on correlation with the customer driving parameters and the best-suited iteration is finalized.

Autonomous vehicle (AV) algorithms need to be tested extensively in order to make sure the vehicle and the passengers will be safe while using it after the implementation. Testing these algorithms in real world create another important safety critical point. Real world testing is also subjected to limitations such as logistic limitations to carry or drive the vehicle to a certain location. For this purpose, hardware in the loop (HIL) simulations as well as virtual environments such as CARLA and LG SVL are used widely. This paper discusses a method that combines the real vehicle with the virtual world, called vehicle in virtual environment (VVE). This method projects the vehicle location and heading into a virtual world for desired testing, and transfers back the information from sensors in the virtual world to the vehicle. As a result, while vehicle is moving in the real world, it simultaneously moves in the virtual world and obtains the situational awareness via multiple virtual sensors. This would allow testing in a safe environment with the real vehicle while providing some additional benefits on vehicle dynamics fidelity, logistics limitations and passenger experience testing. The paper also demonstrates an example case study where path following and the virtual sensors are utilized to test a radar based stopping algorithm.

Commercial vehicles require fast aftertreatment heat-up to move the SCR catalyst into the most efficient temperature range to meet upcoming NOX regulations while minimizing CO2. The focus of this paper is to identify the technology levers when used independently and also together for the purpose of NOX and CO2 reduction toward achieving 2027 emissions levels while remaining CO2 neutral or better. A series of independent levers including cylinder deactivation, LO-SCR, electric aftertreatment heating and fuel burner technologies were explored. All fell short for meeting the 2027 CARB transient emission targets when used independently. However, the combinations of two of these levers were shown to approach the goal of transient emissions with one configuration meeting the requirement. Finally, the combination of three independent levers were shown to achieve 40% margin for meeting 2027 transient NOx emissions while remaining CO2 neutral. These independent levers and combinations were also quantified for meeting the new Low Load Cycle. This paper shows which combinations of technologies meets both the transient emission cycles and low load cycles for NOx with adequate margin while also saving CO2.

Several predictive equations based on the chemical composition of gasoline have been shown to estimate the particulate emissions of light-duty, internal combustion engine (ICE) powered vehicles and are reviewed in this paper. Improvements to one of them, the PEISimDis equation are detailed herein. The PEISimDis predictive equation was developed by General Motor’s researchers in 2022 based on two laboratory gas chromatography (GC) analyses; Simulated Distillation (SimDis), ASTM D7096 and Detailed Hydrocarbon Analysis (DHA), ASTM D6730. The DHA method is a gas chromatography mass spectroscopy (GC/MS) methodology and provides the detailed speciation of the hundreds of hydrocarbon species within gasoline. A DHA’s aromatic species from carbon group seven through ten plus (C7 – C10+) can be used to calculate a Particulate Evaluation Index (PEI) of a gasoline, however this technique takes many hours to derive because of its long chromatography analysis time. A faster (< 15 min.), but lower resolution chromatography technique known as SimDis, which uses wide bore capillary GC and a flame ionization detector (FID) can be used to analyze a gasoline’s boiling point (volatility) characteristics. The PEISimDis equation was developed using multiple Boiling Point range windows from the SimDis GC analysis relating to the aromatic species identified by the DHA. An enhanced PEISimDis equation is shown [Eq. 5] and has been shown to correlate strongly to other predictive particulate emissions equations such as Particulate Matter Index (PMI) and PEI. The SimDis GC method and subsequent enhanced PEISimDis predictive equation provides a rapid estimate of a gasoline’s tendency to produce vehicle particulate emissions [7].As a continuation from previous work (P.Geng et al., 2022), this paper presents a study of the correlation of DHA and SimDis, but this time using a wide range of U.S. market gasoline samples.

Use of electronic systems in the vehicles is increasing day by day. As Electronic Control Modules (ECMs) become a large part of the vehicle, automotive designers need to take diligent decision of selecting electrical and electronic components. Selecting these components for ECM depends on four major factors: meeting stringent vehicle requirements, performance over the lifespan, robustness/reliability and cost. There is always an urge of reducing the cost of the ECM, but robustness of the controller module must not be compromised. One electrical or electronic component failure or false fault detection not only increases warranty cost but may also stall the vehicle, and interrupts customer’s daily routine creating dissatisfaction. This paper emphasizes on the importance of understanding worst-case operating scenarios considering component tolerances over the operating range, datasheet, and impact of tolerances on performance and fault detection. Wide ranges in component tolerances over worst-case operating conditions introduce challenges in optimizing circuit design over the operating environment. This paper exhibits real-life design examples that show the impact of non-ideal aspects of electrical and electronic components over worst-case operating scenarios on meeting the vehicle requirements, fault detection and safe operation. This paper also recommends some design considerations for robust circuit interface design. It is important to study component datasheets carefully to understand parameter variation impact on circuit performance and robustness. It is necessary that circuits are capable of surviving undetectable faults and reduce variation among multiple input power source.