bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Approach for Mechanical Characterization of Angle-Ply Composite Laminates
Authors: Tanaya Sushree , Deb Anindya
Published: April 2024

​Composites made of continuous fibers generally have higher strength-to-weight ratios in fiber directions as compared to those made of discontinuous fibers. However, the latter tend to display quasi-isotropic properties which can be of advantage when directions of mechanical loading can vary. For many real-world applications such as robust design of vehicle body components for crashworthiness, impact loads are stochastic in nature both in terms of magnitude and direction. Hence, in order to realize the true potential of laminated composites with continuous fibers, instead of orthotropic laminates which are most common due to the ease of design and manufacturing, angle-ply laminates are necessary. The latter category of laminates introduce a high degree of flexibility in design options but are also simultaneously challenging in terms of mechanical characterization due to the presence of a larger number of material parameters, as compared to orthotropic laminates, with coupled normal and shear behaviors. A computationally attractive approach in large-scale numerical analysis of laminated composite structures lies in the modeling of laminates with equivalent homogeneous anisotropic properties. A practical strategy in published literature appears to be lacking on the determination of various elastic stiffness or compliance coupling coefficients for a generally-anisotropic angle-ply laminate. In the current exposition, a novel approach is demonstrated whereby using coupon specimens for tensile tests extracted at different directions with respect to a global x-direction for a square angle-ply laminate and one simple in-plane shear test, the membrane normal-shear coupling compliance parameters are determined by solving a set of linear simultaneous equations derived based on transformation of stresses and strains from global to off-axis directions. The current methodology obviates the necessity of using lateral strain gages traditionally deployed in uniaxial tests thereby making material characterization of anisotropic angle-ply laminates less time-consuming and more cost-effective.
Benefit from In-service Life Optimized for Minimum CO2 – Comparison of ICEVs, PHEVs, BEVs and FCEVs
Authors: Watson Harry C.
Published: April 2024

The 2023 FISITA White Paper (for which the author was a contributor) on managing in-service emissions and transportation options, to reduce CO2 (CO2-e or carbon footprint) from the existing vehicle fleet, proposed 6 levers which could be activated to complement the rapid transition to vehicles using only renewable energy sources. Another management opportunity reported here is optimizing the vehicle’s life in-service to minimize the life-cycle CO2 impact of a range of present and upcoming vehicles. This study of the US vehicle fleet has quite different travel and composition characteristics to European (EU27) vehicles. In addition, the embodied CO2 is based on ANL’s GREET data rather than EU27 SimaPro methodology. It is demonstrated that in-service, whole-of-life mileage has a significant influence on the optimum life cycle CO2 for BEVs and H2 fuelled FCEVs, as well as ICEs and PHEVs. Thus, the object is to show how much present, typical in-service life-mileage differs from the optimums against a back drop of steadily improving energy efficiency, as new vehicle designs enter the market along with the greening of electric power supply and conventional fuel supply. The life cycle analysis is more than ‘well-to-wheel’ as the energy content and manufacture of consumables and recycling/reuse of vehicles (as embodied CO2) is included as new vehicles replace older, scrapped ones in the market, with improvements in energy efficiency (and reductions CO2 emissions). It is found that depending on the vehicle size and configuration, the optimum vehicle life ranges from 10 years to more than 20; significantly different from the present fleet median of 17 years. For all forms of EVs the greater the installed battery kWh or H2 tank size and hence range capability, the longer is the optimum service life. As the energy efficiency for new vehicles entering the market improves, vehicles need to stay in use for longer to amortize the embodied energy in manufacturing. It is concluded from the projection results, that PHEVs provide the best path to minimizing CO2 emissions. Across the fleet of technology types, benefits of up to 50% increase in the reduction of life cycle emissions come from optimal age recycling of the vehicle. Under these conditions of optimum age use, the switch to EVs is not so urgent when policies are in place that encourage best use of all vehicles according to their technology.
Comparing the Forward Collision Warning Response of a 2020 Toyota Camry and 2020 Toyota RAV4
Authors: Harrington Shawn , Aguirre Roberto
Published: April 2024

Testing was conducted at four speeds – 35, 50, 60, and 70 mph – to evaluate the performance of the audible and visual forward collision warning (FCW) component of the pre-collision system (PCS) in a 2020 Toyota RAV4 and a 2020 Toyota Camry. Both vehicles were tested in daytime conditions while approaching a Stationary Vehicle Target (SVT). The 2020 Toyota Camry was also tested in nighttime conditions while approaching a live stationary vehicle. Testing measured the time to collision (TTC) values at the issuance of the FCW, the distance from the test vehicles to the target at FCW, and the speed of the test vehicle at FCW utilizing Racelogic VBOX data acquisition systems. A comparison of the performance of the FCW component of two different generations of Toyota Safety Sense – P versus 2.0 – was also made. The results of the testing add higher speed scenarios to the database of publicly available tests from sources like the Insurance Institute for Highway Safety (IIHS), which currently evaluates vehicles at 12 and 25 mph. In addition, the timing of evasive steering maneuvers relative to the target were quantified for the Toyota Camry.
Development of a Dynamic Nonlinear Finite Element Model of the Large Omnidirectional Child Crash Test Dummy
Authors: Challa Balaji Naga Sai Abhishikt , Yang Peiyu , Carlson Michael , Suntay Brian , Stammen Jason , Noll Scott
Published: April 2024

The Large Omnidirectional Child (LODC) developed by the National Highway Traffic Safety Administration (NHTSA) has an improved biofidelity over the currently available Hybrid III 10-year-old (HIII-10C) Anthropomorphic Test Device (ATD). The LODC design incorporates enhancements to many body region subassemblies, including a redesigned HIII-10C head with pediatric mass properties, and the neck, which produces head lag with Z-axis rotation at the atlanto-occipital joint, replicating the observations made from human specimens. The LODC also features a flexible thoracic spine, a multi-point thoracic deflection measurement system, skeletal anthropometry that simulates a child's sitting posture, and an abdomen that can measure belt loading directly. This study presents the development and validation of a dynamic nonlinear finite element model of the complete LODC dummy. Based on the three-dimensional CAD model, Hypermesh was used to generate a mesh of the finite element (FE) LODC model. LS-PrePost was used to specify the material parameters, contact definitions, and initial conditions and then converted to LS-DYNA solver input format. A detailed description of the assemblies of the LODC dummy and their finite element representation is given. Component-level qualification test procedures were simulated using the LODC FE model, replicating the experimental test setups and boundary conditions for the head, neck, thorax, abdomen, and spine. Each component test provides a physical response that is used to calibrate the model's strain-rate-dependent viscoelastic material properties and other characteristics using an inverse method that minimizes the divergence between measured and predicted data. CORrelation and Analysis (CORA) ratings are provided between the simulation and experimental curves. Finally, a system-level sled test was modeled with the dummy upright on a sled bench for validation.
Machine Learning for Road Vehicle Aerodynamics
Authors: Ananthan Vidyasagar , Ashton Neil , Chadwick Nate , Lizarraga Mariano , Maddix Danielle , Maheswaran Satheesh , Moreno Pablo Hermoso , M. Shabestari Parisa , Sovani Sandeep , Subramanian Shreyas , Tadepalli Srinivas , Yu Peter
Published: April 2024

This paper discusses an emerging area of applying machine learning (ML) methods to augment traditional Computational Fluid Dynamics (CFD) simulations of road vehicle aerodynamics. ML methods have the potential to both reduce the computational effort to predict a new geometry or car condition and to explore a greater number of design parameters with the same computational budget. Similar to traditional CFD methods, there exists a broad range of approaches. In particular, the accuracy and computational efficiency of a CFD simulation vary greatly depending on the choice of turbulence model (DNS, LES, RANS) and the underlying spatial and temporal numerical discretizations. Similarly, the end-user must select the correct ML method depending on the use-case, the available input data, and the trade-off between accuracy and computational cost. In this paper, we showcase several case studies using various data-driven ML methods to highlight the promise of these approaches. Whilst these case studies are not comprehensive investigations of the underlying methods and do not include all possible ML approaches (i.e., physics-driven), they highlight the ability of these models to in general predict new designs in near real-time (i.e., less than 5 seconds), after typically less than 1 hour of training on a single GPU. There still exists a need for high quality training data from traditional CFD methods and high-fidelity CFD simulations to validate the ML predictions. Thus, ML approaches should be seen as tools to augment traditional CFD methods rather than to replace them. While this work focuses on preliminary studies, future work will look at more comprehensive real-world/industrial-size calculations for the more promising technologies identified here.
Further Analysis of the Blockage Phenomenon during the Testing of Bluff Automotive Bodies in Closed Wall Wind Tunnels – Revised and Updated
Authors: Gleason Mark , Riegel Eugen
Published: April 2024

The difficulties of testing a bluff automotive body of sufficient scale to match the on-road vehicle Reynolds number in a closed wall wind tunnel has led to many approaches being taken to adjust the resulting data for the inherent interference effects. But it has been impractical if not impossible to experimentally analyze the effects that are occurring on and around the vehicle when these blockage interferences are taking place. The present study is an extension of earlier work by the author and similarly to that study uses the CFD (computational fluid dynamics) analysis of several bodies of differing configurations to examine the interference phenomena in solid wall wind tunnels and the effects that they have on the pressures, forces and force increments experienced by the vehicle model. This is accomplished by executing a series of CFD configurations with varying sized cross sections from 0.2% to 16% blockage enabling an approximation of free air conditions as a reference. The earlier work is extended by using additional body configurations which include reference bodies from the Technical University of Munich, the University of Stuttgart, and Ford Motor Company to complement the simple shapes used in that study along with the use of a SAE Notchback model simple shape and a newly generated Utility Van configuration utilizing chassis components from the Ford GTU pickup. Examination is made of the physical phenomena occurring around the vehicle as the proximity to the walls and ceiling is changed holding the test section aspect ratio and length constant. Static Pressure distortions, skin friction changes, velocity distortions and the distribution of forces on the vehicle body are examined. It is intended that this dataset be used by the SAE Road Vehicle Aerodynamics Forum Committee (RVAC) and the Subsonic Aerodynamic Testing Association combined activity, Commonized Automotive Aerodynamic Test Standards, or CAATS, to evaluate and/or develop wind tunnel blockage techniques for automotive bluff bodies. The report has corrected errors contained in the original paper while revising and updating it.
Integrating Simulation and Test to Develop the Aerodynamics of the New Land Rover Defender
Authors: Eliyas Jehan , Gaylard Adrian P.
Published: April 2024

Meeting customer expectations along with regulatory requirements for efficiency and emissions reduction requires that even highly functional automotive products, such as 4x4s, are developed for aerodynamics efficiency. This is true of iconic vehicles, such as the Land Rover Defender.

This paper discusses the redefinition of an icon: the aerodynamics development of the All-New Land Rover Defender. It outlines a strategy based on integrating simulation and test approaches: unsteady Computational Fluid Dynamics (CFD) simulation and Full-Scale Wind Tunnel testing.

After outlining the integrated development model built around these toolsets, it demonstrates the natural fit between early phase work and simulation, where the focus was on optimizing vehicle volumes and proportions. The growing use of wind tunnel testing, as the design matures, is also explored, starting with full scale clay models before transitioning to a more representative bespoke test property.

The overall development process is illustrated by exploring how the need for a range of changes were identified and executed across a range of geometric zones and vehicle components is described, including: front bumper, body outboard corners, brake cooling ducts, profiled grille shutter vanes, trailing edge design, underbody, and wheel design.
The New China Automotive Technology and Research Center Aerodynamic-Acoustic and Climatic Wind Tunnels
Authors: Waudby-Smith Peter , Bender Trevor , Sooriyakumaran Christopher , Zhang Yilun , Wang Haiyang , Zhao Feng , Fan Guangjun , Sun Jinhong , Liu Xuelong
Published: April 2024

The China Automotive Technology and Research Center (CATARC) has completed two new wind tunnels at its test centre in Tianjin, China: an aerodynamic/aeroacoustic wind tunnel (AAWT), and a climatic wind tunnel (CWT). The AAWT incorporates design features to provide both a very low fan power requirement and a very low background noise putting it amongst the quietest in the automotive world. These features are also combined with high flow quality, a full boundary layer control system with a 5-belt rolling road, an automated traversing system, and a complete acoustic measurement system including a 3-sided microphone array. The CWT, located in the same building as the AAWT, has a flexible nozzle to deliver 250 km/h with an 8.25 m2 nozzle, and 130 km/h with a 13.2 m2 nozzle. The temperature range of the CWT is -40 °C to +60 °C with a controlled humidity range of 5% to 95%. Additional integrated systems include a variable angle solar simulator array, and a rain and snow spray system. This paper provides descriptions of the AAWT and CWT facilities including their sub-systems. The aerodynamic and acoustic performance of the AAWT and the CWT are summarized. Comparisons to published data from comparable wind tunnels are included. In addition, flow uniformity measurements in the AAWT from multiple axial planes are shown together to indicate the development of the flow over the first half of the turntable region.
Digital Twin Modeling Using High-Fidelity Battery Models for State Estimation and Control
Authors: Biju Nikhil , Pandit Harshad
Published: April 2024

Lithium-ion batteries (LIBs) play a vital role in the advancement of electric vehicles and sustainable energy solutions. They are favored over other secondary energy storage systems due to their high energy density, long cycle life, high nominal voltage, and low self-discharge rate. However, the latency of its internal states makes it difficult to predict its performance and ensure it is being operated safely. Fortunately, battery management systems (BMS) can use battery models to predict the internal states of a battery. There is a constant trade-off between accuracy and computational cost when it comes to battery models with only a handful being able to meet the constraints of a BMS. The following paper will showcase a Digital Twin framework that captures the accuracy of high-fidelity electrochemical models while meeting the computational constraints imposed by the BMS. The proposed framework will show that a high-fidelity model can be used to predict slower dynamics such as the state of health (SOH) and more dynamic states such as voltage, temperature, and state of charge (SOC) can be accurately predicted using a lower-fidelity model in Real-Time.
Optimization of Power Module Cooling Plate: An Application of Deep Learning for Thermal Management Devices
Authors: Lombardi Alessandro , Zampieri Luca , Agrawal Monika , Singhal Mohit , Von Tschammer Thomas
Published: April 2024

To meet the ever-increasing demands of the engineering industry, novel approaches to design optimization are essential, especially in fast-paced production environments. Conventional CAD and simulation tools often struggle to keep up with the complexity and speed required for designing critical components. In this context, leveraging Deep Learning technologies presents a promising solution by integrating knowledge from simulations and designs to drastically accelerate product development. With the drive for Electrification, conventional power electronics and systems are becoming more energy dense and hence requires compact and efficient thermal management solutions. Higher energy density is attributed to high power electrical components fitted in packs with shrinking characteristic dimensions and hence needs more efficient and compact thermal management solutions. Conventional engineering design approaches have limitations to push the boundaries of efficiency and power density of air or liquid cooled solutions. This paper explores the application of recent algorithms based on Geometric Deep Learning to construct a predictive model capable of accurately assessing the thermal performance of cooling plates for power modules within a matter of seconds. Leveraging this predictive model, non-parametric design optimization allowed us to rapidly explore an extensive and innovative design space, overcoming the limitations of traditional parametric optimizations. This study reveals significant thermal performance gains achieved through the optimization process, while considering the manufacturing constraints of the cooling module. The flexibility of this approach is demonstrated by comparing the baseline design to those optimized for cost and/or thermal performance. The results showcase cooling modules that significantly outperform the standard product, bringing more than 30% improvement in pressure drop, together with reduced costs, the overall weight being reduced by more than 10%. The Deep Learning based optimization workflow converged towards innovative designs which could not have been achieved with conventional CAD and CAE tools.
Effect of Ambient Pressure on Ammonia Sprays Using a Single Hole Injector
Authors: Shen Li , Leach Felix
Published: April 2024

Ammonia has received attention as an alternative hydrogen carrier and a potential fuel for thermal propulsion systems with a lower carbon footprint. One strategy for high power density in ammonia applications will be direct injection of liquid ammonia. Understanding the evaporation and mixing processes associated with this is important for model development. Additionally, as a prior step for developing new injectors, it is of interest to understand how a conventional gasoline direct injection (GDI) injector would behave when used for liquid ammonia without any modifications. Pure anhydrous ammonia, in its liquid form, was injected from a single hole GDI injector at a fuel pressure of 150 bar into an optically accessible constant volume chamber filled with nitrogen gas for ammonia spray measurements. The chamber conditions spanned a wide range of pressures from 3 − 15 bar at an increment of 1 bar or 2 bar between the test points. These conditions lead to sprays which are both flash boiling and non-flash boiling as well as in a transition region. Spray morphology studies were performed based on high-speed backlit images recorded at 10 kFPS. Droplet size distributions for the bulk spray were simultaneously measured using a laser diffraction technique at the same sampling rate. The results show that at a higher ambient pressure, shorter spray penetration lengths and smaller spray spread widths are observed compared to those at lower pressures. While these macroscopic spray geometrical parameters change gradually at different ambient pressures, the droplet size distribution undergoes a slightly more abrupt transition across the saturation vapor pressure at chamber temperature. These results provide a fundamental dataset for liquid ammonia injection and could be used to validate against simulation data or to build surrogate models.
STEAM & MoSAFE: SOTIF Error-and-Failure Model & Analysis for AI-Enabled Driving Automation
Authors: Czarnecki Krzysztof , Kuwajima Hiroshi
Published: April 2024

Driving Automation Systems (DAS) are subject to complex road environments and vehicle behaviors and increasingly rely on sophisticated sensors and Artificial Intelligence (AI). These properties give rise to unique safety faults stemming from specification insufficiencies and technological performance limitations, where sensors and AI introduce errors that vary in magnitude and temporal patterns, posing potential safety risks. The Safety of the Intended Functionality (SOTIF) standard emerges as a promising framework for addressing these concerns, focusing on scenario-based analysis to identify hazardous behaviors and their causes. Although the current standard provides a basic cause-and-effect model and high-level process guidance, it lacks concepts required to identify and evaluate hazardous errors, especially within the context of AI.

This paper introduces two key contributions to bridge this gap. First, it defines the SOTIF Temporal Error and Failure Model (STEAM) as a refinement of the SOTIF cause-and-effect model, offering a comprehensive system-design perspective. STEAM refines error definitions, introduces error sequences, and classifies them as error sequence patterns, providing particular relevance to systems employing advanced sensors and AI. Second, this paper proposes the Model-based SOTIF Analysis of Failures and Errors (MoSAFE) method, which allows instantiating STEAM based on system-design models by deriving hazardous error sequence patterns at module level from hazardous behaviors at vehicle level via weakest precondition reasoning. Finally, the paper presents a case study centered on an automated speed-control feature, illustrating the practical applicability of the refined model and the MoSAFE method in addressing complex safety challenges in DAS.
Estimating Light-Duty Vehicle Gaseous Emissions Using a Data-Driven Approach in Off-Cycle Measurements
Authors: Hashemi Ashton , Schlingmann Dean
Published: April 2024

As global regulations on automotive tailpipe emissions become increasingly stringent, developing precise tailpipe emissions models has garnered significant attention to fulfill onboard monitoring requirements without some drawbacks associated with traditional sensor-based systems. Within the European Union, there is consideration of mandating real-time measurement of emission constituents to enable driver warnings in cases where constituent standards are exceeded. Presently, available technology renders this approach cost-prohibitive and technologically challenging, with most sensor suppliers either unable to meet the demand or unwilling to justify the development costs associated with sensor commercialization. Efforts to circumvent the sensor-based approach through first principle models, incorporating thermokinetics, have proven to be both computationally expensive and lacking in accuracy during transient operations.

We propose a data-driven solution based on DL (deep learning) to create virtual sensors capable of accurately estimating instantaneous emissions comparable to fast gas analyzers as an alternative to these approaches. To construct such DL models, a highly accurate dataset is essential for training, validation, and testing. This level of precision was achieved by utilizing a PEMS (portable emissions measurement system) to analyze real-world exhaust stream constituents, complemented by the logging of critical powertrain variables. The data recorded by the PEMS comprises a comprehensive inventory of THC (total hydrocarbons), CO (carbon monoxide), and NO (nitrogen oxide) concentrations in the tailpipe, correlated with engine speed, air intake charge, ignition timing, catalyst temperatures, and other key powertrain signals.

During the collection of emissions and powertrain characteristics, test vehicles were driven over diverse city and highway routes, encompassing various ambient conditions, to create an extensive dataset conducive to training. The generated datasets exclude cold-start events, which are subject to rigorous scrutiny in vehicle certification efforts. Furthermore, the model relies on the closed-loop operation of the fuel control system, which is often not the case during cold start conditions. The trained networks exhibit good accuracy, as R2 and error metrics demonstrate. The resulting data-driven model can be integrated into production vehicles as an independent virtual measuring module or with OBM (onboard monitoring).
Using ALPHA v3.0 to Simulate Conventional and Electrified GHG Reduction Technologies in the MY2022 Light-Duty Fleet
Authors: Moskalik Andrew , Kargul John , Butters Karla , Bhagdikar Piyush
Published: April 2024

As GHG and fuel economy regulations of light-duty vehicles have become more stringent, advanced emissions reduction technology has extensively penetrated the US light-duty vehicle fleet. This new technology includes not only advanced conventional engines and transmissions, but also greater adoption of electrified powertrains. In 2022, electrified vehicles – including mild hybrids, strong hybrids, plug-ins, and battery electric vehicles – made up nearly 17% of the US fleet and are on track to further increase their proportion in subsequent years.

The Environmental Protection Agency (EPA) has previously used its Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) full vehicle simulation tool to evaluate the greenhouse gas (GHG) emissions of light-duty vehicles. ALPHA contains a library of benchmarked powertrain components that can be matched to specific vehicles to explore GHG emissions performance. Recently, EPA has updated the ALPHA model with key changes including the addition of new models for electrified vehicle architectures and a robust structure which allows batch processing of large numbers of simulations. This enables large scale characterization of current and future fleets over the EPA city, highway and US06 regulatory drive cycles.

This paper focuses on how ALPHA can be used to approximate fleetwide GHG emissions using a narrow set of engines, electric motors, transmissions, propulsion batteries, and architectures. Simulations of the 2022 model year light-duty fleet show a good overall correlation between modeled GHG emissions and certification results, with the average difference between ALPHA simulation and certification values under 0.2% when the values are production weighted.
Virtual Evaluation of PM Rotor Failure Modes and Magnet Adhesive Debonding with Cohesive Interface Approach
Authors: Karmakar Nilankan , P Praveen , Goel Ashish
Published: April 2024

The modern luxurious electric vehicle (EV) demands high torque and high-speed requirements with increased range. Fulfilling these requirements gives rise to the need for increased efficiency and power density of the motors in the Electric Drive Unit (EDU). Internal Permanent Magnet (IPM) motor is one of the best suited options in such scenarios because of its primary advantages of higher efficiency and precise control over torque and speed. In the IPM motor, permanent magnets are mounted within the rotor body to produce a resultant rotating magnetic field with the 3-phase AC current supply in the stator. IPM configuration provides structural integrity and high dynamic performance as the magnets are inserted within the rotor body. Adhesive glue is used to install the magnets within the laminated stack of rotor. High rotational speed of rotor introduces centrifugal loading on the magnets which can result in multiple failure modes such as the debonding of the magnet, and high radial deflection of rotor which in turn reduces the rotor-stator air gap. In this paper, we are exploring the effect of modelling the magnet-to-lamination bonding with the cohesive interface approach. Also, we are exploring the effect of various parameters of the cohesive interface on the debonding nature of magnet glue, radial deformation of the rotor, and durability over standard duty cycles. Predicted outputs with this analysis approach is found to be in good agreement with the tested data points. Hence, the simulation methodology described in this paper can be used a decision-making tool in the initial design phases of IPM rotors to come up with the appropriate adhesive selection and robust designing.
Effect of Surface Roughness on Tribological and NVH Behaviour of Brake System
Authors: S Gurumoorthy , Bhimchand Naresh , Bourgeau Alyssa , Bhumireddy Yugandhar
Published: April 2024

Brake assemblies are an essential part of any vehicle, and their effective functioning is critical for the safety and comfort of passengers. The surface roughness of brake components plays a vital role in figuring out their tribological and NVH (Noise, Vibration, and Harshness) behavior. It is essential to understand the impact of surface roughness on brake performance to ensure efficient braking and it has been a topic of interest in the automotive industry. In this study, the influence of surface roughness on the wear, and noise characteristics of a brake assembly has been investigated. The study also provides insights into the relationship between surface roughness, frictional behavior, and NVH performance, which can be used to improve the design and manufacturing of brake assemblies. The brake assembly includes of a disc, caliper, and brake pads, which work together to convert the kinetic energy of the vehicle into heat energy, has been considered in this study. First, the tribological behavior of the brake assembly under varying surface roughness conditions has been studied. The roughness of the rotor affected the formation of a transfer layer on the brake pad, which contributed to the frictional behavior of the assembly. Increase in surface roughness results in increased contact area and adhesion between the rubbing surfaces, leading to higher frictional forces and wear. The wear of the brake pads and their respective operational life is calculated according to different surface roughness conditions. Another crucial parameter affected by surface roughness is the NVH behavior of brake assemblies. The surface roughness of brake components alters the contact pressure distribution and affects the vibration behavior of the brake system. The change in NVH performance according to the surface roughness value is predicted. This study highlights the importance of considering surface roughness as a critical parameter in the design and development of brake assemblies. The findings of this study can help the automotive industry and improve the safety and comfort of vehicle occupants.
Development of simulation methodology to evaluate Leaf Spring strength and predict the Leaf Interface stresses and correlating with test
Authors: Balasubramani Sritharkumar , S Kangde Suhas , Mohapatra Durga Prasad , M Ayyappadas
Published: April 2024

Leaf Springs are commonly used as a suspension in heavy commercial vehicles for higher load carrying capacity. The leaf springs connect the vehicle body with road profile through the axle & tire assembly. It provides the relative motion between the vehicle body and road profile to improve the ride & handling performance. The leaf springs are designed to provide linear stiffness and uniform strength characteristics throughout its travel. Leaf springs are generally subjected to dynamic loads which are induced due to different road profiles & driving patterns. Leaf spring design should be robust as any failure in leaf springs will put vehicle safety at risk and cost the vehicle manufacturer their reputation. The design of a leaf spring based on conventional methods predicts the higher stress levels at the leaf spring center clamp location and stress levels gradually reduce from the center to free ends of the leaf spring. In RWUP conditions, the failures of leaf spring can occur at the leaf interfaces (i.e. where the succeeding leaf ends) in addition to center clamp locations. The aim of this paper is to capture and demonstrate the potential failure at leaf interfaces which is not predicted through conventional methodology, in addition to leaf center. The experimental strains measured on leaf spring using strain gauges in vehicle level testing are correlating with the strains predicted by the proposed simulation methodology. The correlation is demonstrated on the front leaf spring of pickup vehicle. An investigation into new simulation methodology is incorporated to study the base design and further the influence of design parameters, nipping, leaf travel, shot peening and stress peening processes were explored to improve the design. Based on these studies the new design is proposed that incorporates stepping, stress peening and diamond cut design which improves the life considerably.
Reduction of Computational Efforts to Obtain Parasitic Capacitances Using FEM in Three-Phase Permanent Magnet Motors
Authors: Lucena Lucas , Peçanha Bianca , de Oliveira Neto Manoel , Taran Narges , Silva Thiago , Lopes Rodolfo , Almeida Oberti
Published: April 2024

The rise in demand for electric and hybrid vehicles, the issue of bearing currents in electric motors has become increasingly relevant. These vehicles use inverters with high frequency switch that generates the common mode voltage and current, the main factor responsible for bearing issues. In the machine structure, there are some parasitic capacitances that exist inherently. They provide a low impedance path for the generated current, which flows through the machine bearing. Investigating this problem in practical scenarios during the design stage is costly and requires great effort to measure these currents. For this reason, a strategy of analysis aided by electromagnetic simulation software can achieve desired results in terms of complexity and performance. This work proposes a methodology using Ansys Maxwell software to simulate two-dimensional (2D) and three-dimensional (3D) model of a three-phase permanent magnet motor with eight poles. The 2D model was used to obtain the parasitic capacitances in the motor plane, and the 3D model was used to acquire the coil head or end-winding effect capacitances. The proposed methodology is capable of predicting favorable results in terms of measuring capacitive bearing currents using a voltage pulse as an input source. The great contribution of this work is the computational reduction of the calculation of parasitic capacitances and the implementation of the machine's common mode circuit to predict the bearing and common mode currents.
Recursive Least Square Method with Multiple Forgot Factor for Mass Estimation of Heavy Commercial Vehicle
Authors: Zheng Hongyu , Xin Yafei , Yan Yang
Published: April 2024

Heavy commercial vehicles have large variations in load and high centroid positions, so it is particularly important to obtain timely and accurate load information during driving. If the load information can be accurately obtained and the braking force of each axle can be distributed on this basis, the braking performance and safety of the entire vehicle can be improved. Heavy commercial vehicle load information is different from passenger vehicles, so it is particularly important to study commercial vehicles engaged in freight and passenger transportation. Presently, numerous research endeavors focus on evaluating the quality of passenger vehicles. However, heavy commercial vehicles exhibit notable distinctions compared to their passenger counterparts. Due to substantial variations in vehicle mass pre and post-loading, coupled with notable suspension deformations, significant changes are observed. Hence, the task of estimating the mass of heavy commercial vehicles proves considerably more intricate than that of passenger vehicles. Nevertheless, the process of mass estimation is intricately linked to vehicular safety. Therefore, delving into the mass estimation of heavy commercial vehicles holds paramount significance in the realm of safety. The demand for precise access to commercial vehicle information is notably heightened in the context of intelligent technology. The Hill Start Assist system necessitates the real-time computation of engine torque, contingent upon the vehicle mass and road gradient, with the objective of minimizing fuel injection during hill starts. In the context of an electronic parking brake system, the determination of ground braking force entails acquiring the mass of the vehicle. The more accurate the mass, the better the braking control effect. In the electronic stability control system for vehicle bodies, the stability factor is affected by the quality of the entire vehicle, and its reliability will affect the judgment of oversteer and understeer. Vehicle quality and road slope are also key parameters for making gear decisions in gear shifting control, and accurate estimation of them can improve the quality of gear shifting control. Therefore, when conducting intelligent vehicle control, it is necessary to obtain real-time vehicle mass and road slope information during vehicle driving. In this paper, a multi forgetting factor recursive least square method is used to identify the vehicle mass and road slope for the problem of inconsistency between the vehicle mass and road slope variation frequency of heavy commercial vehicles. Firstly, a dynamic system model considering the rotational inertia of heavy commercial vehicles is established. Secondly, a multi forgetting factor recursive least square algorithm for vehicle mass and road slope identification is designed. Finally, the identification algorithm is verified at half load and full load respectively.
Wheel & Axle Disconnect Controls on Hybrid Electric Powertrains
Authors: Madireddy Krishna Chaitanya , Verhun Brandon , Xu Chengyi , Sha Hangxing , Tuller Zachary , Patel Nadirsh
Published: April 2024

With the proliferation of electric vehicles in the market, it has become important for Automotive OEMs (Original Equipment Manufacturers) to focus on delivering a higher driving range while also maximizing performance. One approach OEMs are actively considering in meeting this goal is to include a secondary drive axle disconnect into the powertrain which has the potential to improve the overall driving range by about 6-8.3% [4]. This paper outlines the need for a novel controls architecture to make the Powertrain controls software modular and to reduce the development time needed to provide robust powertrain control software. To do this, the electrified powertrain torque controls at STELLANTIS NV takes a decentralized controls architecture approach, by separating the axle disconnect controls subsystem (ADCS) from the primary path of torque controls. The ADCS takes in information such as the desired axle state and controls the axle disconnect actuators to achieve that state. It then communicates back the current status to the primary torque path, like an external “secondary” controller. ADCS also interacts with the primary torque path by providing finalized target motor speeds for synchronization, axle disconnect torque estimates, and axle disconnect torque limits within which the electric machines must remain. This approach enables us to have a common primary path across the powertrain architectures, thereby improving development time, and carrying forward lessons learned from previous powertrain programs.
Analysis of flatness based active damping control of hybrid vehicle transmission
Authors: Achir Ali , Patel Nadirsh , Divet Vincent
Published: April 2024

This paper delves into the investigation of flatness-based active damping control for hybrid vehicle transmissions. The main objective is to improve the current in-production controller performances without the need for additional sensors or observers. The primary goals include improving torque setpoint tracking, enhancing robustness margins, and ensuring zero steady-state torque correction. The investigation proceeds in several steps: Initially, both the general differential flatness property and the identification of flat outputs in linear dynamical systems are revisited. Subsequently, the bond graph formalism is employed to deduce straightforwardly the dynamical equations of the system. Next, a new flat output of the vehicle transmission is identified and utilized to formulate the trajectory tracking controller to align with the required control objectives and to fulfill the system constraints. Lastly, the efficacy of the novel control system is evaluated through simulation, and its performance is highlighted compared to the in-production controller.
A Real-Time Predictive Fuzzy Energy Management Based on Speed Prediction for Range Extended Electric Logistics Vehicle
Authors: Yang Bingjun , Chen Yong , Chen Quang , Lin Xiaozhe , Wei Changyin , Liu Caixia
Published: April 2024

Due to the complexity and timeliness of the dual power source control system for range extended electric vehicles, a real-time predictive fuzzy energy management strategy based on speed prediction, which comprehensively takes into account the demand power of auxiliary power unit, future average speed and driving distance is proposed in this work. Firstly, to improve the topicality and accuracy of the control system, the convolutional neural network with long short-term memory neural network (CNN-LSTM) algorithm is adopted to predict the future driving speed by the speed features and adjacent speeds. Secondly, taking account of the characteristics of the driving conditions for electric logistics vehicles, a three-inputs-one-output fuzzy controller is formulated based on the average predicted speeds, current traveling distance and demand power of the auxiliary power unit, so as to adjust the expected output power to harmonize the fuel consumption, electricity costs for the process of battery charging, discharging and degradation. Besides, the comparative tests are carried out to validate the effectiveness of various control strategies for harmonizing fuel consumption and battery degradation. The results under the World Light-duty Vehicle Test Cycle (WLTC) indicate that compared with the fuzzy control strategy which only takes the current vehicle speed as an input variable and the charge-depleting charge-sustaining (CD/CS) strategy, the total operation cost of the proposed strategy is reduced by 3.04% and 34.44%, respectively. Finally, The robustness and effectiveness of the strategy are verified under various driving conditions, and the real-time performance of the strategy is verified by HIL experiment. Hence, the proposed real-time predictive fuzzy energy management strategy gives out the great control effect on economy improvement and suppression of battery decay for electric extended-range vehicles.
A Zero Trust Architecture for Automotive Networks
Authors: Shipman Maggie E. , Millwater Nathan , Owens Kyle , Smith Seth
Published: April 2024

Since the early 1990’s, commercial vehicles have suffered from repeated vulnerability exploitations that resulted in a need for improved automotive cybersecurity. This paper outlines the strategies and challenges of implementing an automotive Zero Trust Architecture (ZTA) to secure intra-vehicle networks. Zero Trust (ZT) originated as an Information Technology (IT) principle of “never trust, always verify”; it is the concept that a network must never assume assets can be trusted regardless of their ownership or network location. This research focused on drastically improving security of the cyber-physical vehicle network, with minimal performance impact measured as timing, bandwidth, and processing power. The automotive ZTA was tested using a software-in-the-loop vehicle simulation paired with resource constrained hardware that closely emulated a production vehicle network. For example, the vehicle Advanced Gateway electronic control unit (ECU) is utilized to enforce cyber policy, monitor the network, distribute keys, and implement network segmentation. The technical approach applied other security solutions including Secure Onboard Communication (SecOC) for authentication and verification of network traffic and Secure Boot to ensure the system is running authentic software. Implementing these elements and the other security controls was complicated by cost, resource constraints, and the complexity of building and maintaining vehicles.

The project team identified four metrics to demonstrate performance success and feasibility of the implementation. They are as follows: 1. Error monitoring system detected 100% of illicit messages, 2. ECUs refused unauthorized firmware 100% of the time, 3. ECUs discarded unauthenticated messages 100% of the time, 4. Latency at first ignition cycle was less than one second. This research successfully met the four requirements and demonstrated that using ZT principles in an on-vehicle network greatly improved the cybersecurity posture with manageable impact to system performance and deployment.
Comparing Open-Source UDS Implementations Through Fuzz Testing
Authors: Çelik Levent , McShane John , Scott Christian , Aideyan Iwinosa , Brooks Richard , Pese Mert D.
Published: April 2024

In the ever-evolving landscape of automotive technology, the need for robust security measures and dependable vehicle performance has become paramount with connected vehicles and autonomous driving. The Unified Diagnostic Services (UDS) protocol is the diagnostic communication layer between various vehicle components which serves as a critical interface for vehicle servicing and for software updates. Fuzz testing is a dynamic software testing technique that involves the barrage of unexpected and invalid inputs to uncover vulnerabilities and erratic behavior. This paper presents the implementation of fuzz testing methodologies on the UDS layer, revealing the potential vulnerabilities that could be exploited by malicious entities.

By employing both open-source and commercial fuzzing tools and techniques, this paper simulates real-world scenarios to assess the UDS layer’s resilience against anomalous data inputs. Specifically, we deploy several open-source UDS implementations on a Controller Area Network (CAN) testbed and use them as a target for the aforementioned fuzzing tools. The outcomes of the fuzzing campaigns provide both automakers and researchers with insights about the completeness of open-source UDS implementations, as well as existing vulnerabilities. Our recommendations are intended to inform researchers and developers about the current state of these implementations, especially if they consider integrating them into their products. Ultimately, the use of open-source implementations in the automotive domain promises a more secure, easier to maintain, safer, and cheaper development process.

This paper underscores the significance of continuous testing and fortification in ensuring the integrity of automotive systems with a particular focus on UDS, offering a valuable contribution to the advancement of secure vehicular technology.