bluuebunny/crossref_metadata_2025 huggingface dataset:
Scenario-Based Risk Quantification Approach for Assuring Safety in Autonomous Vehicles
Authors: Madala Kaushik , Solmaz Mert
Published: April 2023

Contemporary cutting-edge technologies, such as automated driving brought up vital questions about safety and relativized the safety assurance and acceptance criterion on different aspects. New risk assessment, evaluation, and acceptance justifications are required to assure that the assumptions and benchmarking are made on a reasonable basis. While there are some existing risk evaluation methods, most of them are qualitative in nature and are subjective. Moreover, information such as the safety performance indicators (SPIs) of the sensors, algorithms, and actuators are often not utilized well in these methods. To overcome these limitations, in this paper we propose a risk quantification methodology that uses Bayesian Networks to assess if the residual risk is reasonable under a given scenario. Our scenario-based methodology utilizes the SPIs and uncertainty estimates of sensors, algorithms, and actuators as well as their characteristics to quantify risk using the conditional probability tables that assure no dependencies among vehicle’s elements are overlooked. We also discuss the guidelines that need to be followed when creating the probability tables. To illustrate our methodology, we use a running example, in which we demonstrate how we calculate the risk using our Bayesian approach. We also discuss the merits and limitations of our proposed methodology, and how it is helpful even when we might not have sufficient information from suppliers.
Innovative Architecture of a Smart Bidirectional Input /Output Interface Based upon Frugal Engineering Concepts
Authors: Vaidya Vishwas Manohar
Published: April 2023

Automotive megatrends such as connected, autonomous and electrified vehicles are driving penetration of automotive electronic technology in every vehicle subsystem. However, these electronic units need to be interfaced with input/output devices from outside world. The same need to be interfaced with a robust and reusable architecture conforming to automotive norms. When generic electronic control units (ECUs) need to be reused across diverse vehicle families and segments, it is desirable that every ECU pin interfaced with the outside world needs to be capable of performing multiple tasks. This is particularly true of digital outputs (such as solenoids, lamps, on/off motors etc.) and digital inputs (such as command switches, pushbuttons, pressure switches, thermal switches etc.) Current smart switch peripheral ICs can be used only as outputs and cannot be configured as inputs whenever demanded by application requirements. Also, these smart switch architectures provide conservatively generic solutions which prove to be “over engineered” for many digital output solutions such as simple LED or Solenoid controls. This paper proposes a novel smart switch cum input interface architecture which can be optimally tailored to a given set of output load requirements such as current profile, thermal requirements, protection on one hand and a simple digital on/off input interface on the other hand. The architecture design employs a system engineering technique which lists performance requirements and explores alternate architectures to meet the same. All the alternatives are rated on various performance metrics to arrive at the rightmost choice fitting a given application. The paper ends with a design example which covers both hardware philosophy and a state machine based low level command/diagnostic driver. Future directions and opportunities for this solution are discussed at the end.
Comprehensive Design Methodology of a Vehicle Monocoque: From Vehicle Dynamics to Manufacturing
Authors: Messana Alessandro , Bianco Luca , Carello Massimiliana
Published: April 2023

Climate change has become a real problem in our world. Society is trying to contain it as much as possible, promoting more sustainable behaviors and limiting pollution. For the automotive industry, this leads to progressive electrification and reduction of tailpipe emissions and fuel consumption for conventional vehicles. In this framework, this paper presents the design of a vehicle to compete in the Urban Concept category of Shell Eco Marathon, a competition among universities that has the goal to release a vehicle with the lowest possible fuel consumption. This work describes the monocoque design phases of the vehicle JUNO. The complete design approach is described, through the analysis of the decisional workflow adopted to integrate every technical solution from the aerodynamic constraints to the structural ones passing from the vehicle dynamic requirements. The methodology adopted to size the monocoque is explained in depth through the analysis of the multi-step optimization performed by means of finite element models. Therefore, the load configuration and the design constraints are illustrated with the aim of mass optimization. Specifically, the rollover and the torsional stiffness test have been chosen as the most significative load cases in terms of structural integrity and performance respectively. Furthermore, the definition of the final monocoque geometry and stacking sequence is described together with the virtual results obtained. Finally, a brief explanation of the production process is presented, to complete all the project phases.
Optimization of Body D-Pillar Ring Structure
Authors: Kim Hyungtae , Lee YoungHo , Hur Jungwoo , Choi Jeehwan
Published: April 2023

The body stiffness plays a key role in vehicle performance, such as noise and vibration, ride and handling, durability and so on. In particular, a body D-pillar ring structure is the most sensitive affecting the body stiffness on vehicle with tail gate. Therefore, since D-pillar body ring structure for high stiffness and lightweight is required, an optimized design methodology that simultaneously satisfies the requirements was studied. It focused on a methodology that body engineering designers can optimize design parameters easily and quickly by themselves in the preceding stages of vehicle’s styling distribution and design conceptual planning. First, it is important to establish the body stiffness design strategy by predicting the body stiffness with the vehicle’s styling at early design stage. The methodology to predict body stiffness with the styling and body dimension specification parameters was introduced. Next, design parameters such as a cross-section area, material and thickness of the member, joint structure and detailed shape of the panel were optimized. Finally, the optimized D-pillar structure was applied to the newly developed vehicle and the effect was demonstrated.
Vehicle Underbody Structural Performance Prediction During Waterfording Events Using A One Way Coupled CFD-CAE Approach
Authors: Krishnan Prathap , R Sivakumar , Khedkar Milind R , Mahadule Roshan N , Doroudian Mark , Vanarajan Shankar
Published: April 2023

Water fording events are one of the most challenging situations that vehicles undergo during their lifetime. During these events the underbody components (e.g. Front fascia, Bellypan, wheel liner etc.) are subject to very high loads. Typically, vehicle water fording tests are performed for various depths of water at prescribed vehicle speeds. Water fording tests are usually carried out during the proto phase of the vehicle development program to ensure acceptable performance. If issues are discovered, making changes to the fascia or body panels are typically very expensive. To avoid late changes, a fully virtual methodology was developed to facilitate vehicle water fording performance. The simulation is targeted to evaluate multiple aspects such as air induction system and estimation of hydrodynamic loads on body panel components. This paper describes the approach for coupling CFD (Computational Fluid Dynamics) and CAE (Computer Aided Engineering) to evaluate the stress levels in the body panels and structural components during water fording tests. This study considers that the vehicle is moving through a flat road covered with water at various depths. The CFD simulation uses a commercially available RANS (Reynolds-averaged Navier-Stokes) based software to provide steady state pressure loads for CAE simulation. The CAE models use these pressure loads to compute stress and deflection of the underbody components using a nonlinear CAE solver. This coupled simulation approach has reduced the turnaround times and provided design directions to the product team early in the development cycle.
Side Door Hinge Axis Deviation and Skewness Study on the Door Closing Effort
Authors: Selvan Veera , Askari Hasan , Bhosale Mukund , Unadkat Siddharth , Pandurangan Venugopal
Published: April 2023

The side door closing effort is one of the main evaluating parameters which demonstrates the build quality of the vehicle. The side door hinge axis inclination is one of the key attributes that affect the side door closing effort. Commonly, the hinge axis is inclined in two directions of a vehicle to have necessary door rise during the door opening event. Due to the process and assembly variations in the door assembly, the upper and lower hinge axis of the side door deviates from the design axis. In this paper, the deviations in the side door hinge axis and its effects on the side door closing velocity is discussed. The deviations of the side door hinge axis are studied with a coordinate measuring machine. The side door closing velocity of the vehicle is measured with the velocity meter. The study revealed that side door closing velocity is increasing with an increase in the deviation of the top and bottom door hinge axis from the design hinge axis. The hinge axis skewness between the top and bottom door hinge also increases the side door closing velocity. Based on the study, it is evident that the process and assembly deviations in the door assembly need to be controlled to have the desired door closing velocity.
Characterization and Modeling of Anisotropic Fracture of Advanced High-Strength Steel Sheets
Authors: Hu Jun , Pan Hao , Pavlina Erik , Thomas Grant
Published: April 2023

As an engineering approach of balanced complexity and accuracy, the Generalized Incremental Stress-State dependent damage Model (GISSMO) in LS-DYNA® has now been widely adopted by the automotive industry to predict metallic materials’ fracture occurrences in both forming and crashworthiness simulations. Calibration of the nominal GISSMO is typically based on material characterization data along a certain representative material orientation. Nevertheless, many rolled or extruded metallic materials, such as advanced high-strength steel (AHSS) sheets, exhibit accentuated anisotropic fracture behavior, even though, notably, some of these materials show comparatively weak anisotropic plasticity in the meantime. Accordingly, in this work, the deformation and fracture behavior of a selected AHSS grade, Q&P980 steel, was first characterized based on a series of mechanical experiments under simple shear, uniaxial tension, plane strain, and equi-biaxial tension conditions. Then, material models were calibrated based on the plasticity and fracture data. Two fracture models, either stress- or strain-based, were applied to fit the fracture loci of the target material, which then could be directly implemented into the material cards in LS-DYNA®. Particularly, to simulate the anisotropic fracture behavior of the target material, an extended GISSMO material card (eGISSMO) was introduced and highlighted in this work. Unlike the nominal GISSMO, the eGISSMO integrated the different anisotropic fracture loci and damage accumulation along three material orientations (longitudinal, diagonal, and transverse) into a single material card. In the subsequent validation based on a customized three-point-bending (3PB) testing setup on hat-section samples, only the finite element (FE) model using the calibrated eGISSMO successfully simulated the anisotropic fracture bifurcation observed in the actual experiments.
Speed Determination Using Audio Analysis of Dash Camera Video for Vehicle Accident Reconstruction
Authors: Vega Henry V. , Ngo Justin , Engleman Krystina , Suway Jeffrey
Published: April 2023

Video from dash or surveillance cameras is sometimes used in vehicle accident reconstruction to analyze the speeds of vehicles. However, video captured during nighttime, during poor visibility conditions, or of events out of frame may not always visually capture details needed to determine the speed of the vehicle in question. Prior research has determined speed from vehicle acoustic signals, but little research has analyzed the audio portion of dash camera video for use in accident reconstruction and other forensic settings. The purpose of this study was to outline and test the validity of a method for using the audio portion of dash camera video to determine vehicle speed. Extracting the audio portion from the video recording and further processing it with commercially available software can allow the calculation of vehicle speed and acceleration when traveling over roadway surfaces and detection of turn signal activations while driving. By extracting the audio portion from the recorded video and processing in the frequency domain to remove ambient noise, the speed of the test vehicle was calculated when the wheels of the test vehicle traveled over expansion joints, rumble strips, and grooved concrete roadway surfaces. The reliability of the audio data were then tested against the video and speed data collected for a baseline. The proposed method can be useful to accident reconstructionists in that it provides an additional method for their analysis of vehicle speeds.
Design of a Crease Pattern for Pre-Folded Origami Structures to Improve Vehicle Crashworthiness
Authors: Chaudhari Prathamesh , Valladares Homero , Tovar Andres
Published: April 2023

To promote the progressive collapse of thin-walled vehicle structures and improve their energy-absorbing capabilities, designers allocate collapse initiators such as holes, grooves, humps, and creases. The use of some traditional origami patterns in pre-folded tubes has been particularly effective in this task. However, selecting the optimal origami pattern is a complex multidimensional combinatorial problem. This paper introduces a new origami pattern that triggers an extensional progressive collapse mode in a wide range of thin-walled tubes with a square cross-section. The parameters of the proposed pattern are optimized using a multi-objective Bayesian optimization algorithm to minimize the peak crushing force and maximize the mean crushing force. The crash simulations are supported by the commercial finite element solver Radioss. The optimized pre-folded origami structure depicts extensional progressive collapse under axial loads. Compared to alternative designs, results demonstrate significant improvement in crashworthiness indicators.
Methodology to Deduce the Testing Requirement of Twist Beam by Numerical Comparison of Roll Travel
Authors: Atal Abhishek , Lakhera Vivek , Singh Shamsher , Chaudhari Jeetendra Prakash
Published: April 2023

Twist-beam suspensions are an example of design solution presenting acceptable performance when applied to passenger cars & light vehicles and it can provide an optimal between cost & performance in the automotive market. For these reasons, twist beam is quite popular in use in rear suspension of light vehicles. In contrary to other types of suspension, the twist-beam has a flexible torsion beam connecting the swing arms. The study of the deformation of this flexible element becomes important to understand its performance and durability behavior. As the name signifies, twist beam major performance attribute is control of twist or opposite wheel travel arising from vehicle roll or road input. Current approach for the study this deformation is through FEA & Multi-body dynamics software tools. Conventional FEA approach requires availability of full-scale CAD models to work on, which is quite time consuming and its availability is difficult in early stage of the suspension design/development.

The main objective of this work was to evaluate the performance behavior using simplified mathematical modeling. This approach will give an alternative way to estimate the deformations for the initial stages of the suspension design, comparison between various vehicles/designs in less time as compared to FEA approach.

It also helps to reduce the iterations of RLDA (Road Load Data Acquisition) to understand the difference loads and strains on the structure. This will reduce the time and cost of data collection.
Opinions from Users Across the Lifespan about Fully Autonomous and Rideshare Vehicles with Associated Features
Authors: Gangadharaiah Rakesh , Mims Lauren , Jia Yunyi , Brooks Johnell
Published: April 2023

Fully autonomous vehicles have the potential to fundamentally transform the future transportation system. While previous research has examined individuals’ perceptions towards fully autonomous vehicles, a complete understanding of attitudes and opinions across the lifespan is unknown. Therefore, individuals’ awareness, acceptance, and preferences towards autonomous vehicles were obtained from 75 participants through interviews with three diverse groups of participants: 20 automotive engineering graduate students who were building an autonomous concept vehicle, 21 non-technical adults, and 34 senior citizens. The results showed that regardless of age, an individual’s readiness to ride in a fully autonomous vehicle and the vehicle’s requirements were influenced by the users’ understanding of autonomous vehicles. All of the engineering students understand what a fully autonomous vehicle is and this group was the most willing to ride especially compared to the seniors, where only half of the seniors knew what a fully autonomous vehicle is and 58.8% were not at all ready to ride one. The desire to have a manual control option or the ability to override the vehicle was common (90% of the engineering students, 95.2% of the adults, and 82.4% of the seniors), especially for individuals who reported not being ready to ride in a fully autonomous vehicle. The majority of all three groups of participants (85% of the engineering students, 81% of the adults, and 52.9% of the seniors) considered it essential that the vehicle should convey information about the vehicle’s status and intended behavior. Diagnostic information about the vehicle was desired by the engineering students (71.4%), who had a technical understanding of autonomous vehicles and current automotive related technologies. When autonomous vehicles are available, most participants anticipate preferring to use them as a rideshare service model (75% of the engineering students, 38% of the adults, and 27% of the seniors) rather than owning (5% of the engineering students, 19% of the adults, and 21% of the seniors) the autonomous vehicle themselves. Regarding the topic of sharing rides with strangers, both the automotive engineering students (90%) and the adults (52.6%) were comfortable with the idea of pooled rideshare in comparison to the seniors (29.4%). In future efforts, it will be important to include potential autonomous vehicle users of a wide age range as well as physical, cognitive, and visual abilities.
Adaptive Actuator Delay Compensation for a Vehicle Lateral Control System
Authors: Kennedy William Thomas , Bevly David M.
Published: April 2023

Steering actuator lag is detrimental to the performance of lateral control systems and often leads to oscillation, reduced stability margins, and in some cases, instability. If the actuator lag is significant, compensation is required to maintain stability and meet performance specifications. Many recent works use a high-level approach to compensate for delay by utilizing model-based methods such as model predictive control (MPC). While these methods are effective when accurate models of both the vehicle and the actuator are available, they are susceptible to model errors. This work presents a low-level, adaptive control architecture to compensate for unknown or varying steering delay and dynamics. Using an inner-loop controller to regulate steer angle commands, oscillation can be reduced, and stability margins can be maintained without the need for an accurate vehicle model. The Smith Predictor (SP) control scheme is implemented in the inner-loop to mitigate the effects of the communication delay between the controller and the steering actuator. An algorithm will be presented to estimate both the communication delay between the controller and actuator and the steering dynamics. These estimates will be used to adapt the inner-loop SP to maintain gain and phase margins while reducing oscillation. Estimating the steering lag allows the algorithm to compensate for unknown or changing steering dynamics and communication delay. Results are presented both from simulation and from real-time experiments on a vehicle outfitted with drive-by-wire (DBW) hardware.
A Novel Speed Control Strategy for Electric Vehicles with Optimal Energy Consumption under Multiple Constraints
Authors: Zhang Lijun , Zhang Xianhua
Published: April 2023

Autonomous driving related technologies have become a hot topic in academia and industry. Planning control is one of the core technologies of autonomous driving, which is conducive to vehicles safe and efficient driving. This paper proposes a novel optimal speed control algorithm, which considers the power system's energy consumption, the speed limit on the road, and the safe distance of the vehicle in front. An optimal speed control model of “From battery to wheel” energy consumption is established by constructing a performance index function based on the best-fitting formula of motor power, motor speed and torque. Based on the optimal control principle, the fourth-order ordinary differential equation of the speed control model is established, based on the indirect adjoining approach, the speed control model under the restriction of the road speed limit and safe distance of the preceding vehicle is derived and the analytical expression is obtained. Under the restriction of the road speed limit and safe distance of the preceding vehicle, the model predictive control method is introduced, the urban working conditions in the NEDC cycle are selected for simulation, and the optimal control input is solved in real-time, compared with the intelligent driver driving strategy, this algorithm is proved to be superior in the aspect of vehicle speed optimization control. Finally, the influence mechanism of the road speed limit constraint on the vehicle speed control is analyzed. This paper realizes the design of the optimal speed control algorithm for energy consumption under various constraints. The results can be used to guide the energy-saving driving of vehicles, and provide theoretical guidance for ecological driving technology in the context of intelligent transportation.
Autonomous Eco-Driving Evaluation of an Electric Vehicle on a Chassis Dynamometer
Authors: Motallebiaraghi Farhang , Rabinowitz Aaron , Fanas Rojas Johan , Kadav Parth , A. Miller Damon , Bradley Thomas , Meyer Rick , Asher Zachary
Published: April 2023

Connected and Automated Vehicles (CAV) provide new prospects for energy-efficient driving due to their improved information accessibility, enhanced processing capacity, and precise control. The idea of the Eco-Driving (ED) control problem is to perform energy-efficient speed planning for a connected and automated vehicle using data obtained from high-resolution maps and Vehicle-to-Everything (V2X) communication. With the recent goal of commercialization of autonomous vehicle technology, more research has been done to the investigation of autonomous eco-driving control. Previous research for autonomous eco-driving control has shown that energy efficiency improvements can be achieved by using optimization techniques. Most of these studies are conducted through simulations, but many more physical vehicle integrated test application studies are needed. This paper addresses this research gap by highlighting the Vehicle Hardware-In-the-Loop (VHIL) energy saving potential of autonomous eco-driving control for connected and automated vehicles. A comprehensive system description of autonomous eco-driving control is presented by describing subsystems and their functionalities. Validated autonomous eco-driving optimization methods, including Dynamic Programming (DP), Genetic Algorithms (GA), and Particle Swarm Optimization (PSO) were tested with a control-enabled electric Kia Soul using a 2-wheel-drive chassis dynamometer. VHIL test performance of these methods is evaluated relative to each other as well as a baseline scenario. The conclusions were derived from examinations that were carried out on a chassis dynamometer. The results show that energy efficiency may be enhanced by anywhere from 5 to 15 %, depending on the method that is used. When compared to our earlier simulation results, it is demonstrated that the VHIL outcomes achieve the predicted gain in energy efficiency. The overall results show that the use of the dynamic programming method is the most effective strategy for enhancing energy efficiency. It is shown that the application of methods that are derived from genetic algorithms has the potential to increase energy efficiency when integrated in the test vehicle.
Dynamic Speed Harmonization (DSH) as Part of an Intelligent Transportation System (ITS)
Authors: Kavas-Torris Ozgenur , Guvenc Levent
Published: April 2023

In the last decade, the accelerated advancements in manufacturing techniques and material science enabled the automotive industry to manufacture commercial vehicles at more affordable rates. This, however, brought about roadways having to accommodate an ever-increasing number of vehicles every day. However, some roadways, during specific hours of the day, had already been on the brink of reaching their capacity to withstand the number of vehicles travelling on them. Hence, overcrowded roadways create slow traffic, and sometimes, bottlenecks. In this paper, a Dynamic Speed Harmonization (DSH) algorithm that regulates the speed of a vehicle to prevent it from being affected by bottlenecks has been presented. First, co-simulations were run between MATLAB Simulink and CarSim to test different deceleration profiles. Then, Hardware-in-the-Loop (HIL) simulations were run with a Road Side Unit (RSU), which emulated a roadside detector that spotted bottlenecks and sent information to the Connected Vehicle about the position of the queue and the average speed of the vehicles at the queue. The DSH algorithm was also tested on a track to compare the performance of the different deceleration profiles in terms of ride comfort.
Diagnosis and Prognosis of Chassis Systems in Autonomous Driving Conditions
Authors: Lee Kyung-Woo , Sung Dae-Un , Han Yong Ha , Yoo Yeongmin , Lee Jongsoo
Published: April 2023

Expanding various future mobilities such as purpose built vehicle (PBV), urban air mobility (UAM), and robo-taxi, the application of autonomous driving system (ADS) technology is also spreading. The main point of ADS is to ensure safety by monitoring vehicle anomalies to prevent functional failure or accident. In this study, a model-based diagnosis and prognosis process was established using degradation data generated during autonomous driving simulation. A vehicle model was designed using Modelica/Dymola, and autonomous driving simulation was performed by integrating the lane keeping assistant (LKA) system with the vehicle model using Matlab/Simulink. Degradation data for the 3 components (a shock absorber damper, a suspension bush, and a tire) of the chassis system were input into the integrated simulation model. The degradation behavior was monitored with K-nearest neighbor (K-NN) and Gaussian mixture model (GMM). The remaining useful life (RUL) for each component was estimated using a Gaussian process. As a result, a normal/abnormal data classifier was designed to diagnose the autonomous vehicle simulation model, and the RUL was estimated within the 95% prediction interval.
Analytic Study of China’s Latest New Energy Vehicle Market Subsidies in Facing of the Carbon Neutrality Goal
Authors: Liao Yuhong , Li Xinyi , Wan Yaya , Zhan Zhenfei
Published: April 2023

In recent years, aimed to promote the improvement of China’s new energy vehicle market, a series of incentive policies issued by the Chinese government: including the new energy vehicle subsidy policy, the double credit policy, and the charging pile infrastructure subsidy.Relevant research on new energy vehicle industry is mainly ground on multi-stage game, this paper employs multi-agent games theory, and summarizes the multi-agent decision-making optimization method in differential game based on dynamic programming and reinforcement learning. Then, in the context of new energy vehicles, research and improve the industrial policy of new energy vehicles through this method.A multi-agent differential game decision-making optimization framework is proposed. Complex multi-agent differential game decisions can be solved using the dynamic programming solver or deep reinforcement learning solver in this framework. Case studies and some observations will be given in the end.
Vehicle Forward Collision Warning Based on Improved Deep Neural Network
Authors: Zhan Zhenfei , Zhou Guilin , Fengyao LV , Xue Bingying , He Xin , Wang Ju , Li Jie
Published: April 2023

Forward Collision Warning System is an important part of vehicle active safety system, it can reduce the occurrence of rear-end collision accidents with high fatality rate and improve the safety of driving. At present, there are still some outstanding issues to be addressed among the existing forward collision warning systems, such as the high cost of information acquisition based on LiDAR and other high-definition sensors, and the poor real-time performance of target detection based on vision. In view of the aforementioned issues and in order to improve the detection accuracy and real-time requirements of the target detection function of the early warning system, this paper proposes an enhanced deep learning model-based vehicle target detection method, and improves the key techniques of target detection, ranging and speed measurement and early warning strategy in the warning system. Then, a target positioning scheme by visual fusion method is employed to improve the accuracy of distance detection, followed by an improved multi-target tracking algorithm.to realize the speed estimation of the front vehicle. Finally, the proposed forward collision warning strategy is demonstrated through real world case studies and results are given in the end.
Numerical Modeling and Simulation to Predict Thermal Runaway Propagation in an EV Battery Pack
Authors: U Reghunath , Gudi Abhay , Bonala Sastry
Published: April 2023

As the world is moving towards greener energy solutions, there is a clear transition seen from ICE to EV powertrain solution. The cost of vehicle is primarily controlled by battery pack as it is high capital intense. Though Li-Ion battery is a very promising technology in terms of energy storage and long-term performance, safety of battery is a concern. Battery can undergo self-fire/ thermal runaway due to several factors like aging, internal short, overcharging etc. A numerical investigation is carried out for a conceptual 10S1P prismatic battery pack to model the nail penetration using commercial ANSYS Fluent tool. Vent gas generation has been modelled and its convective effects on Thermal runaway were studied. Vent gas generation is supported through a user defined function which calculates the amount of flow rate that vent gas encounters during thermal runaway.

Further, this procedure of vent gas calculation is extended to a realistic 16S8P cylindrical cell battery pack which has a cooling channel embedded in it. A comparative study is conducted to see the effectiveness of cooling channels by comparing heat transfer rate for same pack with and without the cooling flow. It is observed that cooling channel plays vital role in delaying thermal runaway when compared with results of battery pack without cooling flow. Further this study is extended to evaluate various failure modes of cells by conducting a study and understand worst cell model failure out of all the combinations.
Dynamic Simulation and Optimization of Vehicle-Mounted Multifunctional Mechan-Ical Arm
Authors: Zhang Feiyu , Tan Gangfeng
Published: April 2023

The multi-functional mechanical arm equipped on engineering vehicle can achieve different functions by installing different mechanism devices through the interface at the end of the mechanical arm. It can achieve functions like engineering construction and road rescue. Mechanical arm systems often work in complex environments, which requires good reliability and safety of the boom system.

When the mechanical boom is working, the pressure of each luffing cylinder is large, and the contact force and acceleration of each boom are complex, which requires a certain degree of verification and optimization before it can be put into production. In this paper, a virtual prototype of a vehicle mounted hydraulic mechanical arm with four booms is established. Through ADAMS, the dynamic analysis of mechanical arm under multiple working conditions is carried out, the movement parameter changes and the pressure changes of each luffing cylinder are analyzed. A multi working condition optimization scheme is proposed to parameterize the coordinates of each hinge point of the mechanical boom. Through sensitivity analysis, the parameters of articulation points that have great influence on the force of the boom system are obtained for optimal design, so as to minimize the maximum pressure of each cylinder and improve its working condition.

The maximum pressure of each cylinder of the optimized mechanical arm is reduced by 20% - 50% compared with baseline, and the force curve becomes more gentle, eliminating the sudden change of pressure, which makes the dynamic characteristics of the mechanical arm significantly improved, makes its lightweight design more possible, shortens the deployment speed of its boom, and increases the working efficiency and reliability of the engineering vehicle, This provides a reference for the design of other kinds of mechanical arms.
Improving Handling of a Heavy Truck
Authors: Can Guo , Shuihe Ge , Wei Chen
Published: April 2023

Handling stability is one of the important characteristics to evaluate the vehicle performance. This paper presents the modeling, simulation, optimization of handling for a heavy truck. A detailed heavy truck model was built by ADAMS, which consists of the frame, suspension system, steering system, wheel, the cab, etc. The simulation results fit the experiment data very well, which shows that the dynamic model can accurately predict the performance of the vehicle by modeling and simulation. Handling simulation was carried out to show how the parameters influence the performance of the handling. The optimization was carried out based on the simulation results. The experiment data shows that the handling of the heavy truck such as on center handing, steering returnability, steering-effort has been significantly improved.
Recrystallization Behavior of Non-oriented Electrical Steel Sheets after Skew Cold Rolling
Authors: He Youliang , Sanjari Mehdi
Published: April 2023

Stress-relief annealing is an indispensable processing step for non-oriented electrical steel (NOES) sheets to achieve optimal magnetic properties. The annealing microstructure and texture are not only dependent on the annealing conditions, but also on the prior thermomechanical processing history. To investigate the effect of deformation mode on the recrystallization behavior, a NOES containing 0.9 wt% Si was cold rolled by skew rolling in which the hot-rolled-and-annealed plate was fed at 45° into the rolls to change both the initial texture and the deformation mode. The skew-cold-rolled sheets and those rolled by conventional and cross rolling were then annealed at different temperatures (650 to 1050 °C) for different times (0.5 to 30 min). The recrystallization behavior was characterized using electron backscatter diffraction (EBSD) techniques. It was found that the cold rolling deformation mode and the initial texture have a significant effect on the recrystallization behavior. The recrystallization rates of the skew- and cross- rolled sheets are higher than that of conventionally rolled steel if the annealing temperature is low (650 °C) or the annealing time is short (0.5 min). When the annealing temperature is relatively high (850 °C) and the annealing time is relatively long (2 min), the difference in recrystallization rate is small. In all the cases, skew rolling promotes the formation of the desired <001>//ND (normal direction) texture, and the unfavorable <111>//ND texture is essentially eliminated.
Assessment of Actuator Line and Rotor Disk as Alternative Approaches for the Numerical Simulation of Rotating Wheels
Authors: Evangelista Federico , Semeraro Francesco Fabio , Schito Paolo
Published: April 2023

Wheel and wheelhouses contribute up to 20-30% of the aerodynamic drag of passenger cars. Simulating the flow field around wheels is challenging due to the complexity of the flow structures generated by tires and rims, wheel rotation, tire deformation and contact with the ground. High accuracy is usually obtained with transient simulations that treat rim rotation with the Sliding Mesh (SM) approach, which is also computationally expensive. Previous studies have confirmed that the application of a tangential velocity component to the rim surface is unphysical for open rims, while a Moving Reference Frame (MRF) is lacking accuracy and the averaged results depend on the initial spokes position. These methods do not consider the dynamic nature of the problem. This work proposes the use of the Actuator Line (AL) and Rotor Disk (RD) approaches as alternatives for simulating open rims with much lower computational cost. They are based on the introduction of volume forces to resemble the presence of the spokes and require a preliminary aerodynamic characterization of the body. The new methods are compared to SM, Rotating Wall Boundary Condition (RWBC) and MRF applied to an isolated rotating wheel case. A good agreement is observed for the most relevant flow features, with consistent reductions in computational cost. Specifically, AL keeps the transient features of the problem, while RD provides a much less expensive steady-state solution. As a result, both AL and RD are promising alternatives for the simulation of rotating wheels.
Role of Worst-Case Operating Scenario and Component Tolerance in Robust Automotive Electronic Control Module Design
Authors: Hasan S.M. Nayeem , Irgens Peter , Murphy Thomas
Published: April 2023

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.
Comparison of Deep Learning Architectures for Dimensionality Reduction of 3D Flow Fields of a Racing Car
Authors: Reck Michaela , Hilbert Marc , Hilhorst René , Indinger Thomas
Published: April 2023

In motorsports, aerodynamic development processes target to achieve gains in performance. This requires a comprehensive understanding of the prevailing aerodynamics and the capability of analysing large quantities of numerical data. However, manual analysis of a significant amount of Computational Fluid Dynamics (CFD) data is time consuming and complex. The motivation is to optimize the aerodynamic analysis workflow with the use of deep learning architectures. In this research, variants of 3D deep learning models (3D-DL) such as Convolutional Autoencoder (CAE) and U-Net frameworks are applied to flow fields obtained from Reynolds Averaged Navier Stokes (RANS) simulations to transform the high-dimensional CFD domain into a low-dimensional embedding. Consequently, model order reduction enables the identification of inherent flow structures represented by the latent space of the models. The resulting data from the 3D-DL study are compared to a traditional dimensionality reduction method, namely Proper Orthogonal Decomposition (POD). Flow field features are examined by using methods of local feature importance, aiming for awareness of predominant fluidic phenomena. We show that our data-driven models capture aerodynamically relevant zones around the racing car. 3D-DL architectures can represent complex nonlinear dependencies in the flow domain. The U-Net network demonstrates an R2 reconstruction accuracy of 99.94%, outperforming the results achieved from linear POD with an R2 of 99.57%. Efficiently handling numerous CFD simulations leads to improved post-processing and an accelerated investigation procedure for future aerodynamic development. Finally, the discovered findings provide further knowledge for the serial development to increase efficiency, thereby extending, e.g., the range of electric vehicles.
Deep Learning Based Automotive Requirements Analysis
Authors: D H Sharath , P.C. Karthik , TG Sreekanth , Ansari Asadullah
Published: April 2023

Automotive system functionalities spread over a wide range of sub-domains ranging from non-driving related components to complex autonomous driving related components. The requirements to design and develop these components span across software, hardware, firmware, etc. elements. The successful development of these components to achieve the needs from the stockholders requires accurate understanding and traceability of the requirements of these component systems. The high-level customer requirements transformation into low level granularity requires an efficient requirement engineer. The manual understanding of the customer requirements from the requirement documents are influenced by the context and the knowledge gap of the requirement engineer in understanding and transforming the requirements. The manual way of understanding the requirements of the automotive systems always involve a certain amount of ambiguity, misunderstanding, bias etc. in analyzing the functionality of the requirements. The complex automotive system, which is solely developed based on human understanding always causes some violations in transforming the actual requirements from the stockholders in a product functionality. Hence, to mitigate this human influence on this aspect of requirement understanding, an intelligent system, which either to assists the manual requirement analysis or completely analyze the requirements alone is required. In this regard, an intelligent system is proposed here to analyze the automotive requirements efficiently by reducing human conflict, manual efforts, and to improve design and execution performance of an automotive component. The proposed system uses deep learning based Natural Language Processing (NLP) based algorithms to analyze and understand the requirement corpus from a set of platform requirements. The training of the deep learning CNN algorithms is performed on a huge set of pre-implemented platform requirements. The inference of the new customer requirements is done using the trained deep learning-based models to classify the requirements into one of the pre-defined platform requirement classes, thereby assisting the manual analysis using its intelligent component by also providing the traceability.
Synergizing Artificial Intelligence with Product Recall Management Process
Authors: Krishnamoorthy Bharathi
Published: April 2023

There are a multitude of dynamics faced by any industry. There is also a consistent search and development of technological platforms and services to address these changes. This necessitates a shared work philosophy which involves multiple stakeholders. Verification and validation are integral part of any development irrespective of product, process, or services. Also, every industry has a regulatory compliance to adhere too. But the extent of complexity and the level of dependencies or interactions between modules as well as stakeholders involved, creates slippage at some or other level.

Nowadays the industries are also driven by reuse for cost effectiveness. Though it marks the significant improvement in the capability to compete, compatibility is a key measure to a successful product or service launch and sustainability. In many cases, there is a propagation and ripple effect of an issue at unit level to the product level and this induces safety concerns at times and necessitates massive recalls.

This paper discusses on the major concerns industries face due to recalls. It also details on the current methods adopted towards recall handling and highlights the key challenges associated with the recall with respect to all the stakeholders involved. It discusses on an Artificial Intelligence based strategy for automation of product recall management process.

This paper presents a different facet for product recall management that employs Artificial Intelligence technique and brings out the efficiency and benefits achieved by the proposed strategy.