bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of an Ultra-Low Carbon Flex Dual-Fuel Ammonia Engine for Heavy-Duty Applications
Authors: Hegab Abdelrahman , Bowling William , Cairns Alasdair , Harrington Anthony , Hall Jonathan , Bassett Michael
Published: April 2024

The work examined the practicality of converting a modern production 6 cylinder 7.7 litre heavy-duty diesel engine for flex dual-fuel operation with ammonia as the main fuel. A small amount of diesel fuel (pilot) was used as an ignition source. Ammonia was injected into the intake ports during the intake stroke, while the original direct fuel injection equipment was retained and used for pilot diesel injection. A bespoke engine control unit was used to control the injection of both fuels and all other engine parameters. The aim was to provide a cost-effective retrofitting technology for existing heavy-duty engines, to enable eco-friendly operation with minimal carbon emissions. The tests were carried out at a baseline speed of 600 rpm for the load range of the engine (10-90%), with minimum pilot diesel quantity and as high as 90% substitution ratio of ammonia for diesel fuel. Results demonstrated that at high load conditions, ammonia dual-fuel operation could achieve diesel-like efficiency, while reducing engine carbon emissions by 90% and almost eliminating soot. Engine stability was maintained at acceptable levels, while the peak in-cylinder pressure and the maximum rate of pressure rise were reduced by more than 7% and 34%, respectively. Despite slightly lower combustion and exhaust gas temperatures, ammonia dual-fuel operation resulted in higher NOx emissions compared with conventional diesel operation, potentially owing to the nitrogen-rich fuel. The case was reversed at low load conditions, where ammonia dual-fuel operation produced lower NOx emissions but suffered poor combustion and inferior engine performance, relative to conventional diesel operation. Ammonia slip into the exhaust was excessive (exceeding 10,000 ppm) throughout dual-fuel testing.
Integrating Machine Learning in Pedestrian Forensics: A Comprehensive Tool for Analysing Pedestrian Collisions
Authors: Shrinivas Vadhiraj , Bastien Christophe , Davies Huw , Daneshkhah Alireza , Hardwicke Joseph , Neal-Sturgess Clive , Lamaj Albi
Published: April 2024

Analysis of pedestrian-to-vehicle collisions can be complex due to the nature of the interaction and the physics involved. The scarcity of evidence like video evidence (from CCTV or dashcams), data from the vehicle's ECU, witness accounts, and physical evidence such as tyre marks, complicates the analysis of these incidents. In cases with limited evidence, current forensic methods often rely on prolonged inquiry processes or computationally intensive simulations. Without adequate data, accurately estimating pedestrian kinematics and addressing hit-and-run scenarios becomes challenging. This research provides an alternative approach to enhancing pedestrian forensic analysis based on machine learning (ML) algorithms trained on over 3000 multi-body computer simulations with a diverse set of vehicle profiles and pedestrian anthropometries. Leveraging information such as vehicle profile, damage, and pedestrian attributes like height and weight, the ML algorithm estimates essential parameters like vehicle impact speed, pedestrian gait, crossing speed, and crossing direction. The proposed ML algorithm was evaluated against real-world data from the UK Road Accident In Depth Studies (RAIDS) and proved to be accurate in predicting impact conditions within an error tolerance of 10%. This ML-based technology provides forensic investigators with vital pedestrian collision parameters early in the inquiry, enabling a focused analysis on a reduced collision parameter set. First responders can swiftly estimate speed characteristics, and forensic analysts can streamline their investigations, potentially aiding legal procedures and enhancing post-impact care through the use of this in-situ tool.
Quantifying uncertainty in bicycle-computer position measurements
Authors: Siegmund Gunter P. , Miller Ian L. , Booth Gabrielle , Lawrence Jonathan M.
Published: April 2024

Bicycle computers record and store global position data that can be useful for forensic investigations. The goal of this study was to estimate the absolute error of the latitude and longitude positions recorded by a common bicycle computer over a wide range of riding conditions. We installed three Garmin Edge 530 computers on the handlebars of a bicycle and acquired 9 hours of static data and 96 hours (2214 km) of dynamic data using three different navigation modes (GPS, GPS+GLONASS, and GPS+Galileo satellite systems) and two geographic locations (Vancouver, BC, Canada and Orange County, CA, USA). We used the principle of error propagation to calculate the absolute error of this device from the relative errors between the three pairs of computers. During the static tests, we found 16 m to 108 m of drift during the first 4 min and 1.4 m to 5.0 m of drift during a subsequent 8 min period. During the dynamic tests, we found a 95th percentile absolute error for this device of ±8.04 m. This error was mildly sensitive to the navigation system being used (GPS+Galileo had slightly smaller errors) and more sensitive to the geographic location where the data were acquired (BC errors were larger than CA errors). An absolute error of ±8.04 m is relatively large and limits a forensic investigator’s ability to precisely locate a bicycle within a crash scene based solely on data from this device.
Development of Predictive Model for Flexible Pedestrian Legform Impactor Injury
Authors: Barbhuiya Junaid Hassan , Jain Subhav
Published: April 2024

Road accidents are a major concern worldwide and vulnerable road users make up more than half of the victims of road accident deaths. In order to combat this issue, several countries worldwide have mandated pedestrian safety test regulations viz., AIS100 & UN-R127. One of the requirements of the regulations is when Flexible Pedestrian Legform Impactor (Flex-PLI) is impacted onto the frontal structure of the vehicle at a speed of 40kmph, the Bending moment (BM) of tibia bone of Flex-PLI shall not exceed the regulatory limit of 340Nm. In this paper, we have built a statistical model for predicting the BM of tibia in Flex-PLI using regression analysis. 13 vehicles have been selected from all applicable vehicle categories viz., Sedan, hatchback, Coupe & SUV/MUV for this undertaking. An exhaustive analysis of the vehicle frontal structures and Flex-PLI test videos have been done to identify & measure the design parameters to be used as predictor variables. The vehicles have then been physically impacted with the Flex-PLI and the recorded tibia BMs have been used as the response variables. A regression model has been prepared using the predictor and response variables and an empirical equation has been established. The regression model has been validated by separating the available data into training and test data. Among the 312 evaluated data points across the 13 vehicles, 79.8% of the data points have Full-scale (FS) error less than 10% and 93.6% of the data points have FS error less than 15%. This model can be deployed in the design stage of the vehicle development process to identify concerns in the vehicle design. This enables a quantitative approach to designing vehicles while ensuring regulatory compliance. Also, its time-efficient operation will have a positive impact in reducing the vehicle development timeline.
Investigation of Diffuse Axonal Injury in Rats Induced by the Combined Linear and Rotational Accelerations Using Diffusion Tensor Imaging
Authors: Wang Peng , Song Xuewei , Chen Diyou , Zhu Xiyan , Qiu Jinlong , Wang Nan , Yu Tianming , Zhao Hui
Published: April 2024

Diffuse Axonal Injury (DAI) is the most common type of traumatic brain injury, and it is associated with the linear and rotational accelerations resulting from head impacts, which often occurs in traffic related and sports accidents. To investigate the degree of influence of linear and rotational acceleration on DAI, a two-factor, two-level rat head impact experimental protocol involving linear and rotational acceleration was established using the L4(23) orthogonal table in this paper. Following the protocol, rats head was injured and diffusion tensor imaging (DTI) was performed at 24h post-injury to obtain the whole brain DAI injury, and the fractional anisotropy (FA) value of the corpus callosum was selected as the evaluation indicator. Using analysis of variance, the sum of squared deviations for the evaluation indicators was calculated to determine the degree of influence of linear acceleration and rotational acceleration on DAI. The results show that, 1. For the corpus callosum, the degree of influence of linear and rotational acceleration on DAI was 39.6% and 56.0%, respectively. 2. The degree of influence of linear and rotational acceleration to the corpus callosum's genu, body, and splenium are as follows: genu - linear: 38.6%, rotational: 50.9%; body - linear: 53.5%, rotational: 29.4%; splenium - linear: 29.2%, rotational: 60.5%.3. At 0.7 MPa impact strength, the highest mortality rate (42.9%) was observed in rats with restrained head rotational movements. This study provides a method for quantifying the degree of influence of linear and rotational acceleration on DAI, and the results can be helpful for the development of head protection equipment.
Steering Angle Safety Control for Redundant Steering System Considering Motor Winding’s Various Faults
Authors: Zhao Jian , Dang Ruijie , Wu Hangzhe , Zhu Bing , Chen Zhicheng
Published: April 2024

Reliable and safe Redundant Steering System (RSS) equipped with Dual-Winding Permanent Magnet Synchronous Motor (DW-PMSM) is considered an ideal actuator for future autonomous vehicle chassis. The built-in DW-PMSM of the RSS is required to identify various winding’s faults such as disconnection, open circuit, and grounding. When achieving redundant control through winding switching, it is necessary to suppress speed fluctuations during the process of winding switching to ensure angle control precision. In this paper, a steering angle safety control for RSS considering motor winding’s faults is proposed. First, we analyze working principle of RSS. Corresponding steering system model and fault model of DW-PMSM have been established. Next, we design the fault diagnosis and fault tolerance strategy of RSS. Considering the difference in amplitude frequency characteristics of phase current during DW-PMSM winding faults, the Hanning window and Short-Time Fourier Transform (STFT) is comprehensively used to extract the third harmonic components. These components are then compared with the peak values of the third harmonic under DW-PMSM winding fault-free conditions to diagnose faults. Furthermore, in the main loop and the redundant loop of the RSS, we utilize the backstepping control and the sliding-mode control theory to design precise steering angle following method considering the unmodeled disturbance, which ensures smooth switching of two-circuit windings. Finally, a simulation platform based on MATLAB/Simulink is established. The test results demonstrate that the designed steering angle safety control strategy could accurately identify winding faults in the DW-PMSM, reduce speed fluctuations during two-circuit winding switching, and ensures RSS maintains a steady-state following error within 3° under winding fault conditions.
Effects of Rear Slant Angles on the Bi-Stable Behavior of the Ahmed Body
Authors: Bai Huancheng , Xia Chao , Yu Lei , Fan Yajun , Jia Qing , Yang Zhigang
Published: April 2024

The bi-stable phenomena of the Ahmed model were experimentally studied at different rear slant angles (31.8 °, 42 °, 50 °, 60 °, 75 °, 90°) and different Reynolds numbers (9.2 × 104, 1.84 × 105, 2.76 × 105). The analysis of pressure gradients both on the base and slant indicate that no bi-stable phenomena were observed at different Reynolds numbers when the slant angle was less than or equal to 50°. However, for the rear slant angles greater than or equal to 60°, the pressure gradients consistently shift between two preferred values at various Reynolds numbers, indicating the presence of bi-stable behavior in the spanwise direction. Therefore, the critical angle for the appearance of bi-stability in the Ahmed model lies within the range of 50° to 60°, and the existence of bistable behavior remains unaffected by the Reynolds number. As the slant angle increases, the switching rate decreases significantly until the angle is greater than 60°. Furthermore, with an increase in Reynolds number, the switching rate exhibits a monotonic decrease for most angles, except at 75° and 90°. The rate of decrease, however, diminishes for larger angles. Notably, at both 75° and 90°, the influence of Reynolds number on the switching rate is less pronounced.
The Influence of Hyperparameters of a Neural Network on the Augmented RANS Model Using Field Inversion and Machine Learning
Authors: Tao Yue , Xia Chao , Cai Jianfeng , Zhou Hua , Shi Fanglin , Yang Zhigang
Published: April 2024

In the field of vehicle aerodynamic simulation, Reynold Averaged Navier-Stokes (RANS) model is widely used due to its high efficiency. However, it has some limitations in capturing complex flow features and simulating large separated flows. In order to improve the computational accuracy within a suitable cost, the Field Inversion and Machine Learning (FIML) method, based on a data-driven approach, has received increasing attention in recent years. In this paper, the optimal coefficients of the Generalized k-ω (GEKO) model are firstly obtained by the discrete adjoint method of FIML, utilizing the results of wind tunnel experiments. Then, the mapping relationship between the flow field characteristics and the optimal coefficients is established by a neural network to augment the turbulence model. On the basis of that, the study further investigates the effects of hyperparameters such as epoch, batch size, activation function, and learning rate on the accuracy of the augmented GEKO model. The result shows that with the drag coefficient (CD) as the target, batch size and activation function significantly influence the accuracy of the trained model. When a batch size of 512 and either Softsign or Leaky-ReLU activation function are employed, the trained model predicts CD value closest to the experimental values in the condition of 2000 epochs and a learning rate of 0.001. Increasing the batch size to 1024 or the learning rate to 0.002 provides some improvement in model accuracy, but the effect is not obvious. This work is an important reference for the debugging and improvement of FIML method.
Experimental Investigation of Low-Frequency Flow Phenomena on the Vehicle Underbody Using Particle Image Velocimetry
Authors: Breitenbücher Laura , Wagner Andreas , Wiegand Thomas , Brink Maarten
Published: April 2024

The increasing importance of minimizing drag and the absence of an exhaust system result in battery electric vehicles (BEVs) commonly having a very streamlined underbody. Although this shape of underbody is typically characterized by a low acoustic interference potential, significant flow resonance can be observed for certain vehicle configurations and frequencies below 30 Hz. Since the interior of the vehicle can be excited as a Helmholtz resonator, these low-frequency fluctuations result in reduced comfort for the passengers. As preliminary studies have shown, the flow around the front wheel spoilers significantly influences this flow phenomenon. Flow separation occurs at the front-wheel spoilers and at the front wheels. This leads to the generation of vortices which are growing significantly while being transported downstream with the flow. Even small geometric changes to add-on components on the underbody significantly influence both aerodynamics and aeroacoustics. Thus, the goal is to identify the exact origin of the aforementioned low-frequency flow resonances. An understanding of the fluid mechanical mechanisms on the underbody of the vehicle is necessary in order to subsequently develop stable measures that meet both aeroacoustics and aerodynamic requirements. For this purpose, an abstract model was applied which offers the possibility to investigate the flow phenomena independently from a specific car configuration. Particle Image Velocimetry (PIV) was used to measure the flow field underneath the underbody of the abstract vehicle model. The exact experiment setup in the model wind tunnel is presented below. The evaluation of the results includes instantaneous and time-averaged velocity fields as well as velocity fluctuations.
Innovative Zero-Emissions Braking System: Performance Analysis Through a Transient Braking Model
Authors: Tempone Giuseppe Pio , Imberti Giovanni , de Carvalho Pinheiro Henrique , Carello Massimiliana
Published: April 2024

This paper presents the analysis of an innovative braking system as an alternative and environmentally friendly solution to traditional automotive friction brakes. The idea arose from the need to eliminate emissions from the braking system of an electric vehicle: traditional brakes, in fact, produce dust emissions due to the wear of the pads. The innovative solution, called Zero-Emissions Driving System (ZEDS), is a system composed of an electric motor (in-wheel motor) and an innovative brake. The latter has a geometry such that it houses MagnetoRheological Fluid (MRF) inside it, which can change its viscous properties according to the magnetic field passing through it. It is thus an electro-actuated brake, capable of generating a magnetic field passing through the fluid and developing braking torque.

A performance analysis obtained by a simulation model built on Matlab Simulink is proposed. The model is able to simulate the transient 1D motion of an electric vehicle equipped with four wheels, each having a ZEDS mounted. It has the ability to simulate a road test, supervise the behavior of the vehicle, monitoring parameters such as the State of charge (SoC) of the battery, the current used by the vehicle's battery, speed, drive torque and the decoupling between the regenerative braking torque and the Magneto-Rheological brakes torque.

The primary goal of the model is to verify the capability of the braking system to develop a sufficiently high torque to satisfy safety standards and regulation requests. The study creates also a starting point for thermal analysis of the system.
Robust Control Technology of Autonomous Vehicle on Virtual Magnetic Track
Authors: Chung Kuo-chen , Chiu Hsien-Chang , Su Kuan-Ying
Published: April 2024

Autonomous driving systems, enabled by artificial intelligence and sensing technologies, empower vehicles to navigate autonomously. However, during rainy weather, raindrops may interfere with the vehicle's sensors, leading to inaccuracies in object detection and recognition, which can compromise the safety of autonomous driving. To address these challenges, the magnetic guided autonomous driving system emerges as a promising solution due to its potential to mitigate the impact of rainy weather on autonomous vehicles. Currently, magnetic guided autonomous vehicles rely on continuous or short-distance magnetic tracks for directional control, limiting their practical applicability. To overcome this limitation, we propose an improved technology that not only addresses issues of poor maneuverability but also preserves the inherent advantages of magnetic guided autonomous driving.
Benchmarking of Neural Network Methodologies for Piston Thermal Model Calibration
Authors: Wright Stephen , Ravikumar Avinash , Redmond Laura , McMahan Chris , Lawler Benjamin , Castanier Matthew P. , Gingrich Eric , Tess Michael
Published: April 2024

Design of internal combustion (IC) engine pistons is dependent on accurate prediction of the temperature field in the component. Experimental temperature measurements can be taken but are costly and typically limited to a few select locations. High-fidelity computer simulations can be used to predict the temperature at any number of locations within the model, but the models must be calibrated for the predictions to be accurate. The largest barrier to calibration of piston thermal models is estimating the backside boundary conditions, as there is not much literature available for these boundary conditions. Bayesian model calibration is a common choice for model calibration in literature, but little research is available applying this method to piston thermal models. Neural networks have been shown in literature to be effective for calibration of piston thermal models. In this work, Bayesian model calibration will be compared to two neural network-based calibration methodologies for piston thermal models. The models were compared for both computation time and error across three different data densities. Each data set represents an increasing density of steady-state temperature measurement locations. The results show that the error between the methods is largely consistent across the different data densities, with each model having similar error to the others at each calibration case. On the other hand, computation time highlights the advantage of the neural network methodologies over the Bayesian methodology. At the lowest data density, the Bayesian model calibration methodology had the fastest computation time but only by a few minutes. As the data density increased, the Bayesian model calibration method became hours slower than the Neural network methods, up to 4673.3% slower at the highest data density. Both neural networks-based approaches and the Bayesian model calibration methodology are effective at calibrating at low data densities but for higher data densities, the Bayesian model calibration becomes too computationally expensive.
Experimental and Numerical Momentum Flux Analysis of Jets from a Hydrogen Injector
Authors: Postrioti Lucio , Martino Manuel , Fontanesi Stefano , Breda Sebastiano , Magnani Mauro
Published: April 2024

The use of hydrogen in internal combustion engines is an effective approach to significantly support the reduction of CO2 emissions from the transportation sector using technically affordable solutions. The use of direct injection is the most promising approach to fully exploit hydrogen potential as a clean fuel, while preserving targets in terms of power density and emissions. In this frame, the development of an effective combustion system largely relies on the hydrogen-air mixture formation process, so to adequately control the charge stratification to mitigate pre-ignitions and knock and to minimize NOx formation. Hence, improving capabilities of designing a correct gas jet-air interaction is of paramount importance. In this paper the analysis of the evolution of a high-pressure gas jet produced by a single-hole prototype injector operated with different pressure ratios is presented. The experimental analysis is carried out using global momentum flux measurement with the support of Schlieren imaging and needle lift detection. A combined CFD analysis of the injection process is used to investigate the details of the momentum flux device operation, offering an interesting insight in the measurement mechanisms and in the jet evolution. The final goal of the combined experimental-numerical approach is to provide quantitative description of the injection process dynamics and spatial/temporal jet evolution and morphology so to support the combustion system design.
Development of Optimal Specification of Exhaust Purification System for Euro7 Commercial Diesel Legislation
Authors: Cho Jiho , Choi Sungmu , Lee Sang Min , Hwang Dong Min
Published: April 2024

In this study, an integrated emission prediction model was used to predict whether EURO7-compliant commercial internal combustion engine vehicles would be able to meet upcoming regulations. In particular, the optimal value of Adblue injection and EHC (Electrically Heated Catalyst) control strategy for each combination of the specifications of the close-coupled SCR system (volume, substrate spec., EHC, etc.) was derived. Through this, it was intended to derive the best specification combination in terms of control and emission performance, and to use the results as a basis for decision-making in the early stages of product concept selection.
Motor Control during Gearshift Phase to Reduce the Oscillation in Dual Hybrid Vehicles
Authors: Jing Junchao , Zhang Junzhi , Liu Yiqiang , Huang Weishan , Chen Jialu
Published: April 2024

This paper defines a control method for shift torque exchange stage and a torque distribution control method for speed regulation stage. In the torque exchange stage, the torque distribution problem of active and passive clutches considers the injection of sine curve for local correction, which can solve the fish belly problem of hydraulic response (i.e. the hydraulic response is slow at the beginning and the hydraulic response is fast at the end). In the speed regulation stage, the target speed gradient profile is determined according to different shift types. The determination of the target speed gradient profile integrates different driving modes, throttle, P2 energy and clutch temperature. In the speed regulation stage, the torque distribution control problem of the speed phase including which actuator (P1, engine, C0 clutch) is used preferentially for speed regulation. a) If the speed regulation torque assigned to the input shaft exceeds the input shaft intervention torque capacity, then the excess part is assigned to the clutch for speed regulation. b) If the speed regulation torque assigned to the input shaft is less than the input shaft intervention torque capacity and the speed regulation torque assigned to the input shaft is less than the motor 2 speed regulation torque intervention capacity, the speed regulation torque is assigned preferentially to P2 for lifting torque. The total P2 request torque for P2 is equal to the sum of the speed regulation torque intervention of P2 and the P2 torque requested by ECM because of the fast response and high precision of the motor. c) If the governing torque assigned to the input shaft is less than the input shaft intervention torque capability and the governing torque assigned to the input shaft is greater than the motor 2 governing torque intervention capability, the excess is assigned to the engine. The total engine requested torque is equal to the sum of the engine governing torque intervention and the engine torque requested by the ECM.
Evaluating Safety Metrics for Vulnerable Road Users at Urban Traffic Intersections Using High-Density Infrastructure LiDAR System
Authors: Rath Prabin Kumar , Harrison Blake , Lu Duo , Yang Yezhou , Wishart Jeffrey , Yu Hongbin
Published: April 2024

Ensuring the safety of vulnerable road users (VRUs) such as pedestrians, users of micro-mobility vehicles, and cyclists is imperative for the commercialization of automated vehicles (AVs) in urban traffic scenarios. City traffic intersections are of particular concern due to the precarious situations VRUs often encounter when navigating these locations, primarily because of the unpredictable nature of urban traffic. Earlier work from the Institute of Automated Vehicles (IAM) has developed and evaluated Driving Assessment (DA) metrics for analyzing car following scenarios. In this work, we extend those evaluations to an urban traffic intersection testbed located in downtown Tempe, Arizona. A multimodal infrastructure sensor setup, comprising a high-density, 128-channel LiDAR and a 720p RGB camera, was employed to collect data during the dusk period, with the objective of capturing data during the transition from daylight to night. In this study, we present and empirically assess the benefits of high-density LiDAR in low-light and dark conditions—a persistent challenge in VRU detection when compared to traditional RGB traffic cameras. Robust detection and tracking algorithms were utilized for analyzing VRU-to-vehicle and vehicle-to-vehicle interactions using the LiDAR data. The analysis explores the effectiveness of two DA metrics based on the i.e. Post Encroachment Time (PET) and Minimum Distance Safety Envelope (MDSE) formulations in identifying potentially unsafe scenarios for VRUs at the Tempe intersection. The codebase for the data pipeline, along with the high-density LiDAR dataset, has been open-sourced with the goal of benefiting the AV research community in the development of new methods for ensuring safety at urban traffic intersections.
Comprehensive Evaluation of Behavioral Competence of an Automated Vehicle Using the Driving Assessment (DA) Methodology
Authors: Wang Tinghan , Rahimi Shujauddin , Swaminathan Sunder , Zaidi Mohsin , Wishart Jeffrey , Liu Henry
Published: April 2024

With the development of vehicles equipped with automated driving systems, the need for systematic evaluation of AV performance has grown increasingly imperative. According to ISO 34502, one of the safety test objectives is to learn the minimum performance levels required for diverse scenarios. To address this need, this paper combines two essential methodologies - scenario-based testing procedures and scoring systems - to systematically evaluate the behavioral competence of AVs. In this study, we conduct comprehensive testing across diverse scenarios within a simulator environment following Mcity AV Driver Licensing Test procedure. These scenarios span several common real-world driving situations, including BV Cut-in, BV Lane Departure into VUT Path from Opposite Direction, BV Left Turn Across VUT Path, and BV Right Turn into VUT Path scenarios. Furthermore, the test cases are divided into different risk levels, allowing the AV to be tested in a variety of risk-level situations, with a focus on high-value test cases to increase testing efficiency. Our evaluation leverages the driving assessment (DA) methodology, providing a quantitative framework for objectively assessing AV’s system performance. In this system, scores are systematically assigned based on the comprehensive testing results, yielding a precise understanding of Autoware.ai’s capabilities. This assessment is especially valuable given Autoware.ai’s status as an open-source AV software. The results of our testing provide a promising evaluation of Autoware.ai’s performance. The combination of the Mcity AV Driver Licensing Test and DA methodology contributes to a holistic understanding of Autoware.ai’s behavior competence and demonstrates its capacity to handle safety-critical events in the test scenarios. These findings could not only help understanding of the performance of the vehicle under test (VUT) but also help developers identify the issues in their AV.
Virtual Tire Development for New Electric Vehicle through Driver in Loop Approach
Authors: Asthana Shivam Vivek , Rasal Shraddhesh , Vellandi Vikraman , Uil Rutger , Lanas Gonzalo , Tosolin Guido
Published: April 2024

In recent years, the push for reduced product development timelines has been more than ever with significant changes in the automotive market. High electrification, intelligent vehicle systems and increased number for car manufacturers are a few key drivers to the same. The front loading of development activities is now a key focus area for achieving faster product development. From vehicle dynamics point of view availability of subjective evaluation feedback plays a key role in optimization various system specifications.

This paper discusses an approach for front loading through parallel development of the tire and vehicle chassis system, using advanced simulation and driving simulator technology. The proposed methodology uses virtual tire models which in combination with real-time vehicle model enables subjective evaluation of vehicle performance in driver-in-loop simulators. Three loops of tire tuning are conducted in virtual vehicle development stage, providing valuable insight of potential of achieving improved vehicle stability using tire characteristics. The vehicle under study inherently exhibits lower yaw stability owing to its architectural characteristics. This exercise enables improvement in vehicle stability performance through tire tuning before any physical build of tire or vehicle prototype. Also, understanding on effect of the tire characteristics on vehicle stability enables to take key trade-off decisions related to various chassis components. The objective measurements of prototype vehicle as well as physical tire validate the virtual models used during the activity.
Measured Thermal Performances at Brick and Module Levels in a Battery Pack of a Mid-Size Electric Vehicle under WLTC and FTP Cycles
Authors: Nandagopal Kamaleshwar , Sok Ratnak , Kishida Kentaro , Otake Tomohiro , Kusaka Jin
Published: April 2024

Performances of battery electric vehicles (BEV) are affected by the thermal imbalance in the battery packs under driving cycles. BEV thermal management system (VTMS) should be managed efficiently for optimal energy consumption and cabin comfort. Temperature changes in the brick, module, and pack under the repeated transient cycles must be understood for model-based development. The authors conducted chassis dynamometer experiments on a fully electric small crossover sports utility vehicle (SUV) to address this challenge. A BEV is tested using a hub-type, 4-wheel motor chassis dynamometer with an air blower under the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) and Federal Test Procedures (FTP) with various ambient temperatures. The mid-size BEV with dual-motor featured 80 thermocouples mounted on the 74-kWh battery pack, including the cells, upper tray, side cover, and pack cover. The authors analyzed battery pack temperature distribution behavior by changing the battery’s initial state of charge (SOC) and cell temperatures.

Performance metric data such as battery voltage, current, SOC, pack temperature, coolant properties, pressure drop, and flow rate are recorded and analyzed. The results show the temperature variations under diverse driving conditions, with an average difference of 1°C between modules and 2.3°C between bricks in module 1, and a maximum temperature difference of 3.9°C is recorded in the battery pack. The results provide valuable insights into the optimal operational range for maintaining battery pack temperature stability. The measured results can provide a fundamental understanding of the peak temperature location on the brick-to-brick and pack-to-pack variation under transient cycles. These results provide a fundamental understanding of the thermal performance of battery brick, module, and pack, which can be used to develop a VTMS model.
Research on the Oscillation Reduction Control During High Voltage Battery Failure in Hybrid Electric Vehicles
Authors: Jing Junchao , Zhang Junzhi , Huang Weishan , Liu Yiqiang , Dai Zhengxing
Published: April 2024

In order to achieve seamless mode switching control for hybrid electric vehicles (HEVs) in the event of battery failure, we propose a motor voltage-controlled mode switching method that eliminates power interruptions. This approach is based on an analysis of the dual-motor hybrid configuration's mode switching. We analyze the overall vehicle operation when the high-voltage battery occurs in different hybrid modes. To ensure that the vehicle can still function like a conventional car under such circumstances, we introduce a novel "voltage control" mode. In this mode, instead of operating in its traditional torque control manner, the P1 motor adopts a voltage control strategy. The P1 controller's variable becomes "voltage," and VCU sends the motor's working mode switching request and PCM finishes the mode transition. During system operation, the P1 motor promptly responds to these target voltages to maintain bus voltage within a normal range. The P1 motor voltage is monitored and managed by PCM to ensure safe operation under load conditions and the driver torque request is satisfied by the engine. Tests were conducted on both a motor bench and a sample car to investigate how motor speed and 12V load affect voltage control mode performance. Results demonstrate that smooth mode transition and smooth entry into voltage control mode allow for uninterrupted operation of the motor without any issues or disruptions when disconnecting the high-voltage battery relay during abnormal situations with high-voltage batteries present. This operational approach ensures continued vehicle functionality even during abnormal high-voltage battery situations while enhancing system safety and robustness.
Virtual Chip Test and Washer Simulation for Machining Chip Cleanliness Management Using Particle-Based CFD
Authors: Jan James , Khorran Aaron , Hall Mark , Torcellini Sabrina , Doody David
Published: April 2024

Metal cutting/machining is a widely used manufacturing process for producing high-precision parts at a low cost and with high throughput. In the automotive industry, engine components such as cylinder heads or engine blocks are all manufactured using such processes. Despite its cost benefits, manufacturers often face the problem of machining chips and cutting oil residue remaining on the finished surface or falling into the internal cavities after machining operations, and these wastes can be very difficult to clean. While part cleaning/washing equipment suppliers often claim that their washers have superior performance, determining the washing efficiency is challenging without means to visualize the water flow. In this paper, a virtual engineering methodology using particle-based CFD is developed to address the issue of metal chip cleanliness resulting from engine component machining operations. This methodology comprises two simulation methods. The first is the virtual chip test, which can track the movement of machining chips within internal cavities and tunnels of a machined part, such as the water jackets and oil galleries of a cylinder head, and the simulation results can be used to predict chip clogging locations and severity. Next, the chip clogging data are input into the second method, washer simulation, to design chip washers and washing cycles that can effectively remove the machining chips. The advantage of this methodology lies in its capability to quantify chip cleanliness risks as well as washing efficiencies with numerical quality indices, enabling comparisons of chip cleaning difficulties and evaluations of chip washer performance. The innovation of this methodology is the adaptation of a particle-based CFD method to model the behavior of machining chips as well as the dynamics of water jets in the chip washer.
Design and Simulation of Battery Enclosure for an Electric Vehicle Application
Authors: Gupta Mudit , Gu Randy
Published: April 2024

Making a sturdy battery box or enclosure is one of the many challenging issues that the expansion of electrification entails. Many characteristics of an effective battery housing contribute to the safety of passengers and shield the battery from the harsh environment created by vibrations and shocks due to varying road profiles in the vehicle. This results in stress and deformations of different degrees. There is a need to understand and develop a correlation between structural performance and lightweight design of battery enclosure as this can increase the range of the drive and the life cycle of a battery pack.

This paper investigates the following points:
I)
A conceptualized CAD model of battery enclosure is developed to understand the design parameters such as utilization of different material for strength and structural changes for performance against vibration and strength.

II)
Further, the study would be followed with a series of Finite Element Analyses (FEAs) on simplified models for different load cases, modal analysis, and random excitations of acceleration. The responses of the numerical experiments are the stiffness and natural frequency, evaluated together with the mass of the system.

III)
Finally, Design gauge optimization for the battery enclosure is explored through thickness analysis, showing improvement in the static and dynamic characteristics.

The introduction of numerical procedure and design optimization process is shown to be beneficial to reduce the number of physical tests and product development cost and cycle.
CAE Transfer Path Analysis and Its Accuracy Evaluation Using a Validation Method
Authors: Pimpalkhare Ninad , Mochizuki Shinei
Published: April 2024

In-cabin Noise at low frequency (due to engine or road excitation) is a major issue for NVH engineers. Usually, noise transfer function (NTF) analysis is carried out, due to absence of accurate actual loads for sound pressure level (SPL) analysis. But NTF analysis comes with the challenge of having too many paths (~20 trimmed body attachment locations: engine and suspension mounts, along with 3 directions for each) to work on, which is cumbersome. Physical test transfer path analysis (TPA) is a process of root cause analysis, by which critical contributing paths can be obtained for a problem peak frequency. In addition to that, loads at the attachment points of trimmed body of test vehicle can be derived. Both these outputs are conventionally used in CAE analysis to work on either NTF or SPL. The drawback of this conventional approach is that the critical bands and paths suggested are based on the problem peak frequency of test vehicle which may be different in CAE. Secondly, the force that is derived from measured acceleration, in physical test, when applied to CAE trimmed body will not produce the similar acceleration to that of Physical test. This is due to the correlation differences in transfer functions between physical test and CAE in trimmed body. The study presented in this paper replaces the conventional TPA carried out in physical testing with CAE TPA by using the transfer functions of CAE trimmed body. Hence, the critical bands and ranking of critical paths along with the loads derived through this method would be more accurate in carrying out the NTF and SPL analysis, respectively.
Structural Validation and Correlation of Inverter Gasket
Authors: Duraipandi Arumuga Pandian , Leon Renan , Ribot Herve , Raja Antony Vinoth , Farooqui Altafhussain , Chandrasekaran Vinoth-Roy
Published: April 2024

Inverter is the power electronics component that drives the electrical motor of the electrical driven compressor (EDC) and communicates with the car network. The main function of the inverter is to convert the direct current (DC) voltage of the car battery into alternating current (AC) voltage, which is used to drive the three-phase electric motor. In recent days, inverters are present in all automotive products due to electrification. Inverter contains a printed circuit board (PCB) and electronic components, which are mounted inside a mechanical housing and enclosed by a protective cover. The performance of the electrical drive depends upon the functioning of the inverter. There is a strong demand from the customer to withstand the harsh environmental and testing conditions during its lifetime such as leakage, dust, vibration, thermal tests etc. The failure of the inverter leads to malfunction of the product, hence proper sealing and validation is necessary for inverters to protect the electronic components. Generally, a metallic gasket and rubber gaskets are used as a sealant to protect the electronic components. The influence of design parameters which impact the gasket pressure by selecting the type of screws, number of screws required, minimum distance between the screws and minimum preload required to withstand the thermal conditions are studied and discussed in this paper. The simulation results predict the gasket pressure due to compression, shape of gasket and compressive ratio. The gasket pressure measured from pressure sensitive film test results are well correlated with simulation gasket pressure results. Finally, this robust methodology supports us to virtually validate the gasket design by reducing the number of design iterations and quick evaluation of products.
Observational Study of Passenger Seat Belt Usage Rates on Shuttle Buses
Authors: Gregg Richard H.
Published: April 2024

In 1983, a seat belt use rate survey was published in which 9% shoulder belt use was observed for front-outboard passengers. Nearly forty years later the national estimate of seat belt use has achieved a record high of 91.6% belt use by adult front-seat passengers in 2022. In contrast, there have been very few studies conducted in order to determine seat belt use within large buses and motorcoaches. In 2013, the NHTSA published a final rule amending FMVSS 208 to require seat belts for each seating position in all new over-the-road buses. Beginning in 2016, newly manufactured buses were required to be equipped with lap and shoulder belts for each driver and passenger seat. Recent studies have reported that seat belt use on motorcoaches with relatively long routes was only 2.6%. Similarly, seat belt use in airport shuttle buses was reported to be only 1%. The present observational study was conducted to determine an updated seat belt usage rate of passengers on airport shuttle buses. The aims of this study were two-fold: 1) to personally observe shuttle bus riders and their tendencies to wear or not wear the available seat belts, and 2) to determine whether belt use rates published in recent studies could be confirmed by this study. A single observer completed the shuttle bus trips of this study, which started in July 2022 and continued until July 2023. During each trip, data was recorded for the number of people on the bus and number of people wearing their seat belt. A total of 39 rides on airport shuttle buses in three U.S. cities revealed the seat belt use rate was only 1%, reinforcing previously reported rates.
3DOF Vehicle Dynamics Model for Fuel Consumption Estimation
Authors: De Carlo Matteo , Dragone Paolo , Tempone Giuseppe Pio , Carello Massimiliana
Published: April 2024

The dynamic model is built in Siemens Simcenter Amesim platform and simulates the performances on track of JUNO, a low energy demanding Urban Concept vehicle to take part in the Shell Eco-Marathon competition, in which the goal is to achieve the lowest fuel consumption in covering some laps of a racetrack, with limitations on the maximum race time.

The model starts with the longitudinal dynamics, analysing all the factors that characterize the vehicle’s forward resistance, like aerodynamic forces, altimetry changes and rolling resistance.

To improve the correlation between simulation and track performances, the model has been updated with the implementation of a Single-Track Model, including vehicle rotation around its roll axis, and a 3D representation of the racetrack, with an automatic trajectory following control implemented. This is crucial to characterise the vehicle’s lateral dynamics, which cannot be neglected in simulating its performances on track.

Analysis of suspension geometry, vehicle mass distribution and tire characteristics are made to properly define the parameters of the model, which is used for the optimal race strategy model.

The model has been validated by analysis of performance data obtained by the properly made telemetry system during the 2023 competition, and it predicts with good accuracy the fuel consumption obtained.