bluuebunny/crossref_metadata_2025 huggingface dataset:
Path-Tracking Control and Following Control of Tractor-Semitrailer Combination Based on Improved MPC
Authors: Feng Jiaming , Sun Zhaocong
Published: April 2023

In recent years, the tractor-semitrailer combination has become the primary vehicle of China's long-distance freight system. In this paper, with the aim of optimizing the path tracking control and following control of the tractor-semitrailer combination, a kinematics-based path tracking control scheme is proposed. Firstly, a kinematic model of the tractor-semitrailer combination has been constructed. The control of the tractor-semitrailer combination is simplified to focus on three control points based on the kinematics model. Secondly, the path tracking control algorithm and the following control algorithm of the tractor-semitrailer combination are proposed in this paper. The improved MPC is used for path-tracking control of tractor-semitrailer combinations. The cost function of rolling optimization steps is intended, and the optimal line is determined with the lateral deviation, the variation of lateral error, and the deviation of heading angle as the input. At the same time, the following control algorithm enables the trailer to follow the tractor's trajectory. Finally, this paper simulates the algorithm and establishes the model in MATLAB. In the simulation of the path tracking control algorithm at low speed, the tracking error can be controlled within 50 mm. Finally, in the simulation of the following control algorithm, the error can be controlled within 10 mm. In this paper, the path tracking control algorithm and the following control algorithm are used to solve the off-tracking of the tractor-semitrailer combination. It is expected that the research can promote the intelligent development of the tractor-semitrailer combination.
HIL Demonstration of Energy Management Strategy for Real World Extreme Fast Charging Stations with Local Battery Energy Storage Systems
Authors: Patil Yugandhara Yuvraj , Dobrzynski Daniel , Nystrom Bryan , Wu Zhouquan , Chen Bo
Published: April 2023

Extreme Fast Charging (XFC) infrastructure is crucial for an increase in electric vehicle (EV) adoption. However, an unmanaged implementation may lead to negative grid impacts and huge power costs. This paper presents an optimal energy management strategy to utilize grid-connected Energy Storage Systems (ESS) integrated with XFC stations to mitigate these grid impacts and peak demand charges. To achieve this goal, an algorithm that controls the charge and discharge of ESS based on an optimal power threshold is developed. The optimal power threshold is determined to carry out maximum peak shaving for given battery size and SOC constraints. To validate the effectiveness of the developed strategies and algorithms at the distribution network level, real-time power simulations are performed with a modified IEEE 37-bus test feeder model and loads, including a real-world energy plaza at Argonne National Laboratory (ANL), 4 XFC-ESS sets, 4 commercial and 6 workplace nodes with both level 2 and XFC charging. 3 commercial, 2 workplace, and 8 residential nodes with only level 2 charging. The grid simulates a total of 83 XFC and 300 level 2 stations. To realistically estimate the charging power demand of ANL XFC stations a statistical approach using the probability distribution model of the ANL historical dataset is employed. Unlike other studies, the scope of the paper is not just limited to the simulation study, but it discusses and compares the results with the experimental testing performed using real-time communication with two sets of XFC and ESS at ANL. Furthermore, the peak shaving threshold determination discussed in this paper works irrespective of the generic profile, it considers both charging and discharging of the ESS. The simulation results demonstrate the effectiveness of the presented algorithms to improve voltage distortion by carrying out maximum peak shaving for given battery size and SOC constraints.
Development of 2nd-Generation Solar Charging System and Generating Performance in the USA
Authors: Miyamoto Yuma , Nakado Takashi , Murakami Yukinori , Hayashi Taisuke
Published: April 2023

In response to the steadily worsening impact of global warming, greater efforts are being made to achieve carbon neutrality. Toyota Motor Corporation developed an in-vehicle solar charging system that utilizes generated solar energy to drive the vehicle. While the ignition is off, energy generated from a solar panel is used to charge the main battery. Then, while the ignition is on, this energy is supplied to the 12 V system to reduce consumption of the main battery energy, thereby helping to improve the electric driving range. This 1st-generation solar charging system adopted in the Prius PHV in 2017 was the first mass-produced in-vehicle solar charging system in the world. In 2022, the 2nd-generation solar charging system was developed and adopted in the bZ4X, including performance improvements such as a newly designed solar roof and lightweight charging system. However, further enhancements are necessary to facilitate the adoption of in-vehicle solar charging systems, as well as to help achieve carbon neutrality. Therefore, the 2nd-generation solar charging system has been adopted on the new Prius Prime and launched in the USA for the first time in 2023. Eight representative cities in the USA were selected under the Köppen climate classification, and the annual and monthly energy generated from its solar panels was calculated from solar radiation data. As a result, the performance of the 2nd-generation solar charging system was calculated in the USA and found to show high product value. This paper introduces the development of the 2nd-generation solar charging system and the performance generated from its solar panels in the USA.
Dynamic Charge System Verified in Application under Various Road Conditions
Authors: Tajima Takamitsu , Noguchi Wataru , Aruga Tomohisa , Abe Hiroyuki , Sato Kouichi , Togami Hiroyuki , Shigi Hiroka
Published: April 2023

The electric vehicle (EV) is one means of realizing carbon neutrality for automobiles, but at present, EVs still faces a number of issues. The main issues include, for example, (1) cruising range, (2) installed battery capacity, (3) charging time, (4) infrastructure facilities, and (5) support for Heavy-duty commercial vehicles. Full electrification of heavy-duty trucks, in particular, requires high power output. This means that enormous installed battery capacity is necessary, and as a result, cruising range on a par with engine-driven vehicles cannot be readily achieved.

Therefore a dynamic charge system was devised here that would provide electric power to EVs directly from the infrastructure so that EVs could undergo intermittent charging while driving. This is a conductive charging system that operates from the side of the vehicle (roadside), and research has been underway on the application of this approach to passenger cars and race cars. Attention was further directed to resolving issues with freight vehicles, which account for most of the CO2 emissions in the transportation sector. The system was applied to a heavy-duty truck (4×2 tractor, 45-ton class), which up to now had not been readily convertible to an EV, and the results were reported at WCX 2022.

The present report will describe further support for heavy-duty vehicles by expanded application to a 6×4 tractor (60-ton class). To support use under various road conditions, the equipment was installed in a curved portion and on uphill and downhill sections, and the report will present results from verification with actual vehicles during dynamic charging.

The results include the following:

The vehicle weight of the 6×4 tractor came out at 930 kg (about 10%) lighter than the diesel engine-powered truck that was the base vehicle.

The minimum capacity of the batteries installed in the vehicle came out at 100 kWh, which is about 10% of that in an EV truck that charges while parked.

The charging power was a maximum of 450 kW on the curved road portion, the uphill grade portion, and the downhill grade portion, and the viability of dynamic charging and its control was successfully verified at vehicle speeds from extremely low up to the legal limit of 80 km/h.
Design of a Test Geometry to Characterize Sheared Edge Fracture in a Uniaxial Bending Mode
Authors: Narayanan Advaith , Butcher Cliff
Published: April 2023

The characterization of sheet metals under in-plane uniaxial bending is challenging due to the aspect ratios involved that can cause buckling. Anti-buckling plates can be employed but require compensation for contact pressure and friction effects. Recently, a novel in-plane bending fixture was developed to allow for unconstrained sample rotation that does not require an anti-buckling device. The objective of the present study is to design the sample geometry for sheared edge fracture characterization under in-plane bending along with a methodology to resolve the strains exactly at the edge. A series of virtual experiments were conducted for a 1.0 mm thick model material with different hardening rates to identify the influence of gage section length, height, and the radius of the transition region on the bend ratio and potential for buckling. Two specimen geometries are proposed with one suited for constitutive characterization and the other for sheared edge fracture. It is shown that the local strains at the edge can be accurately resolved using basic image processing without the need for digital image correlation that cannot resolve the strain field at the edge. Finally, a technique to evaluate the location of the unstretched fibre is considered to determine the strain distribution across the gage width for use in constitutive characterization under bending.
A Road Roughness Estimation Method based on PSO-LSTM Neural Network
Authors: Li Zhuoyang , Yang Shichun , Chen Yuyi , Nan Zhaobo , Shi Runwu , Wang Rui , Zhang Mengyue
Published: April 2023

The development of intelligent and networked vehicles has enhanced the demand for precise road information perception. Detailed and accurate road surface information is essential to intelligent driving decisions and annotation of road surface semantics in high-precision maps. As one of the key parameters of road information, road roughness significantly impacts vehicle driving safety and comfort for passengers. To reach a rapid and accurate estimation of road roughness, in this study, we develop a neural network model based on vehicle response data by optimizing a long-short term memory (LSTM) network through the particle swarm algorithm (PSO), which fits non-linear systems and predicts the output of time series data such as road roughness precisely. We establish a feature dataset based on the vehicle response time domain data that can be easily obtained, such as the vehicle wheel center acceleration and pitch rate. A PSO-LSTM network is built to achieve road roughness estimation and prediction, which is compared to the common LSTM network, the backpropagation (BP) neural network, and the wavelet neural network by conducting experiments to evaluate the performance and robustness under different vehicle simulation velocities. The results demonstrate the ability of the proposed model to achieve more precise road roughness estimation, superior prediction accuracy, and better velocity robustness.
Modeling and Verification of Tire Nonlinearity Effect on Accuracy of Vehicle Yaw Rate Calculation
Authors: Min Delei , Wei Yintao
Published: April 2023

The desired yaw rate is a vital target parameter for vehicle stability control, which is currently determined as a steady-state yaw rate by the linear single-track vehicle model. Tire nonlinearity deteriorates the effect of vehicle stability control at larger lateral acceleration. This paper proposes a new calculation method of the steady-state yaw rate considering the tire nonlinearity based on the brush tire model. To validate and verify the proposed method, step steering tests of the target vehicle under different lateral accelerations are carried out on a real proving ground. The results show that when the lateral acceleration is relatively small, the difference between the calculation results of the proposed method and the traditional one is not apparent, and both methods can provide a good estimation for the steady-state yaw rate; however, when the lateral acceleration is relatively large, the difference becomes apparent. It can be shown that the linear tire model cannot be able to calculate the steady-state yaw rate accurately, while the brush tire model agrees well with the observation. This difference shows the importance of considering tire nonlinearity in vehicle stability control, especially in extreme driving conditions. By analyzing the existence of the solution of the yaw rate equation, this paper finds that the vehicle speed upper limit under which the vehicle can be able to achieve steady-state steering is also affected by the tire nonlinearity. A Simulink vehicle model is built to verify the steady-state vehicle speed upper limit for vehicles with different steering characteristics. The results of the modeling and tests provide certain guidelines for developing a vehicle stability control system.
Simulation Based Sensitivity Study of Piston Cooling Performance Parameters in Automotive Diesel Engines
Authors: Chakkamadathil Shirdi Dilipkumar , Gombi Shrirang , Sahu Abhay Kumar
Published: April 2023

The increasing demand for higher power density in Internal Combustion Engines (ICE) leads to an engineering requirement for durable piston design and its cooling. To mitigate the likelihood of failure or damage to pistons, it is important to have a thorough understanding of the physics behind piston cooling along with the design and operating parameters that control piston cooling. Computational Fluid Dynamics (CFD) simulations provide an insightful way to evaluate the impact of different parameters that control piston cooling. This work pertains to diesel engines in particular and drives the sensitivity study of different piston cooling parameters such as oil gallery and Piston Cooling Nozzle (PCN) design features, engine oil and engine operating conditions. The analysis is conducted using a 3D CFD tool with multiphase modeling technique- Volume of Fluid (VOF) approach. Piston cooling parameters such as gallery oil filling ratio, capture efficiency and piston temperatures are used to assess and compare different simulation cases. The rise in oil temperatures inside gallery is also monitored.

Effect of a straight or oil gallery targeted PCN jet vs. inclined or off targeted PCN jet, PCN oil jet flow rate and temperature variation, distance of oil gallery from the piston crown surface are some of the crucial factors affecting the piston cooling which are discussed in detail in this work.

This study serves as a directional guidance and an optimization tool to the designers and stakeholders to gauge the impact of different parameters on piston performance and thus achieve the final design faster leading to significant cost saving.
A CFD-Based Numerical Evaluation, Assessment and Optimization of Conjugate Heat Transfer for Aerodynamic Cooling of a Wheel-Hub-Motors in Micro-Mobility Vehicles
Authors: Mambazhasseri Divakaran Arun , Gkanas Evangelos , Shepherd Simon , Jewkes James , Abo-Serie Essam
Published: April 2023

Micro-mobility vehicles such as electric scooters and bikes are increasingly used for urban transportation; their designs usually trade off performance and range. Addressing thermal and cooling issues in such vehicles could enhance performance, reliability, life, and range. Limited packaging space within the wheels precludes the use of complex cooling systems that would also increase the cost and complexity of these mass-produced wheel motors. The present study begins by evaluating the external aerodynamics of the scooter to characterise the airflow conditions near the rotating wheel; then, a steady-state conjugate heat transfer model of a commercially available wheel hub motor (500W) is created using commercial computational fluid dynamics (CFD) software, StarCCM+. The CAD model of the motor used for this analysis has an external rotor permanent magnet (PM) brushless DC topology. Both internal and external fluid domains are considered to evaluate the combined flow dynamics and conjugate heat transfer from the windings (heat source) to the ambient air. At the maximum speed (482rpm) of the motor, for a total power loss of 180W (η=64%), a maximum temperature of 295°C is observed in the windings. Evaluating the thermal path shows that approximately 58.1% of the total heat generated in the winding is dissipated radially via convection through the air gap, and only 3.66% through the shaft via conduction. The thermal resistance for the shaft is in the range of 22-60 K/W and the rotor components is in the range of 0-2 K/W for the operational speed range of 0-1000rpm. Taguchi’s Design of Experiment (DOE) with Design manager study has been conducted to optimize the performance of design parameters (Fins and air-vents/holes) in cooling the motor. Air vents and external fins on rotor–lid (rotor cover) has a greater effect on cooling the motor than other design parameters.
Influence of Uncertain Factors on Automotive Electronics Thermal Simulation
Authors: Viswanathan Swaminathan , Sridharan Kesav Kumar , Gupta Navneet
Published: April 2023

The electronic content in automotive has increased over recent years and expected to contribute about 50% of the total vehicle cost by 2030. The semiconductor research indicates that focus is on enhancing the functionality of single device and miniaturizing of components to reduce the electronic module size. It is to be ensured, that devices in automotive electronic modules should be within its allowable temperature limit while operating at harsh environment. The accurate virtual simulations using CAE tools prior to proto build can assist in understanding the design risks upfront and aids in arriving at a reliable thermal mitigation solution. The prediction accuracy of thermal simulation is driven by the inputs and modeling approach used in the analysis. Current automotive electronic product development trend indicates, chip development and thermal design of electronics module goes in parallel. Hence the access of device intricate details and accurate power estimation for thermal simulation is not feasible during initial design phase. An electronic module will have multiple devices and modeling each device in detail along with explicit PCB trace modeling requires huge computational resource and time consuming. This indicates that simplified modeling techniques assisted with engineering assumptions are essential for virtual simulation that creates inevitable uncertainty in temperature prediction. In this study a generic automotive electronics system is used as an example to showcase the influence of uncertainty factors such as modeling techniques, power input load and boundary condition on thermal simulation. High fidelity thermal simulation model is used as reference and DOE studies are performed at different levels for the identified uncertainty factors. The uncertainty factors are studied individually and as combination to understand its influence in temperature prediction and its findings are highlighted. The approach showcased in this work can be adapted to understand the influence of uncertainty factors on thermal simulation for any automotive electronic module.
Lithium-Ion Battery Module Internal Temperature Estimation Based on Rauch-Tung-Striebel Smoothing Technique
Authors: Zhu Wenhao , Li Biao , Zhong Hao
Published: April 2023

The temperature monitoring of the lithium-ion battery is crucial for the advanced battery thermal management systems (BTMS) to improve performance and ensure operational safety and reliability of the battery system. In real applications, the core temperature of the battery is unfortunately unmeasurable due to the impracticality of placing a sensor inside the core, and has to be estimated online in real-time. Meanwhile, only the measurement of battery surface temperature can not meet the need for advanced BTMS due to the impact of the large temperature gradient between the surface and internal in high power applications. The battery core temperature estimation will become challenging when encountering sensor bias and noise. In order to improve the accuracy and stability of battery core temperature estimation, the method based on the Rauch-Tung-Striebel smoothing technique and unscented Kalman filter is applied to reconstruct the core temperature for application in battery management systems. The smoothing process can effectively further reduce its estimation error. The unmeasurable parameters in the nonlinear thermal model are identified by the optimization. The electric-thermal coupling model is validated through experiments and CFD simulations. The simulation results show that the average errors of different battery core temperatures are less than 1K.
Experimental Analysis of Spray Topology in the Wake of an Automotive Body
Authors: Crickmore Conor James , Garmory Andrew , Butcher Daniel
Published: April 2023

Advanced driver assistance systems rely on external sensors that encompass the vehicle. The reliability of such systems can be compromised by adverse weather, with performance hindered by both direct impingement on sensors and spray suspended between the vehicle and potential obstacles. The transportation of road spray is known to be an unsteady phenomenon, driven by the turbulent structures that characterise automotive flow fields. Further understanding of this unsteadiness is a key aspect in the development of robust sensor implementations. This paper outlines an experimental method used to analyse the spray ejected by an automotive body, presented through a study of a simplified vehicle model with interchangeable rear-end geometries. Particles are illuminated by laser light sheets as they pass through measurement planes downstream of the vehicle, facilitating imaging of the instantaneous structure of the spray. The tested configurations produce minor changes to the flow field, the impact of which is observed in time-averaged views of the spray. Analysis of the instantaneous data depicts a highly dynamic spray plume, characterised by independent “clusters” of particles, as well as long, tendril-like structures, the development of which may result in misidentifications by classification algorithms. Proper orthogonal decomposition identifies comparable structures in all three configurations, with the fluctuating energy content distributed across many modes, in a further indication of the dynamic nature of the spray. The discussed method aims to aid in the understanding of the physics responsible for downstream spray transport as the industry looks towards the widespread adoption of fully autonomous vehicles.
Track, GoPro, and Prescan Testing of an ADAS Camera
Authors: Bartholomew Meredith , Midlam-Mohler Shawn , Guenther Dennis , Heydinger Gary , Forkenbrock Garrick , Elsasser Devin , Rao Sughosh
Published: April 2023

In order to validate the operation of advanced driver assistance systems (ADAS), tests must be performed that assess the performance of the system in response to different scenarios. Some of these systems are designed for crash-imminent situations, and safely testing them requires large stretches of controlled pavement, expensive surrogate targets, and a fully functional vehicle. As a possible more-manageable alternative to testing the full vehicle in these situations, this study sought to explore whether these systems could be isolated, and tests could be performed on a bench via a hardware-in-the-loop methodology. For camera systems, these benches are called Camera-in-the-Loop (CiL) systems and involve presenting visual stimuli to the device via an external input. In this work, the Mobileye 630 aftermarket monocular camera system was tested across three environments – track based (real life) testing, GoPro CiL bench (recorded real life) testing, and Prescan CiL bench (simulated life) testing. A comparison of the Mobileye outputs was performed to test the response of the camera between the different mediums. Of the two CiL input types, the Prescan environment was found to have the best agreement with the track behavior. While there is potential for both future use and development of CiL testing methodologies, at this time, there are differences which could limit both the applicability and accuracy of the results generated in the bench testing environments, and further exploration is recommended. These differences include reduced detection and FCW distances and variation in response to the same visual input.
Drive-Thru Climate Tunnel: A Proposed Method to Study ADAS Performance in Adverse Weather
Authors: Pao Wing Yi , Li Long , Agelin-Chaab Martin , Komar John
Published: April 2023

The advancement of Advanced Driver Assistance System (ADAS) technologies offers tremendous benefits. ADAS features such as emergency braking, blind-spot monitoring, lane departure warning, adaptive cruise control, etc., are promising to lower on-road accident rates and severity. With a common goal for the automotive industry to achieve higher levels of autonomy, maintaining ADAS sensor performance and reliability is the core to ensuring adequate ADAS functionality. Currently, the challenges faced by ADAS sensors include performance degradation in adverse weather conditions and a lack of controlled evaluation methods. Outdoor testing encounters repeatability issues, while indoor testing with a stationary vehicle lacks realistic conditions. This study proposes a hybrid method to combine the advantages of both outdoor and indoor testing approaches in a Drive-thru Climate Tunnel (DCT). The proposed DCT features a test section that is isolated from the surrounding environment and allows a vehicle to move through a volume of precisely simulated precipitation. It is constructed as a model scale prototype for concept demonstration and preliminary studies. In addition, the DCT’s modular design allows for varying distances, vehicle speeds, and precipitation rates during testing. The model vehicle is equipped with common ADAS sensors, such as optical cameras and LiDARs, which are known to be heavily affected by adverse weather. Quantification metrics are designed and applied to ADAS datasets to investigate sensor performance in conjunction with related phenomena, such as the perceived rain characteristics of a moving vehicle. Therefore, the DCT provides a platform to bridge the gap between outdoor and indoor weather testing for ADAS sensors and open opportunities for sensor perception developments.
Design of a Secure Automated Driving Systems Test Data Interface
Authors: Zagorski Scott , Nguyen An , Heydinger Gary , Abbey Howard
Published: April 2023

Vehicles equipped with Level 4 and 5 autonomy will need to be tested according to regulatory standards (or future revisions thereof) that vehicles with lower levels of autonomy are currently subject to. Today, dynamic Federal Motor Vehicle Safety Standards (FMVSS) tests are performed with human drivers and driving robots controlling the test vehicle’s steering wheel, throttle pedal, and brake pedal. However, many Level 4 and 5 vehicles will lack these traditional driver controls, so it will be impossible to control these vehicles using human drivers or traditional driving robots. Therefore, there is a need for an electronic interface that will allow engineers to send dynamic steering, speed, and brake commands to a vehicle. This paper describes the design and implementation of a market-ready Automated Driving Systems (ADS) Test Data Interface (TDI), a secure electronic control interface which aims to solve the challenges outlined above. The interface consists of a communication port integrated into automobiles which lack traditional manual controls. Via this interface dynamic test scenarios can be executed by plugging in a Vehicle Control Unit (VCU), which is an element of the TDI hardware/firmware intended to be available only to authorized users. Physically, the VCU can interface with the On-board Diagnostics (OBD) OBD-II connector that is present on essentially all modern automobiles. This paper also describes a demonstration of the complete VCU with the secure TDI using a vehicle equipped with a Dataspeed drive-by-wire kit, with its steering, throttle, and brake control systems mimicking the behavior of an autonomous subject vehicle.
Metrics for Machine Learning Models to Facilitate SOTIF Analysis in Autonomous Vehicles
Authors: Madala Kaushik , Avalos Gonzalez Carlos
Published: April 2023

Machine Learning (ML) components are widely adopted in autonomous vehicles to perform tasks such as perception and planning. Despite the multiple uses of machine learning components and their benefits, incorrect outputs from machine learning components can compromise the safety of the system. The limitations of the machine learning algorithms and their acceptable level of performance that results in a reasonable level of residual risk are considered as a part of ISO 21448, the safety of the intended functionality (SOTIF) standard. Currently, to measure the performance of machine learning models, statistical metrics such as accuracy, recall, precision, and F1-measure are often used depending on the nature of the data and task. While these metrics help in understanding which machine learning model is better and can be chosen as a part of the vehicle’s architecture, they do not provide much information regarding safety, in particular, SOTIF. There is a need for new metrics to better assess safety corresponding to these machine learning models. The new metrics need to focus more if an incorrect output from the model results in crashes and near crashes and aid in proposing design changes that help to reduce the residual risk of the vehicle. To achieve this goal, in this paper we discuss the limitation of current metrics with an example architecture that uses machine learning models and propose new scenario-based metrics that help in better analysis of machine learning models for SOTIF.
Hierarchical Neural Network-Based Prediction Model of Pedestrian Crossing Behavior at Unsignalized Crosswalks
Authors: Ćorić Mate , Skugor Branimir , Deur Josko , Ivanovic Vladimir , Tseng H. Eric
Published: April 2023

To enable smooth and low-risk autonomous driving in the presence of other road users, such as cyclists and pedestrians, appropriate predictive safe speed control strategies relying on accurate and robust prediction models should be employed. However, difficulties related to driving scene understanding and a wide variety of features influencing decisions of other road users significantly complexifies prediction tasks and related controls. This paper proposes a hierarchical neural network (NN)-based prediction model of pedestrian crossing behavior, which is aimed to be applied within an autonomous vehicle (AV) safe speed control strategy. Additionally, different single-level prediction models are presented and analyzed as well, to serve as baseline approaches. The hierarchical NN model is designed to predict the probability of pedestrian crossing the crosswalk prior to the vehicle at the high level, and parameters of Gaussian probability distribution of pedestrian entry time to the crosswalk at the low level. On the other hand, the baseline single-level models only provide entry time probability distributions, either in discrete form or in the form of bimodal Gaussian probability distribution. The proposed hierarchical model is validated against the baseline ones for a simplified single-vehicle/single-pedestrian case, where the data obtained through large-scale simulations of a game theory-based pedestrian model and an open-loop driven vehicle are used.
Frictional and Wear Properties of Diamond-Like Carbon Films with Lubricant Additives
Authors: Honda Tomomi , Kasai Moritsugu , Miyake Koji
Published: April 2023

Diamond-Like Carbon (DLC) is a promising engine material for reducing friction and wear on sliding parts. By contrast, MoDTC lubricant additives are known to promote the wear of a-C:H films. However, the mechanism that promotes wear and the formation of tribofilms on DLC parts when in contact with molybdenum-based lubricant additives has not been sufficiently studied. The purpose of this research is to determine the wear promotion mechanism and formation of tribofilm on DLC by lubricant additives by comparing friction and wear properties. We conducted friction and wear tests using a tribometer with DLC (ta-C, ta-C:H, a-C, and a-C:H) blocks, FC250 (cast iron) rings, and oils containing lubricant additives (MoDTC, MoDTP, and Mo without DTC ligand) by observing and analyzing the sliding surfaces of specimens. No wear was observed for any of the DLCs (ta-C, ta-C:H, a-C:H, and a-C) in combination with oils containing MoDTP or Mo without DTC ligands. Oil containing MoDTC revealed low frictional properties for all tested DLCs (ta-C, ta-C:H, a-C:H, and a-C), but arc-shaped wear occurred on a-C:H, which has low hardness and contains hydrogen only in combination with MoDTC-containing oil. In addition, MoO3 was most frequently detected on the worn surface of FC250 in combination with a-C:H and MoDTC-containing oil, whereas Mo carbide was seldom detected by XPS analysis of any worn surface or deposit. These results indicated that the constitution of the DLC and the hydrogen content are the main factors driving wear promotion by MoDTC, while MoO3 is the main factor promoting wear in a-C:H.
Analytical Method to Predict Floor Console Lid Latch Rattle Acoustic Noise
Authors: Behera Dhiren , Jadhav Vishal , Patel Lala Ram , Lopez Uribe Carlos , Auten Julie
Published: April 2023

This paper is a continuation of previously published technical paper SAE 2022-01-0314. The preceding work described an analytical methodology to predict the vehicle interior trim squeak and rattle issues upfront in the design cycle using a “relative displacement” or “contact force” metric; the methodology was implemented on the center floor console armrest latch using a linear finite element model. The work is logically extended to predict the squeak and rattle issues quantitatively using now an “acoustic noise” metric, this enables a direct comparison with the physical test results and helps to further refine the design best practices. This approach combines Finite Element Method (FEM) and Boundary Element Method (BEM) to estimate structural vibration response and acoustic sound pressure respectively. This analysis process encompasses steps such as 1) conversion of frequency domain random input excitation to time series data, 2) estimating non-linear time domain structural response, 3) estimating acoustic radiation using the boundary element method and 4) generating sound metrics in terms of Zwicker loudness. The physics of latch impact phenomenon at the contact interface resulting from road excitation which acts as a secondary excitation source and capable of exciting the higher frequency resonance modes of adjacent panels were captured in the analytical model. Though the resulting radiated acoustic noise due to panel vibration is broadband in nature, but a standard vibro-acoustic method was employed to demonstrate the correlation of analytical result with the experimental data in low and mid frequency range. This analytical method was successfully implemented on the center floor console NVH performance analysis and validated by using numerical simulations for accuracy.
Development of a Novel High Strength Aluminum-Cerium Based Rotor Alloy for Electric Vehicle Induction Motor Applications
Authors: Lombardi Anthony , Byczynski Glenn , Vidanalage Buddhika Guruwatta , Fatima Areej , Kar Narayan
Published: April 2023

To increase vehicle range, light weighting of electric vehicles has been extensively researched and implemented by using aluminum intensive solutions. With regards to traction motors, aluminum alloys that have a desired combination of high electrical conductivity and strength are required for high power output and efficiency. In this research, a novel Al-Ce based alloy, with minor additions of Si and Mg for strengthening, was investigated in different heat treatment tempers to maximize mechanical properties while maintaining a high electrical conductivity. This new alloy system appears to have addressed the classic conundrum of the inverse relationship of mechanical performance verses electrical conductivity for traditional aluminum alloy systems. The results suggest that the Al-Ce-Si-Mg alloy had yield strength in excess of 120 MPa and electrical conductivity of at least 50 %IACS in the T5 and T6 conditions. Simulations of a squirrel cage induction machine show that this combination of properties resulted in similar motor performance to other rotor Al alloys. As a result, the Al-Ce-Si-Mg alloy is a promising alternative to the near pure Al or Cu-based alloys that are currently used in induction motor rotor castings.
Research on Dynamic Leak Detection of Automobile Fuel System
Authors: Dai Chao , He Ren
Published: April 2023

In allusion to the dynamic leak phenomenon caused by the fuel sloshing in the dynamic driving process of vehicle, the dynamic leak detection method was proposed based on the analysis of the dynamic leak mechanism and influencing factors. The six degree of freedom platform was used to simulate the gesture change of fuel tank during driving process. The influence of fuel tank operating conditions and different design styles on dynamic leak were mainly studied. Fiber-optic gyroscope was used to acquire the operating conditions of the fuel tank during driving process in five regions of China: Wuxi Mountain in Anhui, Hongjing Road in Beijing, Jing’anyi in Yunnan, Wuzhi Mountain in Hainan, Gele Mountain in Chongqing. Signal acquired by fiber-optic gyroscope includes the acceleration of the vehicle in the three directions of forward and backward, left and right, up and down as well as the rotation angles around these three directions. The displacement signal after integral reconstruction and angle signal jointly constitutes the operating conditions of fuel tank in different regions. As a comparison, South Mountain operating condition in the United States was also used for analysis. Small type, flat type, L type, saddle type and longitudinal strip type fuel tanks were selected as research objects. The six degree of freedom platform was used to simulate the gesture change of fuel tank during driving process. The result shows that the operating condition of South Mountain in the United States is quite different from those of five regions in China. Among the fuel tank operating conditions in China, the total dynamic leak amount of Wuxi Mountain operating condition in Anhui is the largest, and the leak rate result shows that L type fuel tanks are more prone to dynamic leak. Finally, according to the test results, the best dynamic leak detection operating conditions for fuel tanks with different design styles are proposed.
Admixture Evaluation for Fuel Economy and Emissions by New Indian Motorcycle Driving Cycle
Authors: Sithananthan M. , Kumar Ravindra , Saxena Deepak
Published: April 2023

This research evaluates the fuel economy and emissions of admixtures called gasoline multifunction additive (GMFA) and 10% ethanol blended gasoline against neat gasoline using newly developed Indian Motorcycle Driving Cycle with a BS VI motorcycle, also compared the performance of new cycle with the WMTC for the driving cycle parameters, fuel economy and emissions. The vehicles fuel economy and emissions factors are influenced by not only vehicle type, but also the road infrastructure and traffic patterns followed. The standard driving cycles do not replicate the real-world fuel economy and emissions as these cycles are very old and do not represent current traffic scenario.

A real-world driving cycle was made based on micro-trips approach by analyzing the real-world time-speed data collected in four different pre-determined routes in Delhi-NCR region.. The developed cycle was validated on chassis dynamometer with neat gasoline, admixture gasoline (gasoline blended with multifunctional additive and 10% ethanol blended gasoline) and compared the performance with WMTC.

The fuel economy of new cycle was found lower than the WMTC by 0.5% to 1.5%. The emissions measured were higher than the WMTC by 104.4% to 120.2% for CO, and 13.4% to 24.1% for HC, 0.9% to 4.5% for CO2 emissions and a mixed trend was observed for NOx emissions. The higher vehicular emissions and lower fuel economy show that the new cycle is more severe than the WMTC cycle representing the real-world driving pattern in India. The fuel economy and emissions comparison of admixture gasoline and E10 gasoline against neat gasoline show clear discrimination among the fuels blends; hence the adoption of new cycle for fuel economy and emissions of new generation motorcycles will be more appropriate in India. This study has demonstrated the influence of traffic and road infrastructure on vehicular emissions, and can be used by policy makers to regulate the vehicular pollution in the country.
Study on the Diffusion Law of Electric Vehicle Sharing in Complex Social Network Environment
Authors: Shang Kai , Wang Ning , Tian Hangqi
Published: April 2023

Electric vehicle sharing (EVS) can alleviate traffic congestion and reduce emissions. However, the poor user experience and lack of word-of-mouth effect lead to the low utilization rate of EVS in China. Based on the demand and pain points of EVS, this paper concentrates on travel mode choice behavior of consumers under social networks and establishes an agent-based model for EVS diffusion. The results show that: (1) Social networks can promote the diffusion of EVS and the number of opinion leaders and the number of fans of opinion leaders play an important role. (2) Consumers are more sensitive to travel costs than non-travel time now, but with the improvement of demand for travel experience, consumers are more concerned with non-travel time. (3) The non-travel time of EVS needs to be reduced to 9, 8 and 7 minutes respectively to retain users when the travel cost increases to 0.7, 0.8 and 0.9 Yuan/minute.
Light-duty Plug-in Electric Vehicles in China: Evolution, Competition, and Outlook
Authors: Hao Xu , Ou Shiqi (Shawn) , Liu Kexin , Zhong Ruiheng , Shi Hong , Wang Hewu , He Xin
Published: April 2023

China's plug-in electric vehicle (PEV) market with stocks at 7.8 million is the world's largest in 2021, and it accounts for half of the global PEV growth in 2021. The PEV market in China has dramatically evolved since the pandemic in 2020: over 20% of all new PEV sales are from China by mid-2022. Recent features of PEV market dynamics, consumer acceptance, policies, and infrastructure have important implications for both the global energy market and manufacturing stakeholders. From the perspective of demand pull-supply push, this study analyzes China's PEV industry with a market dynamics framework by reviewing sales, product and brand, infrastructure, and government policies from the last few years and outlooking the development of the new government’s 14th Five-Year Plan (2021-2025). From the demand side, small-sized sedans and compact sport utility vehicles with increased electric ranges are both popular for PEVs, and the electric range of over 60% of new battery electric vehicles in 2021 has been longer than 400 km. From the supply side, although foreign brands like Tesla are still competitive, the products by Chinese domestic automakers like BYD are becoming more attractive and cannibalizing the high-end market. However, the production capacity and cost of PEVs may be limited by the upstream of the supply chain – the battery manufacturing and supply chain inflation. In addition, it is also uncertain how much sales demand impacts will be caused by the potential global economic recession. The government firmly supports electrification and decarbonization of the vehicle industry by emphasizing the importance of the vehicle industry for promoting the greenhouse gas net zero by 2060. The dual-credit policy is regarded as the most critical regulation in a bid to restrain fuel consumption and promote PEV share. Still, the market is facing some technological obstacles, such as battery safety and driving range anxiety, before real prosperity. In addition, the Chinese electric vehicle market is seeing a trend toward the development of new technologies such as vehicle-to-X, autonomous driving, and connected vehicles.
Use of Additive Manufacturing in Product Design & Development
Authors: Vaidya Nandagopal , Awachar Sudhir , Nagi Harpreet Singh
Published: April 2023

Now for past many decades, automotive manufacturing is under perennial pressure owing to consumer aspirations, regulations, globalized competition and sustainability needs. Automotive manufactures are fiercely satisfying all of above constraints through innovative solutions. The resultant vehicles have many complex to very complex systems and subsystems to deliver the performance aspirations. In this game, one of the critical business parameter is rapid time to market. At the current state of the art, time to market directly conflicts with new product development time. Four wheeler new product development consumes upward of 48 months, while risking obsolescence of product features at the time of launch. Automotive manufacturers invest intensely in technologies to achieve this goal of short time to market. Digital technologies delivering high time efficiency have made deep in roads in the automotive product development arena. However yet physical validation is an inevitable step to get vehicle ready for launch. Meaningful validations require functional prototypes. Products being complex in nature, significant lead-time and investments are consumed in prototyping. Conventional part or tool manufacturing works as counterproductive for cost as well as time needs. Hence one more time shortening technique is to build functional prototype for a fixed purpose (validation intent). Built for purpose prototypes deliver extremely high time efficiency, but demand highly skilled prototype manufacturing engineers and technicians. Such rare teams work innovatively combining art and science to compress time lines. This technical paper focuses on smart use of quick manufacturing processes inclusive but not restricting to AM for advancing the product development. The achievement lies in using AM along with allied technologies to achieve the desired fit for purpose functional prototype rapidly. Innovated method would also yield a cost advantage as against the standard prototyping/manufacturing processes. Paper would also discuss on the possibilities of takes these learning for mass manufacturing.