bluuebunny/crossref_metadata_2025 huggingface dataset:
Improved Coated Gasoline Particulate Filters for China 7 and US Tier 4 Legislations
Authors: Schoenhaber Jan , Kawashima Shota , Gotthardt Meike , Schühle Johannes
Published: April 2024

The impending emission regulations in both China (CN7) and the United States (Tier 4) are set to impose more stringent emission limits on hydrocarbons (HC), carbon monoxide (CO), nitrogen oxides (NOx), and particulate matter (PM). CN7 places particular emphasis on reducing particulate number (PN) thresholds, while the forthcoming United States Tier 4 legislation is primarily concerned with reducing the allowable particulate matter (PM) to an assumed limit of 0.5 mg/mile. Given the more stringent constraints on both PN and PM emissions, the development of enhanced aftertreatment solutions becomes imperative to comply with these new regulatory demands. Coated Gasoline Particulate Filters (cGPFs) play a pivotal role as essential components for effective PN and PM abatement. These filters are typically deployed in one of two configurations: close-coupled to the turbocharger positioned downstream of a primary three-way catalyst (TWC) or located further downstream of the exhaust system in an underfloor configuration. Each application imposes distinct technical requirements and specifications on cGPFs, necessitating tailored solutions for both close-coupled and underfloor applications. This research introduces a novel generation of particulate filters optimized for pressure drop and underfloor applications, which exhibit improved performance in terms of light-off and hot conversion efficiency while maintaining comparable backpressure levels to its predecessors. Moreover, a suite of advanced technologies for close-coupled applications is presented, featuring improved three-way conversion efficiency and enhanced thermal durability compared to previous iterations. Significantly, these new technologies demonstrate equivalent three-way conversion capabilities, irrespective of whether employing bi-metallic Pd/Rh or tri-metallic Pt/Pd/Rh Platinum-group-metal (PGM) architectures. Lastly, the study undertakes an analysis of the trade-off between backpressure and the gain in three-way conversion activity provided by these innovative technologies.
Investigation of Fuel Economy Prediction Technology Considering Engine Thermal Flow for Hybrid Electric Vehicle, and Application to Vehicle Development Process
Authors: Ogata Kenichiro , Koide Keijiro , Kubota Shunichi , Takeda Naoaki , Suzuki Yusuke , Toshizane Go , Sugamata Ryohei , Saito Mitsunobu
Published: April 2024

Powertrain development requires an efficient development process with no rework and model-based development (MBD). In addition, to performance design that achieves low CO2 emissions is also required. Furthermore, it also demands fuel economy performance considering real-world usage conditions, and in North America, the EPA (U.S. Environmental Protection Agency) 5-cycle, which evaluates performance in a combination of various environments, is applied. This evaluation mode necessitates predicting performance while considering engine heat flow. Particularly, simulation technology that considers behavior based on engine temperature for Hybrid Electric Vehicle (HEV) is necessary.

Additionally, in the development trend of vehicle aerodynamic improvement, variable devices like Active Grille Shutter (AGS) are utilized to contribute to reducing CO2 emissions. When equipped with AGS, the engine's heat flow environment also changes, resulting in more complex phenomena in the engine compartment compared to the without AGS. As described above, simulation technology that consider behavior based on engine temperature and the behavior of aerodynamic improvement devices are crucial for identifying trade-offs in vehicle performance.

In this study, after clarifying the functions and models required to predict the fuel economy of the EPA 5-cycle, an engine thermal plant have been constructed and coupled with vehicle simulation. The vehicle simulation achieved highly accurate EPA 5-cycle fuel economy prediction. Furthermore, a conceptual-level Co-Simulation environment, suitable for early-stage development was utilized to study performance requirements. This was followed by transitioning to a detailed-level Co-Simulation environment that meets the necessary specifications during the mid-term of development. The conceptual-level Co-Simulation environment allows for clarifying the performance requirements of each device in the early stages of development. The detailed-level Co-Simulation environment enables optimization control after determining the hardware specifications in the mid-term of development.
Application of Large Eddy simulation to Gasoline Engine and Comparison against RANS
Authors: V G Srujan , Servant Cedric , Rathinam Balamurugan
Published: April 2024

The work presented is a comparative analysis between Reynold’s Averaged Navier Stokes (RANS) and Large Eddy simulation (LES) description of turbulence to capture the in-cylinder flow structures. Simulations are performed using RNG k - € RANS model and LES one equation eddy viscosity model with Werner and Wengle wall model. ECFM combustion model has been used to achieve the desired rate of heat release when compared against the experiments. KH-RT spray model has been used to define the primary and secondary breakup of parcels. A Renault engine operating on Miller cycle has been chosen for this study. Consecutive perturbation method (CPM) available in Converge has been used to take the advantage of availability of higher number of cores to reduce the effective CPU time. Results of RANS simulation indicates that increase in valve overlap results in faster tumble decay compared to LES. Moreover, it is also found that LES required lesser tuning of flame stretch due to turbulence and a single value can be applied across different valve lift conditions. A quantitative comparison of standard deviation of IMEP between test and the simulation results has been made. An analysis on fast and slow burning cycles has been performed which showed that velocity plays a major role in affecting the burning speed. Furthermore, additional results obtained from the multiple cycles of LES are analyzed.
WITHDRAWN A Study of Compression Pad, Its Selection and Optimization Process for the Lithium-Ion Cell Module
Authors: Sithick Basha Abubakker , Chalumuru Madhu , Sasikumar K
Published: April 2024

The need for eco-friendly vehicle powertrains has increased drastically in recent years. The most critical component of an electric vehicle is the battery pack/cell. The choice of the appropriate cell directly determines the size, performance, range, life, and cost of the vehicle. Lithium-ion batteries with high energy density and higher cycle life play a crucial role in the progress of the electric vehicle. However, the packaging of lithium-ion cells is expected to meet lots of assembly demands to increase their life and improve their functional safety.

Due to their low mechanical stability, the lithium-ion cell modules must have external pressure on the cell surface for improved performance. The cells must be stacked in a compressed condition to exert the desired pressure on the cell surface using compression foam/pads. The compression pads can be either packaged between each cell or once in every set of cells based on the cell assembly requirements.

This paper describes the need for a compression pad for lithium-ion cell modules and briefly highlights the different types of compression pads and their function. The study also explains how the compression pad selection must be performed for the improved cell performance and life of the cell. The different types of assembly processes of the compression pad are explained. It further shows the list of tests conducted to validate the function and life of the compression pad. The study concludes with the thickness optimization of the compression pad that meets all the requirements and also provides a cost-effective solution.
Powering Tomorrow's Light, Medium, and Heavy-Duty Vehicles: A Comprehensive Techno-Economic Examination of Emerging Powertrain Technologies
Authors: Islam Ehsan Sabri , Nieto Prada Daniela , Vijayagopal Ram , Kim Namdoo , Phillips Paul , Alhajjar Michel , Mansour Charbel , Rousseau Aymeric
Published: April 2024

This paper presents a comprehensive analysis of emerging powertrain technologies for a wide spectrum of vehicles, ranging from light-duty passenger vehicles to medium and heavy-duty trucks. The study focuses on the anticipated evolution of these technologies over the coming decades, assessing their potential benefits and impact on sustainability. The analysis encompasses simulations across a wide range of vehicle classes, including compact, midsize, small SUVs, midsize SUVs, and pickups, as well as various truck types, such as class 4 step vans, class 6 box trucks, and class 8 regional and long-haul trucks. It evaluates key performance metrics, including fuel consumption, estimated purchase price, and total cost of ownership, for these vehicles equipped with advanced powertrain technologies such as mild hybrid, full hybrid, plug-in hybrid, battery electric, and fuel cell powertrains. Comparative assessments are conducted against conventional gasoline, diesel, and natural gas internal combustion engine vehicles, as applicable. A large-scale simulation process is employed, utilizing Autonomie for vehicle sizing and consumption evaluations and TechScape for techno-economic analysis, both of which were developed at Argonne National Laboratory. The research extends technology projections from 2023 to 2050, incorporating two technology progress scenarios: a business-as-usual (BAU) case representing currently projected improvements in vehicle efficiency, and a 'program success' case reflecting potential advancements resulting from the Department of Energy Vehicle Technologies Office and Hydrogen and Fuel Cell Technologies Office research and development investments. The results reveal the significant potential of these emerging technologies to enhance cost-effectiveness and fuel efficiency. Battery-electric and fuel-cell-powered electric vehicles are identified as key contributors to the transition towards economically competitive and environmentally friendly transportation alternatives. The findings underscore the urgency of accelerating the development of component technologies to improve the range and cost-effectiveness of electrified powertrains, particularly in the trucking sector.
Component Sizing Optimization Based on Technological Assumptions for Medium-Duty Electric Vehicles
Authors: Park Dohyun , Jung Jaekwang , Kim Namwook , Islam Ehsan Sabri , Kim Namdoo , Vijayagopal Ram
Published: April 2024

In response to the stipulations of the Energy Policy and Conservation Act and the global momentum toward carbon mitigation, there has been a pronounced tightening of fuel economy standards for manufacturers. This stricter regulation is coupled with an accelerated transition to electric vehicles, catalyzed by advances in electrification technology and a decline in battery cost. Improvements in the fuel economy of medium- and heavy-duty vehicles through electrification are particularly noteworthy. Estimating the magnitude of fuel economy improvements that result from technological advances in these vehicles is key to effective policymaking. In this research, we generated vehicle models based on assumptions regarding advanced transportation component technologies and powertrains to estimate potential vehicle-level fuel savings. We also developed a systematic approach to evaluating a vehicle’s fuel economy by calibrating the size of the components to satisfy performance requirements. We used Autonomie, a high-fidelity vehicle modeling and simulation tool developed by Argonne National Laboratory, integrating Pattern Search solvers to optimize component sizing based on our assumptions. Pattern Search, a direct-method numerical optimization algorithm, is widely used in a variety of applications. The method requires extensive evaluation and iteration but provides good optimization performance for the computational cost. This paper presents the potential energy savings for a medium-duty electric vehicle determined using both rule-based and optimized component sizing.
Application of Triz Methodology in Enhancing Product Life Cycle of an Automotive Diesel Engine
Authors: T Harthik , Kawatkar Amit , Dharan R Bharani
Published: April 2024

This study employs TRIZ, the Theory of Inventive Problem Solving, to optimize a 2.2-liter automotive diesel engine facing challenges from system technology upgrades in the fuel injection system. This system requires the common rail pump. Two pumps were chosen and based on fuel quantity balance (QB) and drive ratio, one pump was finalized as the technical option, and it was studied in a detailed manner to identify the improving and worsening parameters with the help of a contradiction matrix and the 40 TRIZ principle, which are the main core ideas of TRIZ. The worsening parameters (drive torque) are reduced by 21.36%, and the chain load in the 0.5% worn chain condition also fulfills the system requirement.

The chosen pump is further studied. This also helped to identify and categorize the system components of the main engineering system into subsystems and supersystems. Through functional analysis and cause-and-effect chain analysis, the timing issue is identified. and in the trimming stage, using Rule 3, this timing of the pump is resolved. Supporting results and TRIZ findings have been discussed in detail.
Rollover Protection Structure - Gouge and Scratch Analysis in Rollover Crashes
Authors: Warner Mark H. , Swensen Grant , Warner Wyatt Y.
Published: April 2024

Gouges and scratches to rollover protection structures are informative to the reconstruction and analysis of real-world vehicle rollover crashes. Variations in ground surface composition can be correlated with accompanying witness marks on the vehicle rollover protection structure. This paper presents the results of rollover protection structure specimen tests using a variety of test speeds and surface compositions. The test results and analyses that follow are displayed for use in comparison to similar damage on subject crash vehicles. In addition, impact of steel rollover protection structures with various opposing ground surface materials can produce visible sparks in low light conditions. Tests were performed to show the ability of these structures to produce sparks from various surface impacts.
Developing dynamic driver head envelope for passenger cars considering real-time road conditions
Authors: Rajakumaran Sriram , S Rahul , Vasireddy Rakesh Mitra , Nair Suhas
Published: April 2024

Ergonomics plays an important role in automobile design to achieve optimal compatibility between occupants and vehicle components. The overall goal is to ensure that the vehicle design accommodates the target customer group, who come in varied sizes, preferences and tastes. Headroom is one such metric that not only influences accommodation rate but also conveys a visual perception on how spacious the vehicle is. An adequate headroom is necessary for a good seating comfort and a relaxed driving experience. Headroom is intensely discussed in magazine tests and one of the key deciding factors in purchasing a car. SAE J1100 defines a set of measurements and standard procedures for motor vehicle dimensions. H61, W27, W35, H35 and W38 are some of the standard dimensions that relate to headroom and head clearances. While developing the vehicle architecture in the early design phase, it is customary to specify targets for various ergonomic attributes and arrive at the above-mentioned dimensions. In general, specifications that relate to headroom are only a consequence of static assessments carried out inside a laboratory and not on real-time driving condition. The static assessment can be as simple as positioning a digital manikin in CAD environment and then specifying how high or low the interior trim of the headliner be to achieve a certain head clearance. In actual driving scenario, the vehicle would experience rough terrain. In such cases, the road undulations can displace the occupant from their normal seated position in effect reducing the head clearance. Therefore, it is important to understand this dynamic variance of head clearance on actual driving condition. Undertaking a volunteer test to study this variance comes with risk of endangering the participant and has other measurement related complexities. Hence, we adopt a simulation-based approach for the same using Human Body Models (HBMs) of different anthropometry, which are proven having high bio-fidelity. The aim of this study is to validate this hypothesis and develop a head envelope for drivers considering dynamic road conditions, thus enabling vehicle manufactures digitally evaluate head clearance during early development phase. A typical driving scenario with various vehicle speeds on different stochastic roads and braking conditions are simulated using MBS vehicle models and the acceleration signatures from the simulations are used to estimate the vertical lift of driver over the seat. The resulting displaced posture is compared with the normal driving posture and various head clearances are analyzed. The outcome of this work will help in validating and (or) updating the static head envelope and use it for specifying the headroom target for driver in the early phase of the vehicle design.
Potential and Challenges of Using Biomass-Based Resources in Bhutan
Authors: Wangchuk Singye , Kumar Naveen
Published: April 2024

Bhutan is a small nation in the eastern Himalayas, between two of the world's largest neighbors and fastest-growing economies; China, and India. The GDP of the country is $2.707 Billion as of 2022. Bhutan’s largest renewable source is hydropower, which has a known potential of 30,000 MW. However, it has only been able to harvest only 1,480 MW (5% of the potential). The current overall electrification rate is 99% overall with 98.4% in rural areas. It exports 75.5% of total electricity generated in the country to India. However, the reliable supply of electricity remains a big challenge. The government is also pushing the use of renewable energy sources like solar and wind to diversify the energy mix and enhance the power security of the country. The share of renewable energy is very minimal at present amounting to 723 kW Solar PV and 600 kW Wind power. Bioenergy in the form of fuel wood, energy crops & crop residues, and cattle dung has great potential in the country as the country’s total energy demand is projected to grow at an average annual rate of 6.5% which is expected to reach 1,550 kilo ton of oil equivalent (KTOE) by 2030 from 725 KTOE in 2017. However, the country continues to use unsustainable use of biomass due to the use of conventional inefficient cook stoves, a lack of local knowledge, and the inability to produce and use modern and efficient biomass systems. The present study attempts to evaluate the challenges and potential suitability of Biomass-based Resources for meeting existing and future energy requirements of the country as it can’t solely depend upon hydro-power due to many vital reasons. The use of biomass resources for energy also fulfills the country’s mandate of not increasing GHG emissions as the country has the rare distinction of being the only carbon-negative country in the world.
An MBSE Methodology for Cross-Domain Vehicle Performance Development
Authors: Jeong Ilsoo , Yun Sunkil , Alai Shashank , Grange Valentin , Park Hwiseob , Kang Sungho
Published: April 2024

Even if an optimal design is produced in the mid-to-late stages of development, when the maturity of development is increasing, it is already difficult to accept the proposal between the organizations and functions. In case the optimal proposal is made with a small amount of information in the preceding stage, it will be helpful for mutual decision-making. In addition, if all members have a system and development environment that enables access and utilization of necessary data in a timely manner, it is possible to produce quick results through collaboration. To implement such a system and development environment, “digital modeling" of tangible and intangible assets will be essential and to implement an "integrated IT environment" that can access and utilize digital models. Until now, Hyundai Motor Company has not yet fully established a digital development environment that all researchers can simultaneously utilize during the concept development stage. To implement such systems and development environments, we drove this project as a proof of concept (PoC) focusing on vehicle driving performance. We believe this will lay the groundwork for establishing a future Model-Based Systems Engineering (MBSE) platform. This paper proposes Hyundai Motor's new MBSE methodology for vehicle performance development in the early stages of development applied to a driving performance development case study. Model-based collaboration, which is a means of communication in the conceptual design stages, can contribute more to the production of the optimal plan than the later stages following the intermediate stage of development, where the freedom of decision-making between organizations is low. Based on the definition of a common architecture, we propose a parameter linkage between performance, driving performance architecture and performance analysis model linkage, and through these, propose a methodology for collaboration between developers on the platform and present empirical cases.
Evaluating Fire Propagation into the Passenger Compartment via Full-Scale Burn Testing
Authors: Papageorge Michael , Colwell Jeff
Published: April 2024

Two full-scale burn tests were conducted to evaluate the propagation of an engine compartment fire into the passenger compartment of consumer vehicles. In particular, the effect of penetrations in the bulkhead separating the engine compartment from the passenger compartment was examined. The first burn test involved two vehicles of the same year, make, and model. One of the vehicles was left in the original equipment manufacturer (OEM) configuration. The other vehicle was modified by welding steel plates over the pass-through locations in the bulkhead between the engine and passenger compartments. After the fire was initiated in the engine compartment and had reached the onset of flashover, the heat and flames from this fire began to effect the passenger compartment. At about this same time, flames extending from the engine compartment around the hood began impinging directly on the outer face of the windshield. The passenger compartment temperature first increased on both vehicles at an under-dash location near the bulkhead. Once the windshields failed the temperature of the airspace in occupant compartment that the driver or passengers would occupy and the headrest heat flux rapidly increased. A second burn demonstration involved a single mini-van with a windshield that had been fractured in a way similar to what would be expected during a front-end collision. As the fire that was initiated in the engine compartment interacted with the windshield, it failed in locations where there was existing mechanical damage to the windshield. The fire propagated into the vehicle via the broken windshield when the engine compartment fire was visually smaller than in testing in which the windshield was undamaged before the fire.
Design, Analysis, and Comparative Study of Conventional Double Wishbone Control Arms with Modified Split Type Control Arms Design for a Passenger Car
Authors: Shivhare Ghanshyam , Singh Manpreet , Chopra Nitin
Published: April 2024

In today's automotive industry, the preference for suspension systems in high-end passenger vehicles is shifting away from conventional MacPherson or double wishbone setups and toward advanced double wishbones with split-type control arms or multi-link suspensions. This shift not only enhances the ride and handling experience but also introduces greater design complexities.

This paper explains the design limitations of the conventional double wishbone front suspension (with 2 ball joints) and the opportunities presented by advanced double wishbone suspension designs, including split-type lower control arms (with 3 ball joints) and double split-type control arms (with 4 ball joints). Replacing either of the rigid links (upper/lower) of the conventional double wishbone suspension with a four-bar mechanism in the case of split-type control arm wishbone suspension significantly alters the behavior of the kingpin axis, leading to consequential effects on steering and suspension parameters.

The study presented in this paper analyzes the kinematic behavior of the split-type control arm suspension system and its influence on vehicle parameters such as Steering Rack Placement in relation to the wheel center. Furthermore, this study also compares the lateral and longitudinal movement of the wheel center and wheel envelopes with that of a conventional double wishbone suspension design.

In summary, this study provides information about how split-type suspensions work and perform. The comparative analysis with a conventional double wishbone suspension offers a comprehensive perspective on the potential advantages of adopting this advanced suspension configuration in automotive design.
Transient Aerodynamics Simulations of a Passenger Vehicle during Deployment of Rear Spoiler
Authors: Fougere Nicolas , DeMeo Michael , Tuit Farquhar Henry , Oliveira Danilo , Nastov Alexander
Published: April 2024

In the context of vehicle electrification, improving vehicle aerodynamics is not only critical for efficiency and range, but also for driving experience. In order to balance the necessary trade-offs between drag and downforce without significant impact on the vehicle styling, we see an increasing amount of active aerodynamic solutions on high-end passenger vehicles. Active rear spoilers are one of the most common active aerodynamic features. They deploy at high vehicle speed when additional downforce is required [1, 2].

For a vehicle with an active rear spoiler, the aerodynamic performance is typically predicted through simulations or physical testing at different static spoiler positions. These positions range from fully stowed to fully deployed. However, this approach does not provide any information regarding the transient effects during the deployment of the rear spoiler, which can be critical to understanding key performance aspects of the system.

In this paper, we propose a methodology leveraging Computational Fluid Dynamics (CFD) simulations utilizing the Lattice Boltzmann Method (LBM) enabling the accurate simulation of transient aerodynamics forces during deployment of a rear spoiler on a production level passenger vehicle. The simulation results are then compared with full-scale wind tunnel physical test data as a validation of the approach.

This capability enables engineering teams to provide information to guide design decisions and can be generalized to model other types of active systems on cars such as active grilles and front splitters.
Aerodynamic analysis of ultra-efficient vehicle prototype: Effect of spoked wheels and different configurations
Authors: Galvis Chaves David Andrés , Illera-Perozo Danny , Longas Lalinde Luis
Published: April 2024

As the world population and industry increase, the demand for sustainable and efficient energy accelerates each day. One of the most energy-consuming sectors is transportation, which accounted for 27% of the total energy consumption in the US in 2022. This context provides the need to research and innovate on efficient vehicles and academic programs such as SAE supermileage or Shell Eco-Marathon which inspire students to build ultra-efficient vehicles. In vehicle efficiency, aerodynamics has high importance, and designing a low-drag vehicle is a key step. The purpose of this study is to analyze the vehicle aerodynamics of an ultra-efficient vehicle prototype built by Unisabana Herons Electric Vehicles for Shell Eco-Marathon 2022, regarding the influence on drag by having different configurations on spoked wheels, such as open spoked wheels, covered spokes, covered wheels, and a whole different body that follows a streamlined shape. The methodology is to model CAD designs of streamlined shapes to cover the spokes or all the wheels and then solve a three-dimensional, steady-state, turbulent, and incompressible airflow using Computational Fluid Dynamics (CFD) to simulate these designs on Altair AcuSolve. The results show how covering the spokes and the wheels benefits the prototype aerodynamics by reducing the drag and the wake, and the importance of a streamlined body shape for an ultra-efficient vehicle.
Extended Deep Learning Model to Predict the Electric Vehicle Motor Operating Point
Authors: Kolachalama Srikanth , Malik Hafiz
Published: April 2024

The transition from combustion engines to electric propulsion is accelerating in every coordinate of the globe. The engineers had strived hard to augment the engine performance for more than eight decades, and a similar challenge had emerged again for electric vehicles. To analyze the performance of the engine, the vector engine operating point (EOP) is defined, which is common industry practice, and the performance vector electric vehicle motor operating point (EVMOP) is not explored in the existing literature. In an analogous sense, electric vehicles are embedded with three primary components, e.g., Battery, Inverter, Motor, and in this article, the EVMOP is defined using the parameters [motor torque, motor speed, motor current]. As a second aspect of this research, deep learning models are developed to predict the EVMOP by mapping the parameters representing the dynamic state of the system in real-time. The required data is obtained by the testing of a 2023 Cadillac Lyriq electric vehicle (single motor) driven on a specified road segment. The trained functions are developed utilizing the integrated functions of MATLAB, which include the machine learning methods including non-linear autoregressive (NARX), long short-term memory (LSTM), and neural net fit (NNF), suiting vehicle data in the time domain. The performance of the methods is validated by estimating the Error vector representing the conformance of actual and predicted values of randomly selected road snippet data for ten seconds. The RMSE values of the Error, and its first-order derivative (dError) are analyzed as a metric of performance, and thus the best suitable method for modeling the electric vehicle data is identified. It is observed that the NARX method in conjunction with the scaled conjugate gradient (SCG) solver outperformed with the least computational time (< 66.48 s) and lower RMSE, e.g., Error < 3.64 and dError < 4.94, values in all scenarios.
Deep Reinforcement Learning Based Collision Avoidance of Automated Driving Agent
Authors: Chen Haochong , Aksun Guvenc Bilin
Published: April 2024

Automated driving has become a very promising research direction with many successful deployments and the potential to reduce car accidents caused by human error. Automated driving requires automated path planning and tracking with the ability to avoid collisions as its fundamental requirement. Thus, plenty of research has been performed to achieve safe and time efficient path planning and to develop reliable collision avoidance algorithms. This paper uses a data-driven approach to solve the abovementioned fundamental requirement. Consequently, the aim of this paper is to develop Deep Reinforcement Learning (DRL) training pipelines which train end-to-end automated driving agents by utilizing raw sensor data. The raw sensor data is obtained from the Carla autonomous vehicle simulation environment here. The proposed automated driving agent learns how to follow a pre-defined path with reasonable speed automatically. First, the A* path searching algorithm is applied to generate an optimal path from origin to destination for the agent represented by waypoints. Further, preview path tracking errors, steering control and distance to destination are introduced to build the reward function. In addition, raw data from multiple sensors is processed separately and concatenated together to help the proposed agent get a comprehensive understanding of its environment. Two traffic scenarios including traffic rule free urban road and road segment with two intersections, traffic light and stop sign are used to evaluate the performance of the proposed automated driving agent. The performance of proposed Deep Q-Learning (DQN) agent is evaluated in multiple aspects. Compared to traditional mid-to-mid DRL agent with explicit decomposition of high-level behavior decision and low-level control, the proposed DRL agents are expected to have better performance and smaller size since all processing steps are optimized simultaneously. Moreover, the pre-calculated A* path provides a good reference point for subsequent DRL training.
Modelling and Analysis of a Cooperative Adaptive Cruise Control (CACC) Algorithm for Fuel Economy
Authors: Kavas-Torris Ozgenur , Guvenc Levent
Published: April 2024

Connectivity in ground vehicles allows vehicles to share crucial vehicle data, such as vehicle acceleration and speed, with each other. Using sensors such as radars and lidars, on the other hand, the intravehicular distance between a leader vehicle and a host vehicle can be detected. Cooperative Adaptive Cruise Control (CACC) builds upon ground vehicle connectivity and sensor information to form convoys with automated car following. CACC can also be used to improve fuel economy and mobility performance of vehicles in the said convoy. In this paper, a CACC system is presented, where the acceleration of the lead vehicle is used in the calculation of desired vehicle speed. In addition to the smooth car following abilities, the proposed CACC also has the capability to calculate a speed profile for the ego vehicle that is fuel efficient, making it an Ecological CACC (Eco-CACC) model. Simulations were run to model and test the Eco-CACC algorithms with different lead vehicle driving behaviors. The performance of the new Eco-CACC model is then compared to a Proportional Derivative (PD) based Adaptive Cruise Control (ACC) system that aimed to follow the lead vehicle as closely as possible. The PD controller was tuned for nominal performance. The preliminary results show that the proposed CACC model was able to decrease the rate of acceleration and decelerations experienced by the ego vehicle to attain a smooth speed profile that consumed less fuel than its PD-controlled ACC counterpart.
Energy Savings Impact of Eco-Driving Control Based on Powertrain Characteristics in Connected and Automated Vehicles: On-Track Demonstrations
Authors: Jeong Jongryeol , Kandaswamy Elangovan , Dudekula Ahammad Basha , Han Jihun , Karbowski Dominik , Naber Jeffrey
Published: April 2024

This research investigates the energy savings achieved through eco-driving controls in connected and automated vehicles (CAVs), with a specific focus on the influence of powertrain characteristics. Eco-driving strategies have emerged as a promising approach to enhance efficiency and reduce environmental impact in CAVs. However, uncertainty remains about how the optimal strategy developed for a specific CAV applies to CAVs with different powertrain technologies, particularly concerning energy aspects. To address this gap, on-track demonstrations were conducted using a Chrysler Pacifica CAV equipped with an internal combustion engine (ICE), advanced sensors, and vehicle-to-infrastructure (V2I) communication systems, compared with another CAV, a previously studied Chevrolet Bolt electric vehicle (EV) equipped with an electric motor and battery. The implemented control is a universal speed planner that solves the eco-driving optimal-control problem within a receding-horizon framework, utilizing V2I communications for signal phase and timing information. The controller calculates accelerator and brake pedal positions using the vehicle’s state and real-time environmental information. Both the Pacifica, target vehicle, and the Bolt, EV, are equipped with a drive-by-wire system. The experiments encompass five road scenarios repeated three times, covering a 3.7-km track with various stop signs, traffic signals, and speed limits. Three control calibrations are employed to represent human-driver-like, non-connected automated, and V2I-connected driving. First and foremost, the results demonstrate functional eco-driving controls with no extreme acceleration or traffic law violations in the Pacifica (ICE vehicle). Energy savings of up to 6% without connectivity and up to 22% with V2I connectivity are achieved in the ICE vehicle as well. Additionally, a comparison is made between an ICE vehicle and an EV to analyze the energy-saving impacts of eco-driving controls across different powertrain characteristics. In conclusion, this study emphasizes the significance of correlating powertrain design with controls and eco-driving strategies during the development of CAVs.
Toy Model: A Naïve ML Approach to Hydrogen Combustion Anomalies
Authors: Guzmán Mendoza María Gabriela , Garcia Antonio , Molina Santiago , Olcina-Girona Miguel , Pesce Francesco , Gessaroli Davide , Vassallo Alberto , Golisano Roberto , Sacco Nicola
Published: April 2024

Predicting and preventing combustion anomalies leads to safe and efficient operation of the hydrogen internal combustion engine. This research presents the application of three machine learning (ML) models – K-Nearest Neighbors (KNN), Random Forest (RF) and Logistic Regression (LR) – for the prediction of combustion anomalies in a hydrogen internal combustion engine. A small experimental dataset was used to train the models and posterior experiments were used to evaluate their performance and predicting capabilities (both in operating points -speed and load- within the training dataset and operating points in other areas of the engine map).

KNN and RF exhibit superior accuracy in classifying combustion anomalies in the training and testing data, particularly in minimizing false negatives, which could have detrimental effects on the engine. The findings suggest that these naïve models are effective in identifying and flagging operating conditions with high potential for an anomaly occurring and thereby enabling timely intervention and preventive measures. The generalization of the model to conditions outside the training dataset showed sufficiently high prediction capabilities at the early stage of the development, and the new set of tested data can be included into future model training sets to improve the robustness for forthcoming testing at high-anomaly conditions before including the results into control strategies and systems.
Development of a High Power, Low Emissions Heavy Duty Hydrogen Engine
Authors: Bunce Michael , Seba Bouzid , Andreutti Roberto , Yan Ziming , Peters Nathan
Published: April 2024

The hydrogen (H2) internal combustion engine (ICE) is emerging as an attractive low life-cycle carbon powertrain configuration for applications that require high power, high duty cycle operation. Owing to the relative ease of conversion of heavy duty (HD) diesel ICEs to H2 and the potential for low exhaust emissions, H2 ICEs are expected to play a strong role in rapidly decarbonizing hard-to-electrify markets such as off-road, rail, and marine. The conversion of HD diesel ICEs to spark ignited H2 with port fuel injection is typically accompanied by a de-rating of engine power and torque. This is due to several fuel- and system-related challenges, including the high risk of abnormal combustion resulting from the low auto-ignition energy threshold of H2, and boost system requirements for highly dilute operation that is used to partially mitigate this abnormal combustion risk. However, HD ICEs must be adapted to a diverse range of vehicle applications, and so increasing ICE displacement to accommodate the de-rating challenge is not a feasible solution. This study details the research and development of a high power, ultra-low nitrogen oxides (NOx) emissions HD H2 ICE. The engine, converted from a diesel base, leverages an active pre-chamber ignition system to promote stable dilution limit extension, which lowers combustion temperatures and in-cylinder surface temperatures to reduce abnormal combustion. The ignition system also reduces instability-induced abnormal combustion risk. The resulting H2 ICE achieves power and torque levels consistent with those of the base diesel ICE, eliminating de-rating. The additional lean stability, especially during transient operation, leads to ultra-low cycle-average NOx emissions, achieving engine-out NOx of 0.24 g/kWh on a non-road transient cycle (NRTC) with a preliminary transient calibration. Test data was used to correlate a computational fluid dynamics (CFD) model of the engine, developing a simulation toolset that will be used to guide future optimization of the engine.
Proposal and Validation of 3D-CFD Framework for Ultra-Lean Hydrogen Combustion in ICEs
Authors: Sfriso Stefano , Berni Fabio , Breda Sebastiano , Fontanesi Stefano , Ramalho Leite Caio , Brequigny Pierre , Foucher Fabrice
Published: April 2024

In recent months, the increasing debate within the European Union to review the ban on internal combustion engines has led to the pursuit of environmentally neutral solutions for ICEs, as an attempt to promote greater economic and social sustainability. Interest in internal combustion engines remains strong to uphold the principle of technological neutrality. In this perspective, the present paper proposes a numerical methodology for 3D-CFD in-cylinder simulations of hydrogen-fueled internal combustion engines. The combustion modelling relies on G-equation formulation, along with Damköhler and Verhelst turbulent and laminar flame speeds, respectively. Numerical simulations are validated with in-cylinder pressure traces and images of chemiluminescent hydrogen flames captured through the piston of a single-cylinder optical spark-ignition engine. To mitigate the uncertainties related to the modeling of mixture stratification and injection, hydrogen is port-injected and continuously supplied into the intake pipe to ensure mixture homogeneity. Therefore, the main challenge in this study is represented by an accurate characterization of the combustion propagation, which is the key element in the validation of the computational framework. In this regard, a remarkable alignment between simulations and experiments is achieved in terms of pressure traces and flame imaging, evidencing the model’s capabilities. The validation is carried out at different equivalence ratios, demonstrating the reliability of the numerical framework to consistently reproduce results without the need for case-by-case adjustments.
Enhancing Ducted Fuel Injection Simulations: Assessment of RANS Turbulence Models Using LES Data
Authors: Segatori Cristiano , Piano Andrea , Peiretti Paradisi Benedetta , Millo Federico , Bianco Andrea
Published: April 2024

Compression ignition engine-based transportation is nowadays looking for cleaner combustion solutions. Among them, ducted fuel injection (DFI) is emerging as a cutting-edge technology due to its potential to drastically curtail engine-out soot emissions. Although the DFI capability to abate soot formation has been demonstrated both in constant-volume and optical engine conditions, its optimization and understanding is still needed for its exploitation on series production engines. For this purpose, computational fluid dynamics (CFD) coupled with low-cost turbulence models, like RANS, can be a powerful tool, especially in the industrial context. However, it is often challenging to obtain reliable RANS-based CFD simulations, especially due to the high dependence of the various state-of-the-art turbulence models on the case study.

In this scenario, the present work proposes a comparative analysis of the outcome of several RANS turbulence models against high-fidelity large eddy simulation (LES) data related to the DFI case study in constant-volume non-reacting conditions. In particular, the mixing process predicted by each RANS model has been assessed in terms of turbulent kinetic energy and fuel-to-air ratio fields, using the resolved portion of the LES turbulent energy spectrum (characterized by more than 80% of resolved turbulent length scales) as a target. After that, the most reliable models have been selected for the combustion analysis with the aim of providing additional insights regarding the soot formation process. According to these results, although the macroscopic spray characteristics and the flow detachment location are consistently predicted by each RANS turbulence model, the mixing intensity and the related fuel-to-air ratio values in the auto-ignition region are strictly model dependent, drastically modifying the DFI performance in terms of soot reduction compared to the free spray case.
Research on the Anti-Shuffle Control for Hybrid Electric Vehicles in the Parallel Mode
Authors: Jing Junchao , Zhang Junzhi , Wang Zhentao , Liu Yiqiang , Huang Weishan
Published: April 2024

In order to solve the problems of the shuffle caused by internal and external excitation and the difficulty in obtaining the real-time accurate engine torque during the parallel mode operation of hybrid electric vehicles, a dynamic coordination control strategy for suppressing the jitter of hybrid electric vehicles based on the closed-loop control of engine speed was proposed. The engine torque filtering control method based on the slope limit was adopted to limit the rate of change of the engine torque and reduce the impact caused by the sudden change of the engine torque; the engine speed closed-loop control method was used to take the motor speed which is easy to be measured accurately in real time as the feedback control variable, which solved the problem of the real-time accurate estimation of the engine torque online. In parallel mode, the motor torque accounts for a small proportion because the torque distribution method gives priority to the engine. When the amplitude of the engine speed is greater than that of the motor speed and the phase of the engine speed is different from that of the motor speed, if the motor damping control is still adopted, the engine speed jitter cannot be eliminated due to the phase difference. In order to suppress the engine torsional vibration in parallel mode and ensure the good acceleration ability of the system, an active damping control strategy based on the engine speed control was proposed. Kalman filter algorithm is used to calculate the target engine speed signal, and the difference between the target speed and the actual speed is processed by a series of low pass filter, double derivative filter and two-stage series lead correction, forming a compensation torque component and superimposed on the engine torque to suppress the speed fluctuation, thereby increasing the system damping and enabling the system to achieve stable acceleration. In this paper, two-stage series lead phase filter is selected to increase the lead phase in the control system, so as to improve the damping of the transmission system with insufficient natural damping in a closed-loop sense. The center frequency of the filter is set as the natural frequency of the powertrain. A simulation platform is set up by using Matlab/Simulink. The simulation results show that the proposed dynamic coordination control strategy can reduce the fluctuation of the engine speed and vehicle speed, and effectively improve the ride comfort of the hybrid electric vehicle.