Plug-in hybrid electric vehicles have the potential of combining the benefits of electric vehicle in terms of low emissions and internal combustion engine vehicles in terms of vehicle range. With the addition of a renewable fuel, the CO2 potential reduction increase even more. The last trends for PHEV are small combustion engine known as range extender, with battery package between full hybrid and electric powertrains. Thus, allowing an improvement in vehicle’s range, reducing battery materials while converting fuel energy through a highly efficient path. Although these vehicles have been proved to be a convenient strategy for decarbonizing the light vehicles, the use of alternative fuels is poorly studied. In this work, hydrous ethanol is chosen because is already available in some countries, such as USA and Brazil, and have an ultra-low well-to-tank CO2 emission.
Simulators are essential part of the development process of vehicles and their advanced functionalities. The combination of virtual simulator and Hardware-in-the-loop technology accelerates the integration and functional validation of ECUs and mechanical components. In this study, a real-time capable tire model has been developed and coupled with an innovative simulation apparatus. On-track tests were executed to collect data necessary for tire modelling using an experimental vehicle equipped with wheel force transducer, to measure force and moments acting on tire contact patch. The steering wheel was instrumented with a torque sensor, while tie-rod axial forces were quantified using loadcells. The simulation apparatus is composed of a static and a dynamic simulator. The static simulator integrates the entire steering system from the steering column up to tie rods. Tie-rods dynamic forces are applied by two torque motors.
During the development of an Internal Combustion Engine-based powertrain, traditional procedures for control strategies calibration and validation produce huge amount of data, that can be used to develop innovative data-driven applications, such as emission virtual sensing. One of the main criticalities is related to the data quality, that cannot be easily assessed for such a big amount of data. This work focuses on an emission modeling activity, using an enhanced Light Gradient Boosting Regressor and a dedicated data pre-processing pipeline to improve data quality. First thing, a software tool is developed to access a database containing data coming from emissions tests. The tool performs a data cleaning procedure to exclude corrupted data or invalid parts of the test. Moreover, it automatically tunes model hyperparameters, it chooses the best set of features, and it validates the procedure by comparing the estimation and the experimental measurement.
AI-based methods are experiencing a rapid surge in adoption across various applications, particularly in the context of autonomous and trustworthy embedded systems. Since AI-based systems then have the potential to affect with mission critical system properties of trustworthy embedded systems, a higher level of maturity and dependability considerations is required to be followed. This paper combines the findings from the TEACHING project, which focuses on technology bricks for humanistic AI concepts, with insights derived from a facilitation workshop involving subject matter experts for dependability engineering. The paper establishes the body of knowledge and fundamental grounds outcomes discussed in an expert workshop at international conference on computer safety, reliability, and security. The assurance of dependability continues to be an open issue with no common solution yet.
The design of transportation vehicles, whether passenger or commercial, typically involves a lengthy process from concept to prototype and eventual manufacture. To improve competitiveness, original equipment manufacturers are continually exploring ways to shorten the design process. The application of digital tools such as computer aided design (CAD) and engineering (CAE), as well as model-based computer simulation enable team members to virtually design and evaluate ideas within realistic operating environments. Recent advances in machine learning / artificial intelligence can be integrated into this paradigm to shorten the initial design sequence through the creation of digital agents. A digital agent can intelligently explore the design space to identify promising component features which can be collectively assessed within a virtual vehicle simulation.
Roundabouts are intersections at which automated cars seem currently not performing sufficiently well. Actually, sometimes, they get stuck and the traffic flow is seriously reduced. To overcome this problem a V2N-N2V (vehicle-to-network-network-to-vehicle) communication scheme is proposed. Cars communicate via 5G with an edge computer. A cooperative machine learning algorithm orchestrate the traffic. Automated cars are instructed to accelerate or decelerate with the triple aim of improving the traffic flow into the roundabout, keeping safety constraints, providing comfort for passengers on board of automated vehicles. In the roundabout, both automated cars and human-driven cars run. The roundabout scenario has been simulated by SUMO. Additionally, the scenario has been reconstructed into a dynamic driving simulator, with a real human driver in a virtual reality environment. The aim was to check the human perception of traffic flow, driving safety and driving comfort.
Pedestrian Automatic Emergency Braking (P-AEB) is a technology designed to avoid or reduce the severity of vehicle to pedestrian collisions. This technology is currently assessed and evaluated via EuroNCAP and similar procedures in which a pedestrian test target is crossing the road, walking alongside the road, or stationary in the forward vehicle travel path. While these assessment methods serve the purpose of providing cross-comparison of technology performance in a standardized set of scenarios, there are many scenarios which could occur which are not considered or studied. By identifying and performing non-EuroNCAP, non-standardized scenarios using similar methodology, the robustness of P-AEB systems can be analyzed. These scenarios help identify areas of further development and consideration for future testing programs. Three scenarios were considered as a part of this work: straight line braking, curved path braking, and parking lot testing.
The gasoline particulate filter (GPF) represents a practical solution for particulate emissions control in light-duty gasoline-fueled vehicles. It is also seen as an essential technology in North America to meet the upcoming US EPA tailpipe emission regulation, as proposed in the “Multi-pollutant Rule for Model Year 2027”. The goal of this study was to introduce advanced, uncoated GPF products and measure their particulate mass (PM) reduction performance within the existing US EPA FTP vehicle testing procedures, as detailed in Code of Federal Regulations (CFR) part 1066. Various state-of-the-art GPF products were characterized for their microstructure properties and lab-bench performance for pressure drop and filtration efficiency, were then subjected to an EPA-recommended 2000mile on-road break-in, and finally were tested on an AWD vehicle chassis-dyno emissions test cell at both 25C and -7C ambient conditions.
Gasoline particulate filters (GPF) have become a standard aftertreatment component in Europe, China, and since recently, India, where particulate emissions are based on a particle number (PN) standard. The anticipated evolution of regulations in these regions towards future EU7, CN7, and BS7 standards further enhances the needs with respect to the filtration capabilities of the GPFs used. Emission performance has to be met over a broader range in particle size, counting particles down to 10nm, and over a broader range of boundary conditions. The requirements with respect to pressure drop, aiming for as low as possible, and durability remain similar or are also enhanced further. To address these future needs new filter technologies have been developed. New technologies for uncatalyzed GPF applications have been introduced in our previous publications.
Powertrain development requires an efficient development process with no rework and model-based design (MBD), in addition to performance design that achieves low CO2 emissions. Furthermore, it also demands fuel economy performance considering real-world usage conditions, and in North America, the EPA 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 takes into account behavior based on engine temperature. 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.
Engineering design-decisions often involve many attributes which can differ in the levels of their importance to the decision maker (DM), while also exhibiting complex statistical relationships. Learning a decision-making policy which accurately represents the DM’s actions has long been the goal of decision analysts. To circumvent elicitation and modeling issues, this process is often oversimplified in how many factors are considered and how complicated the relationships considered between them are. Without these simplifications, the classical lottery-based preference elicitation is overly expensive, and the responses degrade rapidly in quality as the number of attributes increase. In this paper, we investigate the ability of deep preference machine learning to model high-dimensional decision-making policies utilizing rankings elicited from decision makers.
The greatest threat to human life is climate change. Carbon emission, which is a major cause of climate change, comes from human activities like burning fossil fuels. The automotive sector contributes approximately one fifth of all global carbon emissions. The key solution for addressing the problem of carbon emissions is battery electric vehicles. The high cost of the technology, the lack of charging infrastructure, and the short range of EVs are major barriers to their widespread adoption. The fact that the real range is less than the certified range is another issue for prospective customers of electric vehicles. The certified range of an EV is based on a standard driving cycle, which differs from the actual driving cycle. Therefore, it's crucial to select the right driving cycle for range estimation. In India, MIDC (modified Indian drive cycle) was adopted, which is equivalent to the New European Driving Cycle (NEDC). The maximum speed in the MIDC cycle is set to 90 km/h.
Most of the Automated Driving Systems (ADS) technology development is targeting urban areas; there is still much to learn about how ADS will impact rural transportation. The DriveOhio team deployed level-3 ADS-equipped prototype vehicles in rural Ohio with the goal of discovering technical challenges for ADS deployment in such environments. However, before the deployment on public roads, it was essential to test the ADS-equipped vehicle for their safety limitations. At Transportation Research Center (TRC) proving grounds, we tested one such prototype system on a closed test track with soft targets and robotic platforms as surrogates for other road users. This paper presents an approach to safely conduct testing for ADS prototype and assess its readiness for public road deployment. The main goal of this testing was to identify a safe Operational Design Domain (ODD) of this system by gaining better understanding of the limitations of the system.
In 2022, the U.S. Energy Information Administration reported that the transportation sector led U.S. energy consumption, accounting for 36% of the total, with light-duty vehicles constituting 52% of this. Simultaneously, automotive transportation is experiencing a transformation, incorporating automated driving technologies and enhanced connectivity such as V2V, V2I, and V2X. These advancements not only enhance safety but also offer opportunities to optimize energy efficiency through real-time data. While Connected and Automated Vehicles (CAVs) herald potential energy savings, an increase in Vehicle Miles Traveled driven by CAV convenience might offset these benefits, leading to heightened energy consumption. To tackle these challenges, Phase II of ARPA-E's NEXTCAR Program targets a 30% energy reduction in SAE J3016 L4 light-duty vehicles. Phase I achieved a 20% energy efficiency improvement over 2016-2017 baseline vehicles using SAE J3016 L1-L3 automation levels.
Electrified vehicles represent mobility’s future, but they impose challenging and diverse requirements like range and performance. To meet these requirements, various components, such as battery cells, electric drives, fuel cells, and hydrogen vessels need to be integrated into a drive and storage system that optimizes the key performance indicators (KPI). However, finding the best combination of components is a multifaceted problem in the early phases of development. Therefore, advanced simulation tools and processes are essential for satisfying the customer´s expectation. EDAG has developed a flat storage platform (H2HyBat), which is suitable for both, BEV and FCEV. The platform allows for the flexible and modular integration of batteries and hydrogen vessels. However, package space is limited and the impact of the design choices regarding the vehicle’s KPI need to be considered.