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The European Commission is going to publish the new Euro7 standard shortly, with the target of reducing the impact on pollutant emissions due to transportation systems. Besides forcing internal combustion engines to operate cleaner in a wider range of operating conditions, the incoming regulation will point out the role of On-Board Monitoring (OBM) as a key enabler to ensure limited emissions over the whole vehicle lifetime, necessarily taking into account the natural aging of involved systems and possible electronic/mechanical faults and malfunctions. In this scenario, this work aims to study the potential of data-driven approaches in detecting emission-relevant engine faults, supporting standard On-Board Diagnostics (OBD) in pinpointing faulty components, which is part of the main challenges introduced by Euro7 OBM requirements.

This research focused on developing a methodology for a vehicle dynamics model of a passenger vehicle outfitted with an aftermarket Automated Driving System software package using only literature and track based results. This package consisted of Autoware.AI (Autoware ®) operating on Robot Operating System 1 (ROS™) with C++ and Python ®. Initial focus was understanding the basics of ROS and how to implement test scenarios in Python to characterize the control systems and dynamics of the vehicle. As understanding of the system continued to develop, test scenarios were adapted to better fit system characterization goals with identification of system configuration limits. Trends from on-track testing were identified and paired with first-order linear systems to simulate physical vehicle responses to given command inputs. Sub-models were developed and simulated in MATLAB ® with command inputs from on-track testing.

Highway safety remains a significant concern, especially in mixed traffic scenarios involving heavy-duty vehicles (HDV) and smaller passenger cars. The vulnerability of HDVs following closely behind smaller cars is evident in incidents involving the lead vehicle, potentially leading to catastrophic rear-end collisions. This paper explores how automatic speed enforcement systems, using speed cameras, can mitigate risks for HDVs in such critical situations. While historical crash data consistently demonstrates the reduction of accidents near speed cameras, this paper goes beyond the conventional notion of crash occurrence reduction. Instead, it investigates the profound impact of driver behavior changes within desired travel speed distribution, especially around speed cameras, and their contribution to the safety of trailing vehicles, with a specific focus on heavy-duty trucks in accident-prone scenarios.

This paper details the advancements and outcomes of the NEXTCAR (Next-Generation Energy Technologies for Connected and Automated on-Road Vehicles) program, an initiative led by the Advanced Research Projects Agency-Energy (ARPA-E). The program focusses on harnessing the full potential of Connected and Automated Vehicle (CAV) technologies to develop advanced vehicle dynamic and powertrain control technologies (VD&PT). These technologies have shown the capability to reduce energy consumption by 20% in conventional and hybrid electric cars and trucks at automation levels L1-L3 and by 30% L4 fully autonomous vehicles. Such reductions could lead to significant energy savings across the entire U.S. vehicle fleet.

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.

This paper presents an energy-optimal plan for the allocation of a heterogeneous fleet of delivery vehicles in a dynamic network of multiple distribution centers and fulfillment centers. Each distribution center with a heterogeneous fleet of delivery vehicles is considered as a hub connected with the fulfillment centers through the routes as spokes. The goal is to minimize the overall energy consumption of the fleet while meeting the demand of each of the fulfillment centers. To achieve this goal, the problem is divided into two sub-problems that are solved in a hierarchical way. Firstly, for each spoke, the optimal number of vehicles to be allocated from each hub is determined. Secondly, given the number of allocated delivery vehicles from a hub for each spoke, the optimal selection of vehicle type from the available heterogeneous fleet at the hub is done for each of spokes based on the energy requirement and the energy efficiency of the spoke under consideration.

Driver’s license examinations require the driver to perform either a parallel parking or a similar maneuver as part of the on-road evaluation of the driver’s skills. Self-driving vehicles that are allowed to operate on public roads without a driver should also be able to perform such tasks successfully. With this motivation, the S-shaped maneuverability test of the Ohio driver’s license examination is chosen here for automatic execution by a self-driving vehicle with drive-by-wire capability and longitudinal and lateral controls. The Ohio maneuverability test requires the driver to start within an area enclosed by four pylons and the driver is asked to go to the left of the fifth pylon directly in front of the vehicle in a smooth and continuous manner while ending in a parallel direction to the initial one. The driver is then asked to go backwards to the starting location of the vehicle without stopping the vehicle or hitting the pylons.

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.

The challenge of industrial carbon footprint reduction is led by the engine manufacturers that are developing new technologies and fuels to lower CO2 emissions. Although the deployment of relevant investments for the development of battery electric vehicles, diesel, and gasoline cars are still widely used, especially for their longer operating range, faster refueling, and lower cost. For this reason, more efficient traditional internal combustion engines can guide the transition towards new propulsion systems. In this document, the innovative piston damage and exhaust gas temperature models previously developed by the authors are reversed and coupled to manage the combustion process, increasing the overall energy conversion efficiency. The instantaneous piston erosion and the exhaust gas temperature at the turbine inlet are evaluated according to the models’ estimation which manages both the spark advance, and the target lambda.

The increasing environmental concern is leading to the need for innovation in the field of internal combustion engines, in order to reduce the carbon footprint. In this context, hydrogen is a possible mid-term solution to be used both in conventional-like internal combustion engines and in fuel cells (for hybridization purposes), thus, hydrogen combustion characteristics must be considered. In particular, the flame of a hydrogen combustion is less subjected to the quenching effect caused by the engine walls in the combustion chamber. Thus, the significant heating up of the thin lubricant layer upon the cylinder liner may lead to its evaporation, possibly and negatively affecting the combustion process, soot production. The authors propose an analysis which aims to address the behavior of different typical engine oils, (SAE0W30, SAE5W30, SAE5W40) under engine thermo-physical conditions considering a large hydrogen-fuelled engine.

Nowadays, in the perspective of a full electric automotive scenario, internal combustion engines can still play a central role in the fulfilment of different needs if the efficiency will be improved, and the tailpipe emission will be further limited. Gasoline Compression Ignition engines can offer a favourable balance between NOx, particulate, operating range. Stable operations are ensured by ultra-high gasoline injection pressure and tailored injection patterns in order to design the most proper local fuel distribution. In this context, engine simulations by means of CFD codes can provide insights on the design of the injection parameters, and emphasis must be placed on the capture of spray-wall impingement behaviour under those non-conventional conditions. This paper aims to analyse the spray-wall impingement behaviour of ultra-high gasoline spray using a combined experimental-CFD approach.

Due to the ever increasingly stringent emission regulations for passenger vehicles, the efficiency and performance increase of Spark Ignition (SI) engines have been under the focus of the engine manufacturers. The quest for efficiency and performance increase has led to the development of increasingly complex powertrains and control strategies. The development process requires novel methods that feature a smooth transition between the real and the virtual prototypes. Furthermore, to reduce the development time and cost, developing an engine simulator with a low computational effort and good accuracy, which predicts the engine behavior on the entire operating range, plays a crucial role. This work proposes an Artificial Intelligence-based engine simulator for a Spark Ignition engine. The simulator relies on Neural Networks for the calculation of the main combustion metrics. In the first part of this paper, the data acquired at the engine test cell are analyzed.

In the past years, stringent emission regulations for Internal Combustion (IC) engines produced a large amount of research aimed at the development of innovative combustion methodologies suitable to simultaneously reduce fuel consumption and engine-out emissions. Previous research demonstrates that the goal can be obtained through the so-called Low Temperature Combustions (LTC), which combine the benefits of compression-ignited engines, such as high compression ratio and unthrottled lean operation, with a properly premixed air-fuel mixture, usually obtained injecting gasoline-like fuels with high volatility and longer ignition delay. Gasoline Partially Premixed Combustion (PPC) is a promising LTC technique, mainly characterized by the high-pressure direct-injection of gasoline and the spontaneous ignition of the premixed air-fuel mixture through compression, which showed a good potential for the simultaneous reduction of fuel consumption and emissions in CI engines.

Gear profile deviation is the difference in gear tooth profile from the ideal involute geometry. There are many causes that result in the deviation. Deflection under load, manufacturing, and thermal effects are some of the well-known causes that have been reported to cause deviation of the gear tooth profile. The profile deviation caused by gear tooth profile deformation due to interference-fit assembly has not been discussed previously. Engine timing gear trains, transmission gearboxes, and wind turbine gearboxes are known to use interference-fit to attach the gear to the rotating shaft. This paper discusses the interference-fit joint design and the mechanism of tooth profile deformation due to the interference-fit assembly in gear trains. A new analytical method to calculate the profile slope deviation change due to interference-assembly of parallel axis spur gears is presented.

Internal combustion engines are the primary transportation mover for today society and they will likely continue to be for decades to come. Hybridization is the most common solution to reduce the petrol-fuels consumption and to respect the new raw emission limits. The gasoline engines designed for running together with an electric motor need to have a very high thermal efficiency because they must work at high loads, where engine thermal efficiency is close to the maximum one. Therefore, the technical solutions bringing to thermal efficiency enhancement were adopted on HVs (Hybrid Vehicles) prior to conventional vehicles. In these days, these solutions are going to be adopted on conventional vehicles too. The purpose of this work was to trace development guidelines useful for engine designers, based on the target power and focused on the maximization of the engine thermal efficiency, following the engine rightsizing concept.

This work focuses on the development and validation of a data-driven model capable of predicting the maximum in-cylinder pressure during the operation of an internal combustion engine, with the least possible computational effort. The model is based on two parameters, one that represents engine load and another one the combustion phase. Experimental data from four different gasoline engines, two turbocharged Gasoline Direct Injection Spark Ignition, a Naturally Aspirated SI and a Gasoline Compression Ignition engine, was used to calibrate and validate the model. Some of these engines were equipped with technologies such as Low-Pressure Exhaust Gas Recirculation and Water Injection or a compression ignition type of combustion in the case of the GCI engine. A vast amount of engine points were explored in order to cover as much as possible of the operating range when considering automotive applications and thus confirming the broad validity of the model.

The ported shroud casing treatment for turbocharger compressors offers a wider operating flow range, elevated boost pressures at low compressor mass flow rates, and reduced broadband whoosh noise in spark-ignition internal combustion engine applications. However, the casing treatment elevates tonal noise at the blade-pass frequency (BPF). Typical rotational speeds of compressors employed in practice push BPF noise to high frequencies, which then promote multi-dimensional acoustic wave propagation within the compressor ducting. As a result, in-duct acoustic measurements become sensitive to the angular location of pressure transducers on the duct wall. The present work utilizes a steady-flow turbocharger gas stand featuring a unique rotating compressor inlet duct to quantify the variation of noise measured around the duct at different angular positions.

The water injection is one of the recognized technologies capable of helping the future engines to work at full load conditions with stoichiometric mixture. In the present work, a methodology for the CFD simulation of reacting flow conditions using AVL Fire code v. 2020 is applied for the assessment of the water injection effect on modern GDI engines. Both Port Water Injection and Direct Water Injection have been tested for the same baseline engine configuration under reacting flow conditions. The ECFM-3Z model adopted for combustion and knock simulations have been performed by adopting correlations for laminar flame speed, flame thickness and ignition delay times prediction, to consider the modified chemical behavior of the mixture due to the added water vapor.

With the advent of increasing autonomous vehicles on public roads, the safety of vulnerable road users such as pedestrians, cyclists, etc. has never been more important. These especially include Blind or Visually Impaired (BVI) pedestrians who face difficulty in making confident decisions in road crossings without the help of accessible pedestrian signals (APS). This paper addresses some of the safety measures that can be taken to improve and assess the safety of BVI pedestrians in a controlled environment like a BVI school campus where autonomous vehicles are operated. The majority of research on autonomous vehicle safety does not consider the edge cases of encounters with BVI pedestrians. Based on this motivation, requirements and characteristics of Non-BVI and BVI pedestrians have been stated along with the motion models used to predict their future movements. Existing tools based on Bayesian multi-model filters were used for pedestrian tracking and motion predictions.

Automated driving systems (ADS) are one of the key modern technologies that are changing the way we perceive mobility and transportation. In addition to providing significant access to mobility, they can also be useful in decreasing the number of road accidents. For these benefits to be realized, candidate ADS need to be proven as safe, robust, and reliable; both by design and in the performance of navigating their operational design domain (ODD). This paper proposes a multi-pronged approach to evaluate the safety performance of a hypothetical candidate system. Safety performance is assessed through using a set of test cases/scenarios that provide substantial coverage of those potentially encountered in an ODD. This systematic process is used to create a library of scenarios, specific to a defined domain. Beginning with a system-specific ODD definition, a set of core competencies are identified.