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Accurately predicting real-world vehicle energy consumption is essential for optimizing vehicle designs, enhancing energy efficiency, and developing effective energy management strategies. This paper presents a data-driven approach that utilizes machine learning techniques and a comprehensive dataset of vehicle parameters and environmental factors to create precise energy consumption prediction models. The methodology involves recording real-world vehicle data using data loggers to extract information from the CAN bus systems for ICE and hybrid electric, as well as hydrogen and battery fuel cell vehicles. Data cleaning and cycle-based analysis are employed to process the dataset for accurate energy consumption prediction. This includes cycle detection and analysis using methods from statistics and signal processing, and then pattern recognition based on these metrics.

Internal combustion (IC) engines still power most of the vehicles on road and will likely to remain so in the near future, especially for heavy duty applications in which electrification is typically more challenging. Therefore, continued improvements on IC engines in terms of efficiency and longevity are necessary for a more sustainable transportation sector. Two important design objectives for heavy duty engines with wet liners are to reduce friction loss and to lower the risks of cavitation damages, both of which can be greatly influenced by the piston-liner clearance and the design of the piston skirt. However, engine design optimization is difficult due to the nonlinear interactions between the key design variables and the design objectives, as well as the multi-physics and multi-scale nature of the mechanisms that are relevant to the design objectives.

Protecting the piston ring and liner interface is critical to the proper operation of internal combustion engines. Specifically, the dry region, which is the portion of the liner above the Top Dead Center (TDC) of the Oil Control Ring (OCR), needs proper lubrication to reduce wear and to maintain sustainability. However, the mechanisms by which oil is distributed to such region have not been investigated. This paper presents the first attempt to understand dry region lubrication by means of the oil-gas interaction below the top ring gap through a combination of experimental and modeling approaches. An optical engine with 2D Laser Induced Fluorescence (2D-LIF) technique was applied to visualize the oil flow below the top ring gap. It was observed that the two vortices downstream the top ring gap can cause oil bridging towards the liner, providing lubrication to the ring-liner interface.

Heavy-duty transportation is one of the sectors that contributes to greenhouse gas emissions. One way to reduce CO2 emissions is to use drop-in fuels. However, when drop-in fuels are used, i.e., higher blends of alternative fuels are added to conventional fuels, solubility problems and precipitation in the fuel can occur. As a result, insolubles in the fuel can clog the fuel filters and interfere with the proper functioning of the injectors. This adversely affects engine performance and increases fuel consumption. These problems are expected to increase with the development of more advanced fuel systems to meet upcoming environmental regulations. This work investigates the composition of the deposits formed inside the injectors of the heavy-duty diesel engine and discusses their formation mechanism. Injectors with internal deposits were collected from field trucks throughout Europe. Similar content, location and structure were found for all the deposits in the studied injectors.

The formation of deposits in the fuel systems of heavy-duty engines, using drop-in fuels, has been reported in recent years. Drop-in fuels are of interest because they allow higher levels of alternative fuels to be blended with conventional fuels that are compatible with today’s engines. The precipitation of insolubles in the drop-in fuel can lead to clogging of fuel filters and internal injector deposits, resulting in increased fuel consumption and engine drivability problems. The possible mechanisms for the formation of the deposits in the fuel system are not yet fully understood. Several explanations such as operating conditions, fuel quality and contamination have been reported. To investigate injector deposit formation, several screening laboratory test methods have been developed to avoid the use of more costly and complex engine testing.

ADAS (Advanced Driver Assistance System) functions can help the driver avoid accidents or mitigate their effect when they occur, and are pre-cursors to full autonomous driving (SAE defined as Level 4+). The main goal of this work is to develop a Model-Based system to actuate the Evasive Maneuver Assist (EMA) function. A typical scenario is the situation in which longitudinal Autonomous Emergency Braking (AEB) is too late and the driver has to adopt an evasive maneuver to avoid an object suddenly appearing on the road ahead. At this time, EMA can help improve the driver’s steering and braking operation in a coordinated way. The vehicle maneuverability and response performance will be enhanced when the driver is facing the collision. The function will additionally let the vehicle steer in a predetermined optimized trajectory based on a yaw rate set point and stabilize the vehicle. The EMA function is introduced with some analysis of benchmarking data.

Three-piece oil control rings (TPOCR) are widely used in the majority of modern gasoline engines and they are critical for lubricant regulation and friction reduction. Despite their omnipresence, the TPOCRs’ motion and sealing mechanisms are not well studied. With stricter emission standards, gasoline engines are required to maintain lower oil consumption limits, since particulate emissions are strongly correlated with lubricant oil emissions. This piqued our interest in building a numerical model coupling TPOCR dynamics and oil transport to explain the physical mechanisms. In this work, a 2D dynamics model of all three pieces of the ring is built as the main frame. Oil transport in different zones are coupled into the dynamics model. Specifically, two mass-conserved fluid sub-models predict the oil movement between rail liner interface and rail groove clearance to capture the potential oil leakage through TPOCR. The model is applied on a 2D laser induced fluorescence (2D-LIF) engine.

In order to mitigate the effect of fossil fuels on global warming, biodiesel is used as drop in fuel. However, in the mixture of biodiesel and diesel, soft particles may form. These soft particles are organic compounds, which can originate from the production and degradation of biodiesel. Further when fuel is mixed with unwanted contaminants such as engine oil the amount soft particles can increase. The presence of these particles can cause malfunction in the fuel system of the engine, such as nozzle fouling, internal diesel injector deposits (IDID) or fuel filter plugging. Soft particles and the mechanism of their formation is curtail to understand in order to study and prevent their effects on the fuel system. This paper focuses on one type of soft particles, which are metal soaps. More precisely on the role of the short chain fatty acids (SCFA) during their formation. In order to do so, aged and unaged B10 was studied.

Biofuel can enable a sustainable transport solution and lower greenhouse gas emissions compared to standard fuels. This study focuses on biodiesel, implemented in the easiest way as drop in fuel. When mixing biodiesel into diesel one can run into problems with solubility causing contaminants precipitating out as insolubilities. These insolubilities, also called soft particles, can cause problems such as internal injector deposits and nozzle fouling. One way to overcome the problem of soft particles is by filtration. It is thus of great interest to be able to quantify fuel filters’ ability to intercept soft particles. The aim of this study is to test different fuel filters for heavy-duty engines and their ability to filter out synthetic soft particles. A custom-built fuel filter rig is presented, together with some of its general design requirements. For evaluation of the efficiency of the filters, fuel samples were taken before and after the filters.

The increased rates of research on complex fuel blends in engine applications poses a need for more efficient and accurate fuel blending processes in engine laboratories. Making the fuel blending process automatic, effective, accurate and flexible saves time, storage space and cost without compromising the tests of future fuel alternatives. To meet these requirements, an automatic fuel blending system, following a sequential batch process, was designed and tested for engine laboratory application. The fuel blending system was evaluated in terms of functionality, safety, accuracy and repeatability. The functionality and safety was evaluated through a risk analysis. Whereas, the accuracy and repeatability of the system was investigated through blend preparation tests. The results show that the minimum fuel mass limitation of the system is 0.5 kg. This allows for blends with fuel ratios as low as 7 vol-% to be prepared by the system.

The goal of this interview analysis was to explore and document the perceptions of two unique lane centering systems (S90’s Pilot Assist and CT6’s Super Cruise). Both systems offer a similar type of functionality (adaptive cruise control and lane centering), but have significantly different design philosophies and HMI (Human-Machine Interface) implementations. Twenty-four drivers drove one of the two vehicle models for a month as part of a field operational test (FOT) study. Upon vehicle return, drivers took part in a 60-minute semi-structured interview covering their perceptions of the vehicle’s various advanced driver-assistance systems (ADAS). Transcripts of the interviews were coded by two researchers, who tagged each statement with relevant system and perception code labels. For analysis, the perception codes were grouped into larger thematic bins of safety, comfort, driver attention, and system performance.

Catalytic and non-catalytic engine aftertreatment components, such as the diesel oxidation catalyst (DOC), selective catalytic reduction on filter (SCRF), the gasoline particulate filter (GPF) and the diesel particulate filter (DPF) are complex, multifunctional emissions control technologies that are robustly designed for extended use in harsh automotive exhaust environments. Over the useful component lifetime, lubricant-derived inorganic and incombustible ash accumulates in and/or on the surface of the aforementioned aftertreatment components, resulting in degraded performance and other potential problems. In order to better understand effects of ash in such components, a multiscale analytical approach is necessary, requiring a variety of experimental tools.

The global demand for decreased emission from engines and increased efficiency drives manufactures to develop more advanced fuel injection systems. Today's compression-ignited engines use common rail systems with high injection pressures and fuel injector nozzles with small orifice diameters. These systems are highly sensitive to small changes in orifice diameters since these could lead to deteriorations in spray characteristics, thus reducing engine performance and increasing emissions. Phenomena that could create problems include nozzle fouling caused by metal carboxylates or biofuels. The problems increase with extended use of biofuels. This paper reports on an experimental study of nozzle hole fouling performed on a single-cylinder engine. The aim was to identify if the solubility of the fuel has an effect on deposit build-up and, thus, the reduction in fuelling with associated torque loss, and if there is a probability of regenerating the contaminated injectors.

With the recent advancement in sensing and controller technologies architecture design of an autonomous driving system becomes an important issue. Researchers have been developing different sensors and data processing technologies to solve the issues associated with fast processing, diverse weather, reliability, long distance recognition performance, etc. Necessary considerations of diverse traffic situations and safety factors of autonomous driving have also increased the complexity of embedded software as well as architecture of autonomous driving. In these circumstances, there are almost countless numbers of possible architecture designs. However, these design considerations have significant impacts on cost, controllability, and system reliability. Thus, it is crucial for the designers to make a challenging and critical design decision under several uncertainties during the conceptual design phase.

Reducing cold-start emissions to meet increasingly stringent emissions limits requires fast activation of exhaust system sensors and aftertreatment control strategies. One factor delaying the activation time of current exhaust sensors, such as NOx and particulate matter (PM) sensors, is the need to protect these sensors from water present in the exhaust system. Exposure of the ceramic sensing element to water droplets can lead to thermal shock and failure of the sensor. In order to prevent such failures, various algorithms are employed to estimate the dew point of the exhaust gas and determine when the exhaust system is sufficiently dry to enable safe sensor operation. In contrast to these indirect, model-based approaches, this study utilized radio frequency (RF) sensors typically applied to monitor soot loading levels in diesel and gasoline particulate filters, to provide a direct measurement of stored water levels on the ceramic filter elements themselves.

This paper describes a post-race analysis of team KOMMIT EVT’s electric motorcycle data collected during the 2016 Pikes Peak International Hill Climb (PPIHC). The motorcycle consumed approximately 4 kWh of battery energy with an average and maximum speed of 107 km/h and 149 km/h, respectively. It was the second fastest electric motorcycle with a finishing time of 11:10.480. Data was logged of the motorcycle’s speed, acceleration, motor speed, power, currents, voltages, temperatures, throttle position, GPS position, rider’s heart rate and the ambient environment (air temperature, pressure and humidity). The data was used to understand the following factors that may have prevented a faster time: physical fitness of the rider, thermal limits of the motor and controller, available battery energy and the sprocket ratio between the motor and rear wheel.

Increasing demands for more efficient engines and stricter legislations on exhaust emissions require more accurate control of the engine operating parameters. Engine control is based on sensors monitoring the condition of the engine. Numerous sensors, in a complex control context, increase the complexity, the fragility and the cost of the system. An alternative to physical sensors are virtual sensors, observers used to monitor parameters of the engine thus reducing both the fragility and the production cost but with a slight increase of the complexity. In the current paper a virtual intake manifold cylinder port pressure sensor is presented. The virtual sensor is based on a compressible flow model and on the pressure signal of the intake manifold pressure sensor. It uses the linearized pressure coefficient approach to keep vital performance behaviors while still conserving calibration effort and embedded system memory.

Renewable fuels are essential in the field of heavy duty transportation if we are to reach a fossil-free society in the foreseeable future. However renewable diesel fuels based on fatty acid methyl ester (FAME) might face problems with degradation and with cold flow properties. From the perspective of an engine, this may cause problems in the fuel injection system, such as fuel filter clogging and injector deposits. These phenomena, especially fuel filter clogging, can be connected to gel-like soft particles, which could originate from degradation products as well as from byproducts created during biodiesel refining. In this study, soft particles from the degradation of bio-based diesel fuel were examined. The tested fuels included hydrogenated vegetable oils (HVO), rapeseed methyl ester (RME) and 10% blend of rapeseed methyl ester with standard diesel (B10).

Typically the combustion in a direct injected compression ignited internal combustion engine is open-loop controlled. The introduction of a cylinder pressure sensor opens up the possibility of a virtual combustion sensor which could enable closed-loop combustion control and thus the potential to counteract effects such as engine part to part variation, component ageing and fuel quality diversity. Closed-loop combustion control requires precise, robust and preferably cheap sensors. This paper presents a virtual cylinder pressure sensor based on the signal from the inexpensive but well proven knock sensor. The method used to convert the knock sensor signal into a pressure estimate included the stages: Phase correcting the raw signal, Filtering the raw signal, Scaling the signal to known thermodynamic laws and provided engine sensors signals and Reconstructing parts of the signal with other known models and assumptions.

Cremer impedance, first proposed by Cremer (Acustica 3, 1953) and then improved by Tester (JSV 28, 1973), refers to the locally reacting boundary condition that can maximize the attenuation of a certain acoustic mode in a uniform waveguide. One limitation in Tester’s work is that it simplified the analysis on the effect of flow by only considering high frequencies or the ‘well cut-on’ modes. This approximation is reasonable for large duct applications, e.g., aero-engines, but not for many other cases of interest, with the vehicle intake and exhaust system included. A recent modification done by Kabral et al. (Acta Acustica united with Acustica 102, 2016) has removed this limitation and investigated the ‘exact’ solution of Cremer impedance for circular waveguides, which reveals an appreciable difference between the exact and classic solution in the low frequency range. Consequently, the exact solution can lead to a much higher low-frequency attenuation level.