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The fuel economy and emission of the hybrid vehicle depend largely on the selected engine. And the dedicated hybrid engine (DHE) can be controlled to operate in the optimal operating range because DHE can be decoupled from the vehicle transmission system. The main purpose of this paper is to improve the thermal efficiency of the diesel engine under common operating conditions combined with high compression ratio (CR) and early or late intake valve closing (IVC) angle. According to the vehicle road spectrum data, the optimal operating range of the engine is determined to be 1200-1400 rpm and 70%-90% load. Then CR and IVC angle are optimized by using the calibrated one-dimensional thermodynamic model of the engine under limited peak combustion pressure (Pmax). The results show that the adjustment of IVC angle and CR can control the thermal state at the end of compression stroke. The combination of CR and IVC angle can achieve the optimal fuel consumption improvement.

To many, a digital twin offers “functionality,” or the ability to virtually rerun events that have happened on the real system and the ability to simulate future performance. However, this requires models based on the physics of the system to be built into the digital twin, links to data from sensors on the real live system, and sophisticated algorithms incorporating artificial intelligence (AI) and machine learning (ML). All of this can be used for integrated vehicle health management (IVHM) decisions, such as determining future failure, root cause analysis, and optimized energy performance. All of these can be used to make decisions to optimize the operation of an aircraft—these may even extend into safety-based decisions.

The Homogeneous Charge Compression Ignition (HCCI) combustion eliminates the issues of higher particulate matter and nitrogen oxides emissions that prevail in the traditional compression ignition (CI) combustion mode. The complete replacement of traditional fuels with renewable fuels for internal combustion engines is challenging because significant infrastructure changes in the production and delivery systems are required to ensure renewable fuel availability and economic feasibility. Thus, the use of renewable acetone blended with traditional gasoline has been proposed in the present study to smoothen the transition from the traditional CI to the HCCI engines. HCCI experiments were performed in a light-duty diesel engine at 1500 rpm rated speed. By varying the volumetric proportion of the acetone in the gasoline from 20% to 40%, the HCCI engine load range from 20%-60% was achieved, significantly higher than the limited diesel HCCI load range of 20%-38%.

In these days, not only low exhaust emission but also carbon dioxide reduction is required to achieve carbon neutrality toward resolution of climate change. Though examination of electrification and decarbonized fuel is progressing, industrial machines have issues for high load factor and infrastructure development. Therefore, trends of off-road powertrain are expected to be diversified depending on usage environment or applications. As a result, in terms of diesel engines below 19 kW, it should be the best way for satisfying the social needs to develop new diesel engines which have high environmental performance by optimizing engine combustion. In the case of diesel engines below 19 kW, it is difficult for the engines to adopt the direct injection (DI) combustion system and the common rail system (CRS). The fuel spray of these small displacement engines by DI or CRS easily attaches the wall surface of combustion chamber due to the small bores and causes increasing fuel consumption.

Machine learning is used for the research and development of ITS services and the rider assistance for on-road motorcycle racing. Meanwhile, rider assistance systems for off-road motorcycles have yet to be developed, partly due to the complexity of the measurement conditions, as described in the previous paper. This research aims to create a reliable AI which is capable of classifying typical jump behaviors in off-road riding by machine learning to create a rider assistance system for off-road motorcycles. Motorcycle manufacturers and certain research institutes use motion sensors to collect data, but the data is obtained from a limited number of vehicles and riders. The creation of a rider assistance system requires a large amount of validation data. Furthermore, it is desirable to achieve the target with data that can be measured in mass-produced vehicles, which will make it possible to collect data even from general users.

In recent years there has been a significant focus towards bringing EVs to the market because of the regulations in carbon emissions and increased customer awareness. Unlike the passenger car and two wheelers that are mainly used for personal transportation, the IC engine three wheelers are widely used as commercial vehicles to carry passengers and loads. Converting an existing vehicle in the market to an electric reduces emission and increases sustainability. Large battery capacity is used in these vehicles to achieve the range, thus increasing the initial cost and making it unpopular among customers. However, requirements of the three-wheeler electric vehicles are high in big institutions, resorts, golf courses, hospitals and many other places for internal campus transportation. It is not commercially viable to make a complete design of Body-In-white (BIW) & suspension for this application. Hence, a method of converting an existing three-wheeler into an EV is explained here.

The evaluation of the acceleration, velocity, and travel distance of a motor vehicle is an issue that arises frequently in the analysis of vehicle accidents. It is well known that the acceleration capabilities of a motor vehicle generally reduce as the velocity increases. Vehicle accident reconstruction has traditionally used constant acceleration models with stepped decreases of acceleration as the velocities, distances, and loading increase. For any given vehicle, the energy output of the engine that can be transmitted to the drivetrain remains within a power band of the operating engine. Transmissions efficiently transfer this relatively constant power to the drive wheels. Using the mathematical relationships of the “power equations,” the acceleration, velocity, and travel distances for vehicles can be reasonably evaluated with limited test information. Adjustments for differing load conditions and terrain are readily incorporated into the model.

Low carbon emissions policies for the transportation sector have recently driven more interest in using low net-carbon fuels, including biodiesel. An internal combustion engine (ICE) can operate effectively using biodiesel while achieving lower engine-out emissions, such as soot, mostly thanks to oxygenate content in biodiesel. This study selected a heavy-duty (HD) single-cylinder engine (SCE) platform to test biodiesel fuel blends with 20% and 100% biodiesel content by volume, referred to as B20, and B100. Test conditions include a parametric study of exhaust gas recirculating (EGR), and the start of injection (SOI) performed at low and high engine load operating points. In-cylinder pressure and engine-out emissions (NOX and soot) measurements were collected to compare diesel and biodiesel fuels.

Measures to improve the thermal efficiency of heavy- duty commercial vehicle engines with compression ignition continue to be an important topic in research and development. Increasing the compression ratio (CR) of the engine is a direct way to increase the process efficiency. However, to ensure an optimum combustion and emission behavior at very high compression ratio is challenging. In addition, the combustion and emission behavior of heavy-duty compression ignition (CI) engines with compression ratios beyond 21:1 has hardly been reported in the literature. In this study, a combination of the experimental and 3D-CFD based numerical methods were applied to a high compression ratio heavy duty engine to analyze the combustion process and emissions so as to evaluate the thermal efficiency improvement potential.

Paris Climate Agreement defined the strategy to contrast the current climate change trend. Therefore, a complete and deep review of the entire lifespan of a product is necessary. Recently, in the agri-tech field, also tractors manufacturers have begun to explore the adoption of full-electric or hybrid-electric powertrains to contrast pollutants emissions and to misrepresent tractor functionalities, due to diesel engines stricter regulations in terms of pollutants emissions. The aim of this work is to evaluate the carbon intensity of an ICE and hybrid-electric orchard tractor trough Life Cycle Assessment technique. The assessment has been conducted considering production, use and disposal phases of the tractor. Lastly, the results obtained are illustrated according to gate-to-gate and cradle-to-gate approach.

Lubricating oil consumption (LOC) is a direct source of hydrocarbon and particulate emissions from internal combustion engines. LOC also inhibits the lifetime of exhaust aftertreatment system components, preventing their ability to effectively filter out other harmful emissions. Due to its influence on piston ring- bore conformability, bore distortion is arguably the most critical parameter for engine designers to consider in prevention of LOC. Bore distortion also has a significant influence on the contact forces between the piston ring and cylinder wall, which determine the wear rate of the ring and cylinder wall and can cause durability issues. Two drivers of bore distortion: thermal expansion and head bolt stresses, are routinely considered in conformability and contact analyses. Separately, bore distortion/vibration due to piston impact and combustion/cylinder pressures has been previously analyzed in wet liner engines for coolant cavitation and noise considerations.

A hybrid powertrain offers the potential of a significant fuel saving for heavy-duty Diesel vehicles, which results in CO2 reduction of more than 20%, depending on the application. Using advanced future fuels, like HVO offers additional CO2 saving potential. In addition, the future Diesel engine needs to comply with the next generation of emission legislation, given by the European EUVII and the US EPA2027 regulatory frameworks. To achieve these limits, a combination of different technologies for the engine and the aftertreatment system are required. The proposed paper will present these technology solutions and their impact on CO2 and emissions by means of engine testing and simulation.

Hydrogen-fueled internal combustion engines are highly susceptible to pre-ignition from external sources due to its low minimum ignition energy despite the hydrogen’s good auto-ignition resistance. Pre-ignition leads to uncontrolled abnormal combustion events resulting in knocking and / or backfire (flashback) which may result in mechanical damage, and as such represents tenacious obstacle to the development of hydrogen engines. Current pre-ignition mitigation strategies force sub-optimal operation thereby eroding the efficiency / emissions advantages of hydrogen fuel making the technology less attractive. Hydrogen pre-ignition phenomenon is poorly understood and knowledge gaps about the underlying mechanisms remain. To this end, a phenomenological study of hot-spot induced pre-ignition is carried out in a direct-injection hydrogen-fueled, heavy-duty, single-cylinder optical engine.

Synthesized driving cycles which can reflect the real world driving scenarios are essential for electrification and hybridization of powertrains of heavy duty logistics vehicles (HDLV). Current synthetic methods always neglected weight variation which is crucial for logistic vehicle driving scenarios. This paper proposed a method based on multi-dimensional Markov chains and big data to generate typical driving cycles with consideration of vehicle weight and slope. The validation of the synthesized driving cycle was based on a statistical analysis and the adequacy of the representative to real world driving data was demonstrated.

We propose a novel dual-fuel combustion model for simulating heavy-duty engines with the G-Equation. Dual-Fuel combustion strategies in such engines features direct injection of a high-reactivity fuel into a lean, premixed chamber which has a high resistance to autoignition. Distinct combustion modes are present: the DI fuel auto-ignites following chemical ignition delay after spray vaporization and mixing; a reactive front is formed on its surroundings; it develops into a well-structured turbulent flame, which propagates within the premixed charge. Either direct chemistry or the flame-propagation approach (G- Equation), taken alone, do not produce accurate results. The proposed Dual-Fuel model decides what regions of the combustion chamber should be simulated with either approach, according to the local flame state; and acts as a “kernel” model for the G- Equation model. Direct chemistry is run in the regions where a premixed front is not present.

This study examines the use of hydrogen as a fuel for internal combustion engines to decrease greenhouse gas emissions. The focus is on hydrogen combustion at leaner mixture conditions, which has the potential to increase efficiency and reduce NOx emissions. While metal engine experiments have established these benefits, there are only a few optical studies on pure hydrogen combustion under lean operating conditions. This study reports optical measurements performed in a heavy-duty optical diesel engine converted to spark-ignition operation with port-fuel injections and varying spark timing, at air-excess ratios (lambda) of 2.5 and 3. The engine was equipped with a flat-shaped optical piston that allowed for bottom-view imaging of the combustion process. High-speed natural combustion luminosity images were recorded, along with in-cylinder pressure measurements.

Hydrogen internal combustion engines are a robust and cost-efficient solution to decarbonize the heavy- duty on- and off-highway vehicle sectors. Mature base technology, low additional system costs, and no dependence on expensive raw materials will enable a fast market penetration and thus a rapid reduction of CO2 emissions, provided customer expectations for performance, driving range and total cost of ownership as well as regulatory requirements such as ultra-low pollutant emissions can be met. The power density of a hydrogen engine is currently limited by uncontrolled combustion events such as knocking, pre-ignition and backfiring. Tenneco, TME and FEV show in this paper how the optimization of components and combustion strategies minimizes these risks such that Diesel-like performance can be reached.

Hydrogen ICE can achieve carbon neutrality and is adaptable to medium and heavy-duty vehicles, for which electricity is not always a viable option. It can also be developed using high-quality conventional diesel/gasoline engine technology. Furthermore, it allows for the conversion of existing engines to hydrogen ICE, making it highly marketable. The reliability and durability of MPI hydrogen ICE is better than that of DI, and MPI has an advantage over DI in terms of cruising range because the low-pressure injection of hydrogen reduces the remaining hydrogen in the tank. Improving MPI output is, however, an important subject, and achieving this requires suppressing abnormal combustion such as pre-ignition. In this study, an inline four-cylinder 5L turbo-charged diesel engine was converted to a hydrogen engine. Hydrogen injectors were installed in the intake ports and spark plugs were installed instead of diesel fuel injectors.

An optically accessible hydrogen-fueled, heavy-duty engine was used to investigate the impact of mixture formation on the early flame kernel propagation and the resulting combustion cyclic variability. Direct injection from a centrally mounted medium-pressure outward-opening hollow-cone injector created a fuel- air mixture with a global equivalence ratio of 0.33. The engine was operated at 1200 RPM with dry air at an intake pressure and temperature of 1.0 bar and 305 K, respectively. The charge was ignited at three different locations using focused-laser ignition to allow for undisturbed flame evolution, and the fuel injection timing and injection pressure were varied to influence the mixture inhomogeneity.

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.