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Lean-burn hydrogen internal combustion engines are a good option for future transportation solutions since they do not emit carbon-dioxide and unburned hydro-carbons, and the emissions of nitric-oxides (NOx) can be kept low. However, under lean-burn conditions the combustion duration increases, and the combustion stability decreases, leading to a reduced thermal efficiency. Turbulent jet ignition (TJI) can be used to extend the lean-burn limit, while decreasing the combustion duration and improving combustion stability. The objective of this paper is to investigate the feasibility of a passive pre-chamber TJI system on a heavy-duty hydrogen engine under lean-burn conditions using CFD modelling. The studied concept is mono-fuel, port-fuel injected, and spark ignited in the pre-chamber. The overall design of the pre-chamber is discussed and the effect of design parameters on the engine performance are studied.

Contribution to carbon neutrality is one of the most important challenges for the automotive industry. As CO2 emission has been reduced through electrification such as hybrid electric vehicle (HEV) and plug-in hybrid electric vehicle (PHEV), internal combustion engines (ICEs) equipped in those powertrain systems are still necessary for the foreseeable future, and continuous efforts to improve fuel efficiency are demanded. To improve powertrain thermal efficiency, direct-injection turbocharged gasoline engines have been widely utilized in recent years. Super lean-burn combustion engine has been researched as a next generation of turbocharged gasoline engines. Further utilization of turbochargers is expected. Compared with turbocharged downsized gasoline engines available in the current market, much higher boost pressure must be utilized to realize the super lean-burn engines. As a result, compressor housing temperature will be very high compared with the current market one.

A model-based control approach is proposed to give proper reference for the feed-forward combustion control of Partially Pre-mixed Combustion (PPC) engines. The current study presents a simplified first principal model, which has been developed to provide a base estimation of the ignition properties. This model is used to describe the behavior of a single-cylinder heavy-duty diesel engine fueled with a mix of bio-butanol and n-heptane (80vol% bio-butanol and 20 vol% n-heptane). The model has been validated at 8 bar gross Indicated Mean Effective Pressure (gIMEP) in PPC mode. Inlet temperature and pressure have been varied to test the model capabilities. First the experiments were conducted to generate reference points with BH80 under PPC conditions. And then CFD simulations were conducted to give initial parameter set up, e.g. fuel distribution, zone dividing, for the multi-zone model.

To prevent global warming, further reductions in carbon dioxide are required. It is therefore important to promote the spread of electric vehicles powered by internal combustion engines and electric vehicles without internal combustion engines. As a result, emissions from hybrid electric vehicles equipped with internal combustion engines should be further reduced. Interest in catalytic reactions in an electric field with a higher catalytic activity compared to conventional catalysts has increased because this technology consumes less energy than other electrical heating devices. This study was therefore undertaken to apply a catalytic reaction in an electric field to an exhaust emission control. First, the original experimental equipment was built with a high voltage system used to conduct catalytic activity tests.

Our L2-automated driving system enabling a driver to take his/her hands off from the steering wheel is self-operating on a highway, allowing the vehicle to automatically change lanes and overtake slow-speed leading vehicles. It includes an OTA function, which can extend the ODD after the market launch. To realize these features in reasonably safer and more reliable ways, system architecture must be designed well under hardware and software implementation constraints. One such major constraint is the system must be designed to make the most out of the existing sensor configuration on the vehicle, where five peripheral radars and a front camera for ADAS as well as panoramic-view and rear-view cameras for monitoring are available. In addition, four LiDARs and a telephoto camera are newly adopted for ADS. Another constraint is the system must consist of reliable redundant components for fail-safe operation.

Premixed charge compression ignition (PCCI) combustion is effective in reducing harmful exhaust gas and improving the fuel consumption of diesel engines [1]. However, PCCI combustion has a problem of exhibiting lower combustion stability than diffusive combustion [2, 3], which makes it challenging to apply to mass production engines. Its low combustion stability problem can be overcome by implementing complicated injection control strategies that account for variations in environmental and engine operating conditions as well as transient engine conditions, such as turbocharging delay, exhaust gas recirculation (EGR) delay, and intake air temperature delay. Although there is an example where the combustion mode is switched according to the intake O2 fraction [4], it requires a significant number of engineering-hours to calibrate multiple combustion modes. And besides, such switching combustion modes tends to have a risk of discontinuous combustion noise and torque.

It is well understood that using lower viscosity engine oils can greatly improve fuel economy [1, 2, 3, 4]. However, it has been impossible to evaluate ultra-low viscosity engine oils (SAE 0W-12 and below) utilizing existing fuel economy test methods. As such, there is no specification for ultra-low viscosity gasoline engine oils [5]. We therefore developed firing and motored fuel economy test methods for ultra-low viscosity oils using engines from Japanese automakers [6, 7, 8]. This was done under the auspices of the JASO Next Generation Engine Oil Task Force (“TF” below), which consists mainly of Japanese automakers and entities working in the petroleum industry. Moreover, the TF used these test methods to develop the JASO GLV-1 specification for next-generation ultra-low viscosity automotive gasoline engine oils such as SAE 0W-8 and 0W-12. In developing the JASO GLV-1 specification, Japanese fuel economy tests and the ILSAC engine tests for evaluating engine reliability were used.

This paper focusses on the application of bioalcohols (ethanol and butanol) derived from seaweed in Heavy-Duty (HD) Compression Ignition (CI) combustion engines. Seaweed-based fuels do not claim land and are not in competition with the food chain. Currently, the application of high octane bioalcohols is limited to Spark Ignition (SI) engines. The Reactivity Controlled Compression Ignition (RCCI) combustion concept allows the use of these low carbon fuels in CI engines which have higher efficiencies associated with them than SI engines. This contributes to the reduction of tailpipe CO2 emissions as required by (future) legislation and reducing fuel consumption, i.e. Total-Cost-of-Ownership (TCO). Furthermore, it opens the HD transport market for these low carbon bioalcohol fuels from a novel sustainable biomass source. In this paper, both the production of seaweed-based fuels and the application of these fuels in CI engines is discussed.

Diesel spray mixture formation is investigated at target conditions using multiple diagnostics and laboratories. High-speed Particle Image Velocimetry (PIV) is used to measure the velocity field inside and outside the jet simultaneously with a new frame straddling synchronization scheme. The PIV measurements are carried out in the Engine Combustion Network Spray A target conditions, enabling direct comparisons with mixture fraction measurements previously performed in the same conditions, and forming a unique database at diesel conditions. A 1D spray model, based upon mass and momentum exchange between axial control volumes and near-Gaussian velocity and mixture fraction profiles is evaluated against the data.

Green zones are challenging problems for the thermal management systems of hybrid vehicles. This is because within the green zone the engine is turned off, and the only way to keep the aftertreatment system warm is lost. This means that there is a risk of leaving the green zone with a cold and ineffective aftertreatment system, resulting in high emissions. A thermal management strategy that heats the aftertreatment system prior to turning off the engine, in an optimal way, to reduce the NOx emissions when the engine is restarted, is developed. The strategy is also used to evaluate under what conditions pre-heating is a suitable strategy, by evaluating the performance in simulations using a model of a heavy-duty diesel powertrain and scenario designed for this purpose.

Butanol is an attractive alternative fuel by virtue of its renewable source and low sooting tendency. In this paper, three butanol isomers (n-butanol, isobutanol, and tert-butanol) were induced via port injection respectively and n-heptane was directly injected into the cylinder to investigate reactivity controlled compression ignition in a heavy-duty diesel engine. This work evaluates the potential of applying butanol as low reactivity fuel and the effects of reactivity gradient on combustion and emission characteristics. The experiments were performed from low load to medium-high load. Due to the different reactivities among the butanol isomers, the exhaust gas recirculation rate and the direct injection strategy were varied for a specific butanol isomer and testing load. Particularly, isobutanol/n-heptane can be operated with single direct injection and no exhaust gas recirculation up to medium load due to the high octane rating.

Multiple injection strategies are commonly used in conventional Diesel engines due to the flexibility for optimizing heat-release timing with a consequent improvement in fuel economy and engine-out emissions. This is also desirable in low-temperature combustion (LTC) engines since it offers the potential to reduce unburned hydrocarbon and CO emissions. To better utilize these benefits and find optimal calibrations of split injection strategies, it is imperative that the fundamental processes of multiple injection combustion are understood and computational fluid dynamics models accurately describe the flow dynamics and combustion characteristics between different injection events. To this end, this work is dedicated to the identification of suitable methodologies to predict the multiple injection combustion process.

Rubber is a non-linear viscoelastic material which properties depend upon several factors. In a tire two of these factors, namely the temperature and excitation frequency, are significantly influenced by the vehicle operating conditions. In the past years, applied research studied how rubber viscoelastic characteristics affect structural and frictional tire properties. The present study focuses on how these effects interact with the vehicle handling response. Based on state of the art theory of friction, structural properties of rubber and on experimental evidence, the dependency of key tire parameters on temperature and rolling speed is established. These results are then used in combination with a single-track vehicle model to assess their impact on key vehicle parameters; as an example, the understeer coefficient, yaw resonance peak / damping and maximum acceleration are studied.

CO2 regulations on heavy-duty transport are introduced in essentially all markets within the next decade, in most cases in several phases of increasing stringency. To cope with these mandates, developers of engines and related equipment are aiming to break new ground in the fields of combustion, fuel and hardware technologies. In this work, a novel diesel fuel injector, Delphi’s DFI7, is utilized to experimentally investigate and compare the performance of ramped injection rates versus traditional square fueling profiles. The aim is specifically to shift the efficiency and NOx tradeoff to a more favorable position. The design of experiments methodology is used in the tests, along with statistical techniques to analyze the data. Results show that ramped and square rates - after optimization of fueling parameters - produce comparable gross indicated efficiencies.

Engine starting vibration of hybrid vehicle with Toyota hybrid system has variations even in the same vehicle, and a large vibration that occurs rarely may cause stress to the passengers. The contribution analysis based on the vibration theory and statistical analysis has been done, but the primary factor of the rare large vibration has not been clarified because the number of factors is enormous. From this background, we apply machine learning that can reproduce multivariate and complicated relationships to analysis of variation factors of engine starting vibration. Variations in magnitude of the exciting force such as motor torque for starting the engine and in-cylinder pressure of the engine and timing of these forces are considered as factors of the variations. In addition, there are also nonlinear factors such as backlash of gears as a factor of variations.

The progress of vehicle electrification has reduced engine noise and the improvement of rear differential gear whine noise has become more important for customer satisfaction. Rear differential gear whine noise is a result of the vibration generated by the transmission error of the gears transmitted to the cabin from various paths. As several components have a contribution, identifying key paths to develop an effective countermeasure becomes time consuming. Operational transfer path analysis (OTPA) is one of the TPA methods to determine the main path and contributing part using only the operational data. However, in cases where many reference points are set on the same frame or body, the contribution becomes similar because of high correlation between the reference data set. As a result, finding the main transfer path becomes difficult.

Next generation vehicles driven by motor such as electric vehicles and fuel cell vehicles have no engine noise. Therefore the balance of interior noise is different from the vehicles driven by conventional combustion engine. In particular, road induced noise tends to be conspicuous in the low to middle vehicle speed range, therefore, technological development to reduce it is important task. The purpose of this research is to predict the road induced noise from the signals of sensors adopted for automatic driving for utilizing the prediction result as a reference signal to reduce road induced noise by active noise control (ANC). Using the monocular camera which is one of the simplest image sensors, the road induced noise is predicted from the road surface image ahead of the vehicle by machine learning.

N-butanol, as a biomass-based renewable fuel, has many superior fuel properties. It has a higher energy content and cetane number than its alcohol competitors, methanol and ethanol. Previous studies have proved that n-butanol has the capability to achieve lower emissions without sacrifice on thermal efficiency when blended with diesel. However, most studies on n-butanol are limited to low blending ratios, which restricts the improvement of emissions. In this paper, 80% by volume of n-butanol was blended with 20% by volume of n-heptane (namely BH80). The influences of various engine parameters (combustion phasing, EGR ratio, injection timing and intake pressure, respectively) on its combustion and emission characteristics are tested at different loads. The results showed that when BH80 uses more than 40% EGR, the emitted soot and nitrogen oxides (NOx) emissions are below the EURO VI legislation.

Road Departure Mitigation System (RDMS) is a new feature in vehicle active safety systems. It may not rely only on the lane marking for road edge detection, but other roadside objects This paper discusses the radar aspect of the RDMS testing on roads with grass road edges. Since the grass color may be different at different test sites and in different seasons, testing of RDMS with real grass road edge has the repeatability issue over time and locations. A solution is to develop surrogate grass that has the same characteristics of the representative real grass. Radar can be used in RDMS to identify road edges. The surrogate grass should be similar to representative real grass in color, LIDAR characteristics, and Radar characteristics. This paper provides the 24 GHz and 77 GHz radar characteristic specifications of surrogate grass.

In recent years Road Departure Mitigation Systems (RDMS) is introduced to the market for avoiding roadway departure collisions. To support the performance testing of the RDMS, the most commonly seen road edge, grass, is studied in this paper for the development of standard surrogate grass. This paper proposes a method for defining the resembling grass color and height features due to significant variations of grass appearances in different seasons, temperatures and environments. Randomly selected Google Street View images with grass road edges are gathered and analyzed. Image processing techniques are deployed to obtain the grass color distributions. The height of the grass is determined by referencing the gathered images with measured grass heights. The representative colors and heights of grass are derived as the specifications of surrogate grass for the standard evaluation of RDMS.