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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.

Gasoline-related factors that affect low-speed pre-ignition (LSPI) include the distillation properties of gasoline, manganese (Mn), ethanol, diesel fuel, detergent for aftermarket, and iron (Fe). The combined effect of Mn with ethanol or high calcium engine oil (high-Ca oil) has not been sufficiently clarified. Therefore, appropriate countermeasures for LSPI have not yet been implemented. To clarify the effect of the gasoline properties and additives on LSPI, engine tests were conducted using gasoline with different “PM Index” values, an indicator of distillation properties, different concentrations of Mn, ethanol, diesel fuel, detergent, Fe, and high-Ca oil. The results showed that the LSPI frequency tended to increase with the PM Index, Mn up to 60 ppm, diesel fuel up to 2 vol.%, and detergent up to three times the standard amount.

Vehicle interior wind noise is typically managed through the overall exterior geometry of the vehicle, mirror shape and mounting location, sealing features and glass thickness and damping. Prior research has distinguished between contribution of fluctuating pressure due to air turbulence as compared to acoustic pressure to a passenger vehicles exterior at highway speeds. Because of the large difference in propagation speed between turbulent and acoustic pressure for on-road passenger vehicles, the structural response of the glass to turbulent versus acoustic pressure is not the same. The acoustic coincidence frequency of door glass is typically in the 2-3 kHz range. Turbulent coincidence frequency is much lower, and the effective transmission loss (TL) of the glass depends on the mix of turbulent and acoustic pressure on the exterior surface of the glass.

Wind noise is becoming a higher priority in the automotive industry. Several past studies investigated whether Statistical Energy Analysis (SEA) can be utilized to predict wind noise. Because wind noise analysis requires both radiation and transmission modeling in a wide frequency band, turbulent-structure-acoustic-coupled-SEA is being used. Past research investigated coupled-SEA’s benefit, but the model is usually simplified to enable easier consideration on the input side. However, the vehicle is composed of multiple interior parts and possible interior countermeasure consideration is needed. To enable this, at first, a more detailed coupled-SEA model is built from the acoustic-SEA model which has a larger number of degrees of freedom for the interior side. Then, the model is modified to account for sound radiation effects induced by turbulent and acoustic pressure.

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.

The reduction of particulate emissions is one of the most important challenges facing the development of future gasoline engines. Several studies have demonstrated the impact of fuel chemical composition on the emissions of particulate matter, more particularly, the detrimental effect of high boiling point components such as heavy aromatics. Fuel contamination is likely to become a critical issue as new regulations such as Real Driving Emissions RDE involves the use of market fuel. The objective of this study is to investigate several experimental approaches to detect the presence of Diesel contamination in Gasoline which is likely to alter pollutant emissions. To achieve this, a fuel matrix composed of 12 fuels was built presenting diesel fuel in varying concentrations from 0.1 to 2% v/v. The fuel matrix was characterized using several original techniques developed in this study.

Controllability is defined in ISO 26262 as a driver’s ability to avoid a specified harm caused by a malfunction of electrical and electronic systems installed in road vehicles. According to Annex C of Part 12 of ISO 26262, simulation is one of the techniques that the Controllability Classification Panel (CCP) can use to evaluate comprehensively the controllability class (C class) of motorcycles. With outputs of (i) an index for the success of harm avoidance and (ii) the magnitude of the rider’s compensatory control action required to avoid harm, the simulation is useful for evaluating the C class of the degrees of malfunction that cannot be implemented in practice for the sake of the test rider’s safety. To aim at supplying data that the CCP can use to judge the C class, we try to estimate the vehicle behavior and a rider’s compensatory control actions following a malfunction using vehicle dynamics simulations.

The SAE J300 classification was expanded to 0W-12 and 0W-8 viscosity grades in 2015, and lower viscosity engine oils have been studied in the industry. ILSAC GF-6B that will be introduced in 2020 will specify a 0W-16 requirement, but 0W-12 and 0W-8 grades are not considered. Because engine oil equal to or higher than the 0W-20 grade is recommended for almost all engines globally, suitable engine tests for 0W-12 and 0W-8 do not exist. Therefore, the Japan Automobile Manufacturers Association, Petroleum Association of Japan and Society of Automotive Engineers of Japan decided to establish new 0W-12 and 0W-8 low viscosity engine oil specifications. It is referred to as JASO GLV-1, and together with a new fuel economy engine test procedure, these engine oils for better fuel economy will be put on the Japanese market in 2019. Motoring friction torque tests are widely used to ascertain the friction reduction effect of fuel-economy engine oils.

This study employed a diesel particulate generator (DPG), with an installed engine oil injector for soot and ash accumulation in a diesel particulate filter (DPF). Ash was generated by engine oil injection into the diesel burner flame. The amount of soot accumulation per loading varied from 0.5 g/L to 8 g/L while ash accumulation amount per loading was maintained at 0.5 g/L. Initially, ash accumulation distribution in the DPF was visualized using X-ray computed tomography (CT). It was revealed that the form of ash accumulation changed depending on the amount of soot accumulation before active regeneration, i.e., a large amount of soot accumulation resulted in plug ash, whereas a small amount of soot accumulation resulted in wall ash. To clarify ash accumulation mechanisms, soot and ash transport behavior in DPF during active regeneration process was directly observed using a high-speed camera through an optically accessible D-shaped cut DPF covered with a quartz glass plate.

This work investigated the mechanism of oil dilution by post injection to remove accumulated particulate matter on the diesel particulate filter of diesel engines. We developed a model to simulate post injection spray under low ambient gas pressure conditions. The model can predict the quantity of fuel mass adhered on the cylinder wall. The adhered fuel enters oil sump through the piston ring and cause oil dilution. The fuel in diluted oil evaporates during normal engine operations. We focus on the mechanism of fuel evaporation from diluted oil. The effects of engine speed and oil temperature on the evaporation were investigated. The results showed that the fuel evaporation rate increases with increasing engine speed and oil temperature. Furthermore, we developed an empirical model to predict the fuel evaporation rate of diluted oil through regression analysis with measured data.

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.

The recent growth of available computational resources has enabled the automotive industry to utilize unsteady Computational Fluid Dynamics (CFD) for their product development on a regular basis. Over the past years, it has been confirmed that unsteady CFD can accurately simulate the transient flow field around complex geometries. Concerning the aerodynamic properties of road vehicles, the detailed analysis of the transient flow field can help to improve the driving stability. Until now, however, there haven’t been many investigations that successfully identified a specific transient phenomenon from a simulated flow field corresponding to driving stability. This is because the unsteady flow field around a vehicle consists of various time and length scales and is therefore too complex to be analyzed with the same strategies as for steady state results.

Historically, greenhouse gas (GHG) emissions standards for vehicles have focused on tailpipe emissions. However, sound environmental policy requires a more holistic well-to-wheels (WTW) assessment that includes both production of the fuel and its use in the vehicle. The present research explores the net change in WTW GHG emissions associated with moving from regular octane (RO) to high octane (HO) gasoline. It considers both potential increases in refinery emissions from producing HO fuel and potential reductions in vehicle emissions through the use of fuel-efficient engines optimized for such fuel. Three refinery configurations of varying complexity and reforming capacity were studied. A set of simulations covering different levels of HO gasoline production were run for each refinery configuration.

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.

Social concern with traffic accidents caused by driver’s medical emergencies has been growing for the last several years. In Japan, the government issued technical guidelines in June 2016 to promote systems that deal with such accidents. Based on those guidelines, the Emergency Stop Assist system was manufactured in October 2017 to help reduce such accidents. This article first describes its purpose and core design, then presents an overview of the system, and finally discusses its effectiveness.