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This study investigates if particulates from a GDI engine can be significantly suppressed by use of ultra-high injection pressures under 2 different engine conditions known to be associated with high particulate numbers (PN): warm-up and transients. Experiments were carried out in a single-cylinder GDI engine equipped with an endoscope connected to a high-speed camera to enable combustion visualization. To mimic the warming-up, the coolant temperature was varied between 20 °C and 90 °C. A Diesel injector with modified nozzle was used and the injection pressures were varied between 400 and 1500 bar. The results revealed that increasing the fuel injection pressure decreased engine out HC and PN under warming-up conditions. However, the coolant water temperature was the most dominant factor affecting the emissions. For coolant temperature of 20 °C, the use of 1500 bar fuel injection pressure in comparison to lower fuel pressures resulted in significantly lower PN.

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.

The use of EVs has accelerated worldwide to reduce CO2 emissions and reduce environmental impact. One of the major issues for EVs is their decrease of electric range during heating in winter. This is because the HVAC (Heating Ventilation and Air Conditioning) system with electric heater or HP (Heat pump) system consumes a lot of energy to warm the occupants. That's why many vehicle OEMs consider not only convection heating system with HVAC, but also other systems such as contact heating devices, seat heaters, or steering wheel heaters. Those devices are more efficient at warming the human body, therefore the heating system can save energy and improve the occupant’s comfort. In this paper, the thighs, knees and shins, which cannot be heated by a seat heater, are heated more efficiently than by convection heating by using a high-temperature radiant heater in which the surface heats up to 100°C or more in 1 minute.

The distributed parameter method is used to establish the dynamic simulation model of the electric vehicle thermal management system and various parts, and the finite difference method is used to solve the model. A thermal management system model with same structure is established by AMESIM, and the accuracy of the dynamic simulation model is verified by comparing the deviation of the calculation result between this dynamic simulation model and AMESIM. Based on the established model, the influence of expansion valve opening on the temperature of battery pack and the influence on the heating comfort of the cabin were studied. A control strategy for the rapid cooling of the battery pack was proposed. The results show that the model established by the distributed parameter method provides quite well agreement with commercial equivalent software and can well reflect the flow state of the refrigerant in different zones of the same component.

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.

Growing trends of connected and autonomous vehicles have pushed for increased resolutions of cameras to 8Mpix and displays to 4K/8K, leading to requirements for high-speed interfaces that support 10Gbps and beyond. Unlike data center or enterprise networks which normally operates under controlled indoor environments, in-vehicle networks are required to operate in harsh temperature and interference environments. Due to cost restrictions, the use of single pair wire is prevalent for in-vehicle networks. In general, as data transmission speed increase, signal spectrum spreads across greater frequency range. Since insertion loss of a channel increases in proportion to signal frequency, it becomes more difficult to secure SNR (signal-to-noise ratio) margins as bit rate increases. This makes it increasingly difficult for a device (e.g. ECUs, sensors, and displays) with high-speed communication interface to meet EMC (electromagnetic compatibility) criteria imposed by automotive OEMs.

An important function of an Automated Driving (AD) system is to detect objects including vehicles and pedestrians on the road. Typical devices for detecting those objects include cameras, millimeter-wave RADAR, and light detection and ranging (LiDAR). LiDAR uses the flight time of a short-wavelength electromagnetic wave. Because of that LiDAR is expected to find even small objects such as tire fragments on a road in high resolution. The detection performance required for LiDAR depends on the operational design domain (ODD). For example, while a vehicle is travelling at high speeds, LiDAR needs to detect apparently small objects at long distances, and while it is travelling at low speeds, LiDAR has to detect objects over a wide angular range. Conventional LiDAR is developed to satisfy all requirements, providing performance including detection distance, resolution, and angle of view tends to expose issues such as cost and size when it is mounted onboard.

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.

Recent developments in the commercialization of mobility services have brought unprecedented connectivity to the automotive sector. While the adoption of connected features provides significant benefits to vehicle owners, adversaries may leverage zero-day attacks to target the expanded attack surface and make unauthorized access to sensitive data. Protecting new generations of automotive controllers against malicious intrusions requires solutions that do not depend on conventional countermeasures, which often fall short when pitted against sophisticated exploitation attempts. In this paper, we describe some of the latent risks in current automotive systems along with a well-engineered multi-layer defense strategy. Further, we introduce a novel and comprehensive attack and performance test framework which considers state-of-the-art memory corruption attacks, countermeasures and evaluation methods.

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.

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.

Global exhaust emission regulations are becoming stricter, and vehicles equipped with the idle stop system (ISS) are increasing. Recently, starters for vehicles equipped with ISS are required to improve operation feel when speedily restarted. To satisfy this demand, starters must rotate at higher rotational speeds, and heavier wear in their brushes may cause problems. Tribofilm formed on commutators surface by the brush sliding is an important factor in the brush commutator wear, because tribofilm is said to have a property to increase lubricity and decrease mechanical wear in brushes and commutator, as well as to improve commutation and decrease arc wear. Therefore, for reducing brush commutator wear, it is considered effective to promote film formation by improving materials. However, few researches have been conducted to evaluate the relationship between brush materials and tribofilm formation.

It is important that Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (AD) detect on-road objects, road vehicles and pedestrians. The typical detection devices mounted on ADAS and AD include a camera, a millimeter-wave radar and a Light Detection And Ranging (LiDAR). Since LiDAR can obtain accurate distance and fine spatial resolution due to its short wavelength, it is expected that small objects such as a tire can be detected. However, the conventional LiDAR is equipped with multiple light transmitters and light receivers such as avalanche photo diodes. This causes LiDAR system to be expensive and large in size. Aiming to reduce the cost and size of LiDAR, we employed Single-Photon Avalanche Diode (SPAD) which can be fabricated by CMOS process and easily arrayed. We also developed “Single Chip SPAD Array“ in which the two-dimensional array of SPAD and a signal processing block of range calculation were integrated into a single chip.

With the global adoption of diesel common rail systems and the wide variation in composition of local commercial fuels, modern fuel injection systems must be robust against diverse fuel properties. To bridge the knowledge gap on the effects of compositional variation for real commercial fuels on spray combustion characteristics, the present work quantifies ignition and soot formation/oxidation in three unique, international diesel blends. Schlieren imaging, excited-state hydroxyl radical (OH*) chemiluminescence imaging and diffused back-illumination extinction imaging were employed to quantify vapor penetration, ignition, and soot formation and oxidation for high-pressure sprays in a constant-volume, pre-burn chamber. The three fuels were procured from Finland, Japan and Brazil and have cetane numbers of 64.1, 56.1 and 45.4, respectively.