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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.

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.

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.

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.

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.

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.

Intersection collisions currently account for approximately one-fifth of all crashes and one-sixth of all fatal crashes in the United States. One promising method of mitigating these crashes and fatalities is to develop and install Intersection Advanced Driver Assistance Systems (I-ADAS) on vehicles. When an intersection crash is imminent, the I-ADAS system can either warn the driver or apply automated braking. The potential safety benefit of I-ADAS has been previously examined based on real-world cases drawn from the National Motor Vehicle Crash Causation Survey (NMVCCS). However, these studies made the idealized assumption of full installation in all vehicles of a future fleet. The objective of this work was to predict the reduction in Straight Crossing Path (SCP) crashes due to I-ADAS systems in the United States over time. The proportion of new vehicles with I-ADAS was modeled using Highway Loss Data Institute (HLDI) fleet penetration predictions.

Toyota Motor Corporation has been actively pursuing the development of fuel cell vehicles (FCVs) to respond to global environmental concerns and demands for clean energy. Toyota developed the first fuel cell (FC) bus to receive vehicle type certification in Japan. Subsequently, a new FC bus has been developed, which adopts two FC systems and four high-voltage batteries to achieve the required high power performance and durability. For enhanced durability, the FC system is controlled to maximize usage of the high-voltage batteries and to reduce the number of electric potential changes of the fuel cell. To accomplish this, the voltage of the FC stack must be kept high and FC power must be kept low. The high-voltage batteries were used to actively minimize FC power during acceleration.

This paper presents an improvement in the accuracy of NOx sensors at high NOx concentration regions by optimizing the manufacturing process, sensor electrode materials and structure, in order to suppress the deterioration mechanism of sensor electrodes. Though NOx sensors generally consist of Pt/Au alloy based oxygen pump electrodes and Pt/Rh alloy based sensor electrodes, detailed experimental analysis of aged NOx sensors showed changes in the surface composition and morphology of the sensor electrode. The surface of the sensor electrode was covered with Au, which is not originally contained in the electrode, resulting in a diminished active site for NOx detection on the sensor electrode and a decrease in sensor output. Theoretical analysis using CAE with molecular dynamics supported that Au tends to be concentrated on the surface of the sensor electrode.

To meet the strict emission regulations for diesel engines, an advanced processing device such as a Urea-SCR (selective catalytic reduction) system is used to reduce NOx emissions. The Real Driving Emissions (RDE) test, which is implemented in the European Union, will expand the range of conditions under which the engine has to operate [1], which will lead to the construction of a Urea-SCR system capable of reducing NOx emissions at lower and higher temperature conditions, and at higher space velocity conditions than existing systems. Simulations are useful in improving the performance of the urea-SCR system. However, it is necessary to construct a reliable NOx reduction model to use for system design, which covers the expanded engine operation conditions. In the urea-SCR system, the mechanism of ammonia (NH3) formation from injected aqueous urea solution is not clear. Thus, it is important to clarify this mechanism to improve the NOx reduction model.

This report describes the implementation of the spark channel short circuit and blow-out submodels, which were described in the previous report, into a spark ignition model. The spark channel which is modeled by a particle series is elongated by moving individual spark particles along local gas flows. The equation of the spark channel resistance developed by Kim et al. is modified in order to describe the behavior of the current and the voltage in high flow velocity conditions and implemented into the electrical circuit model of the electrical inductive system of the spark plug. Input parameters of the circuit model are the following: initial discharge energy, inductance, internal resistance and capacitance of the spark plug, and the spark channel length obtained by the spark channel model. The instantaneous discharge current and the voltage are obtained as outputs of the circuit model.

The use of exhaust gas recirculation (EGR) in spark ignition engines has been shown to have a number of beneficial effects under specific operating conditions. These include reducing pumping work under part load conditions, reducing NOx emissions and heat losses by lowering peak combustion temperatures, and by reducing the tendency for engine knock (caused by end-gas autoignition) under certain operating regimes. In this study, the effects of EGR addition on knocking combustion are investigated through a combined experimental and modeling approach. The problem is investigated by considering the effects of individual EGR constituents, such as CO2, N2, and H2O, on knock, both individually and combined, and with and without traces species, such as unburned hydrocarbons and NOx. The effects of engine compression ratio and fuel composition on the effectiveness of knock suppression with EGR addition were also investigated.

Environmental awareness has increased on a global scale which pushed for a heavier demand for weight reduction and high transmission efficiency on manual transmissions (hereafter referred to as the “MT”) in improving vehicle driving and fuel economy performance. Comfortable shift feel is also continuously in demand by the customer because its sensitive performance can be directly recognized by the driver which may determine the transmission’s merchantability. The newly developed 6-speed MT (hereafter referred to as the “6MT”) has achieved size reduction (compact size), weight reduced, better fuel efficiency, and improvement in the shift feel which will continue to maintain its’ competitiveness in the future.

Ongoing research on active stabilizers involves not only control of the roll angle of the vehicle based on steering input but also improving ride comfort by reducing roll vibration caused by the antiphase road surface input. In that context, roll skyhook control, which applies skyhook theory to provide feedback on the vehicle roll and drive the actuators, has already been presented. Although vibration in all frequency bands can be reduced if there is no control delay, time lags or phase delays in control elements such as the communication, computation, low-pass filter, or actuators can amplify vibration. Consequently, a sufficient effect of controlling cannot be obtained. This paper will address wheelbase filtering, which produces a frequency that minimizes roll oscillation, and is used to suppress the influence of the undesirable vibration.