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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.

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.

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.

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.

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.

Vehicle rollovers are one of the more severe crash modes in the US - accounting for 32% of all passenger vehicle occupant fatalities annually. One design enhancement to help prevent rollovers is Electronic Stability Control (ESC) which can reduce loss of control and thus has great promise to enhance vehicle safety. The objectives of this research were (1) to estimate the effectiveness of ESC in reducing the number of rollover crashes and (2) to identify cases in which ESC did not prevent the rollover to potentially advance additional ESC development. All passenger vehicles and light trucks and vans that experienced a rollover from 2006 to 2015 in the National Automotive Sampling System Crashworthiness Database System (NASS/CDS) were analyzed. Each rollover was assigned a crash scenario based on the crash type, pre-crash maneuver, and pre-crash events.

Previous studies and recent practical aerodynamic evaluations have shown that aerodynamic drag of passenger vehicles with “ground simulation” with moving ground and rotating wheels may increase in some cases and decrease in other cases relative to the fixed ground and stationary wheel conditions. Accordingly, the effects of the ground simulation on the aerodynamic drag should be deeply understood for further drag reduction. Although the previous studies demonstrated what is changed by the ground simulation, the reason for the change has not been fully understood. In this article, the effects of wheels and wheel houses attachment and those by the ground simulation with ground movement and wheel rotation on the aerodynamic drag were investigated by quantification of the underfloor flow that plays a crucially important role on the formation of vortical structure around vehicles.

Roadway departure mitigation systems for helping to avoid and/or mitigate roadway departure collisions have been introduced by several vehicle manufactures in recent years. To support the development and performance evaluation of the roadway departure mitigation systems, a set of commonly seen roadside surrogate objects need to be developed. These objects include grass, curbs, metal guardrail, concrete divider, and traffic barrel/cones. This paper describes how to determine the representative color of these roadside surrogates. 24,762 locations with Google street view images were selected for the color determination of roadside objects. To mitigate the effect of the brightness to the color determination, the images not in good weather, not in bright daylight and under shade were manually eliminated. Then, the RGB values of the roadside objects in the remaining images were extracted.

In emerging markets, Port Fuel Injection (PFI) technology retains a higher market share than Gasoline Direct Injection (GDI) technology. In these markets fuel quality remains a concern even despite an overall improvement in quality. Typical PFI engines are sensitive to fuel quality regardless of brand, engine architecture, or cylinder configuration. One of the well-known impacts of fuel quality on PFI engines is the formation of Intake Valve Deposits (IVD). These deposits steadily accumulate over time and can lead to a deterioration of engine performance. IVD formation mechanisms have been characterized in previous studies. However, no test is available on a state-of-the-art engine to study the impact of fuel components on IVD formation. Therefore, a proprietary engine test was developed to test several chemistries. Sixteen fuel blends were tested. The deposit formation mechanism has been studied and analysed.

This study used finite element (FE) simulations to analyze the injury mechanisms of driver spine fracture during frontal crashes in the World Endurance Championship (WEC) series and possible countermeasures are suggested to help reduce spine fracture risk. This FE model incorporated the Total Human Model for Safety (THUMS) scaled to a driver, a model of the detailed racecar cockpit and a model of the seat/restraint systems. A frontal impact deceleration pulse was applied to the cockpit model. In the simulation, the driver chest moved forward under the shoulder belt and the pelvis was restrained by the crotch belt and the leg hump. The simulation predicted spine fracture at T11 and T12. It was found that a combination of axial compression force and bending moment at the spine caused the fractures. The axial compression force and bending moment were generated by the shoulder belt down force as the driver’s chest moved forward.

This paper deals with friction under wet condition in the disk brake system of automobiles. In our previous study, the variation of friction coefficient μ was observed under wet condition. And it was experimentally found that μ becomes high when wear debris contains little moisture. Based on the result, in this paper, we propose a hypothesis that agglomerates composed of the wet wear debris induce the μ variation as the agglomerates are jammed in the gaps between the friction surfaces of a brake pad and a disk rotor. For supporting the hypothesis, firstly, we measure the friction property of the wet wear debris, and confirm that the capillary force under the pendular state is a factor contributing to the μ variation. After that, we simulate the wear debris behavior with or without the capillary force using the particle-based simulation. We prepare the simulation model for the friction surfaces which contribute to the friction force through the wear debris.

The present paper describes an application of non-parametric shape optimization to disc brake squeal phenomena. A main problem is defined as complex eigenvalue problem in which the real part of the complex eigenvalue causing the brake squeal is chosen as an objective cost function. The Fre´chet derivative of the objective cost function with respect to the domain variation, named as the shape derivative of the objective cost function, is evaluated using the solution of the main problem and the adjoint problem. A selection criterion of the adoptive mode number in component mode synthesis (CMS), which is used in the main problem, is presented in order to reduce the computational error in complex eigenvalue pairs. A scheme to solve the shape optimization problem is presented using an iterative algorithm based on the H1 gradient method for reshaping. For an application of the optimization method, a numerical example of a practical disc brake model is presented.

It is important for vehicle concept planning to estimate fuel economy and the influence of vehicle vibration in advance. This can be accomplished using virtual engine specifications and a virtual vehicle frame. In this paper, I will show the power plant model with electric starter and battery that can predict fuel economy, combustion heat results and transient torque. The power plant is a 1.3L 4cyl designed for NA Spark Ignition. The power plant model was realized using an energy based model using VHDL-AMS. Here, VHDL-AMS is modeling language stored in IEC international standard (IEC61691-6) and can realize multi physics in 1D simulation. The modeling language supports electrical, magnetic, thermal, mechanical, fluidic and compressive fluidic domains. The model was created in house using VHDL-AMS and validated on ANSYS SIMPLORER. The simulated results of fuel energy consumption agreed with driving energy and amount of energy losses, e.g. cooling loss, exhaust loss.

Toyota Motor Corporation is developing a series of engines belonging to its ESTEC (Economy with Superior Thermal Efficient Combustion) development concept. This paper describes the development of 8NR-FTS after the subsequent launch of the 2.0-liter DI Turbocharged 8AR-FTS. 8NR-FTS is a 1.2-liter inline 4-cylinder spark ignition downsized turbocharged direct injection (DI) gasoline engine. By following the same basic concepts as 8AR-FTS engine [1], the 8NR-FTS incorporates various fuel efficient technologies such as a cylinder head with an integrated exhaust manifold, the Atkinson cycle using the center-spooled variable valve timing with mid-position lock system (VVT-iW), and intensified in-cylinder turbulence to achieve high-speed combustion.

In recent years, enhancing engine thermal efficiency is strongly required. Since the maximum engine thermal efficiency is especially important for HVs, the technologies for improving engine thermal efficiency have been developed. The current gasoline engines for hybrid vehicles have Atkinson cycle with high expansion ratio and cooled exhaust gas recirculation (EGR) system. These technologies contribute to raise the brake engine thermal efficiency to more than 38%.In the near future the consumers demand will push the limit to 40% thermal efficiency. To enhance engine thermal efficiency, it is essential to improve the engine anti-knock quality and to decrease the engine cooling heat loss. To comply with improving the anti-knock quality and decreasing the cooling heat loss, it is known that the cooled EGR is an effective way.

Improving the engine efficiency to respond to climate change and energy security issues is strongly required. In order to improve the engine efficiency, lower fuel consumption, and enhance engine performance, OEMs have been developing high compression ratio engines and downsized turbocharged engines. However, higher compression ratio and turbocharging cause cylinder pressure to increase, which in turn increases the demand voltage for ignition. To reduce the demand voltage, a new ignition system is developed that uses a high voltage Zener diode to maintain a constant output voltage. Maintaining a constant voltage higher than the static breakdown voltage helps limit the amount of overshoot produced during the spark event. This allows discharge to occur at a lower demand voltage than with conventional spark ignition systems. The results show that the maximum reduction in demand voltage is 3.5 kV when the engine is operated at 2800 rpm and 2.6 MPa break mean effective pressure.