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The flight area of drones and other unmanned aerial vehicles (UAVs) had been highly restricted but has been relaxing, including flights beyond the scope of sight. Deregulation without aircraft-reliability improvement increases the risk of accidents. However, demanding high reliability for all aircraft leads to an increase in the price of the aircraft. Therefore, if airspace restrictions are relaxed for more reliable aircraft, the cost of higher reliability and its benefits can be balanced. This will improve efficiency and optimize cost-effectiveness. The purpose of this proposal is to balance the cost of aircraft-reliability improvement (which allows flight to continue in the event of a failure) and its advantages. Specifically, the author proposes rules that apply more relaxed airspace restrictions to UAVs with higher FCLs (Flight Continuity Possibility Levels) and stricter airspace restrictions to those with lower FCLs.

We propose low inductance batteries and enhance power density for a inverter. Conventionally, the capacitors are used for smoothing ripple of the inverter. The low inductance battery which responds at carrier frequency of inverter can reduce the capacity of the smoothing capacitors and enable to enhance power density for the inverter. For reducing the inductance, it is necessary to separately understand the impact of electrochemical reaction under wide range of assumed conditions and structural reaction on frequency characteristics. Furthermore, it is also necessary to design the low inductance batteries based on combining the both of characteristics. However, there are no study focusing on modeling by combining such different domains. Therefore, we made original inductance model inside battery considering frequency characteristics among all materials and structural influence with electromagnetic field analysis simulator.

Suspension is an important chassis part which is vital to ride comfort [1]. However, it is difficult to achieve our targeted comfortability level in a short time. Therefore, improving efficiency of damper development is our primary challenge. We have launched a project which aims to reduce the workload on developing dampers by introducing analytical approaches to the improvement of ride comfort. To be more specific, we have been putting effort into developing the damping force prediction, the vehicle dynamics prediction and subjective rating prediction. This paper describes subjective rating prediction method which output a subjective rating corresponding to the physical value of the vehicle dynamics with deep learning. As a result of verification using objective data which was not used for learning process, DNN (Deep Neural Network) prediction method could fairly precisely predict subjective rating of the expert driver.

To increase thermal efficiency of internal combustion engines, dilution combustion systems, such as lean burn and exhaust gas recirculation systems, have been developed. These systems require spark-ignition coils generating large discharge current and discharge energy to achieve stable ignition under diluted mixture conditions. Several studies have clarified that larger discharge current increases spark-channel stretch and decreases the possibility of spark channel blow-off and misfire. However, these investigations do not mention the effect of larger discharge current and energy on the initial combustion period. The purpose of this study was to investigate the relation among dilution ratio, initial-combustion period, and coil specifications to clarify the control factor of the dilution limit.

We have developed a cooperative simulation environment for multiple electronic control units (ECUs) including a parallel executions mechanism to improve the test efficiency of a system, which was designed with multiple ECUs for autonomous driving. And we have applied it to a power window system for multiple ECUs with a controller area network (CAN). The power window model consists of an electronic-mechanical model and a CPU model. Each simulator with a different executions speed operates in parallel using a synchronization mechanism that exchanges data outputted from each simulator at a constant cycle. A virtual ECU simulated microcontroller hardware operations and executed its control program step-by-step in binary code to test software for the product version. As co-simulation technology, a mechanism that synchronously executes heterogeneous simulators and a model of an in-vehicle communication CAN connecting each ECU were developed.

Predicting the vibration of a motor gearbox assembly driven by a PWM inverter in the early stages of development is demanding because the assembly is one of the dominant noise sources of electric vehicles (EVs). In this paper, we propose a simulation model that can predict the transient vibration excited by gear meshing, reaction force from the mount, and electromagnetic forces including the carrier frequency component of the inverter up to 10 kHz. By utilizing the techniques of structural model reduction and state space modeling, the proposed model can predict the vibration of assembly in the operating condition with a system level EV simulator. A verification test was conducted to compare the simulation results with the running test results of the EV.

This paper presents the “Virtual Failure Mode and Effects Analysis (vFMEA)” system, which is a high-fidelity electrical-failure-simulation platform, and applies it to the software verification of an electric power steering (EPS) system. The vFMEA system enables engineers to dynamically inject a drift fault into a circuit model of the electronic control unit (ECU) of an EPS system, to analyze system-level failure effects, and to verify software-implemented safety mechanisms, which consequently reduces both cost and time of development. The vFMEA system can verify test cases that cannot be verified using an actual ECU and can improve test coverage as well. It consists of a cycle-accurate microcontroller model with mass-production software implemented in binary format, analog and digital circuit models, mechanical models, and a state-triggered fault-injection mechanism.

In recent years, improvement of in-use fuel economy is required with tightening of exhaust emission regulation. We assume that one of the most effective solutions is ACC (Adaptive Cruise Control), which can control a powertrain accurately more than a driver. We have been developing a fuel saving ADAS (Advanced Driver Assistance System) application named “Sailing-ACC”. Sailing-ACC system uses sailing stop technology which stops engine fuel injection, and disengages a clutch coupling a transmission when a vehicle does not need acceleration torque. This system has a potential to greatly improve fuel efficiency. In this paper, we present a predictive powertrain state switching algorithm using external information (route information, preceding vehicle information). This algorithm calculates appropriate switching timing between a sailing stop mode and an acceleration mode to generate a “pulse-and-glide” pattern.

We developed the numerical simulation tool by using OpenFOAM® and in-house simulation codes for Gasoline Direct Injection (GDI) engine in order to carry out the precise investigation of the throughout process from the internal nozzle flow to the fuel/air mixture in engines. For the piston/valve motions, a mapping approach is employed and implemented in this study. In the meantime, the spray atomization including the liquid-columnbreakup region and the secondary-breakup region are simulated by combining the different numerical approaches applied to each region. By connecting the result of liquid-column-breakup simulation to the secondary-breakup simulation, the regions which have different physical phenomena with different length scales are seamlessly jointed; i.e., the velocity and position of droplets predicted by the liquid-column-breakup simulation is used in the secondary breakup simulation so that the initial velocity and position of droplets are transferred.

The problem of high fatal accident rates due to drunk driving persists, and must be reduced. This paper reports on a prototype system mounted on a car mock-up and a prototype portable system that enables the checking of the drivers’ sobriety using a breath-alcohol sensor. The sensor unit consists of a water-vapor-sensor and three semiconductor gas sensors for ethanol, acetaldehyde, and hydrogen. One of the systems’ features is that they can detect water vapor from human-exhaled breath to prevent false detection with fake gases. Each gas concentration was calculated by applying an algorithm based on a differential evolution method. To quickly detect the water vapor in exhaled breath, we applied an AC voltage between the two electrodes of the breath-water-vapor sensor and used our alcohol-detection algorithm. The ethanol level was automatically calculated from the three gas sensors as soon as the water vapor was detected.

A virtual power window control system was built in order to look into and demonstrate applications of microcontroller models. A virtual ECU simulated microcontroller hardware operations. The microcontroller program, which was written in binary digital codes, was executed step-by-step as the virtual ECU simulation went on. Thus, production-ready codes of ECUs are of primary interest in this research. The mechanical system of the power window, the DC motor to lift the window glass, the H-bridge MOSFET drivers, and the current sensing circuit to detect window locking are also modeled. This means that the hardware system of the control system was precisely modeled in terms of mechanical and circuit components. By integrating these models into continuous and discrete co-simulation, the power window control system was analyzed in detail from the microscopic command execution of the microcontroller to the macroscopic motion of the window mechanism altogether.

Highly automated driving systems have a responsibility to keep a vehicle safe even in abnormal conditions such as random or systematic failures. However, creating redundancy in a system to respond to failures increases the cost of the system, and simple redundancy cannot detect systematic failures because some systematic failures occur in each system at the same time. Systematic failures in automated driving systems cannot be verified sufficiently during the development phase due to numerous patterns of parameters input from outside the system. A safety concept based on a “safety sustainer” for highly automated driving systems is proposed. The safety sustainer is designed for keeping a vehicle in a safe state for several seconds if a failure occurs in the system and notifying the driver that the system is in failure mode and requesting the driver to take over control of the vehicle.

In previous study, a method of combustion detection for homogeneous charge compression ignition (HCCI) using a crank angle sensor and a knock sensor has been estimated [1]. In addition, an ion-current sensor has been used as a countermeasure against abnormal combustion with downsizing and higher compression ratio engines. An ion-current sensor has been newly adopted in engine systems. In this study, detection performance of combustion conditions in HCCI and spark ignition (SI) using with the ion-current sensor was estimated. The purpose of this study was to confirm detectable combustion conditions using with the ion-current sensor, and to confirm a requirement of applied voltage for the ion-current sensor. A detection signal of the ion-current sensor was changed by combustion style (HCCI,SI). Experimental results showed a heat release rate increased with ion signals increasing approximately at the same time in HCCI and SI.

A recent trend in high-pressure gasoline pumps is increasing the outlet pressure. One of the most important topics for increasing this pressure is improving volumetric efficiency. Therefore, the purpose of this research is to quantify the breakdown of efficiency loss factors and to suggest a new design for improving volumetric efficiency. Authors developed a method of quantifying the efficiency loss breakdown of high-pressure gasoline pumps by using 1D fluid pressure simulation results and conducting evaluation experiments regarding sensitivity. Authors separated pump movement into three phases; suction, compression, and delivery. Authors then investigated the loss factors in each phase. As a result, authors obtained an equation for predicting the final output volume. The equation consists of a limit output volume and other types of leakage volumes.

Recently emissions regulations are being strengthened. An air-fuel ratio cylinder imbalance causes emissions to increase due to universal exhaust gas oxygen (UEGO) sensor error or exhaust gas oxygen (EGO) sensor error. Various methods of reducing an air-fuel ratio cylinder imbalance have been developed. It is preferable for a control system to operate over a wide range of conditions. Our target is to expand the operating conditions from idling to high load conditions. Our approach is to use both an UEGO sensor and a crank angle sensor. A two-revolution frequency component calculated from the UEGO sensor output signal and angular acceleration calculated from the crank angle sensor output signal are used to identify the cylinder where the air-fuel ratio error occurs.

We have achieved injection quantity range enhancement by using the current waveform control technique for direct injection (DI) gasoline injectors. In this study, we developed an injection quantity simulator to find out the mechanism of non-linear characteristics. We clarified the non-linear production mechanism by using the simulator. This simulator is a one-dimensional simulator that incorporates calculation results from both unsteady electromagnetic field analysis and hydraulic flow analysis into the motion equation of this simulation code. We investigated the relation between armature and the injection quantity by using the simulator. As a result, we clarified that the non-linearity was produced by the bounce of the armature in the opening action. Thus, we found that it is effective to reduce the armature bounce to improve the linearity of the injection quantity characteristics.

In-vehicle networks are generally used for computerized control and connecting information technology devices in cars. However, increasing connectivity also increases security risks. “Spoofing attacks”, in which an adversary infiltrates the controller area network (CAN) with malicious data and makes the car behave abnormally, have been reported. Therefore, countermeasures against this type of attack are needed. Modifying legacy electronic control units (ECUs) will affect development costs and reliability because in-vehicle networks have already been developed for most vehicles. Current countermeasures, such as authentication, require modification of legacy ECUs. On the other hand, anomaly detection methods may result in misdetection due to the difficulty in setting an appropriate threshold. Evaluating a reception cycle of data can be used to simply detect spoofing attacks. However, this may result in false detection due to fluctuation in the data reception cycle in the CAN.

A technique of estimating particulate matter (PM) from gasoline direct injection engines is proposed that is used to compute mass density and particle number density of PM by using fuel mass in rich mixtures obtained by using non-combustion computational fluid dynamics (CFD). The CFD code that was developed by the authors employed a Cartesian coordinates system as a discretization method and large eddy simulation (LES) as a turbulence model. Fuel spray droplets were treated with the discrete droplet model (DDM). The code was verified with some experimental data such as those obtained from in-cylinder gas-flows with a laser Doppler velocimeter (LDV) and in-cylinder fuel concentration with laser induced fluorescence (LIF). PM emissions from a single-cylinder gasoline direct injection engine were measured with an electrical low pressure impactor (ELPI) to determine the model constants that were required in the estimation model.

The authors developed a multi-swirl type injector characterized by a short spray penetration length and fine atomization to improve exhaust emissions and fuel consumption for port fuel injection (PFI) gasoline engines. In PFI gasoline engines, fuel adhesion to an intake manifold causes exhaust emission. In addition, good mixing of fuel and air causes high combustion efficiency, and as a result the fuel consumption improves. Injectors therefore require two improvements: first, a short spray penetration to avoid fuel adhesion to the intake manifold, and second, a fine atomization spray to generate a good mixture formation of fuel and air. In this study, the authors developed a multi-swirl type injector equipped with multiple orifice holes featuring swirl chambers upstream of each orifice. The key feature of the proposed injector is “involute curve-formed swirl chambers” for generating a uniform thin liquid-film in the orifices.

The cooled EGR system has been focused on as a method for knocking suppression in gasoline engines. In this paper, the effect of cooled EGR on knocking suppression that leads to lower fuel consumption is investigated in a turbo-charged gasoline engine. First, the cooled EGR effect is estimated by combustion simulation with a knock prediction model. It shows that the ignition timing at the knocking limit can be advanced by about 1 [deg. CA] per 1% of EGR ratio, combustion phasing (50% heat release timing) at the knocking limit can be advanced by about 0.5 [deg. CA] per 1% of EGR ratio, and the fuel consumption amount can be decreased by about 0.4% per 1% of EGR ratio. Second, the effect of cooled EGR is verified in an experimental approach. By adding inert gas (N2/CO2) as simulated EGR gas upstream of the intake pipe, the effect of EGR is investigated when EGR gas and fresh air are mixed homogeneously. As a result, the ignition timing at the knocking limit is advanced by 7 [deg.