The potential of a sustainable fuel composed of lignin and ethanol for marine engines was investigated. A lignin-fuel with 44 wt% lignin, 50 wt% ethanol, and 6 wt% water was experimentally evaluated with a modified small-bore compression ignition engine. The ignition delay was measured and analyzed by the Arrhenius equation. The ignition quality of the lignin-fuel is similar to 1-pentanol (CN = 18.2), which is acceptable for large-scale 2-stroke marine engines. The spray formation was also evaluated from a view of the break-up mechanism and the Sauter-mean diameter, and proper atomization is expected in a large-scale 2-stroke marine engine condition.

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In the design of vehicle dynamics, suspension characteristics are a key design element. These characteristics include non-linear spring properties like bump rubbers, geometric properties of suspension arms, and compliance properties of rubber bushings. We have developed a suspension characteristic model that expresses these properties using mechanically meaningful parameters directly linked to the numerical values used in design. This model can be utilized in the design of vehicle dynamics. Furthermore, the parameters of this model, along with the characteristics of other components, have been accumulated in a database. We have established a Model-Based Development (MBD) environment for vehicle dynamics.

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Increasing the rigidity of the torque sensor is effective in enhancing vehicle responsiveness. However, since the rigidity of the system changes, the vibration transmission characteristics also change significantly. Therefore, we proposed EPS control that makes it possible to shape the vibration transmission characteristics. It was shown that the vibration transmission characteristics can be changed by extracting a specific frequency of the torque signal and adding the assistive current based on this signal. As a result, it was confirmed that the torque sensor can be made high rigidity and the vehicle responsiveness is enhanced.

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Generative Design is an artificial intelligence technology that presents thousands of solutions based on a given combination of criteria with the help of cloud computing. We applied the generative design approach to the gear structure of a diesel engine to eliminate the conflict between gear noise reduction and friction reduction. Sensitivity studies were conducted by the gear train CAE model which surrogates characteristic values to verify the validity of the input conditions for generative design. A generative design was performed, and a prototype was selected from a huge number of design results. The engine test results with prototype gears showed that the generative design was effective in reducing noise at the same level as conventional parts without deteriorating friction.

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In a driver assistance system, the information presented at inappropriate timing reduces reliability and acceptability. Therefore, we conducted a DS experiment to investigate the relationship between the necessity for notifying approaching bicycles and their position and speed. As a result, it was found that whether or not the notification is judged appropriate could be estimated by the TTC of the bicycle. it was also found that informing the reason is effective to help prevent distrust when the bicyclist changed his/her path after the notification.

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This paper describes a vehicle motion and driver behavior during tire blowout. Effects on vehicle motion and driver behavior differ depending on the position at which the tire blowout occurs. To investigate these effects, a movable driving simulator environment was constructed to simulate front-right, front-left, rear-left and rear-right tire blowouts respectively. The results of the experiments using this driving simulator confirmed that drivers reacted differently to front and rear tire blowouts. It was also confirmed that the driver's reaction differed depending on whether the vehicle was turning towards the oncoming lane or the side wall, and whether the burst tire was the steered wheel or not.

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The present study analyzes crossing incidents at intersections with motorcycles using the near-miss database recorded by drive recorders. Characteristics of crossing incidents with motorcycles are examined by comparing cases with passenger cars. The inversed time to conflict point (TTC−1) of the ego vehicle with motorcycles is significantly larger than case with passenger cars when the object vehicle appears at the intersection. The TTC−1 of motorcycles is also larger than the passenger cars. No significant difference in the TTC ratio between the ego and object vehicles is confirmed depending on the type of object vehicle.

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Driver monitoring systems (DMS) have been studied to discriminate driver status to assure driver safety and comfort. In this study, we focused on stress coping styles (active and passive coping) that people exhibit in response to external stimuli and explored the possibility that a DMS that discriminates stress coping styles could be useful in assessing driver safety and comfort. As a result of conducting driving experiments in several driving environments and analyzing hemodynamics, it was confirmed that stress coping styles clearly change due to the driving environment and driving characteristics, indicating that a DMS that discriminates stress coping styles may be useful in evaluating driver safety and comfort.

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The effects on autonomic nervous activity were investigated by supplying aromas to drivers whose alertness was impaired. α-pinene and limonene were compared with no-aroma control in a driving simulator. In the limonene-supply condition, autonomic indices suggested that mental tension might have been sustained, and the increase in the variance of respiratory frequency could be associated with maintained alertness. Physiological indices in the α-pinene condition showed the activation of sympathetic nervous activity, suggesting an enhancement in drivers’ efforts to maintain alertness. This study showed that supplying aroma might be effective in improving driver alertness.

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In order to provide timely attractive vehicles to customers, it is necessary to shorten and bring forward the prediction period for vehicle performance while ensuring quality. Aerodynamic noise is one of the important factors in vehicle product value, and it needs to be optimized during the exterior design phase of the vehicle. Aerodynamic noise refers to the phenomenon where sound waves (exterior wind noise) induced by turbulent flow around a vehicle, propagates and transmits inside the cabin and affects the passengers. Recently, an IDS (Intensity Density Source) method using CFD (Computational Fluid Dynamics) for exterior wind noise prediction has been proposed, however it requires a significant amount of simulation time to output the results. In this paper, a prediction method for exterior wind noise base on combination of generative AI and IDS data is proposed to solve the simulation time problem and its effectiveness is confirmed by numerical validation results.

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Due to the recent strengthening of noise regulations, vehicles with internal combustion engines are required to reduce exhaust noise. To improve development efficiency, we have been required to develop a technology that quantitatively predicts the pulsating noise caused by engine combustion and the airflow noise caused by exhaust gas flow, which constitute exhaust noise. Especially, the frequency characteristics of airflow noise could not be predicted before. This report describes the development of a technique for quantitatively predicting the frequency characteristics of exhaust airflow noise using a hybrid method that combines CFD (Computational Fluid Dynamics) and acoustic analysis.

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To enhance occupant protection in various crash configurations, it is essential to control the deceleration of the passenger compartment. In this study, the Energy-Derivative Method was extended to a car-to-car frontal collision for a simple structure model as well as full car models by expressing velocities in the centroid coordinate system. This method allows investigation of the contribution of each component of both cars toward controlling of the deceleration of the passenger compartment in car-to-car collisions.

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In the past, machine learning has been applied mainly to recognition functions in autonomous driving systems, but recently the scope of application has expanded to decision-making functions, and data-driven planners are attracting attention. Machine learning models constructed by learning to imitate the driving behavior of a normative driver have the potential to achieve the same or even better performance than conventional rule-based or model-based planners. However, data-driven planners do not work properly for vehicles with different specifications than the one used during training, and thus must be re-trained for each vehicle. Moreover, data acquisition for each vehicle is also required for re-training. The computational cost of re-training and the man-hours required for data acquisition are issues when using a data-driven planner in the product design phase. In this study, we propose a method that combines a data-driven planner and model following control to enable the application of the planner to vehicles with various specifications without re-training.

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Based on a cellular automaton model, the trade-off between an increase in flow and a destabilization of mixed traffic comprising connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs) were elucidated from the perspective of cluster dynamics. CAVs behave synchronously with multiple CAVs ahead via communication, while HDVs travel based on a single vehicle traveling immediately ahead. CAVs clustered, whereas HDVs formed congested flow beyond the critical density. As the CAV penetration rate increases and/or the connection range expands, the flow rate increases by cluster size expansion. On the other hand, HDV’s congestion induced the velocity fluctuation of the CAV cluster.

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Advanced mobility and services such as automated driving, CASE, and Mobility as a Service (MaaS) will revolutionize the automotive society and are said to be the once-in-a-century mobility revolution. These changes in social conditions have various effects not only on cars but also on road infrastructure. Further advancement of road space utilization and restructuring of road space to meet diverse needs are being considered. In urban smart cities utilizing MaaS and AI with automated driving technology, etc., it is expected that a variety of activities by people and a variety of moving vehicles such as automated vehicles and personal mobility vehicle (PMV) will be mixed in the road space. When introducing PMV into a road space where pedestrians and vehicles coexist, it is necessary to ensure not only safety but also acceptance of pedestrians. This study proposes a path planning algorithm that is acceptable to pedestrians. The relationship between the vehicle's position and pedestrian acceptability is represented as pedestrian sensitivity model. A path of autonomous vehicle is designed based on pedestrian sensitivity and risk potential models. The effectiveness of the proposed method is evaluated through simulation experiments.

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To prevent extreme climate events, it is urgent to reduce greenhouse gas emissions, and research on alternative fuels for internal combustion engines is necessary. In this study, diesel spray and flame characteristics were investigated when Fatty Acid Methyl Ester (FAME) was blended with Hydrotreated Vegetable Oil (HVO) in any ratio. As a result, blending HVO with FAME promoted atomization and improved evaporation, resulting in a lean mixture. However, the KL factor increases due to the dominant oxygen content, but can be reduced to the same level as a fuel blended with 20% FAME by increasing the injection pressure.

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The estimate method of mass combustion fraction 50% crank angle (MBF50%CA) using the amplitude of the 4th order sine component in the cylinder pressure (b₄) has been confirmed. As b₄ is period 90 degrees, the maximum cylinder pressure crank angle (θ_Pmax) of a general gasoline engine exists in the positive calculation section of the sine wave. When θ_Pmax is delayed, b4 value of calculation section including θ_Pmax of the b4 calculation process becomes smaller, and b₄ becomes smaller, and MBF50%CA becomes late. From the above, it is possible to estimate MBF50%CA by b₄.

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This study uses Large Eddy Simulation (LES) to analyze cycle-to-cycle variations in a single-cylinder engine under different fuel injection methods (port fuel injection (PFI) and direct injection (DI)) and air-fuel ratios. By focusing on fuel concentration near the spark plug and subsequent combustion variability, the study examines whether LES can accurately replicate these variations and how different mesh resolutions affect the results. This study demonstrated that LES could reproduce the impacts of different fuel injection methods and air-fuel ratios on cycle-to-cycle variations in fuel concentration and combustion. The resolution of the computational mesh influenced the accuracy of these simulations, suggesting that finer meshes provide more accurate representations of in-cylinder flows and fuel distributions.

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In this study, multi-cycle combustion simulations were conducted using Large Eddy Simulation (LES) to analyze cycle-to-cycle variations in a single-cylinder engine under varying conditions of fuel injection methods (port fuel injection (PFI) and direct injection (DI)), air-fuel ratio, and engine load. Specific focus was placed on the fuel concentration, velocity, and sub-grid scale (SGS) turbulence intensity near the spark plug at the spark timing, examining their relationship with cycle-to-cycle variations and subsequent combustion characteristics.

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In this study, we focused on the eco-driving speed change pattern of electric heavy-duty vehicles. In the first report, we performed optimization calculations for minimizing vehicle driving energy consumption starting from the energy output of vehicle batteries. We also used chassis dynamometer test data to verify the optimization results. In this paper, we report results of additional studies with consideration of the battery internal loss and the cable loss.

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Thermal management for the lithium-ion battery in the electric vehicle, which controls the battery temperature, is important to counteract degradation and overheating of the battery. To design an optimal thermal management system, it is effective to use a vehicle system 1D simulation model. In the previous report, we studied several thermal management systems for the battery electric vehicle and evaluated the battery warm-up performance at cold start of these systems based on this model. In this report, we improved the battery thermal model and these studied systems. Then we analyzed the battery cooling performance at high temperature and the quick charge performance at low temperature of each system.

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Motors, batteries, and air conditioning systems for xEVs are typically thermally managed via separate oil, water, and refrigerant pathways. A centralized system utilizing CO₂ refrigerant with electric insulation and low viscosity is being developed, enabling both direct cooling and lubrication. This approach demonstrated improvements in thermal management and a reduction in rotor agitation resistance. In addition to these advancements, the study revealed the need to address bearing wear during boundary lubrication and to enhance the cooling of the sliding parts in the mechanical seals of the motor shaft.

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To improve the performance prediction accuracy of surrogate models for exterior panels, a cycle that explores and detect a lack of training data in design space, creates new training data using 3D shape generation AI and CAE for the detected area and iteratively update the surrogate model is established. The process was applied to the surrogate model of hood outer rigidity performance and the prediction accuracy is improved．

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This study proposes a simple method for estimating increase of tire/road noise due to acceleration torque from the rigidity of tread block. The validity of the method is examined for tires with different tread block rigidity which were prototyped by adopting different tread compounds. For the calculation of tread block rigidity, finite element analysis or an analytical model based on mechanics of materials is utilized, and the temperature dependency of rubber stiffness is considered. Indoor tire-alone drum tests are conducted to determine the increase of noise due to acceleration torque. The results reveal that a negative correlation can be confirmed between the increase of tire/road noise due to acceleration torque and the tread block rigidity. Thus, tread block rigidity can be a simple and useful parameter to estimate the increase of tire/road noise due to acceleration torque.

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This study examines the Automated Driving System Marker Lamp, an external HMI for automated vehicles. A lamp unit capable of emitting blue-green light was installed on the vehicle’s front roof. An indoor evaluation experiment simulating nighttime conditions was conducted to assess the impact of luminous intensity, modulation patterns, and modulation cycle on the lamp’s visibility, annoyance, glare, and the perception of the direction-indicator. Results suggest that excessive luminous intensity or short modulation cycles increase both annoyance and glare, and impair the perception of the direction-indicator, suggesting these setting may be inappropriate.

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The purpose of this study is to establish resistance seam welding technology for stainless steel foil materials. To establish the technology, we investigated whether the joint strength was sufficient and stable (Target: 80% of base metal strength), and whether nuggets were formed in a continuous overlapping pattern. Sufficient and stable joint strength was achieved for all foil thicknesses tested (200 μm, 100 μm, 50 μm, 20 μm). The important parameters in resistance seam welding technology for foil materials are current value and curvature radius of electrode. For 200 μm and 100 μm thick foils, fracture pattern was base metal fracture. The fracture occurred at the softened zone near the welding zone. For 50 μm and 20 μm thick foils, fracture patterns were divided into base metal fracture and joint interface fracture. Differences in fracture patterns may cause variations in strength. As the thickness in the foil material decreases (50 μm, 20 μm), the nugget disappears and the continuity of the nugget in the welding zone is lost. Nuggets could not be formed even when the current value and curvature radius of electrode were changed. However, the target values for joint strength were also achieved for solid phase bonding.

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In this study, we attempted to reduce mold wear in CFRTP injection molding by mixing long and short fiber pellets with different fiber lengths to reduce CF content and maintain strength. In addition, the internal structure of the molded specimens was visualized using CT scans to clarify the relationship between internal molding defects, CF distribution and orientation, CF length, and tensile strength. Tensile strength increased with increasing CF content in 100% short fiber. For 100% long fibers, the strength increased with CF content from 10wt% to 15wt%, but the strength decreased at 20wt% from 10wt%. At content rates of 10, 15, and 20 wt%, internal defects (voids) and uneven distribution of CF occur in long-fiber CFRTP at a content rate of 20 wt%, resulting in a decrease in strength. No internal defects or uneven CF distribution were observed in the short-fiber CFRTP at any content rate. By mixing 50wt% long-fiber pellets and 50wt% short-fiber pellets in the molding process, the same tensile strength as that of 100% short-fiber pellets with a CF content of 20wt% could be secured at a CF content of 15wt%. Therefore, it was clear that mixing pellets can reduce CF content and ensure strength. Factors that contributed to the maintenance of strength by pellet blending were that the CF distribution became uniform while suppressing the generation of defects such as voids, and that the fiber length inside the specimen became longer by mixing long fibers in addition to short fibers.

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In this study, PVD coatings were applied to press dies for high-tensile steel with a tensile strength of 1470 MPa, the highest tensile strength among automotive steel sheets currently in use, to improve durability. Detailed observation of the die surfaces during the pressing process was conducted to clarify the damage mechanism and to create a highly durable PVD coating. The results obtained are shown below. Damage to PVD-coated molds during press working is caused by adhesion of the work material in the concave areas where droplets generated during film deposition are removed by polishing. The surface pressure increases at the adhesion area, and the load on the die locally increases, resulting in destruction of the coating. By coating the die surface with a PVD coating that has multiple layers and a gradient layer with a small difference in hardness, the load stress on the die during machining can be alleviated and damage can be suppressed.

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When the mastic hardens and shrinks during the painting process, sink mark caused by the retraction of the outer panel becomes an issue. Panel thickness is known to be a factor that affects sink mark, but the influence of design curvature has not been clarified. The influence of design curvature on sink mark was investigated by experiments using a laboratory model and CAE. It became clear that the effect of design curvature on distortion was greater than that of panel thickness.

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During collisions, base metals may incur ductile fracture without evident defects under complex stress conditions. Herein, we propose a test method for evaluating the propagation behavior of internal ductile cracks in metals under tensile stress conditions. The applicability of the developed specimen to the evaluation of ductile crack propagation properties was discussed based on the results of tensile fracture tests and finite element analyses. It is found that the energy dissipated to crack propagation per unit area was constant in the tensile stress state regardless stress triaxiality conditions.

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Software-defined vehicles (SDVs) enable flexible use-cases such as software update, hardware plug-and-play and fail-operational. The SDV requires reconfiguration of in-vehicle network upon change for communications of software. Software-defined network (SDN) is a technology enabling network reconfiguration. However, introducing SDN to SDV has an issue of confirming performance feasibility for multiple SDV use-cases. In this study, we implemented real-time network reconfiguration middleware resembling the architecture of standard automotive middleware on automotive-grade devices. Evaluation results showed that the performance satisfies or is expected to satisfy the requirements, and we got prospect for introducing SDN to SDV.

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