For further improvement of thermal efficiency in super-lean burn spark ignition (SI) engine by reducing unburned loss, it is important to clarify unburnt hydrocarbon emission mechanism. In this study, time-resolved total hydrocarbon (THC) mole fraction measurement by a fast response flame ionization detector (FFID) was conducted in super-lean burn single cylinder SI engine with port fuel injection, Gas sampling probes were mounted at about 47 mm (upstream measurement position) and 209 mm (downstream measurement position) downstream from the exhaust valve. This study investigated the relationship between THC emissions and combustion characteristics in each cycle of a super-lean burn spark ignition engine, in order to clarify the mechanism of unburned hydrocarbon (UHC) formation and emissions. The THC in the cylinder were measured by placing the tip of the FFID probe at two locations with the distances from wall surface of 0 mm and 5 mm. The results showed that the THC mole fraction at 5 mm from the wall was strongly affected by CA90, while the effect of CA90 on THC was weak at 0mm from the wall. Furthermore, the measured THC in exhaust pipe only in stable combustion cycles were extracted and related with the operation conditions of the engine. Even by excluding the measured data of misfire cycles, the results showed THC emissions in each cycle increase with the increase in excess air ratio between 1.0 and 2.0.
The progressive depletion of petroleum-based energy reserves, coupled with the intensifying threat of global climate change, has catalyzed an urgent global imperative to explore alternative energy sources to conventional fossil fuels. Although electric vehicles offer a promising solution by eliminating tailpipe emissions, their widespread adoption is hindered by range anxiety and insufficient charging infrastructure. In contrast, Hybrid Electric Vehicles can serve as a practical transitional technology, offering improved fuel economy with reduced emissions by operating both the IC engine and electric motor in their best efficiency regions. The process of developing HEVs involves various modeling techniques, including Model-in-Loop (MiL), Software-in-Loop (SiL), and Hardware-in-Loop (HiL). In MiL, kinematic, quasi-static, and dynamic modeling techniques are employed to develop accurate digital twins and predict vehicle behavior across various driving conditions. This study introduces a novel hybrid simulation framework that integrates forward and backward simulation techniques to develop a full-parallel P3-type hybrid electric vehicle architecture. The input data for the developed model are generated from actual power plant testing to improve the system's accuracy. The proposed framework facilitates a comprehensive evaluation of energy management strategies and fuel consumption metrics. A deterministic, rule-based control algorithm incorporating PID tuning and engine start-stop functionality is developed to optimize power distribution between the internal combustion engine and the electric motor. Simulation results demonstrate a fuel-efficiency improvement of approximately 30% relative to a conventional baseline vehicle, with strong correlation with experimental data, with deviations of 4–6%.
This study focused on the driver's brain activity in the prediction phase, which is the stage before recognition, and analyzed the effects of superior or inferior brain activity on recognition and driving performance on curved roads through a driving simulator experiment. A circuit course was set up, and changes in cerebral blood flow and gaze distribution were analyzed across 10 corners, together with the average of lane deviation in the first third of each curve. The results showed that activation of the frontal lobe during the prediction phase led to faster curve recognition and reduced lane departure in the curve sections. Overall, it was found that activation of the frontal lobe in the prediction phase improves both curve recognition and driving performance on curved roads.
One of the longest and most complex quality control procedures during high-voltage battery production is the leak test that must be performed on all batteries. The test consists of applying a controlled pressure variation inside the battery. To improve this process, a CFD model was developed, and the different phases of the test were numerically reproduced and then compared with experimental data. The numerical model was used in a simplified setup for validation and then implemented to the battery. The flow inside the battery was studied to identify impacting factors like geometry to increase the test accuracy in future work.
Personal mobility supports social participation and well-being, yet highway merging remains stressful and risky. Driving behavior is influenced by prediction errors between actual traffic and drivers' predictions. We hypothesized that designing traffic environments to improve driver prediction accuracy can effectively assist drivers. We previously proposed position-triggered speed management using Cooperative Adaptive Cruise Control (CACC) vehicles near highway merging sections to smooth merging by reducing speed differentials between merging and mainline vehicles. However, empirical validation of speed differentials' impact on drivers' merging experience was lacking due to no methods for evaluating human interactions in mixed traffic involving human-driven vehicles (HDVs) and CACC vehicles. To address this, we developed a networked multi-driver driving simulator (NMDDS) allowing ten participants to interact in a virtual merging environment, essential for studying complex driver dynamics in real traffic. Experimental results showed CACC speed management effectively moderated mainline HDV speeds near merging sections, with effectiveness depending on CACC penetration rate. We clarified how personality traits relate to subjective evaluations like perceived danger and merging difficulty. Structural Equation Modeling (SEM) also indicated relative speed between merging and mainline vehicles significantly influences these factors. These findings empirically support that reducing speed differentials improves merging comfort and safety. Future research will implement methods to further minimize speed differentials, including managing merging vehicle speeds and refining adaptive speed control strategies, aiming to enhance safety and driver intuitiveness during highway merging.
The integration of Advanced Driver Assistance Systems (ADAS) within modular robotic frameworks presents significant opportunities for scalable autonomous mobility research. This paper introduces a comprehensive ROS 2 Humble-based simulation framework for implementing and evaluating core ADAS functionalities-Lane Keeping Assist (LKA), Adaptive Cruise Control (ACC), Forward Collision Warning (FCW), and Automatic Emergency Braking (AEB)-on a differential-drive robotic platform modeled in Gazebo. Our architecture uniquely combines multi-sensor perception, a dual-SLAM strategy (2D mapping via SLAM Toolbox and dense 3D reconstruction/localization via RTAB-Map), and real-time navigation control using Nav2 within a unified, extensible ROS 2 node graph. The system integrates (i) a URDF/Xacro vehicle equipped with LiDAR, RGB and depth cameras for multimodal perception; (ii) modular AI perception pipelines for lane detection, object recognition and obstacle classification; (iii) control-theoretic ADAS implementations (PID and MPC) for lateral stabilization and adaptive longitudinal regulation; (iv) a Time-to-Collision (TTC) safety module enabling predictive FCW/AEB; and (v) a hierarchical arbitration mechanism that dynamically interfaces with and-under safety-critical conditions-overrides the navigation stack. The architecture is designed with clearly defined topic interfaces, deterministic message flows, and fail-safe prioritization logic to meet real-time constraints; simulation experiments in the Gazebo environment demonstrate robust behavior across dynamic obstacle scenarios, sensor noise profiles and timing stresses. Finally, a computational feasibility analysis on the Raspberry Pi 4 highlights trade-offs between modular and monolithic architectural strategies in latency, scalability and maintainability.
In Japan, the prevention of traffic accidents on community roads, which frequently have poor visibility at intersections, is an ongoing issue. As a solution to this issue, we focus on Vehicle-to-Network-to-Vehicle (V2N2V) safety systems, which use cellular networks and allow vehicles to share information over a wider area. In V2N2V safety systems, vehicles provide their self-localization data and local observations of surrounding traffic participants to a central server, and then the system predicts potential collisions for other vehicles based on the provided information. However, in the coordinate transformation of information from vehicles, the system must consider two sources of uncertainty: (1) self-localization uncertainty of the vehicle and (2) local observation uncertainty of the surrounding participants. Based on this motivation, we propose a framework that integrates these two uncertainties in coordinate transformation and then estimates the global state of the traffic participant observed by the vehicle. We validate this framework through both simulation and real-world experiments under conditions that assume on-board sensors of widespread commercial vehicles. In the simulation, we compare the proposed framework with a baseline that does not consider self-localization uncertainty. The results demonstrate that incorporating self-localization uncertainty is crucial for appropriately estimating the overall uncertainty after the coordinate transformation under the assumed on-board sensor setup. In addition, results of real-world experiments demonstrate that the framework can integrate states with appropriate uncertainty. The proposed framework will enable a broader range of vehicles to provide information in V2N2V-based safety systems and contribute to more comprehensive road safety.
This study proposes a novel scheme for the simultaneous optimization of degradation parameters and the selection of degradation scenarios for Li-ion battery cells based on capacity retention data. The proposed method identifies the most probable degradation mechanisms through posterior probability. Validation using synthetic data confirmed the scheme's ability to accurately select intended models, such as thin film growth in anode and structural transitions in cathode. Furthermore, the scheme demonstrated robust extrapolation performance, predicting future capacity fade. This framework provides a powerful tool for identifying battery health states and predicting long-term reliability in practical automotive applications.
In developing countries, small electric vehicles (SEVs) offer a promising solution for crowded cities by helping to solve traffic problems. One of the most critical challenges in the deployment of SEVs is the charging infrastructure, which involves both topology and technology considerations. This paper presents a model of an onboard charger that integrates a DC/DC converter with a battery system for SEVs. The proposed model focuses on low-cost implementation. Flyback converters, commonly used in household appliances, are selected for the DC/DC conversion stage. The battery pack consists of 60 lithium-ion cells connected in series. An analysis model is developed by combining the flyback converter model with a first-order Thevenin equivalent battery model. Experimental hardware was assembled to obtain parameter values for the analysis model. Additionally, a neural network model was trained using a dataset generated from the analysis model. The outputs of both models - charging current and output voltage - were compared. The results demonstrate that the neural network model accurately represents the charging current and output voltage under varying duty cycles, source voltages, and battery open-circuit voltages.
Electric vehicles (EVs) have undergone a significant transformation recently in an effort to get greater attention from the global automotive industry. Battery packs are considered as the key components that govern the performance, range, and cost of EVs. Thus, creating and designing battery packs for EVs have become important areas of research as well as growth in the automotive industry. Normal battery design for EVs sometimes lacks scalability, cost-effectiveness, and flexibility. However, the modular design for the battery pack in EVs will provide a low cost for the manufacturing and maintenance of EVs. It also enables the customization of the battery pack to suit the specific needs of the EV model. This paper provides a thorough overview of the design elements of EV battery pack modularity, metrics for evaluating EV battery pack modularity, and the main advantages of modular design. Their consistent findings indicate that the energy density of modular design of battery packs in EVs is superior when compared with ordinary existing battery designs. Not only energy density, there are other significant metrics like cost, cycle life, and power density that optimize the modular design performance..
This study explores the impact of frequent handovers on connected car communication, focusing on high-speed environments like highways. Data from Tokyo highways show significant latency spikes and packet loss during handovers, degrading real-time applications. While Multi-access Edge Computing (MEC) reduces latency in wired segments, it is less effective for wireless networks. Carrier aggregation is a key strategy to improve network stability by leveraging asynchronous handover timings across multiple carriers. The findings highlight the need for advanced handover management and future integration of next-generation networks to ensure reliable communication for connected vehicles.
In this study, an accident risk evaluation method for curves with a curvature radius of 50 m or more was developed using Digital Road Map, which provide information on the road alignment except for cross-fall of road. The analysis was conducted on national and prefectural roads around Suwa City and Chino City in Nagano Prefecture, and its validity was verified. The results showed that the accident risk index by this accident risk assessment method and the accident rate, which is the number of accidents divided by the number of curves, are positively correlated. This indicates that the accident risk index for curves can evaluate the accident rate for curves with a radius of curvature of 50 m or more. However, for sharp curves with a radius of curvature of less than 50 m, no correlation was observed. There is room for further study for sharp curve, such as consideration of real-vehicle speed.
In an aging society, electric wheelchairs have become indispensable assistive devices for older adults with physical limitations, helping them maintain and improve their quality of life by enabling activities such as shopping, visiting hospitals, and participating in the local community. This study presents an intuitive human-machine interface (HMI) for situation awareness by vibration stimulation using tactile apparent motion for handle-type electric wheelchairs. The HMI has a total of eight vibration devices attached to the handle of the electric wheelchair, two at the front, two at the rear, and two on each side. Experiments were conducted at an intersection with vehicles approaching from four directions (left, right, front, and rear), and the correct response rates and response times were compared across three conditions: tactile apparent movement, normal vibration, and no vibration. Results showed that the correct response rate for tactile apparent motion in the left-right scenarios were as high as or higher than that in the no vibration condition, in which the surrounding situation was perceived visually. Similarly, response times for the tactile apparent motion condition in the left-right scenarios were the fastest compared to the normal vibration and no vibration conditions. Additionally, the result of a five-point subjective evaluation regarding sense of security granted by tactile apparent motion and normal vibration showed a significantly higher score for tactile apparent motion. These findings suggest that tactile apparent motion is particularly effective for alerting users of the presence of vehicles approaching from the left and right.
Reducing aerodynamic drag is crucial to improve the efficiency of vehicles. In this study, the feasibility of releasing the cooling flow into wake region to enhance automobile’s aerodynamic drag performance was investigated. A realistic general vehicle model for aerodynamic study was used as an investigation target, and a parametrically optimized cooling ventilation duct which leads to vehicle’s base surface, including the radiator, was equipped to reduce the aerodynamic drag. Compared with the original sample in aerodynamic ideal configuration which shut out the radiator ventilation, the modified one shows a similar level of drag but with the cost of losing half radiator ventilation volume compared to the original model in baseline configuration. By comparing the flow field around samples during the optimization process and original model, the mechanism between the cooling flow which in wake region and the drag performance variation was primarily discussed.
In order to accurately predict the impact condition of each body region to the car surface from the initial impact condition between the car and powered two wheeler, the influence of the initial impact condition on the trajectory of the driver of the powered two wheeler was investigated by using FE collision simulations. Impact angle, and velocities of the both car and powered two wheeler were used as the parameter to define the initial impact condition. The trajectories of the Head, T1, Upper Thorax and Pelvis and velocities of these body parts were obtained from the time history of the coordinate data of the FE collision simulations. The trajectories of each body region showed following tendency: 1) The influence of impact angle, car velocity and powered two wheeler velocity on the trajectories became larger as the initial location of body parts was lower. Specifically, the Head moves approximately straight along with initial velocity direction until the Head impacts to the car. 2) Decrease of resultant velocity of each part of the body becomes small when the impact angle is different from perpendicular. The findings from this study can be used as the basic knowledge for the powered two wheeler driver’s trajectories to accurately predict the impact condition of each body region to the car surface from the initial impact condition between the car and powered two wheeler.
Traffic accidents on community roads, which frequently have intersections with poor visibility, are one of the social issues in Japan. Although safety technologies that utilize roadside sensors are expected to be effective for Japanese community roads, only a few roadside sensors and limited sensor coverage are available on community roads in practice. In such an environment, it is difficult to consistently track multiple traffic participants by associating sensor observations of them from one sensor coverage to another coverage. To address this difficulty, we propose a data association method for multi-target tracking on the assumption that targets can be outside the sensor coverage. The proposed method calculates the existence probability of each target being within the sensor coverage at each time step and incorporates it as a prior probability in the data association process. In the simulation experiments, comparisons with existing methods demonstrate that the proposed method achieves a higher association success rate in various conditions. Furthermore, real-world experiments validate the feasibility of the proposed method.
A nondestructive safety diagnosis for lithium-ion battery modules was demonstrated with experimental data. The charging curve analysis (CCA) was selected for estimating the internal state of a lithium-ion battery cell and the cell operating conditions in a module. The safety threshold set by using CCA data was validated by thermal runaway tests for battery cells using an external heating method. The diagnosis for the module revealed not only its safety but also its discharge capacity (state of health (SOH)). An output image with comprehensive information including indicators to accumulate remaining battery performance values was successfully displayed.
The quick and accurate prediction of occupant injuries in motor vehicle collisions helps emergency services respond more effectively and reduce casualties. Existing studies have mainly concentrated on predicting overall injury severity rather than examining injuries to specific body parts, which limits the precision of injury assessment and targeted emergency response. In this study, we developed a random forest-based model to predict injury severity in different body parts, including the head, face, neck, chest, abdomen, spine, and limbs. This enables emergency services to deliver precise and targeted responses after collisions. Furthermore, it facilitates a correlation analysis between various collision-contributing factors and body part-specific injuries.
This study has developed a novel method to estimate the singular stress field at the bonded edge by applying an inverse analysis on the deformed shape of the adherend, without explicitly considering microscopic behaviors of the bonded interface. The mathematical model used in the inverse analysis does not need to be a model of the entire adherend, and can be a partial model of the bonded member based on the free body diagram concept. In the inverse analysis, the Tikhonov regularization is applied to reduce the influence of displacement measurement errors on the estimated values. In order to verify the basic validity of the estimation method, peel tests were performed in numerical simulations and actual tests, and the singular stress field at the bonded edge was estimated by the estimation method. In the verification, the regularization parameter selection in the inverse analysis was discussed based on the test results. The numerical simulations show that both the stress intensity factor and the stress singularity index remained almost constant between a certain range of the regularization parameters, and were close to the correct values. Then, the inverse analysis results agree with the correct values. The actual test results show good agreement with each other, and are close to the direct analysis results. These results indicate that the proposed estimation method is effective for estimating the singular stress field at the bonded edge.
To evaluate unsafe driving, it is essential to identify the driving behavior characteristics of skilled drivers in identical situations as a standard for safe driving. Previous studies on skilled drivers have reported individual differences in behavior depending on specific conditions or indices. These variations pose challenges in understanding driving behavior characteristics in complex driving scenarios. In this study, we focus on probabilistic decision-making theory and propose a method to extract the positional commonality of skilled drivers' driving behavior selection in scenarios requiring multiple driving behaviors. Utilizing data collected through self-localization technology, which accurately determines the vehicle's position, we analyzed the driving behavior selection characteristics of skilled drivers in complex driving situations. The results revealed that the driving behavior of skilled drivers in complex scenarios varies across three distinct zones. The extracted characteristics functioned as a safe driving index in evaluating unsafe driving, confirming the validity of the proposed method.
