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This study presents a safety management model for hydrogen refueling stations, integrating digital twin technology and artificial intelligence (AI) to enhance operational safety. Given the risks associated with high-pressure gas handling and potential fires from hydrogen leaks, real-time safety monitoring is crucial. The proposed model is based on a digital twin, a virtual replica of the physical system using real-time data, including temperature, pressure, and state of charge, collected from an actual hydrogen refueling station in Samcheok, Gangwon Province. Out of nine tested machine learning and deep learning algorithms, the convolutional neural network (CNN) demonstrated the highest performance (accuracy: 1, F1 score: 0.993) for risk prediction. Using AI libraries like Scikit-Learn and TensorFlow, the model achieved prediction times of 68 milliseconds, enabling decision-making at intervals of 1 s. Developed with the Unity 3D modeling tool, the digital twin visualizes predicted risk situations, allowing users to quickly identify and respond to potential hazards. This approach offers a robust solution for improving the safety of hydrogen refueling stations. [ABSTRACT FROM AUTHOR]

Equipping electric vehicles with a two-speed gearbox allows for achieving high torque and maximum speed through appropriate gear ratio adjustments. Additionally, tuning motor operating points to efficient zones, considering energy efficiency, significantly enhances the vehicle's overall performance. This paper presents an AWD system configuration method, integrating a two-speed transmission to improve energy efficiency and driving performance through front and rear motor torque distribution and powertrain specification optimization. Based on vehicle simulations conducted using MATLAB/Simulink, a strategy for torque distribution between the front/rear axles was established using fuzzy logic, considering energy efficiency and driving stability. Furthermore, a multi-objective optimization was performed using a surrogate model trained through MATLAB parallel simulations. When the optimization results were applied to various vehicle specifications, it was observed that energy efficiency was improved, and acceleration performance was increased compared to a baseline vehicle without optimization. [ABSTRACT FROM AUTHOR]

As the automotive industry transitions from internal combustion engine vehicles to the era of electric cars, extensive research is being conducted in the field of electric vehicles. While a significant portion of this research focuses on the electrification of passenger cars, commercial vehicles have experienced relatively modest changes towards electric propulsion. Particularly, challenges related to power and efficiency have prompted a concentrated effort in addressing these issues. However, improvements in the efficiency of motors and inverters are reaching their limits, necessitating the development of multi-speed transmissions for electric commercial vehicles to enhance overall system efficiency. In this paper, the development of a 4-speed transmission with a synchronizer designed for electric commercial vehicles is presented as part of a project. A transmission shift map was developed, and verification of increased power and efficiency was conducted through a comparison with the existing product (a pneumatic 4-speed internal combustion engine transmission) installed in the target commercial vehicle. The study utilized vehicle dynamics, component modeling, and simulation environments to assess the improvements in performance. [ABSTRACT FROM AUTHOR]

This study analyzed the trends in change and influencing factors based on particulate matter data from subway stations in 2019 and 2021 (before and during COVID-19). The measurements were based on subway stations on the Seoul Metro from lines 1 to 9. To confirm the changes in particulate matter concentrations before and after COVID-19, the trends of PM10 and PM2.5 concentrations in subway stations in 2019 and 2021 were analyzed. A correlation analysis was subsequently performed for a comparison of the factors influencing changes in the particulate matter concentrations. Overall, both PM10 and PM2.5 concentrations decreased during the outbreak compared to before the COVID-19 outbreak, with rates of 20.7% and 22.8%, respectively. In conclusion, the PM10 and PM2.5 concentrations in the Seoul Metro subway stations during COVID-19 were reduced. The reduction in PM10 and PM2.5, during the COVID-19 period, was the result of government policies such as telecommuting recommendations, subway congestion forecasting systems, and increased ventilation. [ABSTRACT FROM AUTHOR]