With an increase in global warming, battery electric vehicles (BEVs), which are environmentally friendly, have been rapidly commercialized to replace conventional vehicles with internal combustion engines. Unlike traditional internal combustion engine vehicles, the powertrain system of BEVs operates with high efficiency, resulting in lower heat generation. This poses a challenge for cabin heating under low-temperature conditions. Conversely, under high-temperature conditions, the operating temperature of a high-voltage battery (HVB) is lower than the ambient air temperature, which makes cooling through ambient air challenging. To overcome these challenges, in this study, we proposed an integrated thermal management system (ITMS) based on a heat pump system capable of stable thermal management under diverse climatic conditions. Furthermore, to assess the ability of the proposed ITMS to perform thermal management under various climatic conditions, we integrated a detailed powertrain system model incorporating BEV specifications and the proposed ITMS model based on the heat pump system. The ITMS model was evaluated under high-load-driving conditions, specifically the HWFET scenario, demonstrating its capability to perform stable thermal management not only under high-temperature conditions, such as at 36 °C, but also under low-temperature conditions, such as at −10 °C, through the designated thermal management modes.

In the automotive industry, extensive research has been conducted to eliminate factors negatively impacting product quality, such as wrinkles, cracks, and thickness distribution in components. The application of drawbeads often relies on the experience of field workers, leading to considerable trial and error before stabilizing the production process. Therefore, to efficiently transform these inefficiencies related to time and cost, there is a need for real-time predictive technology for forming quality based on the position of drawbeads and the bead force. This study proposes a method for predicting formability in real-time, based on a digital twin framework that considers the position of drawbeads and holder force. A digital twin was developed to predict the sheet metal forming process using Support Vector Machine, Random Forest, Gradient Boosting Machine, and Artificial Neural Networks. The machine learning models were trained using finite element analysis data corresponding to the position and bead force of drawbeads, enabling the real-time prediction of wrinkles and crack occurrences. The accuracy of the machine learning models was demonstrated, achieving 100% accuracy in determining crack occurrence, with a mean squared error (MSE) of 0.141 for wrinkle prediction and 0.038 for crack prediction, thereby ensuring the accuracy of the forming prediction model based on drawbead applications. Based on these predictive models, a user-friendly GUI has been developed, which is expected to reduce design time and costs while facilitating real-time predictions of forming quality, such as wrinkles and cracks, on-site.

In recent years, there has been a growing demand for multipurpose drones that can handle surveillance, environmental monitoring, and urgent deliveries. This trend has spurred the need for increased power and longer flight times for drones. Hence, many researchers introduced various hybrid systems to enhance endurance. In particular, a hybrid system that integrates solar cells, fuel cells, and batteries can substantially enhance a drone’s endurance. However, linking multiple power sources necessitates a control strategy that prioritizes safety and durability. It is also essential to analyze the control characteristics of each component as the dynamic behavior of individual components, coupled with environmental factors, significantly impacts the overall dynamic characteristics of drone systems. This study introduces a PEMFC–battery drone model. The model’s dynamic characteristics can be evaluated based on changes in environmental conditions and the control strategies of primary components. The validity of this model is confirmed by analyzing the dynamic characteristics of drone systems. As a result, the MRAC logic applied to the flight-level control and thrust motor of the drone was found to amplify the characteristics of the underlying PI and IP controllers. These control characteristics can lead to the development of control strategies for improving the flight performance or power durability of the aircraft by being properly applied to the flight environment of the drone.

Since the 2015 Paris Agreement, efforts for environmental protection have gained prominence worldwide. Accordingly, electric vehicles have become increasingly relevant. Thus, improving the performance of the water pump, a key component of cooling systems in electric vehicles, is crucial. Electric vehicles operate on batteries and motors, making their cooling systems remarkably complex. Efficient operation of the water pump is directly related to the stable performance of electric vehicles and is therefore critical. This study conducted numerical analyses using Ansys Fluent to evaluate water pump performance by varying key parameters, namely, number of blades and outer diameter of the impeller. When the number of blades was changed to 7, 9, 11, and 13, the efficiency, head, and thrust tended to increase. In particular, for blade counts greater than 11, the fluid flow was found to stabilize with negligible effect on pump performance. When the outer diameter of the impeller was 70, 69, 68, and 67 mm, although efficiency decreased, the head and thrust tended to increase. Based on these comprehensive results, a structure was proposed for the shape of the optimized water pump. The development of efficient and stable water pumps is expected to contribute to the performance improvement of electric vehicles.

A six-phase motor with a high degree of freedom can be converted into a three-phase motor in order to be used in a traction system. In addition, when phase-change technology is applied, it is possible to establish an efficient control strategy tailored to the driving environment of the EVs. Therefore, in this paper, a down-scaled 3 kW permanent-magnet-assisted synchronous motor (PMa-SynRM) capable of phase switching was designed, and its driving states in controlled fault modes were analyzed through experiments. The PMa-SynRM selected for this study was a machine that had good fault-tolerance capabilities and was less expensive than an IPMSM with the same performance; it was designed using the lumped-parameter method (LPM) having a fast calculating speed and a genetic algorithm. In addition, the effectiveness of the optimal design was verified by comparing the analytical results of the FEM and the LPM. Lastly, a phase switching experiment was conducted to analyze the steady-state and transient-state characteristics, and the results are presented.

This paper proposes a method to improve the output torque of a six-phase permanent-magnet synchronous machine (PMSM) within the same current peak limit through a fifth-harmonic injection into each phase current of the stator. Compared to the fifth + seventh-harmonic current-injection method used to improve the output torque of the six-phase PMSM, the control system can be stably controlled, and the controller design complexity decreased. This is because the harmonic component was converted into a direct current (DC) component and controlled by a proportional-integral (PI) controller instead of the fifth + seventh-harmonic injection method, which converts the harmonic component into an alternating current (AC) component and controls it with a resonance controller. The appropriate fifth-harmonic ratio for maximum output torque through fifth-harmonic injection was selected through optimization using values analyzed via fast Fourier transform (FFT) for stator phase harmonic current terms caused by inverter nonlinearity and motor design errors. Therefore, it was possible to optimize the fifth-harmonic ratio to be injected without requiring torque modeling using the physical properties of the motor. The experimental results were obtained under the rated current condition with six-phase PMSM in the laboratory, and the average output torque increase under fifth-harmonic injection was about 5% compared to the method without harmonic injection.