Jaeyoung Han

The use of conventional fluorinated refrigerants, such as R134a and R1234yf, is being progressively restricted as regulations on persistent substances, including per- and polyfluoroalkyl substances (PFAS), become increasingly stringent. Accordingly, environmentally friendly refrigerants are receiving increasing attention. R744 (carbon dioxide, CO₂) is considered a promising alternative for thermal management systems in electric vehicles (EVs) owing to its low global warming potential and favorable environmental characteristics. However, R744 operates under transcritical conditions, and high operating pressure and energy losses during expansion require improved thermal management technologies. The vortex tube has attracted attention as an alternative device because it can separate high-pressure refrigerant into hot and cold streams without moving parts, allowing pressure energy to be utilized as thermal energy. However, most previous studies have focused on energy separation characteristics and performance of the vortex tube as a standalone device, while applications to electric vehicle integrated thermal management system (EVITMS) remain limited. Therefore, this study developed an R744-based integrated thermal management system (ITMS) incorporating the vortex tube. A vehicle-level simulation model reflecting actual driving conditions was established to analyze cabin heating and the thermal management performance of battery and power electronic components and confirm the applicability of vortex tube-based ITMS.

In robotic reinforcement learning, multiple auxiliary rewards are commonly combined with the task reward to alleviate sparse reward signals and accelerate policy learning. The relative influence of these rewards is determined by their weights, which are typically fixed throughout training in existing methods. However, because the importance of each reward term to task learning changes as training progresses, reward weights should be adaptively adjusted to reflect this variation. In this paper, we propose an adaptive reward weighting method that dynamically adjusts auxiliary reward weights based on the interdependence between the task reward and each auxiliary reward. We quantify the interdependence using the mutual information (MI) between their reward trajectories. The estimated interdependence is used to periodically update auxiliary reward weights, assigning higher weights to auxiliary rewards that are more relevant to task learning. Experiments are conducted on the Allegro Hand Repose Cube, Franka Panda Cabinet Opening, and Shadow Hand Handover environments in NVIDIA Isaac Lab. The experimental results show that the proposed method achieves faster convergence and higher final performance than conventional fixed-weight methods. Furthermore, additional experiments on the MI estimation method and the degree of MI modulation demonstrate that performance improvement is achieved not by arbitrarily adjusting reward weights, but by adaptively emphasizing auxiliary rewards based on their interdependence with the task reward.

Vanadium hexacyanoferrate as a multivalent redox platform.

Air‐cooled polymer electrolyte membrane fuel cells (PEMFCs) offer structural simplicity and advantages in compact system design; however, nonuniform thermal management induced by fan‐based cooling can lead to hotspot formation, resulting in increased membrane resistance, performance degradation, and reduced durability. In particular, variations in airflow characteristics caused by different fan configurations directly influence stack temperature distribution and voltage stability, indicating the necessity for systematic experimental and quantitative investigation. Accordingly, this study derives a critical operating temperature based on the temperature–voltage characteristics of an air‐cooled PEMFC stack and applies a scheduled PI control strategy targeting experimentally identified hotspot locations using multipoint temperature sensors to enhance thermal stability. Three cooling configurations including single large fan, dual small fans, and quad small fans were experimentally evaluated in terms of stack voltage performance, fan energy consumption, and temperature distribution. Quantitative assessment was conducted using the Cooling Uniformity Index (CUI) and Hotspot Severity Index (HSI). The results demonstrate that the single large fan provides the most stable voltage performance and superior hotspot suppression under high‐load conditions, whereas the quad small fans configuration exhibits the highest cooling efficiency in terms of average temperature reduction and fan power consumption. In contrast, the dual small fans configuration does not present any significant advantage in either thermal stability or energy efficiency.

Multi-stack Polymer electrolyte Membrane Fuel Cell (PEMFC) systems are increasingly adopted in heavy-duty mobility to overcome the power limitations and thermal instability of single-stack configurations. However, the overall energy efficiency, hydrogen utilization, and thermal behavior of multi-stack fuel cell trucks are highly dependent on the applied Power Management System (PMS). In this study, high-fidelity, system-level dynamic model of multi-stack fuel cell truck was developed using Matlab/Simscape^TM, and three PMS approaches (rule-based control, state-machine control, and fuzzy logic control) were comparatively evaluated. The analysis includes coolant temperature regulation, hydrogen consumption, battery State of Charge (SoC) dynamics, and the parasitic power demand of Balance of Plant (BoP) components. Results show that the fuzzy logic PMS provides the most balanced operating profile by smoothing transient fuel cell loading and actively leveraging the battery during high-demand periods. In the thermal domain, the fuzzy logic PMS reduced temperature overshoot by up to 61.20%, demonstrating the most stable thermal control among the three strategies. Hydrogen consumption decreased by 3.08% and 0.89% compared with the rule-based and state-machine PMS, respectively, while parasitic power consumption decreased by 7.12% and 3.32%, confirming improvements in overall energy efficiency. TOPSIS-based multi-criteria decision analysis further showed that the fuzzy logic PMS achieved the highest closeness coefficient (0.9112), indicating superior system-level performance. These findings highlight the importance of PMS design for achieving energy-optimal and thermally stable operation of multi-stack PEMFC trucks and provide practical guidance for future control strategies, heavy-duty mobility applications, and next-generation hydrogen powertrain optimization.

The overall energy efficiency of a multi-compressor system varies greatly depending on its operating strategy. In most industrial facilities, the strategy is still determined empirically by operators, which often fails to achieve optimal energy efficiency. The optimization of multi-compressor operation is inherently complex, as it must simultaneously consider various operational factors such as on/off combinations, type-specific flow capacities, and flow constraints. This study proposes a parallel-search simulated annealing algorithm that employs a branch-based exploration mechanism. In the proposed algorithm, multiple search branches independently explore the solution space and periodically exchange information, enabling a broader search and faster convergence than conventional simulated annealing. Simulation results show that the proposed approach reduces total power consumption by about 8% compared with existing heuristic methods while maintaining stable and consistent performance across large-scale scenarios.

The International Maritime Organization (IMO) has strengthened regulations on ship emissions, increasing interest in eco‐friendly marine propulsion systems. Among these, the polymer electrolyte membrane fuel cell (PEMFC) is a promising alternative for small ships. This study aims to develop a system‐level high‐fidelity integrated fuel cell–battery hybrid small ship model and evaluate the performance of different power management strategies, focusing on energy efficiency and thermal management. A comprehensive model was developed using Matlab/Simulink and Matlab/Simscape, integrating a PEMFC stack, high‐voltage battery, power conversion units, motor, and thermal management system (TMS). Two power management systems (PMSs), rule‐based and fuzzy‐based, were implemented and analyzed under realistic operational profiles, including trim angle effects. Simulation results showed that the fuzzy‐based PMS reduced hydrogen consumption by 8.6%, improved overall energy efficiency by 17.8%, decreased overshoot by 21.6%, and shortened settling time by 121 s compared to the rule‐based PMS. TMS energy consumption was also reduced due to precise control of coolant pumps and valves. Qualitatively, the fuzzy‐based PMS stabilized battery state of charge (SOC) and mitigated fuel cell overload, demonstrating improved operational stability during dynamic ship operation.