Compared to single fuel cell powertrain, the multi-source hybrid system addresses the slow dynamic response and inability to recover and recycle braking energy capability inherent to fuel cell system. In aviation, hybrid systems enhance energy management efficiency and enable rapid power response to dynamic demands through the synergistic integration of multiple power sources. Concurrently, the energy management strategy coordinates the operational states of all sources in real time and optimally allocates power loads, thereby enhancing system operational stability and extending service life. Integrating Equivalent Consumption Minimization (ECM) principle, an Optimized Parasitism-Predation Algorithm (OPPA) is proposed. Compared with the traditional PPA, the optimized algorithm features two key enhancements. First, it integrates gradient information analysis and adopts a probabilistic acceptance mechanism for suboptimal solutions, thereby significantly expediting the local search process. Second, certain fixed parameters within the algorithm are adaptively adjusted over time, enabling the algorithm to effectively achieve the exploration-exploitation trade off. Simulation results demonstrate that the proposed strategy effectively balances multiple performance metrics within the model. When benchmarked against the State Machine Strategy and the Equivalent Consumption Minimization Strategy (ECMS), it outperforms in enhancing system efficiency and stability while concurrently reducing system costs.

The latest social VR technologies have enabled users to attend traditional media and arts performances together while being geographically removed, making such experiences accessible despite budget, distance, and other restrictions. In this work, we aim at improving the way remote performances are shared by designing and evaluating a VR theatre lobby which serves as a space for users to gather, interact, and relive the common experience of watching a virtual opera. We conducted an initial test with experts ($\mathrm{N}=10$, i.e., designers and opera enthusiasts) in pairs using our VR lobby prototype, developed based on the theoretical lobby design concept. A unique aspect of our experience is its highly realistic representation of users in the virtual space. The test results guided refinements to the VR lobby structure and implementation, aiming to improve the user experience and align it more closely with the social VR lobby's intended purpose. With the enhanced prototype, we ran a between-subject controlled study ($\mathrm{N}=40$) to compare the user experience in the social VR lobby between individuals and paired participants. To do so, we designed and validated a questionnaire to measure the user experience in the VR lobby. Results of our mixed-methods analysis, including interviews, questionnaire results, and user behavior, reveal the strength of our social VR lobby in connecting with other users, consuming the opera in a deeper manner, and exploring new possibilities beyond what is common in real life. All supplemental materials are available at https://github.com/cwi-dis/IEEEVR2024-VRLobby.

For the operation process of proton exchange membrane fuel cells (PEMFCs) system, fault diagnosis plays a crucial role in ensuring the safety and reliability of system. To improve the accuracy and rapidity of fault diagnosis, a fault diagnosis method of PEMFC based on data space feature decision tree group (DTS) and extreme learning machine (ELM) is proposed. Firstly, principal component analysis (PCA) is used to reduce the dimension of sensor data such as current, temperature and gas flow rate of system, which reflects the operating states of fuel cell. Besides, spatial features of system can be extracted and processed by random rotation matrix. Furthermore, multiple sets of spatial rotation feature data are set to diagnose the health state of fuel cell system by combining decision tree group and extreme learning machine (DTS-ELM). Finally, the proposed method was verified and analyzed, and the experimental results indicate that this method can quickly identify four operating states such as normal state, flooding, membrane dying and hydrogen leakage. The diagnostic accuracy and operating time of this method are 99.54% 0.261s, respectively. Therefore, on-line rapid fault diagnosis of proton exchange membrane fuel cell system can be realized by the proposed method.

Electrochemical impedance spectroscopy (EIS) has broad application prospects in the field of structural analysis and performance optimization of proton exchange membrane fuel cells (PEMFC), and the establishment of an equivalent impedance model based on EIS can intuitively and accurately characterize the internal characteristics of fuel cells. In this paper, an EIS diagram is drawn based on experimental data, and an equivalent impedance model that can well characterize PEMFC is proposed, and compared with commonly used equivalent models. The correspondence between each topology in the model design and the actual PEMFC electrochemical process is explained, and the electrochemical reaction principle of the catalytic layer is explained based on the model. The fitting analysis shows that the accuracy (chi-square error) of the designed model on the experimental data is improved by about 58.16%. This model can guide the process of EIS image analysis and establishing equivalent circuit models, and provide guiding ideas for related mechanism research.