The key technologies of fuel cell systems have a great impact on their performance and lifetime. However, most of the current analyses in reviews only focus on a single system or one aspect of the technology. It is highly necessary to systematically analyze the new development and research trends of vehicular fuel cell systems. This paper proposes a quantitative and visual analysis based on bibliometrics to more intuitively reveal the research direction in this field. Key information on hydrogen/air subsystems, energy management strategies, and thermal management subsystems are analyzed by combining the literature analysis and technical discussion. In detail, the possible reasons behind the variations in the number of articles published within different periods of the subsystem are revealed. Then, through qualitative analysis of representative papers with high citations in different periods, the main research methods adopted by scholars in different periods are derived. Finally, the technology research trends of control methods that need to be addressed in the future are summarized for different subsystems. This paper provides a reference for researchers in the field to understand more clearly the current trends in the field and provide new ideas to break through the challenges of developing high-performance fuel cell systems.

The successful reduction of carbon emissions in major sectors such as heavy industry and long-distance transport depends crucially on the ability to produce green hydrogen on a large scale. This involves generating hydrogen via water electrolysis, utilizing power sourced from renewable energies. However, persistent challenges, such as dynamic inefficiencies, material degradation, and renewable intermittency, demand a paradigm shift from static control strategies to adaptive, self-optimizing systems. This perspective argues that the synergistic integration of digital twins (DTs) and machine learning (ML) offers a transformative framework for real-time optimization, predictive maintenance, and resilient grid integration. By synthesizing physics-based modeling with data-driven intelligence, DT-ML systems enable closed-loop control architectures that dynamically adapt to operational uncertainties. We analyze the technical foundations of this integration, address critical barriers, and propose actionable pathways for stakeholders to accelerate the hydrogen economy's transition from promise to practice.

This study addresses the joint state and fault fusion estimation problem for mobile robot localization under the energy-harvesting sensors. Under such a circumstance, the sensors can harvest energy from the external environment and then consume an amount of energy when transmitting measurements to the corresponding estimator. Based on the energy harvesting mechanism's probability distribution, the probability of measurement loss is computed at each step. The main objective of this study is to tackle the mobile robot localization problem by designing local estimators for each sensor node, where the upper bound of the local estimation error covariance is guaranteed and then minimized by appropriately tuning the estimator parameters. Furthermore, the local estimates are fused using the covariance intersection (CI) fusion approach. Finally, a numerical experiment is presented to demonstrate the effectiveness of the proposed fusion estimation algorithm.

Traffic congestion and safety concerns pose major challenges in dense urban and highway networks, while vehicle platooning offers a promising solution by coordinating movements, reducing aerodynamic drag, and optimizing road utilization to enhance traffic efficiency and safety. Most existing multi-vehicle cooperative lane-change methods adopt a synchronous lane-change strategy, which could result in speed fluctuations and potential safety risks in dense traffic. To address these issues, this study proposes a forward-looking sequential asynchronous lane-change strategy, which dynamically computes the forward shift of vehicle trajectories using relative motion theory. Each following vehicle shifts its predecessor's trajectory forward by a calculated distance, ensuring lane changes occur precisely when the time headway (THW) to the preceding traffic vehicle (PTV) reaches a predefined threshold. Additionally, a spring-damper-based physical model is integrated to regulate longitudinal spacing, ensuring stable vehicle following dynamics. The proposed method is validated in CARLA simulations across various traffic densities, ranging from 2.5 to 10 veh/km/lane. Results show that it consistently outperforms the baseline synchronous strategy in lane travel time, speed fluctuation rate, and THW improvement. The best performance is observed at 5 veh/km/lane, where the proposed method reduces lane travel time by 7.5%, decreases speed fluctuation by 47.0%, and increases THW between the last platoon vehicle and the following traffic vehicle by 3.37 s compared to the baseline. These findings suggest that the proposed strategy could be a foundation for future advancements in cooperative driving, enabling more adaptive and resilient lane-changing behaviors in a cloud-vehicle-road integrated traffic environment.

Room-temperature ferromagnetism in graphene is a crucial step toward the practical application of spintronic devices. While hydrogen adsorption on graphene has been shown to induce magnetic moments, the overall efficiency remains low due to the clustering of hydrogen atoms and weak magnetic coupling. This study demonstrates a highly effective vacancy-assisted hydrogenation method to synthesize hydrogenated graphene (HG) with robust room-temperature ferromagnetism. The introduction of vacancies inhibits hydrogen clustering, increases magnetic edge atoms, and enhances the coupling between magnetic moments. As a result, HG exhibits a Curie temperature of 540 K and a saturation magnetization of 0.69 emu/g at 300 K. Our findings provide a new approach for the efficient hydrogenation of graphene, paving the way for its applications in spintronic devices.