Chain

Open Access

ISSN(Print): 2097-3470 | ISSN(Online): 2097-3489 | CN: 10-1915/TB

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Aims & Scope

Chain, is a scholarly peer-reviewed, open-access journal sponsored by The Nonferrous Metals Society of China and Beihang University, published by Youke Publishing, and hosted on IEEE Xplore. Its primary mission is to showcase significant advancements in comprehensive multi-dimensional transportation, encompassing waterways, railways, roadways, aviation and space, and comprehensive transportation. Chain welcomes submissions of full-length articles, short communications, review articles, perspectives, highlights, invited interviews, news and views, etc., featuring cutting-edge research, spanning foundations, methodologies and applications across fields such as transportation science & technology, computer science, engineering, electrical & electronic engineering, vehicle engineering & mobility, energy & fuels, materials science, advanced manufacturing. More specifically, its scope encompasses the following areas, though not exclusively: • Comprehensive Transportation: the tri-level of comprehensive transportation, reflecting the spatial structure of elevated, ground-level, and underground on land, as well as underwater, aerospace, and outer space. • Hybrid Cyber-Ecosystem: hybrid systems empowered by AIoT, digital twins, and computational intelligence (machine learning/deep learning/evolutionary algorithms) through a data-driven paradigm; artificial intelligence applied to transportation. • Adaptive Green Transit: electric vehicles, green mobility, intelligent infrastructure, ecosystems, low-carbon transport, and sustainable transportation. • Integration-Internet: vehicle-to-everything (V2X), cooperative vehicle-infrastructure systems (CVIS), integration of information, facilities and services (such as vehicle-road-cloud integration system), and integration of transportation and energy. • Next-Generation: low-altitude economy and equipment (eVTOL and flying cars), self-driving cars, hyperloop, etc., using disruptive materials and technologies.

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Experimental optimization of process parameters for Pt/Al₂O₃-catalyzed hydrogen elimination and corresponding regulation strategies

To address safety hazards arising from trace H₂ leakage in hydrogen energy systems, catalytic elimination using a Pt/Al₂O₃ catalyst was investigated. A test system was constructed to study the effects of Pt loading, inlet temperature, and space velocity on hydrogen conversion, along with catalyst microstructure analysis. Single-factor experiments indicate that increasing Pt loading from 1 wt% to 4 wt% significantly raises conversion rate from approximately 30% to over 85% at 1 vol% H₂. Elevating the inlet temperature effectively overcomes the activation barrier, with a particularly pronounced effect at low temperatures. In contrast, the influence of space velocity is relatively weak and negatively correlated with hydrogen conversion. Multivariable synergistic analysis identifies inlet temperature as the dominant factor for overcoming surface activation limitations at low H₂ concentrations, while Pt loading enhances reaction kinetics via increased active site density. At H₂ concentrations above 2 vol%, the system enters an "interfacial reaction saturation" regime where all parameter effects diminish. Numerical simulations confirm these mechanisms, clarifying that synergistic mode of action wherein temperature dominates activation while Pt loading increases the reaction rate.

Disruptive generational leap: an embedded AI battery management system for power and energy storage systems

Battery management systems (BMSs) play an undeniably critical role in both power and energy storage systems. As applications continue to expand into various complex scenarios, BMSs have increasingly become the key determinant of the overall performance of advanced battery systems. Beyond conventional basic performance metrics, lifetime and safety gradually emerge as core concerns for battery systems. However, existing BMSs are increasingly inadequate in supporting these two aspects. The development of high-safety, long-lifetime BMSs has become a common focus in battery systems across various application scenarios. This paper reviews the current state of BMS technology, analyzes its shortcomings in both hardware and software, and summarizes the latest technological advancements across four key areas: multi-dimensional parameter measurement, multi-modal fusion modeling, active management, and embedded artificial intelligence (AI) deployment. It objectively evaluates the strengths and weaknesses of these technologies for future BMS applications. Finally, the paper innovatively proposes the fundamental concept of an embedded AI BMS, highlighting its potential for deployment in both power battery and energy storage battery applications. As the core conclusion of this paper, the embedded AI BMS plays a significant role in next-generation battery systems. The discussion of key technologies in this paper also provides comprehensive references and analytical insights for the development of the next-generation smart BMS.

A review of connected and automated vehicle platoon control under mixed traffic

With the rapid advancement of Vehicle-to-Everything (V2X) communication and automated driving technologies, connected and automated vehicle (CAV) platoons have emerged as a promising solution for enhancing road capacity, energy efficiency, and traffic safety. Unlike the ideal fully CAV environment, real-world traffic will remain in a mixed traffic phase for an extended period, where CAVs and human-driven vehicles (HDVs) coexist. Factors such as cut-in by surrounding vehicles, HDV leader disturbances, and communication delay significantly increase the complexity of platoon control. This paper provides a systematic review of CAV platoon control methods under mixed traffic from three interrelated perspectives: control architecture, HDV interaction mechanisms, and control methods. From the architectural perspective, centralized management, distributed cooperation, and hybrid hierarchical architectures are comparatively examined in terms of decision efficiency, communication dependency, and scalability. Regarding HDV interaction mechanisms, HDV behavior modeling and prediction approaches based on model-driven, data-driven, and game-theoretic frameworks are synthesized, highlighting the trade-offs between modeling fidelity and uncertainty quantification capability. In terms of control methods, the technical characteristics and applicability limits of model-driven control, data-driven control, and hybrid model-data-driven control are systematically analyzed. Building upon this comparative assessment, the advantages, limitations, and application scenarios of these approaches are summarized to provide guidance for control strategy design and selection. Finally, future research directions for CAV platoon control under mixed traffic flow are outlined, aiming to provide theoretical support for establishing safe, stable, and efficient CAV platoons in complex mixed traffic environments.

2026 2025 2024

Review of digital twin technology applications in hydrogen energy

Hydrogen energy is a clean and versatile energy carrier, increasingly recognized for its role in a sustainable energy future due to its clean and abundant energy production. Bridging the gap between potential and practicality, digital Twin (DT) technology emerges as a pivotal artificial intelligence tool, providing a virtual modelling platform that enhances the operation and integration of hydrogen energy into modern energy systems. This review firstly explores the multifaceted applications of DT technology across different stages of the hydrogen energy lifecycle, including production, storage, transport, and utilization. It commences with a detailed introduction to DT technology, elucidating its definition, core principles, and structural nuances, thus laying the groundwork for understanding its pivotal role in energy systems. The core of the review delves into the applications of DT technology in hydrogen energy, segmenting the discussion into production, storage, transport, and utilization processes. Specific focus is given to optimizing fuel cells and hybrid electric vehicles through DT models, along with the seamless integration of hydrogen systems with broader energy networks. It further dissects the working mechanism of DT, highlighting the key features that contribute to itsgrowing prominence in the energy sector.

Integrating digital twins and machine learning for advanced control in green hydrogen production

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.

Machine learning and density functional theory for catalyst and process design in hydrogen production

Hydrogen plays a vital role in achieving NetZero emissions as a carbon-free energy carrier. However, its production, especially green hydrogen generated from renewable sources, is hindered by low efficiency and limited yield, primarily due to the performance of the catalysts used. Developing efficient catalysts typically involves extensive experimental work and trial-and-error processes. For instance, screening for effective catalysts still heavily relies on human-lab-work, a process that is time-consuming. Facing this critical challenge, machine learning (ML) emerges as a promising solution. ML, a core component of data mining and analysis that uses statistical algorithms without explicit instructions, can rationalize the design of catalysts through the use of big data, including DFT results. This approach makes a significant shift from traditional trial-and-error approaches to more computationally driven strategies, offering a more effective path to uncovering vital methodologies for catalyst development. This review aims to capture and evaluate the impact of ML algorithms that have driven progress in catalyst research over the past three years. It presents an overview of the existing ML algorithms, exploring their specific functionalities, benefits, and limitations. Besides, this review also considers prospective solutions and future directions for applying ML to enhance the efficiency of green hydrogen production, particularly through electrochemical and biological processes.

A novel design of wheel-propeller based aerial-ground amphibious transportation platform

Benefiting from the synergistic integration of aerial agility and terrestrial endurance, aerial-ground amphibious platforms can effectively traverse unstructured environments, demonstrating considerable potential for emergency response and reconnaissance applications. However, most existing systems adopt a decoupled configuration, utilizing independent propulsion units for ground and aerial modes. Although this approach enables dual-domain operation, the duplicated actuators and transmission chains inevitably incur structural redundancy and additional mass, which reduce overall system integration and payload efficiency. To address these limitations, this paper proposes a novel aerial-ground amphibious platform based on an integrated wheel-propeller structure. In the proposed design, a single electric motor actuates both the wheel and ducted propeller through a shared powertrain, where an electromagnetic clutch selectively engages the transmission path to switch between terrestrial locomotion and aerial propulsion. Building upon this mechanism, an amphibious platform configuration incorporating eight-wheel-propeller units is developed. By establishing a dynamic simulation model of the platform, the dynamic response characteristics during the drive-to-fly transition were investigated. The results demonstrate the feasibility of the operational principle underpinning the platform. The research provides a feasible technical approach for lightweight and highly integrated aerial-ground platforms, laying the foundation for future experimental implementation.

Ionomer adsorption governs coverage structure: interfacial engineering of fuel cell electrodes toward high-efficiency proton/oxygen transport

The ionomer coating structure on the catalyst surface critically determines the performance and durability of proton exchange membrane fuel cell (PEMFC) electrodes. Conventional fabrication often yields non‑uniform overlayers, increasing oxygen transport resistance, blocking active sites, and accelerating interfacial degradation. Advancing from random deposition to controlled coating structure is therefore a key objective. Ionomer adsorption is governed by its intrinsic properties, support characteristics, and catalyst morphology. In liquid environments, adsorption involves backbone hydrophobicity and side‑chain chemical bonding, with the latter dominating the final configuration. Solvent composition, particularly water content, modulates dispersion, and adsorption behavior. Optimization strategies focus on two main approaches: suppressing detrimental adsorption (e.g., by tailoring support porosity or using additives like ionic liquids) and constructing favorable mass‑transport pathways (e.g., by designing ionomers with cyclic/porous structures or engineering porous overlayers). Rational design of ionomer properties, interface regulation, and integration of these strategies can synergistically enhance proton conduction, mass transport, and stability, laying a foundation for high‑performance, durable fuel cell electrodes.

Mechanistic insights into the electrochemical and thermal safety degradation of lithium titanate batteries under constant voltage overcharge conditions

Lithium titanate oxide (Li₄Ti₅O₁₂, LTO) anode-based batteries are widely recognized for their excellent safety characteristics and long cycle life. However, constant-voltage overcharging (CVOC), arising from cell-to-cell variations and delays in the response of a battery management system (BMS), can accelerate capacity degradation and increase the risk of thermal runaway. In this work, we systematically investigated the effects of CVOC at different voltages on the capacity retention and thermal safety of LTO-based batteries. By subjecting cells to overcharging cycling at elevated voltages, we elucidate the aging mechanisms and degradation pathways. Noninvasive diagnostics combined with postmortem analyses are employed to correlate the electrochemical behavior with the electrode morphology and composition under CVOC conditions. A critical voltage of 3.5 V is identified, beyond which severe degradation occurs. At 4.0 V, continuous CVOC induces the growth of a thick organic-rich solid-electrolyte interphase (SEI) and byproducts, leading to only 60% capacity retention compared with nearly 100% retention under 3.0 V CVOC or conventional constant-current/constant-voltage charging. Furthermore, the self-heating onset temperature decreases by 44.8 °C, indicating a significant reduction in thermal stability associated with high-voltage overcharge. These findings are corroborated by detailed postmortem characterization. Overall, this study demonstrates that the CVOC critically impacts both the electrochemical performance and thermal safety of LTO-based batteries, offering important insights for the design of high-stability, highly safe power sources for electric transportation systems.

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