Search Results (128)

As hydrogen fuel cell vehicles gain momentum as crucial zero-emission transportation solutions, the urgency to address hydrogen permeability through the polymer liner becomes paramount for ensuring the safety, efficiency, and longevity of Type IV hydrogen storage tanks. This paper synthesizes existing research findings, analyzes the influence of different materials and structures on gas permeability, elucidates the dissolution and diffusion mechanisms of hydrogen in plastic liners, and discusses their engineering applications. We focus on measurement methods, influencing factors, and improvement strategies for liner gas permeability. Additionally, we explore the prospects of Type IV hydrogen storage tanks in fields such as automotive, aerospace, and energy storage industries. Through this comprehensive review of liner gas permeability, critical insights are provided to guide the development of efficient and safe hydrogen storage and transportation systems. These insights are vital for advancing the widespread application of hydrogen energy technology and fostering sustainable energy development, significantly contributing to efforts aimed at enhancing the performance and safety of Type IV hydrogen storage tanks. Full article

Nickel’s durability and catalytic properties make it essential in the aerospace, automotive, electronics, and fuel cell technology industries. Wastewater analysis typically relies on sensitive but costly techniques such as ICP-MS, AAS, and ICP-AES, which require complex equipment and are unsuitable for on-site testing. This study introduces a novel screen-printed electrode array with 16 chemically and, optionally, electrochemically coated Au electrodes. Its electrochemical response to Ni²⁺ was tested using Na₂SO₃ and ChCl-EG deep eutectic solvents as electrolytes. Ni²⁺ solutions were prepared from NiCl₂·6H₂O, NiSO₄·6H₂O, and dry NiCl₂. In Na₂SO₃, the linear detection ranges were 20–196 mM for NiCl₂·6H₂O and 89–329 mM for NiSO₄·6H₂O. High Ni²⁺ concentrations (10–500 mM) were used to simulate industrial conditions. Two linear ranges were observed, likely due to differences in electrochemical behaviour between NiCl₂·6H₂O and NiSO₄·6H₂O, despite the identical Na₂SO₃ electrolyte. Anion effects (Cl⁻ vs. SO₄²⁻) may influence response via complexation or ion pairing. In ChCl-EG, a linear range of 0.5–10 mM (R² = 0.9995) and a detection limit of 1.6 µM were achieved. With a small electrolyte volume (100–200 µL), nickel detection in the nanomole range is possible. A key advantage is the array’s ability to analyze multiple analytes simultaneously via customizable electrode configurations. Future research will focus on nickel detection in industrial wastewater and its potential in the multiplexed analysis of toxic metals. The array also holds promise for medical diagnostics and food safety applications using thiol/Au-based capture molecules. Full article

Proton exchange membrane fuel cells (PEMFCs) are ideal for fuel cell vehicles due to their high specific power, rapid start-up, and low operating temperatures. However, their limited lifespan presents a challenge for large-scale deployment. Accurate assessment of remaining useful life (RUL) is essential for enhancing longevity. Automotive PEMFC systems are complex and nonlinear, making lifespan prediction difficult. Recent studies suggest deep learning approaches hold promise for this task. This study proposes a novel EMD-TCN-GN algorithm, which, for the first time, integrates empirical mode decomposition (EMD), temporal convolutional network (TCN), and group normalization (GN) by using EMD to adaptively decompose non-stationary signals (such as voltage fluctuations), the dilated convolution of TCN to capture long-term dependencies, and combining GN to group-calibrate intrinsic mode function (IMF) features to solve the problems of modal aliasing and training instability. Parametric analysis shows optimal accuracy with the grouping parameter set to 4. Experimental validation, with a voltage lifetime threshold at 96% (3.228 V), shows the predicted degradation closely aligns with actual results. The model predicts voltage threshold times at 809 h and 876 h, compared to actual values of 807 h and 872 h, with a temporal prediction error margin of 0.250–0.460%. These results demonstrate the model’s high prediction fidelity and support proactive health management of PEMFC systems. Full article

Proton exchange membrane fuel cells (PEMFC), distinguished by rapid refueling capability and zero tailpipe emissions, have emerged as a transformative energy conversion technology for automotive applications. Nevertheless, their widespread commercialization remains constrained by technical limitations mainly in operational longevity. Precise prognostics of performance degradation could enable real-time optimization of operation, thereby extending service life. This investigation proposes a hybrid prognostic framework integrating steady-state modeling with dynamic condition. First, a refined semi-empirical steady-state model was developed. Model parameters’ identification was achieved using grey wolf optimizer. Subsequently, dynamic durability testing data underwent systematic preprocessing through a correlation-based screening protocol. The processed dataset, comprising model-calculated reference outputs under dynamic conditions synchronized with filtered operational parameters, served as inputs for a recurrent neural network (RNN). Comparative analysis of multiple RNN variants revealed that the hybrid methodology achieved superior prediction fidelity, demonstrating a root mean square error of 0.6228%. Notably, the integration of steady-state physics could reduce the RNN structural complexity while maintaining equivalent prediction accuracy. This model-informed data fusion approach establishes a novel paradigm for PEMFC lifetime assessment. The proposed methodology provides automakers with a computationally efficient framework for durability prediction and control optimization in vehicular fuel cell systems. Full article

The exhaust purge on the anode side is a critical step in the operation of fuel cell systems, and optimizing the exhaust interval time is essential for enhancing stack efficiency and hydrogen utilization. This paper proposed a method to determine the purge strategy of hydrogen supply system based on theoretical and simulation analysis. To investigate the impact of anode purge strategy on the performance of automotive fuel cells, a model of a 100 kW fuel cell stack and a dual-ejector hydrogen supply system was developed in MATLAB/Simulink(R2022b) using principles of fluid dynamics, simulation, and experimental data. This model effectively captures the accumulation and exhaust of hydrogen, nitrogen, and vapor within the anode. Simulations were conducted under seven different exhaust interval times at varying current densities to study the effect of exhaust interval on the performance of the fuel cell. The results indicate that for a 100 kW fuel cell, the exhaust interval time should be controlled within 25 s and should decrease as the current density increases. At low current density, increasing the exhaust interval has a more significant effect on improving hydrogen utilization. At high current density, reducing the exhaust interval helps maintain a stable hydrogen excess ratio and shortens the time required for the output voltage to reach a stable state. Full article

Proton Exchange Membrane Fuel Cells (PEMFCs), recognised for their high efficiency and zero emissions, represent a promising solution for automotive applications. Despite their potential, durability challenges under real-world automotive operating conditions—arising from chemical, mechanical, catalytic, and thermal degradation processes intensified by contaminants—limit their broader adoption. This review aims to systematically assess recent advancements in understanding and modelling PEMFC degradation mechanisms. The article critically evaluates experimental approaches integrated with advanced physicochemical modelling techniques, such as impedance spectroscopy, microstructural analysis, and hybrid modelling approaches, highlighting their strengths and specific limitations. Experimental studies conducted under dynamic, realistic conditions provide precise data for validating these models. The review explicitly compares physics-based, data-driven, and hybrid modelling strategies, discussing trade-offs between accuracy, computational demand, and generalizability. Key findings emphasise that hybrid models effectively balance precision with computational efficiency. Finally, the article identifies apparent research gaps. It suggests future directions, including developing degradation-resistant materials, improved simulation methodologies, and intelligent control systems to optimise PEMFC performance and enhance operational lifespan. Full article

Hydrogen (H₂) is a key energy carrier and industrial feedstock, with growing interest in its production from syngas and water–gas shift (WGS) syngas. Effective purification methods are essential to ensure high hydrogen purity for various applications, particularly fuel cells, chemical synthesis, or automotive fuel. Pressure swing adsorption (PSA) has emerged as a dominant separation technology due to its efficiency, scalability, and industrial maturity. This study reviews PSA-based hydrogen purification and proposes an experimental framework based on literature insights. Key process variables influencing PSA performance, such as adsorbent selection, cycle sequences, pressure conditions, and flow configurations, are identified. The proposed experimental methodology includes breakthrough adsorption studies and PSA process evaluations under dynamic conditions, with variations in column configuration, adsorption pressure (8–9 bar), and process concept (Berlin and Linde Gas). The purpose of the review is to prepare for syngas separation by the selected process in terms of hydrogen recovery and purity using ITPE’s advanced technological facilities. The findings are expected to contribute to improving PSA-based hydrogen purification strategies, offering a pathway for enhanced industrial-scale hydrogen production. This work provides a foundation for bridging theoretical PSA principles with practical implementation, supporting the growing demand for clean hydrogen in sustainable energy systems. Full article

With the increasing severity of global environmental issues and the pressure from the strict pollutant emission regulations proposed by the International Maritime Organization (IMO), the shipping industry is seeking new types of marine power systems that can replace traditional propulsion systems. Marine fuel cells, as an emerging energy technology, only emit water vapor or a small amount of carbon dioxide during operation, and have received widespread attention in recent years. However, research on their application in the shipping industry is relatively limited. Therefore, this paper collects relevant reports and literature on the use of fuel cells on ships over the past few decades, and conducts a thorough study of typical fuel cell-powered vessels. It summarizes and proposes current design schemes and optimization measures for marine fuel cell power systems, providing directions for further improving battery performance, reducing carbon emissions, and minimizing environmental pollution. Additionally, this paper compares and analyzes marine fuel cells with those used in automotive, aviation, and locomotive applications, offering insights and guidance for the development of marine fuel cells. Although hydrogen fuel cell technology has made significant progress in recent years, issues still exist regarding hydrogen production, storage, and related safety and standardization concerns. In terms of comprehensive performance and economics, it still cannot effectively compete with traditional internal combustion engines. However, with the continued rapid development of fuel cell technology, marine fuel cells are expected to become a key driver for promoting green shipping and achieving carbon neutrality goals. Full article

Because of the higher energy density of hydrogen as a clean energy source, the use of proton exchange membrane fuel cells (PEMFCs) for aviation applications has become an important research topic in recent years. Unlike batteries, fuel cells require a lot of peripheral aggregates to operate properly. The peripheral aggregates of a fuel cell, which constitute the so-called balance of plant (BoP), consume a certain part of the power generated by the fuel cell stack, which reduces the overall efficiency of the fuel cell system. One of the greatest challenges in the design of a fuel cell system is the sizing of the fuel cell stack and the determination of the internal power consumption of the BoP. This paper models the power demand of the BoP of a fuel cell system based on an automotive fuel cell power system. Furthermore, the effect of flight altitude on the power demand of the BoP is investigated. Full article

New energy vehicles have emerged as a prominent focus in the automotive industry. This study develops a comprehensive modeling specification for fuel cell systems in new energy vehicles and establishes a framework for fatigue life analysis in the frequency domain. First, a finite element model of the fuel cell system was created in accordance with established standards, followed by grid convergence analysis and grid quality correction to enhance model accuracy. Next, random vibration analysis was performed to determine the root mean square (RMS) stress distribution of the fixed plate assembly in a random vibration environment, and the results were validated through experimental tests. Finally, Miner’s linear cumulative damage rule and the rainflow distribution model for random processes were applied to predict the fatigue life of the fixed plate assembly and connecting bolts. Critical locations for potential structural fatigue were identified, and the simulation results were corroborated through fatigue testing. The findings validate the accuracy of the proposed fatigue analysis framework and offer valuable insights for the continued development of fuel cell systems. Full article

Rising greenhouse gas emissions stemming from road transport have intensified the need for efficient and environmentally friendly propulsion technologies. Hybrid and fuel cell electric vehicles have emerged as a viable solution, integrating internal combustion engines and fuel cells with electric motors to optimize fuel efficiency and reduce emissions. This article reviews and analyzes energy management strategies for the principal powertrain topologies of hybrid electric vehicles, focusing on achieving solution optimality in real-time applications. A thorough and comprehensive overview of rule-based, optimization-based, and learning-based energy management strategies is presented, highlighting their main attributes and providing a comparative analysis in terms of fuel economy improvements, real-time implementation feasibility, and computational complexity, while simultaneously identifying and uncovering areas requiring further research in the field. We found that while rule-based methods offer simplicity and real-time capability, their adaptability remains limited. Optimization-based and learning-based approaches, although often achieving near-optimal solutions, face challenges due to their high computational demands and integration complexities. Our analysis also revealed the importance of leveraging vehicle connectivity and intelligent transportation systems for future energy management developments, which will contribute to broader sustainability goals in the automotive sector. Full article

Fuel cell electric vehicles (FCEVs) have received significant attention in recent times due to various advantageous features, such as high energy efficiency, zero emissions, and extended driving range. However, FCEVs have some drawbacks, including high production costs; limited hydrogen refueling infrastructure; and the complexity of converters, controllers, and method execution. To address these challenges, smart energy management involving appropriate converters, controllers, intelligent algorithms, and optimizations is essential for enhancing the effectiveness of FCEVs towards sustainable transportation. Therefore, this paper presents emerging energy management strategies for FCEVs to improve energy efficiency, system reliability, and overall performance. In this context, a comprehensive analytical assessment is conducted to examine several factors, including research trends, types of publications, citation analysis, keyword occurrences, collaborations, influential authors, and the countries conducting research in this area. Moreover, emerging energy management schemes are investigated, with a focus on intelligent algorithms, optimization techniques, and control strategies, highlighting contributions, key findings, issues, and research gaps. Furthermore, the state-of-the-art research domains of FCEVs are thoroughly discussed in order to explore various research domains, relevant outcomes, and existing challenges. Additionally, this paper addresses open issues and challenges and offers valuable future research opportunities for advancing FCEVs, emphasizing the importance of suitable algorithms, controllers, and optimization techniques to enhance their performance. The outcomes and key findings of this review will be helpful for researchers and automotive engineers in developing advanced methods, control schemes, and optimization strategies for FCEVs towards greener transportation. Full article

Reducing reliance on fossil fuels has driven the development of innovative technologies in recent years due to the increasing levels of greenhouse gases in the atmosphere. Since the automotive industry is one of the main contributors of high CO₂ emissions, the introduction of more sustainable solutions in this sector is fundamental. This paper presents a novel energy management system for fuel cell hybrid electric vehicles based on dynamic programming and adaptive neuro fuzzy inference system methodologies to optimize energy distribution between battery and fuel cell, therefore enhancing powertrain efficiency and reducing hydrogen consumption. Three different approaches have been considered for performance assessment through a simulation platform developed in MATLAB/Simulink 2023a. Further validation has been conducted via a rapid control prototyping device, showcasing significant improvements in hydrogen usage and operational efficiency across different drive cycles. Results manifest that the developed controllers successfully replicate the optimal control trajectory, providing a robust and computationally feasible solution for real-world applications. This research highlights the potential of combining advanced control strategies to meet performance and environmental demands of modern heavy-duty vehicles. Full article

The Proton Exchange Membrane Fuel Cell (PEMFC) is a fast-developing battery technology, and the key to its reliability and lifespan improvement lies in the accurate assessment of durability. However, the degradation mechanism of the PEMFC is hard to determine and its internal parameters are highly coupled. Thus, the development of a more accurate life prediction model that meets the actual scenarios is to be investigated urgently. To solve this problem, a multi-feature fusion life prediction method based on the Temporal Convolutional Network-Gated Recurrent Unit (TCN-GRU) is proposed. A TCN algorithm is used as the prediction base model, and two GRU modules are included with the model to strengthen the model’s expression ability and improve its predictive accuracy. Two widely recognized datasets and two operating conditions are utilized for model training and prediction, respectively. Comparisons are made with single-feature parameter models in terms of Root Mean Square Error (RMSE) and the Determination Coefficient (R²). The results show that the prediction accuracy of the TCN-GRU multi-feature fusion model is higher than that of the single-feature models in terms of stability and anti-interference under both operating conditions. The accuracy of the TCN-GRU (three-feature) model is the most optimal in a steady-state condition at 80% of the training set ratio (RMSE = 3.27 × 10⁻³, R² = 0.965). Furthermore, with the increase in the input feature parameter, the TCN-GRU model is closer to the real value, which proves once again that the proposed model can meet the accuracy requirements of the life prediction of the PEMFC. Full article

The global shift towards sustainable energy solutions has prompted a reevaluation of traditional transportation methods. In this context, the replacement of electric cars with hydrogen-powered vehicles is emerging as a promising and transformative alternative. This publication explores the essence of this transition, highlighting the potential benefits and challenges associated with embracing hydrogen as a fuel source for automobiles. The purpose of this work is to provide a comprehensive comparison of electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs), analyzing their respective advantages and disadvantages. Additionally, this work will outline the significant changes occurring within the automotive industry as it transitions towards sustainable mobility solutions. Full article