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Existing research on fault diagnosis for polymer electrolyte membrane fuel cells (PEMFC) has advanced significantly, yet performance is hindered by variations in data distributions and the requirement for extensive fault data. In this study, a cross-domain adaptive health diagnosis method for PEMFC is proposed, integrating the digital twin model and transfer convolutional diagnosis model. A physical-based high-fidelity digital twin model is developed to obtain diverse and high-quality datasets for training diagnosis method. To extract long-term time series features from the data, a temporal convolutional network (TCN) is proposed as a pre-trained diagnosis model for the source domain, with feature extraction layers that can be reused to the transfer learning network. It is demonstrated that the proposed pre-trained model can hold the ability to accurately diagnose the various fuel cell faults, including pressure, drying, flow, and flooding faults, with 99.92 % accuracy, through the effective capture of the long-term dependencies in time series data. Finally, a domain adaptive transfer convolutional network (DATCN) is established to improve the diagnosis accuracy across diverse fuel cells by learning domain-invariant features. The results show that the DATCN model, tested on three different target domain devices with adversarial training using only 10 % normal data, can achieve an average accuracy of 98.5 % (30 % improved over traditional diagnosis models). This proposed method provides an effective solution for accurate cross-domain diagnosis of PEMFC devices, significantly reducing the reliance on extensive fault data.

The wind–thermal bundled power system achieves energy complementarity and optimized scheduling, which is an important way to build a new type of energy system. For the safe and stable operation of the wind–thermal bundled power system, accurate data-driven analysis is necessary to maintain real-time balance between electricity supply and demand. By summarizing the development and characteristics of wind–thermal bundled power system in China and different countries, current research in this field can be clearly defined in two aspects: short-term wind power prediction for wind farms and performance evaluation of automatic generation control (AGC) for thermal power generation units. For short-term wind power prediction, it is recommended to focus on historical data preprocessing and artificial intelligence methods. The technical characteristics of different data-driven wind power prediction methods have been compared in detail. For performance evaluation of AGC units, a comprehensive analysis was conducted on current evaluation methods, including the “permitted-band” and “regulation mileage” methods, as well as the issue of evaluation failure in traditional evaluation methods in practical engineering. Finally, the relative optimal dynamic performance of AGC units was discussed and the future trend of data-driven research in wind–thermal bundled power system was summarized.

Cold start is a critical operating scenario for the proton exchange membrane fuel cell (PEMFC), particularly in the field of transportation. Under sub-freezing temperatures, the water inside the cell will freeze and obstruct gas flow paths as well as cover catalyst reaction sites, resulting in a failed startup. This study proposes an optimization method for the -30°C cold start of PEMFC based on a data-driven surrogate model to improve cold start performance and reduce irreversible damage to the cell. A validated PEMFC cold start mechanism model is utilized as the basis for developing an extreme learning machine (ELM) based data-driven surrogate model, which is trained using data collected from the mechanism model and has higher computational efficiency compared with the original model. In addition, the NSGA-II multi-objective optimization algorithm is employed to optimize the current loading strategies and operating parameters using the surrogate model as fitness function. The objectives are to enhance the minimum voltage and reduce startup duration time. Moreover, experimental validation confirms the effectiveness of the proposed method. The test results demonstrate that a cold start from -30°C is achieved within 97 s, with the minimum voltage reaching 0.44 V. Notably, there is a reduction in startup time by 26 s and an increase in the minimum voltage by 0.06 V compared to the base case. This study establishes a foundation for researchers to adjust operating settings during cold start based on diverse applications and requirements.

Transport processes are crucial for the performance of electrochemical energy conversion devices and attract wide attentions. This work focuses on the critical oxygen transport process in the ionomer electrolyte film on a Pt electrode, which highly limits the performance of low Pt-loading fuel cells. Reduction of oxygen transport resistance may be achieved by optimizing the ionomer film morphology. Therefore, the relationship between ionomer film morphology and oxygen transport characteristics is explored by altering ionomer side chain lengths in this work. The results show that the swollen structure with larger water agglomerates in the ionomer film with shorter ionomer side chains is detrimental to the formation of oxygen transport paths. However, the multilamellar structure with an alternating alignment of water agglomerates and PFSA ionomer agglomerates in the ionomer film with longer ionomer side chains has a larger water-PFSA interface, which provides more oxygen transport paths and thus reduces the oxygen transport resistance. This work inspires the novel design concept of the ionomer electrolyte film with low local oxygen transport resistance, i.e., enlarging the water-PFSA interface parallel to the oxygen transport direction via altering the ionomer material properties, which is valuable for the development of low Pt-loading fuel cells.

Offshore wind power stands out as a promising renewable energy source, offering substantial potential for achieving low carbon emissions and enhancing energy security. Despite its potential, the expansion of offshore wind power faces considerable constraints in offshore power transmission. Hydrogen production derived from offshore wind power emerges as an efficient solution to overcome these limitations and effectively transport energy. This study systematically devises diverse hydrogen energy supply chains tailored to the demands of the transportation and chemical industries, meticulously assessing the levelized cost of hydrogen (LCOH). Our findings reveal that the most cost-efficient means of transporting hydrogen to the mainland is through pipelines, particularly when the baseline distance is 50 km and the baseline electricity price is 0.05 USD/kWh. Notably, delivering hydrogen directly to the port via pipelines for chemical industries proves considerably more economical than distributing it to hydrogen refueling stations, with a minimal cost of 3.6 USD/kg. Additionally, we assessed the levelized cost of hydrogen (LCOH) for supply chains that transmit electricity to ports via submarine cables before hydrogen production and subsequent distribution to chemical plants. In comparison to offshore hydrogen production routes, these routes exhibit higher costs and reduced competitiveness. Finally, a sensitivity analysis was undertaken to scrutinize the impact of delivery distance and electricity prices on LCOH. The outcomes underscore the acute sensitivity of LCOH to power prices, highlighting the potential for substantial reductions in hydrogen prices through concerted efforts to lower electricity costs.

As a high efficiency hydrogen-to-power device, proton exchange membrane fuel cell (PEMFC) attracts much attention, especially for the automotive applications. Real-time prediction of output voltage and area specific resistance (ASR) via the on-board model is critical to monitor the health state of the automotive PEMFC stack. In this study, we use a transient PEMFC system model for dynamic process simulation of PEMFC to generate the dataset, and a long short-term memory (LSTM) deep learning model is developed to predict the dynamic performance of PEMFC. The results show that the developed LSTM deep learning model has much better performance than other models. A sensitivity analysis on the input features is performed, and three insensitive features are removed, that could slightly improve the prediction accuracy and significantly reduce the data volume. The neural structure, sequence duration, and sampling frequency are optimized. We find that the optimal sequence data duration for predicting ASR is 5 s or 20 s, and that for predicting output voltage is 40 s. The sampling frequency can be reduced from 10 Hz to 0.5 Hz and 0.25 Hz, which slightly affects the prediction accuracy, but obviously reduces the data volume and computation amount.

Energy storage is an effective way to address the instability of renewable energy generation modes, such as wind and solar, which are projected to play an important role in the sustainable and low-carbon society. Economics and carbon emissions are important indicators that should be thoroughly considered for evaluating the feasibility of energy storage technologies (ESTs). In this study, we study two promising routes for large-scale renewable energy storage, electrochemical energy storage (EES) and hydrogen energy storage (HES), via technical analysis of the ESTs. The levelized cost of storage (LCOS), carbon emissions and uncertainty assessments for EESs and HESs over the life cycle are conducted with full consideration of the critical links for these routes. In order to reduce the evaluation error, we use the Monte Carlo method to derive a large number of data for estimating the economy and carbon emission level of ESTs based on the collected data. The results show that lithium ion (Li-ion) batteries show the lowest LCOS and carbon emissions, at 0.314 US$ kWh-1 and 72.76 gCO2e kWh-1, compared with other batteries for EES. Different HES routes, meaning different combinations of hydrogen production, delivery and refueling methods, show substantial differences in economics, and the lowest LCOS and carbon emissions, at 0.227 US$ kWh-1 and 61.63 gCO2e kWh-1, are achieved using HES routes that involve hydrogen production by alkaline electrolyzer (AE), delivery by hydrogen pipeline and corresponding refueling. The findings of this study suggest that HES and EES have comparable levels of economics and carbon emissions that should be both considered for large-scale renewable energy storage to achieve future decarbonization goals.

Proton exchange membrane fuel cell (PEMFC) is regarded as one of the most promising energy conversion devices, but cost and durability are two challenges that hinder its large-scale application. Short-term performance degradation prediction is of great importance for the optimization of operating conditions and maintenance strategies to improve the durability and reduce costs of the PEMFCs. This paper proposes a novel data-driven short-term prediction method combining discrete wavelet transform (DWT), gaussian process regression (GPR) and particle swarm optimization (PSO) algorithm. The DWT decomposes the voltage time series waveform into numerous sub-waveforms with different characteristics. The GPR is utilized to construct individual prediction models for distinct sub-waveforms and the final prediction outcomes are obtained by adding the results of each model. The PSO achieves the automatic hyper-parameters optimization in order to improve the accuracy and robustness of the GPR model. In addition, the effectiveness of the proposed method is fully validated by the degradation datasets of PEMFCs in constant and quasi-dynamic load current conditions as well as real road working conditions. Finally, compared with widely used data-driven models such as artificial neural networks (ANN), support vector machine (SVM), long short-term memory (LSTM) and newly proposed methods in relevant studies, the proposed method exhibits superior accuracy and stability for degradation prediction.

Paper-based microfluidic fuel cells (PMFCs) gradually become alternative energy sources due to their advanced features, including simple structure, low cost, without requiring auxiliary equipment. However, the applications of PMFCs are limited by the poor power density and expensive cost. Therefore, a novel design of catalyst layer with stepped Pd distribution is proposed to improve the PMFC performance and reduce the cost. An experimental investigation is performed to understand the underlying influence mechanism of the catalyst layer properties, including Nafion solution content catalyst layer area, catalyst loading and catalyst distribution. We found that the proportioin choice of 4.76% Nafion solution in preparing catalyst layer achieves a shortest reaction time and a highest output performance. In addition, choosing a small catalyst layer area and high catalyst loading is beneficial to improve the current density. With the hybrid arrangement of stepped catalyst layers, the PMFC reduces the cost and presents highest current density of 44.10 mA cm−2 and peak power density of 12.87 mWcm−2. Finally, our designed PMFC stack is employed to power a thermohygrograph and a blood glucose meter for at least 5 h. These practical applications are in favor of supporting the developments of integrated device with energy generation and electrochemical detection.

Abstract Proton exchange membrane (PEM) fuel cell faces the inevitable challenge of the cold start at a sub‐freezing temperature. Understanding the underlying degradation mechanisms in the cold start and developing a better starting strategy to achieve a quick startup with no degradation are essential for the wide application of PEM fuel cells. In this study, the comprehensive in situ non‐accelerated segmented techniques are developed to analyze the icing processes and obtain the degradation mechanisms under the conditions of freeze–thaw cycle, voltage reversal, and ice formation in different components of PEM fuel cells for different freezing time. A detailed degradation mechanism map in the cold start of PEM fuel cells is proposed to demonstrate how much degradation occurs under different conditions, whether the ice formation is acceptable under the actual operating conditions, and how to suppress the ice formation. Moreover, an ideal starting strategy is developed to achieve the cold start of PEM fuel cells without degradation. This map is highly valuable and useful for researchers to understand the underlying degradation mechanisms and develop the cold start strategy, thereby promoting the commercialization of PEM fuel cells.

Proton exchange membrane fuel cells (PEMFCs) are promising power sources owing to their high-power/energy densities and low pollution emissions. With the increasing demand for electricity for various low-power devices, small-scale storage of electricity encountered bottle-neck, which provides new opportunities for PEMFC. Owing to the high specific energy of PEMFCs, the utilization of this technology for small-scale applications has recently attracted significant attention. In recent years, considerable effort has been made to advance PEMFC technology and applications, especially in the small-scale PEMFC commercial market. The current review provides a holistic overview of the cutting-edge developments of small-scale PEMFCs in the transportation, stationary, and portable power generator fields. In particular, we examine current literature on the development of small-scale PEMFCs and discuss the operating complexity of PEMFC systems for different applications. Finally, we provide a forward-looking perspective on the strategies for small-scale high-specific-power PEMFC systems.

Data-driven modelling methods are being developed in the quest to achieve more accurate performance prediction of protons exchange membrane fuel cell (PEMFC) systems in response to their complicated physicochemical phenomena. However, there is little research in this field detailing the pre-processing and selection of balance of plants (BOP) features for the input layer of system performance prediction at different current densities. Furthermore, most of the previous research applies neural networks based on simulation data rather than real-time bench or vehicle operation datasets which leads to low robustness and unreliable practical results. This paper details the application of a novel algorithm denoted XGBoost-Boruta, which utilises the combination of an ensemble learning approach and a wrapping approach, to improve the robustness of feature selection and to increase the accuracy and robustness of PEMFC system performance prediction. By introduction of the Z score and shadow features to eliminate the randomness of conventional ensemble learning methods, seven key controllable BOP variables of the hydrogen anode, air cathode and cooling subsystems are selected as the original input variables to determine their dependency on the stack voltage. Two case studies are presented for verification and validation of the proposed algorithm based on the real-time dataset of bench experimental data and data obtained from heavy truck operation at current densities ranging from 100 to 1500 mA/cm2. The feature selection strategy, based on the proposed XGBoost-Boruta algorithm, largely decreases the RMSE by 23.8% and 14.1% and the R2 increases by 0.06 and 0.04 of both the bench experimental and the heavy truck validation datasets respectively.

The O2 permeation barrier across the nanoscale ionomer films on electrocatalysts contributes to a major performance loss of proton exchange membrane (PEM) fuel cells under low Pt loading. Enhancing O2 transport through the ionomer films is essential for developing low Pt loading catalyst materials in high-performance PEM fuel cells. This study found that adding an ionic liquid (IL) can effectively mitigate the dense ionomer ultrathin sublayer formed on the Pt surface, which severely hinders O2 transport to the catalyst sites. The molecular dynamics simulation results show that adding the IL significantly alters the ionomer ultrathin sublayer structure by inhibiting its tight arrangement of perfluorosulfonic acid chains but scarcely impacts the ultrathin sublayer thickness. Additionally, the IL addition provides a larger free space for O2 dissolution in the ultrathin sublayer. Consequently, due to IL molecules’ presence, the O2 density in the ultrathin sublayer on the Pt surface is improved by an order of magnitude, which will benefit the catalytic efficiency, and the O2 permeation flux across the ionomer film is increased by up to 8 times, which will reduce the O2 transport loss of the catalyst layer.

Abstract The flow field structure of a proton exchange membrane fuel cell (PEMFC) is a determining factor for improving the cell power density. In this study, a universal alternating flow field design for the first time is proposed, which arranges structural units with different flow resistances in an alternating way to significantly improve the gas transfer rate into the electrode, with the advantages of easy machining and low pumping loss. Based on the design, it is proposed and tested large‐scale fuel cells with three novel flow fields by combining a parallel channel, baffled channel, serpentine channel, and narrowed channel. The results show that the design can significantly enhance the gas supply efficiency and that the novel baffled flow field improves the PEMFC performance by 23% with low pumping loss. The design employed in the study offers additional options for flow field optimization and contributes to the early achievement of next‐generation ultrahigh power density fuel cells.

The present research proposes an innovative multi-physics coupled model of different configurations of an integrated coal gasification fuel cell combined cycle (IGFC) system employing Solid Oxide Electrolytic Cell (SOEC) for CO2 capture. Full-system simulation is carried out to examine efficiency. The model incorporates a Solid Oxide Fuel Cell (SOFC), a SOEC, a gas turbine (GT), and multiple recirculation loops operated by two ejectors. The results reveal that compared with traditional power plants, the proposed IGFC system equipped with SOEC can reduce CO2 emission by almost 80%, and operates environmentally beneficial. The efficiency of the system varies greatly depending on the design parameters implemented. The CO2 enrichment phenomenon by SOFC and capture measures of CO2 by SOEC are simultaneously analyzed. In addition, parametric analysis is performed to evaluate the coupling influence of multiple operating parameters on the IGFC system. Recirculation ratios of 0.75 with four times recirculations are found to be the optimal conditions for both SOFC fuel electrode and SOEC air electrode aimed at getting to the highest power generation efficiency and total CO2 capture rate of the system. After systematic optimization of the design parameters, the electrical efficiency and CO2 capture rate of the proposed system could achieve 68.47% and 87.88%, respectively, which are about 20% and 60% greater than those of traditional power plants. Furthermore, after optimizing the control strategy, the fuel utilization rate of the system increases from 63.09% to 83.40%.

Degradation prediction of proton exchange membrane fuel cell (PEMFC) stack is of great significance for improving the rest useful life. In this study, a PEMFC system including a stack of 300 cells and subsystems has been tested under semi-steady operations for about 931 h. Then, two different models are respectively established based on semi-empirical method and data-driven method to investigate the degradation of stack performance. It is found that the root mean square error (RMSE) of the semi-empirical model in predicting the stack voltage is around 1.0 V, while the predicted voltage has no local dynamic characteristics, which can only reflect the overall degradation trend of stack performance. The RMSE of short-term voltage degradation predicted by the DDM can be less than 1.0 V, and the predicted voltage has accurate local variation characteristics. However, for the long-term prediction, the error will accumulate with the iterations and the deviation of the predicted voltage begins to fluctuate gradually, and the RMSE for the long-term predictions can increase to 1.63 V. Based on the above characteristics of the two models, a hybrid prediction model is further developed. The prediction results of the semi-empirical model are used to modify the input of the data-driven model, which can effectively improve the oscillation of prediction results of the data-driven model during the long-term degradation. It is found that the hybrid model has good error distribution (RSEM = 0.8144 V, R2 = 0.8258) and local performance dynamic characteristics which can be used to predict the process of long-term stack performance degradation.

Abstract All‐solid‐state batteries (ASSBs) have become an important technology because of their high performance and low‐risk operation. However, the high interface resistance and low ionic conductivity of ASSBs hinder their application. In this study, a self‐developed electrochemical model based on an open‐source computational fluid dynamics platform is presented. The effect of contact area reduction at the electrode/solid‐state electrolyte interface is investigated. Then, a new conceptual 3D structure is introduced to circumvent the existing barriers. The results demonstrate that the discharge time is shortened by over 20% when the area contact ratio reduces from 1.0 to 0.8 at 1 C‐rate, owing to the increased overpotential. By adopting the new 3D pillar design, the energy density of ASSBs can be improved. However, it is only when a 3D current collector is contained in the cathode that the battery energy/power density, capacity, and material utilization can be greatly enhanced without being limited by pillar height issues. Therefore, this work provides important insight into the enhanced performance of 3D structures.

In this study, a 3-D multiphase model of the proton exchange membrane (PEM) electrolyzer is developed on an open-source computational fluid dynamics (CFD) platform, OpenFOAM. A pseudo-coupling method is presented to consider the detailed two-phase distribution conditions at the interface between the flow channel and liquid/gas diffusion layer (L/GDL) in anode flow channel of a 3-D PEM electrolyzer model. The detailed two-phase distribution conditions can be obtained by high-speed optical images or volume of fluid (VOF) simulation results. Through this method, it is possible to predict the effect of two-phase flow existing in the anode flow channel on the transport phenomena in the porous electrode, and thus cell performance. The 3-D model is validated with experimental test results at three current density conditions (0-1, 0-2, 0-3 A cm−2) considering the existence of two-phase flow. It is found that if the two-phase flow in the anode flow channel is neglected, the simulation polarization curves will deviate from the experimental test results, especially at high current density conditions. In contrast, the simulated polarization curves manage to fit the experimental data accurately with the pseudo-coupling method, indicating that it is necessary to consider the effect of two-phase flow. In addition, this pseudo-coupling model can not only take the multi-phase mass transport into consideration but also the distribution of temperature and current density inside a PEM electrolyzer. With the help of this model, a novel double-layer flow field is proposed to enhance the performance of electrolyzer. It is found that the cell performance of the proposed flow field is 0.171 V better that of the traditional parallel flow field at 3 A cm−2. Also, the temperature distribution and current density distribution of the new flow field are more uniform, which could benefit the durability and performance of PEM electrolyzer.

Proton exchange membrane fuel cells often suffer from low lifetimes and high cost. Here, the authors enhance the transient power performance and durability of these fuel cells by integrating a thin layer of tungsten oxide within the anode, which acts as a hydrogen reservoir, oxygen scavenger, and a regulator for the hydrogen-disassociation reaction.

As a clean electrochemical energy conversion device, proton exchange membrane (PEM) fuel cell will play great effect in the carbon neutral society. It is widely recognized as a challenge to couple the multi-phase flow in gas flow channel and the electrochemical activities in the electrodes in PEM fuel cell modeling. In this study, a two-fluid model is developed to describe the multi-phase flow in the whole cell, in conjunction with the transport phenomena and electrochemical reaction kinetics. We assume the analogy between micro channel and porous media, and derive the gas/liquid velocity ratio for multi-phase flow in channel through Darcy's law for two-phase flow, which is expressed as a function of gas/liquid viscosity ratio and liquid saturation. Integration of the two-fluid model makes it able to predict the two-phase pressure drop and local liquid saturation in gas flow channels in an operating fuel cell. A sub-model of liquid water coverage at the GDL (gas diffusion layer) surface is also developed and incorporated into the model to study the impact of various contact angles at GDL surface on cell performance. The liquid saturation profiles along the flow direction are compared with literature data with reasonable agreement achieved. The model is employed to study a flow field of five parallel channels connected with an inlet/outlet manifold. It shows that multi-phase flow in the gas flow channel leads to more non-uniform flow and oxygen distributions, and hence a larger concentration loss under high current densities than the single-phase channel flow model.

The morphological evolution caused by Ni coarsening is one of the main causes of performance degradation in solid oxide fuel cell (SOFC) anode. In this study, we perform a model-based quantitative analysis to investigate the influences of morphological evolution on SOFC performance and durability. A phase-field model is developed to track the temporal evolution of microstructure as inputs of an electrode numerical model for evaluating the electrochemical performance degradation over time. After model validation, parametric simulations are conducted. It is found that Ni coarsening manifests mainly in two aspects: the reduction of total three-phase boundary (TPB) length, and the lengthening of single TPB. Based on the quantified microstructure parameters, the overpotential increase caused by Ni coarsening is obtained. Besides, the results highlight the importance of Ni content on the temporal evolution. The performance analysis provides an optimal range of Ni content, which can maintain a relatively low overpotential during the whole process.

Proton exchange membranes with short-pathway through-plane proton conductivity are attractive for proton exchange membrane fuel cells. Here the authors align proton conducting channels orthogonal to the plane of composite proton exchange membranes using a magnetic field for improved fuel cell performance.

Hydrogen starvation leads to the extreme deterioration of fuel cell performance due to the induced voltage reversal and carbon corrosion in the anode catalyst layer (ACL) and gas diffusion layer. In this paper, reversal-tolerant anodes (RTAs) with different ACL configurations are proposed, where IrOx/C is used as a water electrolysis catalyst. Experimental results show that the separate IrOx/C catalyst layer of MEA samples, layered reversal-tolerant catalyst-coated membrane (layered-RTA), and reversal-tolerant gas diffusion electrode (GDE-RTA) significantly enhance the reversal tolerance and cell performance compared to conventional anode and common RTA consisting of a homogeneous catalyst layer mixed with IrOx/C and Pt/C (hybrid-RTA). Of these, GDE-RTA possessed a reversal tolerance time of 86 min, a power density of 1.42 W cm−2, and a minimum degradation rate of 2.4 mV min−1, suggesting it to be the best RTA structure. Cyclic voltammetry and electrochemical impedance spectrum were used to detect the properties of each sample. Additionally, the degradation mechanisms of the three RTAs are thoroughly investigated and discussed by means of microstructural characterization through scanning electron microscopy and transmission electron microscopy. This work provides novel ideas for the fabrication of a robust RTA by tuning the ACL configuration, which is practical for the commercialization of fuel cells.

The development of multi-physics-resolved digital twins of proton exchange membrane fuel cells (PEMFCs) is significant for the advancement of this technology. Here, to solve this scientific issue, a surrogate modelling method that combines a state-of-the-art three-dimensional PEMFC physical model and data-driven model is proposed. The surrogate modelling prediction results demonstrate that the test-set relative root mean square errors (rRMSEs) of the multi-physics fields range from 3.88% to 24.80% and can mirror the multi-physics field distribution characteristics well. In summary, for multi-physics field prediction, the data-driven surrogate model has a comparable accuracy to the comprehensive 3D physical model; however, it considerably reduces the cost of computation and time and achieves the efficient multi-physics-resolved digital-twin. Two model-based designs based on the as-developed digital twin framework, i.e. the PEMFC healthy operation envelope and the PEMFC state map, are demonstrated. This study highlights the potential of combining data-driven approaches and comprehensive physical models to develop the digital twin of complex systems, such as PEMFCs.

Polymer electrolyte membrane (PEM) fuel cells are electrochemical devices that directly convert the chemical energy stored in fuel into electrical energy with a practical conversion efficiency as high as 65%. In the past years, significant progress has been made in PEM fuel cell commercialization. By 2019, there were over 19,000 fuel cell electric vehicles (FCEV) and 340 hydrogen refueling stations (HRF) in the U.S. (~8,000 and 44, respectively), Japan (~3,600 and 112, respectively), South Korea (~5,000 and 34, respectively), Europe (~2,500 and 140, respectively), and China (~110 and 12, respectively). Japan, South Korea, and China plan to build approximately 3,000 HRF stations by 2030. In 2019, Hyundai Nexo and Toyota Mirai accounted for approximately 63% and 32% of the total sales, with a driving range of 380 and 312 miles and a mile per gallon (MPGe) of 65 and 67, respectively. Fundamentals of PEM fuel cells play a crucial role in the technological advancement to improve fuel cell performance/durability and reduce cost. Several key aspects for fuel cell design, operational control, and material development, such as durability, electrocatalyst materials, water and thermal management, dynamic operation, and cold start, are briefly explained in this work. Machine learning and artificial intelligence (AI) have received increasing attention in material/energy development. This review also discusses their applications and potential in the development of fundamental knowledge and correlations, material selection and improvement, cell design and optimization, system control, power management, and monitoring of operation health for PEM fuel cells, along with main physics in PEM fuel cells for physics-informed machine learning. The objective of this review is three fold: (1) to present the most recent status of PEM fuel cell applications in the portable, stationary, and transportation sectors; (2) to describe the important fundamentals for the further advancement of fuel cell technology in terms of design and control optimization, cost reduction, and durability improvement; and (3) to explain machine learning, physics-informed deep learning, and AI methods and describe their significant potentials in PEM fuel cell research and development (R&D).

The dead-ended anode (DEA) and anode recirculation operations are commonly used to improve the hydrogen utilization of automotive proton exchange membrane (PEM) fuel cells. The cell performance will decline over time due to the nitrogen crossover and liquid water accumulation in the anode. Highly efficient prediction of the short-term degradation behaviors of the PEM fuel cell has great significance. In this paper, we propose a data-driven degradation prediction method based on multivariate polynomial regression (MPR) and artificial neural network (ANN). This method first predicts the initial value of cell performance, and then the cell performance variations over time are predicted to describe the degradation behaviors of the PEM fuel cell. Two cases of degradation data, the PEM fuel cell in the DEA and anode recirculation modes, are employed to train the model and demonstrate the validation of the proposed method. The results show that the mean relative errors predicted by the proposed method are much smaller than those by only using the ANN or MPR. The predictive performance of the two-hidden-layer ANN is significantly better than that of the one-hidden-layer ANN. The performance curves predicted by using the sigmoid activation function are smoother and more realistic than that by using rectified linear unit (ReLU) activation function.

The compression of the gas diffusion layer (GDL) greatly affects the electrochemical performance of proton exchange membrane fuel cells (PEMFCs) by means of both the equivalent value and distribution of contact pressure, which depends on the packing manner of the fuel cell. This work develops an intelligent approach for improving the uniformity and equivalent magnitude of contact pressure on GDLs through optimizing the clamping forces and positions on end plates. A finite element (FE) model of a full-size single fuel cell is developed and correlated against a direct measurement of pressure between the GDL and a bipolar plate. Datasets generated by FE simulations based on the optimal Latin hypercube design are used as a driving force for the training of a radial basis function neural network, so-called the agent model. Once the agent model is validated, iterations for optimization of contact pressure on GDLs are carried out without using the complicated physical model anymore. Optimal design of clamping force and position combination is achieved in terms of better contact pressure, with the designed equivalent magnitude and more uniform distribution. Results indicate the proposed agent-based intelligent optimization approach is available for the packing design of fuel cells, stacks in particular, with significantly higher efficiency.

This paper evaluates the liquid water at the gas diffusion layer-channel (GDL-channel) interface of reconstructed GDL microstructures with uniform and non-uniform fiber diameters in wave-like channels. A non-uniform GDL microstructure is reconstructed for the first time at the GDL-channel interface to evaluate droplet motion. The three-layer GDL microstructures are generated using the stochastic technique and implemented using the OpenFOAM computational fluid dynamics (CFD) software (OpenFOAM-6, OpenFOAM Foundation Ltd., London, UK). The present study considers the relationship between reconstructed GDL surfaces with varying fiber diameters, wettability, superficial inlet velocity and droplet size. Results show that the droplet detachment and the average droplet velocity decrease with an increase in the fiber diameter as well as the structural arrangement of the fibers. Under the non-uniform fiber arrangement, the removal rate of water droplets is not significantly improved. However, the choice of smaller fiber diameters facilitates the transport of droplets, as hydrophobicity increases even at slight surface roughness. The results also indicate that the average droplet velocity decreases under low inlet velocity conditions while increasing under high inlet velocity conditions. Therefore, the structural make-up of the GDL-channel interface influences the droplet dynamics, and the implementation of a non-uniform GDL structure should also be considered in the GDL designs.

This study numerically investigates liquid water dynamics in a novel hybrid sinusoidal flow channel of a proton exchange membrane fuel cell (PEMFC). The two-phase flow is examined using a three-dimensional, transient computational fluid dynamics (CFD) simulation employing the coupled level set and volume of fluid (VOF) method. Simulations for hybrid and non-hybrid sinusoidal flow channels, including a straight flow channel, are compared based on their water exhaust capacities and pressure drops. Additionally, the effects of inlet gas velocity, wall wettability, and droplet interaction in the flow channel on the dynamic behaviour of liquid water are investigated. Results reveal that the novel hybrid sinusoidal channel designs are consistent in terms of quicker water removal under varying hydrophilic wall conditions. Also, it is found that the liquid surface coverage, detachment, and removal rate depends on droplet proximity to the walls, inlet gas velocity, and wall contact angle. Also, the time a droplet makes contact with the side walls affect the discharge time. Additionally, there is an improvement in the gas velocity magnitude and vertical component velocity across the hybrid sinusoidal channel designs. Therefore, the unique geometric configuration of the proposed hybrid design makes it a viable substitute for water management in PEMFC applications.

The interaction of acridine orange (AO) with double-stranded (ds) DNA in aqueous solution was investigated by linear sweep polarography (LSP) on a dropping mercury working electrode (DME). In pH 2.5 Britton–Robinson (B–R) buffer solution, AO had a sensitive linear sweep polarographic reductive peak at –0.89 V (vs. SCE), which could be greatly inhibited by the addition of dsDNA, with a positive shift of the peak potential. Based on the decrease of the reductive peak current, a new quantitative electrochemical determination method for dsDNA was developed with a linear range of 2.0−20.0 mg l-1 and the linear regression equation: ΔIp” (nA) = 111.90 C (mg l-1)+125.32 (n = 9, γ = 0.997). The influences of commonly co-existing substances, such as metal ions, amino acid, etc., on the determination were also investigated. The method is sensitive, rapid and simple with good selectivity. The new proposed method was further applied to the detection of RNA and three synthetic samples containing dsDNA with satisfactory results. The binding number and the equilibrium constant between dsDNA and AO were calculated by an electrochemical method.

In this paper hemoglobin (Hb) was used to catalyze the oxidative reaction of ο-tolidine (OT) with H2O2. The oxidative product of OT with H2O2 was an azo substrate, which was electroactive and had a sensitive linear sweep voltammetric reductive peak at -0.52 V (vs. SCE) on hanging mercury drop working electrode (HMDE) in pH 5.0 Britton-Robinson (B-R) solution. The conditions of Hb catalytic reaction and voltammetric detection were optimized. Under the optimal conditions, the electrochemical behaviour of the oxidative product was carefully investigated and the electrode process of the product on mercury electrode was proposed. Based on the increase of the reductive peak current of the oxidative product with the concentration of the H2O2 or Hb, a new electrochemical method for the determination of trace amount of H2O2 or Hb was proposed. The calibration graph had a linear range of 6.0 x 10-8 to 4.0 x 10-5 M for H2O2 and 1.0 x 10-9 to 7.0 x 10-7 M for Hb with the detection limit of 1.0 x 10-8 M H2O2 and 5.0 x 10-10 M Hb (3σ), respectively. This new proposed method was further attempted to determine the content of H2O2 in fresh rainwater with satisfactory results.

In a weak acidic Britton–Robinson (B–R) buffer of pH 4.0, human serum albumin (HSA) can react rapidly with carminic acid (CAA) to form a supramolecular complex. Microelectrostatic fields in the molecular structure of HSA cause the electrostatic binding reaction of the positively charged CAA with the negatively charged HSA. CAAhas a sensitive, second order derivative linear sweep voltammetric reductive peak at – 0.54 V (vs. SCE) on a dropping mercury electrode (DME). After the addition of HSA to a CAAsolution, the reductive peak current of CAAdecreased without a shift of the peak potential. Based on the decrease of the peak current, a sensitive voltammetric method for the determination of microamounts of the protein is proposed. The conditions for the binding reaction and the voltammetric detection were optimized. Under the optimal conditions, the decrease of the voltammetric detection were optimized. Under the optimal conditions, the decrease of the voltammetric peak current was in proportion to the quantity of HSA in the range of 2.0 to 70.0 mg l-1, with a linear regression equation DIp”(nA) = 11.74 C (mg l-1)–12.33 and a detection limit of 2.0 mg l-1 (3s). The method was further applied to the detection of different kinds of proteins, such as bovine serum albumin (BSA), oval albunim (OVA), lipase, etc. The established method was further applied to the determination of the albumin content in human serum samples and the results obtained by this method were in good agreement with the traditional Coomassie Brilliant Blue G-250 (CBB G-250) spectrophotometric method. The binding mechanism is discussed and the binding ratio was calculated from the voltammetric data.

An electrochemical assay of the enzyme alkaline phosphatase (ALP) using ascorbic acid 2-phosphate (AAP) as a new voltammetric substrate has been described in this paper. In the alkaline buffer solution the ALP enzymatic hydrolysis product of AAP was ascorbic acid (AA), which was an electro-active substance and had a sensitive differential pulse voltammetric (DPV) oxidative response on glassy carbon electrode (GCE) at +380 mV (versus Ag/AgCl), so the activity of ALP could be monitored voltammetrically of the oxidative peak current of AA. The electrochemical behaviours of AA were carefully studied and the AA standard solution could be measured by DPV method in the linear range from 10.0 to 1000.0 μmol/L with the detection limit of 8.0 μmol/L. The optimal conditions for ALP enzymatic reaction and the voltammetric detection were optimized. Under the optimal conditions the calibration curve for ALP assay exhibited a linear range from 0.4 to 2000.0 U/L with a detection limit of 0.3 U/L. This proposed method was further applied to determine the ALP content in healthy human serum and the results were in good agreement with the traditional p-nitrophenyl phosphate spectrophotometric method. The kinetic constants of enzymatic reaction were also investigated with the apparent kinetic constant Km as 2.77 mmol/L and the maximum velocity Vmax as 0.33 mol/min.

Thirteen new triazoles containing 1,3-dioxolane rings were synthesized and their identities confirmed by means of IR, NMR, MS, elemental analysis and X-ray crystallography. The results of preliminary biological tests show that all of these compounds possess some fungicidal and plant growth regulant activities.

The crystal and molecular structures of [Ni(Im)6](dtp)2 (Im = imidazole, dtp = O,O′-diphenyldithiophosphate) have been determined by X-ray crystallography. It crystallizes in the triclinic system, space group Pī, with cell parameters a = 9.375 (2), b = 12.324(3), c = 13.285(3) Å, α = 107.86(3), β = 102.28(3), γ = 109.24(3), and Z = 1. The crystal structure of the title compound is built up of discrete monomeric molecules of [Ni(Im)6](dtp)2. The nickel (II) ion is hexacoordinated by six imidazole molecules and the coordination environment of Ni (II) is of octahedral geometry. In the solid state, a network of N-H···S intermolecular hydrogen bonds connect the Ni(Im)6 moieties and O,O′-diphenyldithiophosphate molecules, forming a three-dimensional structure.

In this study, a 3D + 1D (three-plus-one dimensional) multiphase PEM (proton exchange membrane) fuel cell model is developed, in which the detailed channel two-phase flow is also considered based on the two-phase Darcy's law. Compared with conventional 3D fuel cell models, the 3D + 1D model improves calculation efficiency and stability. Utilizing this model, the simulation work is conducted on an automobile PEM fuel cell (309.12 cm2) with the full flow field morphology considered. Its accuracy is comprehensively validated at this scale against the experimentally measured polarization curves and HFR (high frequency resistance) under different anode and cathode stoichiometric ratios and pressures and operating temperatures, indicating the model reliability. Subsequently, we numerically investigate the influence of four flow field configurations on cell performance characteristics, including the wave-like, dotted, baffled, and straight flow fields. It is found that the dotted and baffled configuration effectively improved cell performance, but the wave-like flow field has little influence on cell performance compared with straight flow field. Moreover, it is found that decreasing the channel/rib width decreases the concentration loss, but it also increases the ionic ohmic loss, resulting in an optimal channel/rib width for cell performance improvement. [ABSTRACT FROM AUTHOR]