A comprehensive understanding of spatially non-uniform degradation under high current density conditions is essential for health management of proton exchange membrane fuel cell (PEMFC). In this study, a segmented PEMFC is subjected to a 600-hour durability test at 2.0 A & sdot;cm-2. PEMFC is uniformly divided into 11 equal segments (S1-S11, 21 mm each) along air flow direction from inlet to outlet. At end-of-life (EOL), the average platinum particle radius increases to 2.52 nm in segment S11 and 3.35 nm in segment S1. This non-uniform platinum irreversible degradation reduces current density in segments S1-S4, while increases current density in segments S5-S11. Meanwhile, during operation periods, the current density decreases in segments S1 and S8S11 but increases in segments S2-S7, confirming the spatially non-uniform reversible degradation. To interpret these observations, a three-dimensional degradation model is developed incorporating platinum Oswald ripening, coalescence and ionomer sulfonate adsorption. The model reveals that the difference in ionomer water content between operation periods and recovery steps accelerates sulfonate coverage onto platinum, with coverage ratio reaching 0.15-0.437 in segments S1-S2 and S8-S11, thereby inducing severe reversible degradation. Reversible degradation modifies the spatial evolution of electrochemical active surface area (ECSA) under irreversible degradation, shifting ECSA distribution from an initially low-inlet/high-outlet pattern to a middle-peaked profile. In contrast, the spatial trend of proton conductivity degradation remains consistent with that driven by irreversible degradation. This study provides a predictive PEMFC model for non-uniform degradation under high current density, offering guidance for the development of PEMFC health management and degradation mitigation strategies.

Against the backdrop of sustainable energy development, the integration of renewable energy and carbon trading mechanisms has emerged as an effective pathway for reducing carbon emissions in integrated energy systems (IES). However, existing studies predominantly focus on the performance assessment of single-type distributed energy system architectures and lack a multi-dimensional comparative evaluation of wind-solar (WS) and wind-solar-gas (WSG) energy systems. Moreover, current research mainly emphasizes energy, economic, and environmental dimensions, often overlooking the critical aspect of energy supply autonomy. To address these gaps, this study constructs two system architectures: wind-solar IES and wind-solar-gas IES. A multi-objective optimization framework incorporating the ladder carbon trading mechanism is established, with optimization objectives including primary energy savings ratio (PESR), annual total cost savings ratio (ATCSR), carbon dioxide emission reduction ratio (CDERR), and grid interaction level (GIL). The Pareto fronts are obtained using the NSGA-II algorithm, and the entropy-weighted TOPSIS method is applied for multi-objective decision-making to compare the optimal configurations and overall performance of the two architectures. The results indicate that, compared with WSG, WS achieves improvements of 5.3% in PESR and 7.1% in ATCSR. However, its CDERR decreases by 2.7%. The higher emissions in WS despite lower energy consumption result from greater reliance on coal-dominated grid electricity (0.968 kgCO2/kWh) compared to WSG’s use of natural gas (0.203 kgCO2/kWh). In addition, the GIL of WS increases by 23.8%, indicating stronger grid dependence. These findings reveal that WS exhibits superior economic and energy performance but relatively weaker environmental benefits and reduced energy supply autonomy. This study provides a valuable analytical paradigm for the optimal design and comprehensive evaluation of IES.

Accurately estimating the state of health of proton exchange membrane fuel cell (PEMFC) and predicting the degradation trend are essential prerequisites for effective health management to enhance durability. This paper proposes a generalized hybrid degradation prediction method for PEMFC that is applicable to diverse operating conditions. Firstly, the internal polarization dynamics are characterized via the distribution of relaxation times method, and a third-order equivalent circuit model is established to quantify the polarization losses. The voltage losses are quantified using a polarization curve model. Degradation characteristic analysis from both approaches consistently reveals that deterioration in mass transfer kinetics and charge transfer kinetics is the primary cause of performance degradation. Subsequently, component-level degradation indexes are extracted based on degradation models, and a novel weighted fusion method is proposed to construct a hybrid degradation index characterizing the overall degradation state of PEMFC. Finally, the Bayesian-optimized Bi-directional long short-term memory (Bi-LSTM) model is employed to predict PEMFC degradation trend under various prediction horizons, enabling accurate estimation of remaining useful life (RUL). The results show that the optimized Bi-LSTM achieves higher RUL estimation accuracy than the baseline Bi-LSTM, and the hybrid method outperforms the AutoML-based method and the cascaded echo state network reported in previous studies. For the first stack, the estimation error remains below 7.78%, with a minimum error of 0.50%. For the second stack, the estimation error does not exceed 12.28% overall and drops below 10% when the prediction horizon is within 300 h, with a minimum error of 2.67%.

The development trend of high-current-density and large-reaction-area in proton exchange membrane fuel cell (PEMFC) results in severe reactant distribution non-uniformity along the flow direction, imposing substantial constraints on both performance and durability. While gradient electrode design and operating parameter regulation have been proposed as promising strategies to mitigate these limitations, a systematic and broadly applicable optimization framework remains absent. To address this, a high-accuracy long-channel segmented 3D PEMFC model is developed to comprehensively characterize the effects of gradient Pt loading, gradient gas diffusion layer (GDL) porosity, cathode inlet relative humidity, and cathode backpressure on three novel indexes, including voltage (Vcell), local current density uniformity (sigma), and the peak water saturation in the midstream (sathigh). Subsequently, three advanced machine learning models are constructed based on the computational data from the numerical model, with an accurate deep neural network (DNN) selected as the surrogate model. Furthermore, the black-box model interpretation method is incorporated to quantify the contribution of each variable and to enhance the transparency of the surrogate model. Finally, the combination of DNN, nondominated sort genetic algorithm (NSGA-II), and the evaluation strategy accelerates the acquisition of optimal solutions under different decision-making requirements. The results demonstrate that the proposed integrated optimization framework has sufficient predictive and analytical capabilities. The selected optimal solution achieves a 5.79% increase in Vcell, with sigma and sathigh reduced by 60.24% and 16.53%, respectively. The work provides a promising strategy for the future development of functionally graded electrodes and achieving the goals of high performance and long lifetime for PEMFC.

The cold start of proton exchange membrane fuel cells (PEMFCs) at subzero temperatures is hindered by ice formation during the process. We developed a hierarchical mesoporous carbon structure, denoted as TMC-GC, showing strong suppression of heterogeneous ice nucleation. The water/TMC-GC mixture exhibited a phase transition temperature 4.2 degrees C lower than that of the mixture containing commercial carbon black (XC72). Low-field nuclear magnetic resonance reveals that TMC-GC can sustain a larger fraction of unfrozen water with higher molecular mobility compared to XC72 at -30 degrees C, thereby favoring more effective removal of supercooled water during cold start. Adopting TMC-GC as the carbon support, the Pt/TMC-GC catalyst achieved an isothermal operational time of 25.3 min at -10 degrees C, 3.7 times that of commercial Pt/C (6.8 min), in single-cell PEMFC tests, demonstrating the state-of-the-art cold-start performance. Stack-level theoretical projections indicate that replacing Pt/C with Pt/TMC-GC enhances PEMFC robustness, enables faster startup, and reduces preheating energy demand (by 28.9% at -20 degrees C) during cold starts. Owing to the superior mass-transport characteristics of the hierarchical mesoporous carbon structure, the Pt/TMC-GC delivers key performance metrics that surpass the U.S. Department of Energy targets and are competitive with leading catalysts under normal-operation conditions.

This work extensively examines how various operational conditions (temperature, pressure, humidity, and stoichiometric ratio) affect both the local current density (LCD) uniformity and performance of a long-channel proton exchange membrane fuel cell (PEMFC), utilizing a segmented anode flow field plate equipped with LCD measurement capabilities. As current density increases, charge transfer resistance (Rct) decreases rapidly before stabilizing at a lower level, while mass transfer resistance (Rmt) exhibits an exponential increase. Under full humidification, LCD linearly decreases along the channel and pronounced non-uniformities in LCD arise due to the "flooding" of the cathode outlet at higher current densities. Higher temperatures, pressures, and stoichiometric ratios effectively mitigate "flooding", decreasing Rmt and improving LCD uniformity, thereby enhancing PEMFC performance. However, lower cathode relative humidities can improve voltage but worsen the LCD uniformity under medium and high current densities, and both the voltage and LCD uniformity can be improved by appropriately reducing anode relative humidity. Crucially, a linear relationship between LCD nonuniformity and Rmt/Rct has been identified, which is useful for comparing the LCD non-uniformity under different combinations of operating conditions, particularly under high current densities. These findings highlight the value in quickly screening suitable operating conditions through electrochemical impedance spectroscopy to optimize the PEMFC performance, as well as the guidelines for the development of high-powerdensity and long-life PEMFCs.

Proton exchange membrane fuel cells (PEMFCs) face persistent challenges in cost, performance, and durability, particularly under high current density (HCD) conditions. While degradation mechanisms under low current density are well-documented, those under HCD remain inadequately understood. This work investigates the in-plane non-uniform degradation behavior of PEMFC over 600 h at 2.0 A/cm2, employing in-situ monitoring and numerical simulations. The results reveal that voltage decay rates of 0.069 mV/h, 0.071 mV/h, and 0.098 mV/h at 0.5 A/cm2, 1.0 A/cm2, and 2.0 A/cm2, respectively, indicate accelerated degradation at higher current densities. Importantly, a substantial portion of the voltage losses is reversible, as recovery occurs following shutdown procedures. Furthermore, the cathode catalyst layer is identified as the primary site of degradation, evidenced by a 60.45 % reduction in electrochemical surface area and notable increases in charge transfer and mass transfer resistances. In addition, Platinum particle degradation exhibits spatial non-uniformity, with the particle radius decreasing from 3.35 nm at the cathode inlet to 2.52 nm at the outlet, resulting in an average radius of 2.84 nm-nearly double the initial value of 1.51 nm. The observed spatial heterogeneity is driven by the non-uniform distribution of local current density. These findings indicated the presence of spatially heterogeneous degradation mechanisms in PEMFCs, highlighting the need for mitigating in-plane non-uniformity to improve performance and durability under HCD conditions.

Dead-end operation of proton-exchange-membrane fuel cells (PEMFCs) causes significant water accumulation, and the resulting water states strongly modulate catalytic activity, heat transfer, and mass transport. Accurate, real-time knowledge of these internal water states is therefore indispensable for effective PEMFC health management. Nevertheless, current experimental and modeling techniques cannot yet deliver dynamic, online predictions. To address this gap, we propose the LSTM-Inception-Transformer, a multi-modal data-fusion network tailored for water-state estimation. The network is trained exclusively on data produced by a validated, three-dimensional, non-isothermal, two-phase, single-channel PEMFC model. By combining long short-term memory (LSTM), Inception, and Transformer blocks, the architecture achieves cross-modal feature fusion and yields reliable water-state estimates under arbitrary load voltages and times. In contrast to conventional water-management strategies that depend only on output voltage/current signals or empirical rules, the proposed approach supplies direct, dynamic decision support. Numerical experiments demonstrate a 51.8% accuracy gain relative to a single-modal LSTM baseline.

With the swift electrification of mobility and transportation, low temperature heating methods (LTHM) have garnered widespread attention and have significantly advanced in enhancing the low-temperature adaptability of power batteries. In order to reveal the global research progress and hot trends of LTHM for power batteries, different from the existing human-experienced literature review, knowledge graphs of LTHM for power batteries based on bibliometrics are conducted in this paper. The research on LTHM for power batteries is analyzed qualitatively and quantitatively focusing on the volume of published papers, keywords, burst terms, technical characteristics and heating models. Wherein, China is the country with the largest publication frequency and centrality contribution in this field. Global scholars have formed research hotspots mainly on lithium batteries, heating properties, phase change materials, anode materials and thermal management. The heating methods are categorized into three types: internal, external and combined heating methods, with the heating rates distributed in 0.5-5 degrees C/min mostly. The internal heating methods exhibit higher heating rates compared to external heating methods on the whole. However, the practical application is constrained by several factors, including lifespan influence, securities, and immature technology. Furthermore, the modeling methods and parameter identification methods for theoretical models of the internal heating methods differ for different heating methods. For alternative current heating, without restricting safety limits, larger current amplitudes can result in more rapid heating rates. Finally, based on the current research status, some prospects for the future have been made.

The exponential growth in demand for computing power has resulted in a rapid expansion of energy consumption and CO2 emissions from data centers. Consequently, the full utilization of renewable energy sources is regarded as the most effective strategy for data centers to achieve near-zero carbon emissions. However, due to the mismatch between the intermittency of renewable energy and the time-varying workloads. Data centers still face challenges in integrating renewable energy and exploiting the regulation potential of computing tasks. Therefore, this study proposes a novel multi-featured collaborative optimization framework for low-carbon data center integrated energy systems (DCIES) that integrates task scheduling mechanism, renewable energy uncertainty and hybrid cooling. Firstly, the renewable energy scenario generation is based on the generative adversarial network with gradient penalty. The two-stage distributionally robust optimization model for DCIES based on data-driven uncertainty set is established to address the renewable energy uncertainty. Secondly, this study exploits the flexibility regulation potential of data center by formulating the workload scheduling mechanism for multiple tasks with different server execution times and delay-tolerant times. The results reveal that the DCIES collaborative optimization scheme, which integrates the task scheduling mechanism, renewable energy uncertainty and hybrid cooling, could reduce the total cost and CO2 emissions by 23.2 % and 28.4 %, respectively, while reducing the renewable energy curtailment by 7.4 %. This multi-featured collaborative optimization of data center computing electricity and thermal provides a novel approach to the low-carbon and sustainable development of data center buildings.

Prognostics and health management (PHM) is an effective method to improve the durability of proton exchange membrane fuel cells (PEMFCs). Accurate lifetime prediction is an essential prerequisite for health management. This paper proposes a hybrid prediction method that combines degradation mechanisms with deep learning neural networks to predict the degradation trends and estimate the remaining useful life (RUL) of PEMFCs under dynamic load cycle conditions. Firstly, the polarization curve model is employed to extract degradation-related parameters and quantify the overvoltage. The relationship between overvoltage and membrane electrode assembly (MEA) degradation is analyzed, revealing that cathode catalyst and membrane are the key components influencing the degradation. Secondly, a comprehensive degradation index (CDI) is developed. A novel method for quantifying the weight coefficients of the CDI is proposed for the first time. The effects of catalyst and membrane degradation on the overall performance degradation are quantified, which are 82.2 % and 17.8 %, respectively. Finally, the long short-term memory (LSTM) and gated recurrent unit (GRU) models are employed to predict the degradation trend. The results show that GRU outperforms LSTM in this study. The maximum RUL estimation error of the proposed hybrid method is 9.50 %, with all errors within the 10 % confidence interval.

The widely adopted dual-evaporator integrated thermal management system is crucial for ensuring driving safety and occupant comfort in electric vehicles. However, given the different cooling requirements for occupants and power batteries, existing systems are unable to efficiently decouple the evaporation temperatures of the cabin evaporator and battery evaporator. The intermediate-pressure port of the vapor injection compressor may be utilized to directly integrate the battery evaporator for cooling purposes. Building on this concept, a "cabin + battery" type integrated thermal management system employing a dual-suction scroll compressor is proposed and experimentally investigated at different evaporation temperatures of both evaporators and compressor speeds. The novel system is comprehensively evaluated against both a conventional baseline and a modified system equipped with a pressure reducer. Results demonstrate that the dual-suction system operates stably under two distinct evaporation temperatures and exhibits enhanced cooling performance compared with both baseline systems. Quantitative improvements over the reference modified system include 16.5-43.7 % higher cooling capacity, 1.8-29.4 % higher coefficient of performance, and 6.4-18.4 degrees C lower compressor discharge temperature. Additionally, for the dual-suction system, adopting a strategy that reduces the evaporation temperature of the cabin evaporator while increasing that of the battery evaporator proves notable effectiveness in enhancing the cooling capacity of the battery evaporator. A promising energy-saving and efficient integrated thermal management system solution for electric vehicles is provided.

Sustainability assessment and flexibility enhancement are the key to achieve efficient and economic, supply-demand matching and comprehensive evaluation of integrated energy systems (IES). Therefore, a joint optimization model of economic, environmental and exergy for IES combining waste heat driven organic Rankine cycle (ORC) power generation and multi-energy storage is developed, and the emergy theory is introduced for system sustainability assessment. Firstly, the mixed-integer nonlinear optimization models for three scenarios of gas turbine (GT) without ORC, GT with ORC (GT-ORC) and solid oxide fuel cell (SOFC) with ORC (SOFC-ORC) are established, and the Pareto curve is obtained by using the augmented epsilon constraint. Secondly, the economic, environmental and exergy efficiency of the optimal decision-making scheme are analyzed, and the system sustainability is evaluated by solar emergy. The results reveal that in the optimal scenario, the SOFC-ORC scenario reduces CO2 emission by 33.2 %, but the annual cost is increased by 45.7 %, and exergy efficiency is lowered by 8.1 % compared to that of the GT-ORC scenario. Then, the emergy sustainability index of the optimal solution for SOFC-ORC scenario is 0.031, which is significantly lower than that in GT-ORC scenario, which is 0.152. The proposed emergy analysis method covers energy, economy, society and environment, and elucidate the developmental sustainability of the system.

Reducing platinum group metal (PGM) usage in a proton exchange membrane fuel cell (PEMFC) is essential for its broad implementation. To ensure the performance of low-PGM-loading PEMFCs, the contact between the flow-field plates and the membrane electrode assembly (MEA) is critical. We found the MEA with lower catalyst loading is more sensitive to the change of contact uniformity, which can be quantified as average contact pressure and proportion of contact area. When the contact pressure distribution becomes less uniform and the average contact pressure between the flow-field ridge and MEA decreases from 1.05 to 0.15 MPa, the MEAs with the PGM loading of 0.100 mg/cm2 and 0.060 mg/cm2 exhibit 7.8 % and 37.8 % power drop at 2.0 A/cm2, respectively. The experimental data is consistent with the theoretical study and can be explained as lower catalyst loading comes along with a lower volume fraction of conductive carbon support and fewer platinum sites, making the electrochemical reaction’s ohmic and mass transfer overpotential more sensitive to the environmental change. More specifically, the theoretical study shows that the MEA with lower loading (0.04 mgPGM/cm2) suffers a more than doubled ohmic overpotential increase compared to the MEA with higher loading (0.12 mgPGM/cm2) when average contact pressure reduces from 0.8 MPa to 0.2 MPa. Also, the lower catalyst loading MEA faces four times more mass transfer overpotential increase when the proportion of contact area reduces from 100 % to 40 %. Our findings suggest that the requirement of mechanical design and manufacturing accuracy of the components should be higher for PEMFC with lower catalyst loading.

Data centres are causing an increase in global energy demands. To prevent this new energy demand increasing CO2 emissions, data centres need to shift from being consumers to active prosumers. Policies and technologies to support this shift across computing, electrical and thermal energy systems will be crucial for reducing the energy consumption and emissions of data centres. The increasing demand for data centres risks greatly increasing greenhouse gas emissions. To prevent this problem from happening, data centres need to transition from being consumers to being prosumers.

With the rapid development of the digital economy, the energy demands and environmental pressures of data centers have become increasingly prominent. Traditional optimization and evaluation methods based on single indicators are no longer sufficient to comprehensively reflect the system's economic viability, environmental benefits, and resource utilization efficiency. This paper, based on emergy theory, converts the inputs of various energies, resources, equipment, and labor within the system into solar emergy, thereby establishing a cross-dimensional comprehensive evaluation framework. For the first time, the Emergy Sustainability Index (ESI) is introduced as the optimization objective of the data center integrated energy system (DC-IES). A bi-level optimization model is proposed in this study. In the upper level, a genetic algorithm is employed to optimize equipment capacity configuration to maximize the ESI. In the lower layer, typical daily operations are addressed by formulating a mathematical programming model that maximizes the renewable emergy input, thereby achieving a coordinated optimization of system capacity and operational scheduling. The TOPSIS method is used to select the optimal solution from the multi-objective Pareto curve, and the approach is validated through a case study of an actual data center. The results show that the ESI-based optimization case achieves an ESI value of 0.1518. Compared to the case optimized for both annual cost and carbon emissions, this solution yields a 296 % increase in ESI (from 0.0382 to 0.1518), a 61 % reduction in carbon emissions, and a 51 % increase in annual costs. Despite the moderate economic trade-off, it significantly improves system sustainability and environmental performance. Finally, the paper summarizes the current advantages and limitations of applying emergy theory in analyzing and optimizing DC-IES, thereby providing new theoretical directions for future research.

In proton exchange membrane fuel cells (PEMFCs) with large active areas, uneven distributions of reactants (e.g., water and oxygen) and electrochemical parameters (e.g., local current density) significantly affect performance, particularly under low stoichiometric ratios and high current densities. To address these issues, this study introduces a high-precision three-dimensional model integrating aggregate modeling with multi-parameter verification. The model accurately predicts key metrics such as hydrogen permeation current density and pressure differentials, with maximum deviations of 0.07 mA/cm2 and 0.64 kPa, respectively. Validation against segmented PEMFC experimental data demonstrates excellent agreement for output voltage, ohmic overpotential, and activation overpotential, with maximum relative errors of 1.0 %, 8.5 %, and 1.2 %, respectively. The study further reveals that ohmic and activation overpotentials may compensate for each other, improving polarization curve accuracy and underscoring the necessity of thorough verification of these parameters. Additionally, the model captures the linear-to-exponential growth of the non-uniformity index of local current density (sigma) with increasing current density, while demonstrating that higher pressure, temperature, stoichiometric ratios, and cathode humidity significantly reduce sigma, especially under high current density conditions. These findings highlight the model's strong predictive capabilities and its value for optimizing PEMFC performance through detailed internal physical field analysis.

The increasing adoption of electric delivery fleets introduces significant challenges related to uneven energy utilization and suboptimal scheduling efficiency. Vehicle-to-Vehicle (V2V) energy sharing presents a promising solution, but its effectiveness critically depends on precise matching and co-optimization within dynamic urban traffic environments. This paper proposes a hierarchical optimization framework to minimize total fleet operational costs, incorporating a comprehensive analysis that includes battery degradation. The core innovation of the framework lies in coupling high-level path planning with low-level real-time speed control. First, a high-fidelity energy consumption surrogate model is constructed through model predictive control simulations, incorporating vehicle dynamics and signal phase and timing information. Second, the spatiotemporal longest common subsequence algorithm is employed to match the spatio-temporal trajectories of energy-provider and energy-consumer vehicles. A battery aging model is integrated to quantify the long-term costs associated with different operational strategies. Finally, a multi-objective particle swarm optimization algorithm, integrated with MPC, co-optimizes the rendezvous paths and speed profiles. In a case study based on a logistics network, simulation results demonstrate that, compared to the conventional station-based charging mode, the proposed V2V framework reduces total fleet operational costs by a net 12.5% and total energy consumption by 17.4% while increasing the energy utilization efficiency of EV-Ps by 21.4%. This net saving is achieved even though the V2V strategy incurs a marginal increase in battery aging costs, which is overwhelmingly offset by substantial savings in logistical efficiency. This study provides an efficient and economical solution for the dynamic energy management of electric fleets under realistic traffic conditions, contributing to a more sustainable and resilient urban logistics ecosystem.

Temperature vacuum swing adsorption (TVSA) is currently one of the potential methods for the direct air capture (DAC). However, the adsorption capacity commonly presents downtrend along the bed at end adsorption step and the different operation conditions directly influence the energy and exergy performance. In this study, the cascade electrical-temperature heating method (C-ETVSA) is proposed matching the adsorption capacity distribution and a numerical model is established to analyze the overall performance. Results demonstrate that the total recovery (eta(Rec)) is 82.5 % under the cascade heating of 363/383 K when the adsorption bed temperature (T-ad) reaches 360 K, which is 5.4 % higher than that under constant heating of 363/363 K. Simultaneously, T-ad within high adsorption zone is higher than that of low zone under cascade heating but presents opposite trend under constant heating, showing the supplied energy is utilized more effectively in former method. Specific energy consumption (E-total) realizes the minimum value of 395.8 kJ/mol under the cascade heating temperature of 393/418 K at distribution proportion of 1:2. And T-ad is increased as the adsorption duration time of C/C-0 (outlet/inlet CO2 concentration ratio) is extended from 1 % to 5 % because more heat is supplied to the adsorbent. Furthermore, E-total decreases from 395.8 to 375.4 kJ/mol when the target recovery increases from 90 % to 98 % and exergy efficiency (eta(ex)) raises from 5.36 % to 5.77 % because of simultaneous decreasing E-total and lifting minimum separation work consumption.