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• Propose a combined activation procedure reducing time and gas consumption. • Validate effectiveness of the procedure at single-cell and stack level. • Reveal change of electrochemical factors and microstructure in activation. • Activation performance is related to hydration status and oxide reduction. Activation is a crucial procedure to improve the performance of newly fabricated fuel cell stacks to meet the power requirements in actual application scenarios. However, traditional activation procedures generally last for several hours or even days, which greatly increases the cost and limits the production efficiency. In response to this problem, this paper proposes a rapid activation procedure composed of hydrogen pumping (HP) and limiting-current activation with limited air (LCA). The influence of current density for HP and air supply for LCA on activation effect is studied and the best operating conditions are determined. The importance of water supply to the cathode is highlighted during HP. The influence of air supply for LCA on activation effect is limited, which contributes to gas saving. According to the experiments conducted on a single cell and a 10-cell stack, only 40 min are needed with this combined procedure, which is much shorter than that of the traditional step-current activation. Furthermore, nearly 75% of hydrogen is saved due to the short activation time and the control of gas supply, thus significantly reducing the cost during activation. Furthermore, to reveal the underlying mechanisms, membrane electrode assembly parameters before and after activation are obtained by micro-current excitation. A decrease in ohmic resistance and an increase in roughness factor of catalyst are observed, which contribute to performance improvement. According to the characterizations in the microstructure of membrane electrode assembly and platinum catalyst valence state, the electrolyte hydration and oxide reduction are determined as key factors and principles during activation. [ABSTRACT FROM AUTHOR]

[Display omitted] • Originally propose in-situ gas distribution characterization method for stacks. • Establish theoretical dual-chamber model decoupled by micro-current excitation. • Reveal distribution features and precisely recognize channel-choked fuel cell. • Perfectly verify characterization results by specially designed step-current test. • Fundamentally support breakthrough of power and durability of large-scale stacks. Uniform gas distribution contributes greatly to output performance, operating reliability, and durability of fuel cell stacks, which still lacks detection means and remains a great challenge. In this study, a non-invasive and in-situ characterization method of gas distribution is originally presented, based on the dual-chamber model that describes the mathematical relationship among gas flow, hydrogen crossover, and hydrogen transfer in the cathode side during electrochemical testing (N 2 /H 2). By means of micro-current excitation, the hydrogen crossover of every cell in a stack is synchronously identified and then the gas distribution can be decoupled. In a specially designed step-current test that aims for direct evidence, the voltage response characteristics to artificially induced air starvation coincide perfectly with the identified gas distribution. Typically, as the current density steps up to 175 mA·cm−2, the voltages of under-supplied cells, classified by the identified stoichiometric ratios of nearly-one and within 1.13–1.15, decline rapidly to around zero and abnormally to around only 0.5 V, respectively, while the sufficiently supplied cells remain normal. Based on the in-situ method, the artificially channel-choked fuel cell is precisely recognized, and different choke degrees can be distinguished. As identified at the corresponding current densities of 50, 100, and 500 mA·cm−2 and the average stoichiometric ratio of two, the gas flow rate of a certain cell only reduces by 22.8 %, 20.2 %, and 13.7 %, respectively, even with all the cathodic channels choked. The significant role of convection in the mass transfer is experimentally confirmed in fuel cells, and correspondingly, the mass transfer indicators are available by this electrochemical means. [ABSTRACT FROM AUTHOR]

• Originally build stack-level diagnosis framework to evaluate SOH and mass transfer. • In-situ diagnose MEA degradation and consistency deterioration in startup-shutdown. • Reveal evolution of CCL structure damage and mass transfer deterioration. • Determine hydrophobicity loss as primary factor of startup-shutdown-induced aging. • Contribute to stack-level durability investigation and lifespan extension. The durability of proton exchange membrane fuel cell stacks is a key limiting factor in transportation application due to severe conditions, where startup-shutdown and local fuel starvation most severely threaten the structure stability. In this paper, potentiostatic test at the average of 1.4 V is conducted to accelerate the carbon corrosion and structure damage in the cathode in a seven-cell commercial stack. A complete diagnosis framework based on micro-current excitation is established to systematically and in-situ evaluate the state-of-health of the cathode and the membrane, the mass transfer state, and the cell inconsistency in stack-level. Afterwards, the structure damage of cathode catalyst layer is characterized with scanning electron microscopy and energy disperse spectroscopy to provide direct evidences. In the early stage, the electrochemical surface area attenuates severely attributed to detachment and agglomeration of Pt particles, and the catalyst layer framework becomes more porous and hydrophilic, thus raising the double-layer capacitance. The weak catalyst layer structure tends to collapse starting from the lower layer close to the membrane, thereby leaving the dense lower layer and the porous upper layer in all cells. The damage mode is accurately manifested by the stepwise increase in the hydrogen transfer coefficient in the cathode that is identified in electrochemical testing. Meanwhile, the performance decay during operation exhibits typical characteristics of water flooding deterioration, as indicated by the three stages of steady voltage decay, drastic and periodic voltage fluctuation, and cell reversal. Therefore, the increase in water sensitivity and its dominant role in startup-shutdown-induced degradation are confirmed, which are also supported by the carbon-corrosion-induced ionomer agglomeration surrounding the enlarged pores in the upper layer. The enlarged pores are prone to be flooded, and the collapsed partial structure inherently limits the mass transfer. The aging process, evolution mechanisms of internal damage, and dominant factors during startup-shutdown are therefore identified and clearly revealed in stack-level. [ABSTRACT FROM AUTHOR]

• Evaluation methods of fuel cell stack consistency are reviewed. • Influencing factors and reasons of fuel cell stack consistency are discussed. • Main detection methods of MEA parameters are summarized and compared. • Effect of MEA parameters on fuel cell performance and lifetime are analyzed. • Methods and directions for improving fuel cell stack consistency are proposed. The proton exchange membrane fuel cell (PEMFC) is an ideal energy conversion device. The performance of a fuel cell stack depends on the coordination of many aspects. Consistency is an important factor affecting the performance of fuel cell stacks. However, it is still not very clear how to evaluate and improve fuel cell stack consistency. In this review, the evaluation methods of fuel cell stack consistency are summarized, and various influencing factors of consistency are discussed, including operation conditions, operating mode, control strategy, structure design, component materials, and manufacturing processes. The membrane electrode assembly (MEA) is the core component of PEMFC, and the differences between MEAs also determine the voltage consistency of fuel cell stacks. The detection of MEA parameters is an important method to evaluate its quality, and the main detection methods of MEA parameters are reviewed and compared. Moreover, the influence of MEA parameters on the performance and lifetime of fuel cell stacks is systematically analyzed in this review. Finally, research orientations and methods for improving fuel cell stack consistency are proposed from MEA preparation, stack design, and operation optimization. [ABSTRACT FROM AUTHOR]

• Identify ohmic resistance and update excitation model for precision upgrading. • Conduct systematic error analysis concerning decoupling algorithm and stack state. • Originally reveal condition sensitivity for identifying MEA inconsistency. • Validate adaptability to single excitation for a great advance in test efficiency. • Contribute to validity and reliability of MCE in stack-level MEA diagnosis. The inconsistency of membrane electrode assemblies (MEAs) greatly restricts the development of high-performance and long-lifetime fuel cell stacks. Micro-current excitation (MCE) method has promising prospects in consistency evaluation due to its capacity in MEA component diagnosis at the stack level. In this study, the ohmic resistance is identified with the initial voltage jump characteristic upon MCE and then introduced into the excitation-response model for precision upgrading. A detailed error analysis is then conducted concerning the decoupling algorithm, the simplified model, and the stack state, demonstrating the significance of error revision for degraded stacks. Based on the updated method, the condition sensitivity of inconsistency identification is further investigated, involving the operating temperature, the gas humidity, and the gas flow rate. It is essential to ensure the same hydration state of fuel cells for simultaneous component inconsistency diagnosis. Heating and minor gas humidification are sufficient and necessary. The MCE is proven to be very sensitive to the N 2 flow rate in the cathode. An extremely low N 2 flow rate enables a relatively uniform gas distribution and hydrogen adsorption saturation, and is thus recommended. Finally, the updated MCE is validated to adapt to a single excitation for inconsistency diagnosis, which means great progress in test efficiency. [ABSTRACT FROM AUTHOR]

• Parameters in 9-state model, improved voltage model, and controller are identified. • Throttle factor of cathodic inlet is time-variant with motor voltage and its step. • Delay effect of motor voltage is noticeable and cannot be ignored. • Clear boundary estimation for super-twisting sliding mode controller is presented. The super-twisting sliding mode control (ST-SMC) is widely used in fuel cell system control due to the simple control law and strong robustness. However, it requires an accurate system model. Generally, literature usually reduces the system order and directly gives the empirical value for the system and controller parameters or conducts coefficient identification of the fuel cell voltage model, lacking the specific identification method for system physical parameters and controller parameters. In this paper, a relatively complete control-oriented nine-state fuel cell system model was established, including the model of compressor flow using the artificial neural network method, and the improved voltage model. Then, the data-driven method for key parameter identification was proposed, including the fuel cell throttle factor and motor voltage changing rate considering time delay. In addition, the parameter tuning method for controller design was proposed as well. These two methods are of originality. After the model validation in the perspective of steady and transient performance, the comparison was carried out between the ST-SMC and PID controller. It is found that the throttle factor of the cathodic fuel cell inlet and the delay effect in terms of changing rate of motor voltage impact the system model, where the throttle factor is time-variant, and the delay is noticeable which differs with the step magnitude of the motor voltage. The parameter tuning and boundary estimation of ST-SMC are very specific, owing to the process of treating flow rate as a state, not speed, and are convenient to be generalized. The better ability in anti-flooding exhibits the importance of parameter identification. Although the study is conducted in a low-pressure system, the method proposed in this paper is universal and could be applied to other fuel cell controls for better system efficiency and reliability. [ABSTRACT FROM AUTHOR]

Highlights • A comprehensive review of researches on gas starvation issue of PEM fuel cell. • Include causes, severe consequences, diagnostic methods and mitigation measures. • Summarize the variable methods and compare their performance in different aspects. • Provide guidance to the diagnose methods, system control strategy and structure design. Abstract The short service life of fuel cell is a key problem that restricts the commercialization of fuel cell vehicles. Many scholars have found that gas starvation is one of the most important causes of the proton exchange membrane fuel cell lifetime decay, which leads to a series of severe consequences such as carbon support corrosion, cell reversal and output performance degradation. However, accurate diagnosis and effective mitigation of fuel cell gas starvation are not achieved currently. Gas starvation is a condition that the reaction gas of proton exchange membrane fuel cell working in the sub-stoichiometric state. In this paper, we will study the causes, severe consequences, diagnostic methods and mitigation measures of the gas starvation in proton exchange membrane fuel cells through previous literature review. This research is aim to provide guidance to the diagnose methods, to optimize the system control strategy and structure design and to contribute to the studies which are focus on prolong the proton exchange membrane fuel cell lifetime. [ABSTRACT FROM AUTHOR]

• A method for calculating the optimal interval of air stoichiometry is proposed. • Synthetically consider fuel cell power, working efficiency and gas starvation. • Calculate air stoichiometry optimal interval under different operating conditions. • Changing trends of optimal interval varying with operating conditions are analyzed. • Evaluate gas starvation by calculating the proportion of gas-starvation area. Cathode air stoichiometry is one of the crucial factors affecting the electrical performance and local gas starvation of proton exchange membrane fuel cells. A suitable air stoichiometric ratio can not only improve the generated power and working efficiency, but also effectively reduce or eliminate internal reactant gas starvation. At present, the optimal value of cathode air stoichiometry and its changing trends under different operating conditions remains to be further studied, and the optimization indicator in existing researches mainly tends to be maximum power, with rare attention to fuel cell degradation. This paper proposes a quantitative method for calculating the optimal interval of air stoichiometry, which considers synthetically the fuel cell generated power, working efficiency and internal gas starvation reduction, to make these three indicators reach the optimal balance. The local gas starvation inside is evaluated by proportion of gas-starvation area on fuel cell electrode surface. Based on the proposed methodology, a three-dimensional model of a five-channel serpentine flow field fuel cell is established in this paper to simulate and calculate the optimal interval of cathode air stoichiometry under different operational parameters and electrical load conditions. The changing trends of the optimal air stoichiometry with different operating conditions and the impacts of key operational parameters and loads on air stoichiometry optimal interval are also carefully analyzed. Among them, the operating pressure and current density have a significant influence on the optimal interval value. Increasing the working pressure can make the optimal air stoichiometry smaller, and the increase in current density results in a substantial raise of optimal air stoichiometry. The calculation method of cathode air stoichiometry optimal interval proposed in this paper can provide references for fuel cell air supply strategy research, and the conclusions can be beneficial for fuel cell performance optimization and long-lifetime design. [ABSTRACT FROM AUTHOR]

• Fuel cell system-level modeling is achieved by Simulink and Fluent co-simulation. • Variable load processes in this paper are designed based on the DST protocol. • Simulate impacts of subsystem's response characteristics and operating parameters. • Analyze mechanism behind these impacts, combining internal distribution changes. • Can not only obtain response of subsystems, but also monitor internal distribution. The lifetime of vehicular proton exchange membrane fuel cell is one of the key factors restricting the commercialization of fuel cell vehicles. It's well recognized variable load conditions have the greatest impact on fuel cell degradation. Studying dynamic load characteristics is very crucial for fuel cell long-life design and optimal control. Since experiments are not easy to monitor fuel cell internal distribution, the dynamic response studying is commonly implemented in model simulation. The fuel cell system has complicated structures and large differences in length scale, to make up for the insufficient precision and limited research content in existing models, this paper uses an innovative modeling method, Simulink and Fluent co-simulation method to establish a fuel cell system-level model. It can obtain not only response characteristics of auxiliary subsystems and the system dynamic performance, but also the internal physical quantities distribution changes. Multiple simulations and comparisons are made to observe voltage dynamic response and internal concentration distribution. Impacts of subsystem's response characteristics and system's critical operational parameters and mechanism behind them are analyzed. The co-simulation method and obtained results in this paper can be used for future research of fuel cell system-level modeling and provide theoretical basis for dynamic capacity optimization. [ABSTRACT FROM AUTHOR]