Stack Voltage Research Articles

Parameter Estimation of Proton Exchange Membrane Fuel Cells Using Chaotic Newton-Raphson-Based Optimizer

This paper presents a novel improved metaheuristic algorithm, Chaotic Newton-Raphson-Based Optimizer (CNRBO), for the Proton Exchange Membrane Fuel Cells (PEMFCs) parameter extraction problem. In this study, ten distinct chaotic maps are integrated within the Newton-Raphson-based optimizer (NRBO) to improve its performance. The chaotic maps are employed to define the probability of the trap avoidance operator (TAO). The proposed CNRBO algorithm is validated using standard benchmark optimization problems. Results proved its advantages over the standard NRBO algorithm in terms of accuracy, robustness, and convergence speed. For PEMFC parameters extraction using the proposed CNRBO algorithm, the objective function to be minimized is the sum of squared errors (SSE) between estimated and measured stack voltages across various data points. To validate the effectiveness and reliability of the proposed CNRBO, its performance is compared with recent algorithms on eight different PEMFC stack models including NedStackPS6, BCS 500W, Horizon 500W, 250W stack, Avista SR-12 500W, Temasek 1KW, Ballard Mark V 5KW and Horizon H-1000XP stack. Statistical measures are employed to assess the superiority and robustness of the proposed CNRBO. Statistical analyses demonstrate that the proposed CNRBO algorithm outperforms existing algorithms in terms of accuracy, search capability, and convergence speed, solidifying its position as a powerful tool for PEMFC parameter estimation.

Prediction of fuel cell degradation trends using long short term memory optimization algorithm based on four-module experimental reactor validation

To solve the problem of life prediction of proton exchange membrane fuel cells (PEMFCs), a novel stack with four modules was used to conduct experiments. With the consistent conditions of the experimental stack, the stack voltage data was used as a life indicator to predict the remaining life of PEMFCs and the trend of performance degradation, which was advantageous for early detection of stack operation problems and timely implementation of maintenance measures. Due to the difficulty of obtaining optimal hyperparameter combinations for traditional Long-Short Term Memory (LSTM) neural networks through limited experiments, which affects the prediction accuracy, the Grey Wolf Optimization (GWO) algorithm is introduced. This improved the accuracy of the test set for Module 1 by 10.154 %, and reduced the prediction error for remaining service life by 11.3 %. Modules 2 and 3 were validated using the optimization algorithm, the accuracy of the test set was improved by 12.289 % and 11.044 %, The prediction error for the remaining service life has been reduced by 21.17 and 28.21 h, respectively. The four-module experimental fuel cell stack can provide multiple operating conditions simultaneously to verify the accuracy and effectiveness of the hybrid prediction model proposed in this paper.

Experimental study of voltage uniformity and stability of H2/O2 PEM fuel cell stack with dead-end anode and recirculation cathode

Dead-end anode and recirculation cathode is an operating mode effectively improving the gas reactant utilization of hydrogen–oxygen proton exchange membrane fuel cell (H2/O2 PEMFC) stack. However, the difference of single cells in the stack, will be further expanded due to the flooding and impurities and necessary purge for removing them. In this work, the voltage uniformity and stability of H2/O2 PEMFC under different operating conditions are investigated based on a dead-end anode and recirculation cathode PEMFC stack. Research indicates that enhancing the current density and temperature, lowering the inlet gas pressure will diminish the uniformity in stack voltage. The analysis of the experimental pattern reveals a positive correlation between the voltage uniformity and stability. This is due to the water flooding or membrane dehydration phenomenon of the single-cell leading to a reduction of the single-cell voltage and the voltage uniformity of the stack, while the water flooding or membrane drying phenomenon makes the stack more unstable and the purge interval shorter. Based on these experimental results, the best operating scheme for the designed stack is proposed, which is of some guidance and reference value for the practical application of air-cooled PEMFC in the hundred-watt class.

Accurate key parameters estimation of PEM fuel cells using self-adaptive bonobo optimizer

The present study introduces an efficient Self-Adaptive Bonobo Optimizer (SaBO) for identifying the unknown parameters of the proton exchange membrane fuel cell (PEMFC). A comparative analysis between recent robust approaches, such as Gradient-based Optimizer (GBO), Bald Eagle Search Algorithm, and Rime-Ice algorithm (RIME), has been introduced. The basic concept is to minimize the mean bias error between the measured and predicted stack voltage. The main results show that although the techniques were close, in contrast, the SaBO optimizer provides a better superiority than GBO, BES, and RIME for an optimum forecast of the PEMFCs model. Moreover, the best fitness was achieved with the SaBO at 0.0367 (V) for the Heliocentris FC-50, and 0.1150 (V) for Nexa® 1200, also, with the minimum deviation of 0.0027 & 0.0172, and high efficiency. These achievements denote that the SaBO algorithm is more stable and robust for PEMFC parameter estimation.

Experimental Investigation on the Backpressure Effect on the Performance of a High Temperature Proton Exchange Membrane Fuel Cell Stack

This study investigated the impact of backpressure of anode and cathode on the performance of a high-temperature proton exchange membrane fuel cell stack consisting of 30 cells, initially operating at a current density of 0.4 A/cm², temperature of 160 ℃, and 0 bar with pure H2. Subsequently, the backpressure on both sides was varied from 0 bar to 1.5 bar, and the changes in stack performance were recorded throughout the testing period. Additionally, the impact of backpressure was also investigated with reformates (H2/CO2/CO/N2 = 69.4%/22.3%/1.40%/6.9%) as anode feed for the stack. The results showed that increase in operating backpressure improves the fuel cell performance. A stack voltage increase of 20 mV per cell and 40 mV per cell were achieved for the pure hydrogen operation and reformate gas operation respectively when increasing backpressure from 0 bar to 0.5 bar. This signifies potential to improve the system’s overall efficiency with only minor penalty in terms of auxiliary power loss to increase the back pressure.

Experimental Investigation on the Backpressure Effect on the Performance of a High Temperature Proton Exchange Membrane Fuel Cell Stack

This study investigated the impact of backpressure of anode and cathode on the performance of a high-temperature proton exchange membrane fuel cell stack consisting of 30 cells, initially operating at a current density of 0.4 A/cm², temperature of 160 ℃, and 0 bar with pure H2. Subsequently, the backpressure on both sides was varied from 0 bar to 1.5 bar, and the changes in stack performance were recorded throughout the testing period. Additionally, the impact of backpressure was also investigated with reformates (H2/CO2/CO/N2 = 69.4%/22.3%/1.40%/6.9%) as anode feed for the stack. The results showed that increase in operating backpressure improves the fuel cell performance. A stack voltage increase of 20 mV per cell and 40 mV per cell were achieved for the pure hydrogen operation and reformate gas operation respectively when increasing backpressure from 0 bar to 0.5 bar. This signifies potential to improve the system’s overall efficiency with only minor penalty in terms of auxiliary power loss to increase the back pressure.

Generative adversarial networks for stack voltage degradation and RUL estimation in PEMFCs under static and dynamic loads

Accurately predicting the degradation and remaining useful life (RUL) of Proton Exchange Membrane Fuel Cells (PEMFCs) is crucial for enhancing their reliability, particularly in automotive and energy sectors. Traditional models often fail to capture the complex, non-linear degradation patterns of PEMFCs, resulting in suboptimal predictions. This study addresses these technological gaps by introducing a novel approach, the Self-Attention Temporal Dual Discriminator Generative Adversarial Network (SAT-DD-GAN). This model is designed to overcome the limitations of existing methods by integrating advanced Long Short-Term Memory (LSTM) networks for time-series analysis, a self-attention mechanism to focus on critical degradation signals, and dual discriminators to improve prediction accuracy and robustness. By addressing the gap in handling long-term dependencies and complex degradation patterns, the proposed SAT-DD-GAN bridges the disconnect between traditional models and the real-world degradation behavior of PEMFCs. The SAT-DD-GAN achieved groundbreaking predictive performance, with the lowest RMSE (0.983E-3 for static load, 1.012E-3 for dynamic load), MAPE (0.036E-1 and 0.253E-1), MAE (0.776E-3 and 0.806E-3), and the highest R2-score (0.9998 and 0.9983), alongside zero relative error for both datasets. These results demonstrate the model's ability to capture the intricate degradation dynamics more effectively than existing state-of-the-art methods. By filling the technology gap of accurately modeling PEMFC degradation under varying load conditions, this study establishes a new standard for PEMFC prognostics and paves the way for future innovations in optimizing fuel cell performance and longevity.

Red-Billed Blue Magpie Optimizer for Electrical Characterization of Fuel Cells with Prioritizing Estimated Parameters

The red-billed blue magpie optimizer (RBMO) is employed in this research study to address parameter extraction in polymer exchange membrane fuel cells (PEMFCs), along with three recently implemented optimizers. The sum of squared deviations (SSD) between the simulated and measured stack voltages defines the fitness function of the optimization problem under investigation subject to a set of working constraints. Three distinct PEMFCs stacks models—the Ballard Mark, Temasek 1 kW, and Horizon H-12 units—are used to illustrate the applied RBMO’s feasibility in solving this challenge in comparison to other recent algorithms. The highest percentages of biased voltage per reading for the Ballard Mark V, Temasek 1 kW, and Horizon H-12 are, respectively, +0.65%, +0.20%, and −0.14%, which are negligible errors. The primary characteristics of PEMFC stacks under changing reactant pressures and cell temperatures are used to evaluate the precision of the cropped optimized parameters. In the final phase of this endeavor, the sensitivity of the cropped parameters to the PEMFCs model’s performance is investigated using two machine learning techniques, namely, artificial neural network and Gaussian process regression models. The simulation results demonstrate that the RBMO approach extracts the PEMFCs’ appropriate parameters with high precision.

An embedding layer-based quantum long short-term memory model with transfer learning for proton exchange membrane fuel stack remaining useful life prediction

The battery management system (BMS) plays a critical role in electric vehicles (EVs), with the prediction of remaining useful life (RUL) being of utmost importance. This functionality enables real-time monitoring and enhances driver assistance through intelligent driving mechanisms. To improve the precision of stack voltage degradation and RUL prognostication for proton exchange membrane fuel cell (PEMFC) aging, especially with limited real-time training data, we propose a novel approach using a layer-enhanced quantum long short-term memory model with transfer learning. The proposed model is trained offline using historical stack voltage degradation data from a source stack, which is then fine-tuned and transferred to a target stack. Comparative evaluations demonstrate the model's robustness, with consistently low root mean square errors in stack voltage predictions, even with increased training data. The proposed model significantly improves RUL prediction accuracy, achieving a maximum relative accuracy enhancement of 1.33 % when initiating predictions at 712 h. These results affirm the effectiveness of our approach in enhancing the accuracy of PEMFC aging prognosis, thereby offering valuable insights for practical integration within the BMS framework for EVs.

Voltage Stacking: A First-Order Modelization of an m × n Asynchronous Array for Chip and Architectural Design Exploration

Voltage stacking is a technique in which multiple integrated circuits are stacked in series between the supply voltage instead of in parallel, thus improving the energy efficiency of the power distribution network. Unfortunately, voltage stacking presents stability challenges for integrated circuits within the stack. A first-order model to quantify variability, stability, and power metrics for an array of voltage-stacked asynchronous integrated circuits is presented. Voltage variability and power consumption are accounted for and discussed. Limitations of the model are identified outside of the nominal behavior. The number of columns in the architecture, chip leakage, and supply voltage are shown to be the key contributors to the stability, performance, and energy efficiency of a system of voltage-stacked asynchronous processors. A higher leakage to active power ratio, though usually avoided by chip designers, is shown to improve stability and be key in designing stacks without external balancing. Outputs of the model enable system and chip designers to evaluate first-order trade-offs in energy efficiency, performance, and system cost. These fundamental data allow designers to make informed design and optimization trade-offs between asynchronous voltage-stacked architectures and the integrated circuits used therein. Analysis of this model shows that various voltage-stacked configurations, such as one with a 48 V supply using 100 rows and 11 columns, can be designed with less than 10% voltage variation per chip, mitigating the need for external voltage balancing.

Enhancing parameter identification for proton exchange membrane fuel cell using modified manta ray foraging optimization

In this paper, an accurate model of proton exchange membrane fuel cell (PEMFC) for optimal identification of PEMFC parameters has been developed. The optimization methodology is based on the modified version of Manta Ray Foraging Optimization (MMRFO) technique for minimizing the sum of squared errors (SSE) between the Experimentally measured stack voltage and the estimated voltage produced by the optimized model. In the modified methodology, the sine-cosine method has been utilized to enhance the global searching capability in the exploration phase and the local searching capability in the exploitation phase of the MRFO algorithm. In order to validate the effectiveness of the suggested methodology, four different case studies comprising standard benchmark 250 W PEMFC, BCS-500 W PEMFC, SR-12 500 W FC, and 1 kW Temasek stacks were utilized, and the attainments have been compared with the measured polarization characteristics. The attainments have been intensively compared with several metaheuristic algorithms (MA) including Tree growth Algorithm (TGA), Grey wolf optimizer (GWO), Whale optimization algorithm (WOA), Salp swarm algorithm (SSA), and original Manta Ray Foraging Optimization (MRFO), to confirm the superiority of the MMRFO against the compared techniques. The obtained results give a satisfactory agreement between the MMRFO-based model and the experimentally measured data. Finally, the achievements confirmed the effectiveness of the MMRFO over the basic MRFO algorithm and other novel metaheuristic algorithms in identifying PEMFC parameters.

Energy harvesting from fuel cell bicycles for home DC grids using soft switched DC–DC converter

Fuel cell vehicles (FCVs) are gaining significance due to their potential to reduce greenhouse gas emissions and dependence on fossil fuels. Their efficient fuel cell cycle makes them ideal for last-mile transportation, offering zero emissions and longer range compared to battery electric vehicles. Additionally, the generation of electricity through fuel cell stacks is becoming increasingly popular, providing a clean energy source for various applications. This paper focuses on utilizing the energy from fuel cycle bicycles when it's not in use and feeding it into the home DC grid. To achieve this, a dual-phase DC to DC converter is proposed to boost stack voltage and integrate with the 24 V DC home grid system. The converter design is simulated using the PSIM platform and tested in a hardware-in-the-loop (HIL) environment with real-time simulation capabilities.

Multi objective optimization using artificial neural network to maximize the power output of PEMFCs

ABSTRACT Designing a PEM fuel cell model is exceedingly challenging because of its multivariate in nature. Optimization is required to achieve highest operating condition. Neural Network Model is one of the possible methods to solve complex problems. The polarisation curve of a PEMFC (Proton Exchange Membrane Fuel Cell) is investigated in this paper in relation to the effects of seven parameters, including temperature, relative humidity in the cathode, relative humidity in the anode, anode stoichiometry, cathode stoichiometry, partial pressure of H2, and partial pressure of O2, using an ANN (artificial neural network) model. Where model geometric parameters i.e. Channel width, Channel depth, Channel length, Rib width, Cell width, GDL thickness, CL thickness, Membrane thickness of PEMFC was constant. Initially single Objective Function (Output Power) is predicted. The research presented here makes predictions about a PEMFC stack's electrical performance under multiple operating conditions. Mathematical model was further verified using laboratory data. Co-efficient of Determination (R2), Mean Square Error (MSE), and Mean Absolute Error (MAE) was determined using the fuel cell stack voltage model and stack power model. The model results show the possibility of using ANN in the implementation of such models to predict the PEMFC system's steady-state behaviour.

Highly robust and porous cathode current collecting layer for flat-tubular solid oxide fuel cell stack applications

Solid oxide fuel cells (SOFCs) have gained great attention as a stationary power generation application due to their high efficiency, fuel flexibility and environmental friendliness. The devices and power age could be revolutionized with the commercialization of these technologies. The SOFC-based stationary power generator is one step closer to commercialization however, it is delayed due to the inherent bottleneck of insufficient long-term durability. The cathode current collecting layer (CCCL) is crucial in stack applications to minimize the contact loss between cell and metallic interconnects. Herein, we report an easy-to-fabricate and highly robust CCCL to boost electrochemical performance and long-term durability of flat tubular (FT) short stack. The tailored structure of the CCCL was employed in the fabrication of the FT short-stack. The G/Ag porous structure exhibited increased porosity of 61.2 %, thereby enhancing the mass transport and improving the overall performance and long-term durability. The four-cell FT stack was manufactured without an interconnect and open air-gas channel. Subsequently, the FT short-stack is tested for performance and long-term durability for elapsed 5000 h at a chronopotentiometry test. The porous layer of graphite/Ag on the cathode layer maintained structural integrity without defects and mechanical failure. Consequently, the stack voltage remains stable throughout operation owing to improved structural stability and uniform temperature distribution across four FT cell stack.

Efficiency and longevity trade-off analysis and real-time dynamic health state estimation of solid oxide fuel cell system

Solid oxide fuel cell (SOFC) is a promising energy conversion technology due to its advantages of high efficiency, low emissions, and fuel adaptability. Addressing the balance between efficiency and longevity is crucial for their application on a large scale. Accurate estimation and prediction of the health state of SOFC systems is essential for the study of optimization between system efficiency and degradation. In this work, the influence law of degradation, the relationship between efficiency and longevity, and their joint optimization, alongside the state of health (SOH) estimation of the SOFC system have been deeply investigated, based on a rigorously validated model of a degraded SOFC system. First, the influential variables that predominantly influence system degradation are identified and analyzed via principal component analysis, including stack average temperature, stack temperature gradient, and fuel utilization. Then, the exploration of the trade-off relationship between system efficiency and longevity under all operating conditions is conducted, and the influence laws of influential variables on system degradation are revealed. In light of the lack of accuracy during load changes and the high time cost for existing SOH estimation methodologies, a novel improved SOH estimation strategy, considering both stack voltage and current-related states, is proposed for real-time estimation of system SOH under varying fuel compositions and dynamic power scenarios. The performance of the proposed method is demonstrated by comparing with existing methodologies. This comprehensive study lays the groundwork for the effective and sustainable health management of SOFC systems, ensuring their efficiency and longevity are maintained at an optimal balance.

Diagnostic and prognostic for prescriptive maintenance and control of PEMFC systems in an industrial framework

This paper designed and defined the framework of prescriptive maintenance and its four axes, for hydrogen-energy systems, considering the constraints of actual systems, to improve durability, availability, reliability, performance, safety and operating costs of these systems. Different data-driven approaches for diagnostic and different approaches for prognostic of Remaining Useful Life (RUL) for Proton Exchange Membrane Fuel Cell (PEMFC) systems are discussed. The use of measures always available on actual systems allows to validate the approach in an industrial framework. Several diagnostic algorithms (classification and clustering) are compared to find the best compromise between computation time and accuracy in an online diagnostic framework. For the prognostic part, the data set comes from experimental tests performed under steady load for a PEMFC stack. A degradation trend of PEMFC stack voltage is extracted from these data and two well-known prediction algorithms namely Bidirectional Echo State Network (BiESN) and Bidirectional Long-Short Term Memory network (BiLSTM) are tested and compared to evaluate the robustness of the PEMFC voltage prediction for RUL estimation. The diagnostic and prognostic approaches proposed in this paper are first steps towards future work related to prescriptive maintenance for hydrogen-energy systems (PEMFC, Proton Exchange Membrane Water Electrolyzer and hybridization of the two).

Anticipating the lifespan: Predicting the durability of an anode-supported solid oxide fuel cell short stack over 50,000 h

In the present study, the operational lifetime of a solid oxide fuel (SOFC) short stack is predicted by investigating the performance degradation of both the short stack and its cells throughout 1000 ​h at 800 ​°C. The short stack and integral cell voltages are continuously measured during the long-term test, with electrochemical impedance spectroscopy (EIS) conducted every 200 ​h. The short stack voltage decreased rapidly for the initial 200–300 ​h and afterwards, it decreased at a slow rate due to the increase in the Ohmic and polarization resistances in the same manner. Scanning electron microscopy results show that there is no delamination or cracking among constituent layers of the short-stack cells. The single degradation effects of the Ni coarsening in the anode, cation migration and surface segregation in cathode and oxide scale growth in metallic interconnect mesh are successfully integrated into a comprehensive lifetime prediction model. The experimentally measured voltage degradation data of the short stack fits well with the developed mathematical model and allows the successful prediction of the lifetime up to 50,000 ​h.

Novel electrochemical and thermodynamic conditioning approaches and their evaluation for open cathode PEM-FC stacks

The Air-cooled and open cathode proton exchange fuel cell (PEMFC) offers particular advantages in terms of complexity and weight reduction. Therefore, it seems very attractive to support the aviation industry in their de‑carbonisation process. Major challenges in terms of operational stability and reliability hinders this technology to be used in commercial applications.Therefore, this study thoroughly examines the conditioning and optimization of open cathode PEMFC stacks, drawing insights from experimental findings. Key areas of focus include the enduring impact of pre-humidification, the quantification of efficiency enhancements through reconditioning via oxygen starvation, and the refinement of Electrochemical Impedance Spectroscopy (EIS) data analysis under non-stationary conditions.The pre-humidification enhances stack performance by improving cell voltages from 40.09 V to 41.37 V at 30 A, increasing membrane humidity and improving efficiency. Furthermore, the residual water in the stack also functions as evaporative cooling and can assist in limiting the operating temperature of the stack during system start up. However, it is demonstrated that excessive soaking with water leads to severe flooding phenomena at the beginning of operation.A reconditioning period of 100 s through oxygen starvation induces a notable increase in stack voltage, from 41.37 V to 47.79 V at 35 A, which decreases to 43.56 V after 10 min of operation. This corresponds to an average increase in electric energy provision of about 6.2% at constant hydrogen consumption, attributed to PtOx reduction and increased water production.Despite limitations outside the medium frequency range (approximately 10 Hz – 15 kHz) for non-stationary conditions, EIS aids in understanding of the stack behaviour and supports the interpretation of current and voltage results. A novel evaluation method enables the quantitative description of the condition using EIS data. This data reveals a considerable drop (on average about 21,5% for the 10 A point and about 27,4% for the 30 A point) in charge transfer resistances of the fuel cell from initial operation to measurements after overnight soaking and the first series of oxygen starvation recovery.

Transfer learning-based deep learning models for proton exchange membrane fuel remaining useful life prediction

Proton exchange membrane fuel cells (PEMFCs) offer power generation capabilities for diverse applications including commercial enterprises, industrial sectors, and residential technologies. Nevertheless, the comprehensive integration of PEMFC applications could be improved by challenges related to degradation and durability. The imperative development of efficient performance prognostic models assumes a pivotal role in the prognosis of remaining useful life (RUL), health monitoring, and effective utilization of PEMFCs. This paper centers on the prognostication of critical components within PEMFCs and introduces a transfer learning approach based on variational autoencoder and bi-directional long short-term memory with an attention mechanism (Bi-LSTM-AM) model. This approach combines feature fusion, knee-point detection, and a sophisticated deep-learning-based predictive model. Notably, incorporating the variational autoencoder as the framework for feature fusion introduces a novel perspective previously unexplored. Identifying the knee point and knowing the start point on the training data, facilitates optimized parameter computation. The application of transfer learning facilitates the transfer of optimal model parameters and weights from a source to a target dataset. Conclusively, the estimation of stack voltage degradation and real-time RUL prediction based on the test dataset is executed by implementing our proposed method. The stack voltage prediction findings showcase the Bi-LSTM-AM model’s superior performance relative to comparison models. The proposed online rolling prediction model, utilizing a sliding window technique for RUL prediction, yields significantly enhanced accuracy, culminating in a relative error margin ranging from approximately 1.69% to 5.04%.

Energy Harvesting and Storage with a High Voltage Organic Inorganic Photo‐Battery for Internet of Things Applications

Integrated local energy harvesting and storage is a critical prerequisite for energy autonomy of distributed sensing arrays required for the implementation of the internet of things (IoT). In this context, the monolithic integration of solar cells with metallic lithium‐based batteries into stacked high voltage photo‐batteries allows to provide said energy autonomy, with the smallest possible footprint and the highest resource efficiency. However current photo‐battery designs require additional electronics to control and match power output like maximum power point (MPP) tracking and overcharge protection. Herein a novel and compact monolithic photo‐battery design is provided, advantageously combining an organic solar cell with a NMC 622 versus metallic lithium‐based battery, matched in terms of VOC and cut‐off voltage, thereby achieving photo‐charging without any control electronics and energy release on demand at a very compact footprint of only 2.5 cm × 2.5 cm × 0.2 cm. Additionally, by applying a power profile simulating a temperature and humidity sensor over the course of a 24 h light and dark cycle, real world applicability of the photo‐battery in this type of applications is demonstrated.