Fuel Cell System Research Articles

Fuel Cell System Modeling Dedicated to Performance Estimation in the Automotive Context

In this paper, a meticulous modeling approach is proposed not only for a fuel cell stack itself but also for all auxiliary components that collectively form the fuel cell system. This comprehensive modeling approach encompasses a wide range of components, including, but not limited to, the hydrogen recirculation pump and the air compressor. Each component is thoroughly analyzed and modeled based on the detailed specifications provided by suppliers. This involves considering factors such as efficiency, operating parameters, response times, and interactions with other system elements. By integrating these detailed models, a holistic understanding of the entire fuel cell system’s performance can be attained. Such an approach enables engineers and designers to simulate various operating scenarios, predict system behavior under different conditions, and optimize the system design for maximum efficiency and reliability. Moreover, it allows for informed decision-making throughout the system’s development, deployment, and operational phases, ultimately leading to more robust and effective energy systems. The model validation is performed by comparing experimental data to theoretical results, and the observed difference does not exceed 3%.

Emergoenvironmental analysis and sustainable flexible peaking of a novel solar-assisted solid oxide fuel cell cogeneration system

The development of green and sustainable conversion technologies is imperative to alleviate the current severe energy and environmental crises and to achieve the goal of carbon neutrality. This project presents a novel cogeneration system that integrates a solid oxide fuel cell afterburning system, an energy recovery system, and a parallel heating system equipped with multistage utilization, solar-assisted, and flexible peaking technologies. Following the establishment and validation of the system, power source analysis, parameter sensitivity studies, three-objective optimization, and internal exergy flow investigation are carried out. Subsequently, the positive impacts of multistage utilization, solar-assisted, and flexible peaking technologies, as well as the thermoeconomic and emergoenvironmental operating characteristics of the system, are discussed. The numerical results demonstrate that the emergoenvironmental factor and emergy environmental impact difference of the system are 70.15 % and 82.52 %, and the energy efficiency is improved by 1.41 %∼19.09 %, respectively, compared with similar solid oxide fuel cell systems, confirming its favorable thermodynamic performance and emergoenvironmental impact. The system achieves daily heating savings of 3302 kWh and life cycle economic benefits of $19,310,524, with a repayment of the investment cost in 4.17 years, reflecting considerable economic benefits. This project also demonstrates exergy flow, module defects, and improvement mechanisms from the perspective of emergoenvironmental, providing a valuable reference for the promotion of green energy.

Modeling and Control of Multi‐Stack Fuel Cell Air System based on Nonlinear Model Predictive Control Method

Hydrogen is crucial for achieving SDGs by driving energy transition and combating climate change. Proton exchange membrane fuel cell technology, leveraging hydrogen, faces challenges in meeting high‐power demands. The multistack fuel cell system (MFCS) tackles this by integrating multiple substacks, yet its air supply needs meticulous control. Proportional integral derivative (PID) decoupling from single‐stack falls short of MFCS. This article proposes nonlinear model predictive control (NMPC) for optimized air flow and pressure decoupling. Modeling MFCS's air system and designing a predictive model, it is aimed to ensuring precise control of air flow and pressure in each substack. The decoupling experiments show that NMPC outperforms PID, accurately managing air flow and pressure and reducing load fluctuations. For air mass flow, NMPC cuts mean‐absolute error (MAE) by 64.56% and root‐mean‐square error (RMSE) by 81.36%. For pressure, MAE drops 81.23% and RMSE 83.59%. Comprehensive step load tests confirm NMPC's precise, dynamic regulation too, compared to PID, NMPC lowers average MAE for air mass by 20.67%, pressure by 32.22%. RMSE improvements of 31.08% and 33.23% highlight NMPC's strength. NMPC's quick response mitigates coupling issues, enhancing vehicle load adaptability.

Optimization of cold startup strategy with quasi 2-D model of metal hydride hydrogen storage with fuel cell

In this work, a novel cold startup strategy is presented for a large-scale metal hydride–fuel cell system. Due to the thermal requirement of the system, when the initial temperature of metal hydride and ambient air is low, the system cannot be started because the hydrogen pressure in the metal hydrides storage system is lower than the minimal pressure required by the fuel cell. The cold startup is achieved by maintaining a part of metal hydride storage at a target temperature and using coolant control to transfer heat to the remaining cold metal hydride storage upon operation of the fuel cells. The strategy is studied numerically and a novel Quasi-2-Dimensional approach is developed to describe heat transfer within metal hydrides and validated. By the proper selection of the value of the control variable, both fast startup and immediate uninterrupted fuel cell power supply are achieved. The effect of initial conditions on the startup process is studied and the strategy consumed 30 % less energy than the alternative without control. The proposed strategy is suitable and efficient for the cold startup of large-scale metal hydride-fuel cell systems.

Efficiency optimization of fuel cell systems with energy recovery: An integrated approach based on modeling, machine learning, and genetic algorithm

Maximizing the efficiency of fuel cell systems (FCS) with energy recovery capabilities is crucial for advancing high-efficiency FCS technology. This research aims to explore the maximization of FCS efficiency through the optimization of operational parameters and to elucidate the synergistic effects between parameter optimization and energy recovery. Employing an integrated approach that combines model simulation, experiments, machine learning, and genetic algorithm (GA), this work addresses key technical challenges in developing an efficient FCS model and a high-precision surrogate model. It also tackles the critical problem of achieving maximum system efficiency through the optimization of operational parameters and energy recovery. The findings indicate that the data-injection-based model simplifies the computational process and effectively validated over long time scales. Feature selection and network quality assessment ensure the precision and reliability of the long short-term memory (LSTM) network. Optimization using GA in conjunction with the LSTM surrogate model enables a rapid and accurate determination of the system's maximum net output power and corresponding operational conditions. The results demonstrate a 6 % increase in the system's net output power, with the system efficiency consistently surpassing 55 %. Moreover, energy recovery consistently boosts optimized system efficiency by about 1.6–1.7 %.

Thermal performance of MHD quadratic convection of nano fluid flow in a square cavity filled with a porous medium with radiation and heat generation effects

The dynamics of enclosure flows are crucial for temperature management in fuel cell systems. Effective temperature control is essential for maintaining optimal operating conditions, thereby enhancing the efficiency and longevity of fuel cells. By refining coolant flow pathways, simulations play a pivotal role in efficiently dissipating the heat generated during electricity production. This study aims to explore the numerical simulation of buoyancy-driven magnetized nanofluid flows within a square enclosure saturated with a porous medium. The goal is to understand how various factors, such as magnetic fields, nanoparticle suspension, thermal radiation, porosity, and internal heat generation/absorption, collectively impact fluid dynamics and thermal distribution. The study employs the finite difference-based Marker-and-Cell (MAC) method, validated against previous studies, to accurately simulate buoyancy-driven flow phenomena. The transverse uniform magnetic field, Nield conditions, and heat generation are considered, with the nanofluid characterized by Buongiorno’s model. The MAC solver is used to solve the dimensionless conservative equations of thermal transport, mass, and momentum transport, ensuring appropriate wall conditions. The impact of magnetic number (0 ≤ Ha ≤ 30), heat generation (−2 ≤ Q ≤ 2), Darcy parameter (10−4 ≤Da ≤10−1), Rayleigh number (104 ≤Ra ≤ 106), Thermal radiation (0 ≤ Rd ≤ 7) and Non-linear temperature parameter (0 ≤λ ≤3) on the fluid flow and thermal transport have been examined. The study comprehensively analyses the interplay of the magnetic field, nanoparticle suspension, thermal radiation, porosity parameter, and internal heat generation/absorption. It concludes that these factors significantly influence fluid dynamics and thermal distribution within the enclosure, providing valuable insights for optimizing temperature management in fuel cell systems.

Temperature Maintenance of Proton Exchange Membrane Fuel Cell System Based on Genetic Algorithm

Temperature is one of the main factors affecting proton exchange membrane fuel cells(PEMFC). In severe cold conditions, when equipment with PEMFCs is shut down for a short period of time, the battery temperature will drop to below zero degrees Celsius. Under this condition, the generation of ice will increase the battery start-up time, and cold start of PEMFC often requires additional heat sources. At the same time, repeated cold starts will seriously reduce the lifespan of proton exchange membrane fuel cells. Aiming at this problem, a temperature maintenance strategy is proposed for short-term low-power output of PEMFCs in severe cold conditions based on the temperature dynamic model of PEMFCs, which reduces energy loss through genetic algorithm effectively.

Feasibility analysis of hybrid photovoltaic, wind, and fuel cell systems for on–off‐grid applications: A case study of housing project in Bangladesh

AbstractThis study investigates the viability of hybrid photovoltaic (PV), wind, and fuel cell (FC) systems for on‐grid and off‐grid operations for the Ashrayan‐3 housing project in Bangladesh, with an increased focus on sustainable energy solutions. Motivated by the issue of the delivery of proper and sustainable energy services to remote locations, we conducted an extensive analysis of load demand and found that an average daily demand of 46,176.65 kWh exists, with a peak load of 4852.8 kW. In this research, the HOMER software has been used to make a simulation of five different hybrid system configurations with differing mixes of renewable technologies. From the analyses, the systems based 100% on renewable resources suffer more initial capital costs, with a total net present cost increase of up to 20%, in comparison to conventional systems. On the other hand, the systems give much lower operational costs and cost of energies (COEs) of a minimum of $0.0253/kWh, reported from the on‐grid PV‐based system. On the other hand, the off‐grid PV–FC–wind turbine system showed a COE of $0.286/kWh, along with a decrease in CO2 emissions by about 15,000 kg/year, showing a 30% decrease, compared with on‐grid systems. The results form a basis for the conclusion that such hybrid renewable energy systems are both economically and environmentally feasible. They can reduce COEs by up to 70% in off‐grid systems. This proves that the quality of life and energy security in developing regions will be highly increased, supporting the goals of sustainable development.

Enhancing the efficiency of medium-scale dual-chamber microbial fuel cell systems through the utilization of novel electrodes material and proper selection of catholyte and external resistance

Dual-chamber microbial fuel cells (DC-MFC) are devices that can be used to generate electricity through the degradation of substrates. In this study, the performance of DC-MFC with novel electrode materials is evaluated under different external resistance using a hydrochloric acid solution as catholyte. Hydrophilic-treated graphene was used as the electrode material, DuPontTM Nafion 117 was used as the proton exchange membrane and domestic wastewater served as the substrate. The maximum power density achieved was 32.05 mW⋅m−2, obtained by degrading 69.8% of organic matter when an external resistance of 100 Ω was used as electrical load. This power density was 32 times higher than the power density obtained in the control (1.01 mW⋅m−2). This result obtained was similar to those reported in the literature for small-scale DC-MFC systems. And, contrary to the reported trend that as the scale of MFCs increase, the efficiency decreases. It can be stipulated with these results that is possible to scale up DC-MFC to medium-scale systems without loss performance quality by selecting the appropriate external resistance, catholyte and electrode materials.

Innovative air supply management in fuel cells: An economic predictive control approach without pre-measured OER

Existing fuel cell air supply control methods improve the economic performance of Proton Exchange Membrane Fuel Cells (PEMFC) under steady-state conditions through oxygen excess ratio curve, while ignoring the transient process of the air supply system under varying operating conditions. Furthermore, the oxygen excess ratio curve may exhibit deviations due to fuel cell aging and measurement inaccuracies, which would further diminish the economic performance derived from the control process. Not relying on pre-measuring the oxygen excess ratio curve, an EMPC (Economic Model Predictive Control) method is proposed for fuel cell air supply systems. It incorporates an economic cost function reflecting the economic objectives of the air supply system. By dynamically optimizing the parasitic power of the air compressor, this method enhances the overall economic performance by achieving performance optimization for both transient and steady-state processes of the fuel cell system. The asymptotic stability of PEMFC is achieved through the utilization of terminal penalty functions and terminal domain constraints. Stability and convergence analysis is carried out by employing auxiliary optimization problems and the Lyapunov method. In transient processes, EMPC method can achieve a maximum 4.97 % improvement in the economic performance of PEMFC. A hardware in loop (HIL) experiment was finally conducted to show the real-time capacity of the proposed EMPC method.

Evaluating a 10 kW PEM fuel cell system for unmanned aerial vehicles at different cruising altitudes: A thermal analysis

Evaluating a 10 kW PEM fuel cell system for unmanned aerial vehicles at different cruising altitudes: A thermal analysis

Degradation prediction of fuel cell systems based on different operating conditions in dynamic cycling condition

In this paper, the degradation of PEMFC under different operating conditions in dynamic cycle condition is studied. Firstly, according to the failure mechanism of PEMFC, various operating conditions in dynamic cycle condition are classified, and the health indexes are established. Simultaneously, the rates and degrees of the output voltage decline of the PEMFC under different operating conditions during the dynamic cycling process were compared. Then, a model based on variational mode decomposition and long short-term memory with attention mechanism (VMD-LSTM-ATT) is proposed. Aiming at the performance of PEMFC is affected by the external operation, VMD is used to capture the global information and details, and filter out interference information. To improve the prediction accuracy, ATT is used to assign weight to the features. Finally, in order to verify the effectiveness and superiority of VMD-LSTM-ATT, we respectively apply it to three current conditions under dynamic cycle conditions. The experimental results show that under the same test conditions, RMSE of VMD-LSTM-ATT is increased by 56.11 % and MAE is increased by 28.26 % compared with GRU attention. Compared with SVM, RNN, LSTM and LSTM-ATT, RMSE of VMD-LSTM-ATT is at least 17.26 % higher and MAE is at least 5.65 % higher.

A single-bridge interleaved three-level LLC resonant converter with current sharing capability for fuel cell system

This paper propose a wide gain single-bridge interleaved three-level LLC resonant converter with current sharing capability. This novel converter offers numerous advantages, including low cost, low current ripple, reduced voltage and current stress, widely gain range, high efficiency, good current sharing capability and versatile application scalability. Utilizing the three-level inverter + full-wave rectifier LLC converter as a representative case, the paper conducts an in-depth analysis and research on the proposed method. Finality, a 600 W experimental prototype was constructed and tested. Experimental results reveal that the proposed converter exhibits lower current ripple and a broader gain range. Moreover, the converter shows good current sharing capability (with a resonant element tolerance of 10%, the current error between the two phases does not exceed 12%) and high efficiency (peaking at 95.8%).

Energy Management System for a Hybrid Fuel Cell Unmanned Aerial Vehicle

Energy Management System for a Hybrid Fuel Cell Unmanned Aerial Vehicle

A Nonlinear Active Disturbance Rejection Feedback Control Method for Proton Exchange Membrane Fuel Cell Air Supply Subsystems

The control strategy of the gas supply subsystem is very important to ensure the performance and stability of the fuel cell system. However, due to the inherent nonlinear characteristics of the fuel cell gas supply subsystem, the traditional control strategy is mainly based on proportional integral (PI) control, which has the disadvantages of large limitation, large error, limited immunity, and inconsistent control performance, which seriously affects its effectiveness. In order to overcome these challenges, this paper proposes an optimal control method for air supply subsystems based on nonlinear active disturbance rejection control (ADRC). Firstly, a seven-order fuel cell system model is established, and then, the nonlinear ADRC and traditional PI control strategies are compared and analyzed. Finally, the two strategies are simulated and compared. The validation results indicate that the integral absolute error (IAE) measure of PI control is 0.502, the integral square error (ISE) measure is 0.1382, and the total variation (TV) measure is 399.1248. Compared with the PI control, the IAE and ISE indexes of ADRC were reduced by 61.31% and 58.03%, respectively. ADRC is superior to PI control strategy in all aspects and realizes the efficient adjustment of the system under different working conditions. ADRC is more suitable for the nonlinear characteristics of the gas supply system and is more suitable for the oxygen excess ratio (OER).

Self-Aerated Osmotic Microbial Fuel Cell System Based on Siphon Principle: Performance Investigation and Application Significance

Self-Aerated Osmotic Microbial Fuel Cell System Based on Siphon Principle: Performance Investigation and Application Significance

Long-Term Continuous Gas Diagnostics For Fuel Cell Systems: Development Of An Automatized Raman Metrology

Knowledge of the composition of gas flows in fuel cell systems is crucial for their understanding. In current research, this data is rarely recorded inline and in real-time due to the lack of suitable metrology. Here, we present an optical gas sensor. Green light (532 nm) from a 450 mW laser excites Raman scattering. A spectrometer analyzes the scattered light and a Python software extracts gas composition data automatized and in real-time. We designed a measurement cell that allows optical inline access to gas flow systems and is easy to implement. Therefore, common signal enhancement methods with more complex setups can not be applied. Literature data on scattering cross sections does not offer a reliable basis for calibration what constitutes the need for a gas conditioning system that allows fast preparation of well defined mixtures. We used it to present two principles of calibration. First, calibration based on relative scattering cross sections is the more stable possibility, because it is still valid in cases where laser power and integration time are changed. However, in consequence the results for one species are not independent from the other species. A poor signal, e.g. following from a low mole fraction in a single component, will introduce uncertainty in all components. We avoid that problem through direct linear correlation of signal strength of one species to its partial pressure, which is the second calibration variant. In this case, there is an influence of laser power and integration time. The additional information on overall pressure can be used in model systems, where the presence of undetected components can be ruled out. We can compare that information to pressure measurements to do plausibility checks on the Raman results in real-time. We apply the sensor in fuel cell system research, where measurement intervals of one second yield results of sufficient precision. Increasing integration time can further decrease the uncertainties where favorable. The measurement and data evaluation routines work stable and constant enough to monitor gas compositions in longterm operation. To date, the sensor can measure mixtures of nitrogen, oxygen, hydrogen and water vapor.

Rotordynamics of a rotor supported by foil bearings under various housing excitation

Proton Exchange Membrane (PEM) Fuel cell Systems(FCS) are rapid-growing technology in transportation systems. In such applications, a motor-driven oil-free air compressor is one of the critical auxiliary subsystems. Foil bearings are the perfect choice as oil-free bearings for the compressors due to high rotordynamics stability and shock-resistance. The platform of a motor-driven oil-free compressor can also be used as an electric turbocharger for traditional internal combustion engines. All electromechanical subsystems for electric vehicles must satisfy vibration endurance requirements following ISO 16750–3:2012, characterized by certain g-loading at specific frequencies. For the compressor to satisfy ISO 16750 requirement, detailed rotordynamics simulations with externally excited compressor housing under certain g-loading are essential to design the foil bearings considering the static and dynamic loads to the bearing and rotordynamics stability. The main objectives of the current research are 1) to develop a four degree of freedom (4-DOF) dynamic model of the rotor supported by foil bearings under external excitation to the compressor, 2) to simulate linear and non-linear rotordynamics of the compressor rotor supported by two radial foil bearings, and 3) to provide appropriate design guideline such as bump stiffness and axial length of the foil bearings. The simulations show that the rotor is stable for both under single frequency excitation and simultaneous excitations of all the frequencies in ISO 17650 standard. 4-DOF modal analyses were applied to identify natural mode of the system under the housing excitation.

Feedback-linearization decoupling based coordinated control of air supply and thermal management for vehicular fuel cell system

The coordinated control of the coupled system of air supply and thermal management is one of the important ways to improve the efficiency of proton exchange membrane fuel cell (PEMFC) system. The novelty of this paper is to design a feedback-linearization decoupling based coordinated controller combining with active disturbance rejection control (ADRC) method. Aiming at strong coupling of air supply and thermal management system, we have proposed a three-dimensional particle swarm coordinated optimization algorithm to calculate the steady-state reference, then, a feedback-linearization decoupling controller is designed to achieve independent control of air flow, cathode pressure and temperature. Besides, aiming at time-varying, nonlinear interference, we have designed an ADRC based on the decoupled method to achieve better dynamic response and stability. The current step load test reveals that the overshoot and the average tracking error are much smaller under the air flow-pressure-temperature decoupling coordinated controller compared that under the proportional-integral-derivative (PID) controller and the air flow-pressure decoupling controller. The test results under different ambient temperatures indicate that the proposed coordinated strategy reduces the tracking error by over 10 %, the efficiency of fuel cell system is increased by 3.5%–4.5 %.

An Indirect Ammonia Fuelled Solid-Oxide Fuel Cell System with free cracking from Waste Heat

An Indirect Ammonia Fuelled Solid-Oxide Fuel Cell System with free cracking from Waste Heat