Stack Temperature Research Articles

4E analysis and tri-dimensional optimization of a hybrid energy system based on biogas from the digester and high-efficiency fuel cell: An attempt to reach sustainability with decreased emission and increased efficiency

Scientists and researchers have made numerous efforts to reduce global carbon emissions. One of the greatest methods to reach this goal is using renewable energies, especially benefiting from biomass, because of their higher efficiency and lower GHG emissions. In this regard, this paper represents a fuel cell based on biogas fuel, in which the city sludge is initially treated and then fed to a digester. The gas is cleaned and put into the fuel cell for power generation and heating. The system has superiority over other biogas-fed SOFC schemes over its better installment of subsystems. The system is analyzed from technical and environmental aspects, and parametric sensitivity is carried out. Also, cooling is generated from the waste heat to minimize the effects of environmental pollution. It is exhibited that fuel utilization factor, stack temperature, and gas composition seriously affect system performance. Finally, the optimization is conducted, and the best solution point is found with an effectiveness of 56.2%, emission of 0.021 kg/kWh, and a cost of 0.05472 $/kWh.

A new concept of water management diagnosis for a PEM fuel cell system

This paper presents a new diagnosis view of fuel cell water management. The faults related to water management in the fuel cell are addressed from the control point of view and their occurrence is considered as a consequence of a temperature sensor fault. To ensure proper operation of the polymer electrolyte membrane fuel cell (PEMFC), the stack temperature and inlet pressure are controlled by a model-free control. A fault in the temperature sensor that causes an imbalance in water content in the stack is detected by a new fault detection approach in model-free context. The validation of the proposed strategy is performed on a 1.2 kW fuel cell with real time detection of temperature sensor faults, leading to water condensation in the cells or membrane dehydration. Fuel cell control and diagnosis are achieved without any requirement of an accurate system analytical model and additional sensors. However, faults can be detected in quasi static operation only and not un transient state.

Stack cooling system coupled with secondary heat pump in fuel cell electric vehicles

Fuel cell electric vehicles (FCEVs) requires proper thermal management of a proton-exchange membrane fuel cell (PEMFC) stack, but conventional thermal management systems (TMSs) cannot manage the thermal requirements of PEMFCs under high-power density at high ambient temperatures. In this study, a stack cooling system coupled with a secondary heat pump in an FCEV is suggested. The enhanced thermal flexibility of the secondary heat pump system enabled extensive cooling with various TMS configurations. The cooling performance with different TMS configurations was evaluated, considering the thermal and electrical coupling of the heat pump system and the PEMFC stack. The results show that the stack temperature was lowered up to 2.6 °C when the excessive cooling load of the stack radiator was distributed to the cabin radiator under a relatively low-power output condition. To dissipate a large amount of waste heat from the stack, TMS requires a temporary shutdown of the cabin cooling system. In this case, utilizing the inactive outdoor heat exchanger as an additional radiator decreased the temperature up to 18.4 °C by extending the heat transfer area for stack cooling. This study proposes a cooling scheme for an FCEV and suggests appropriate cooling methods and configurations under various operating conditions.

Backpropagation Neural Network‐Based Prediction Model for Fuel Cell Thermal Management System

The temperature prediction of fuel cell thermal management systems (FCTMS) has been the focus of research in fuel cell vehicle. Herein, a prediction model of FCTMS based on the backpropagation neural network is proposed. Predictive models are applied to fuel cell TMSs for predicting temperature changes within the system. First, the fuel cell TMS is established based on Amesim software and verified by using experimental data. Then a prediction model is established based on the simulation data of the system model. After the validation calculation, the highest accuracy of the stack temperature prediction was found, with a relative error of 0.75%. The heat sink outlet temperature prediction accuracy is the worst, with a relative error of 4.3%. The mean square error of the overall output of the prediction model is 0.043, and the mean absolute percentage error of the three results is 0.23%, 0.48%, and 0.16%. Both are below 5%. Therefore, the prediction model has more precise prediction performance, which helps the parameter study and control decision setting of FCTMS.

Experimental Results of a 10/40 kW-Class Reversible Solid Oxide Cell Demonstration System at Forschungszentrum Jülich

In 2018, a 5/15 kWDC reversible solid oxide cell system was developed and successfully operated by Forschungszentrum Jülich. Based on the knowledge gained with this first system, an optimized system in the power class of 10/40 kWAC was developed afterwards in 2019 that uses the well-established Integrated Module. This module consists of four 20-layer sub-stacks, two heat exchangers and five heating plates. It represents the main components of the system. The basic system layout was retained in general from the previous system and adjusted in accordance with the higher power level, as well as the supporting balance of plant components. The layout of the demonstrator system and its results are described. During the experimental evaluation in fuel cell mode, the system could provide an electrical output power from 1.7 to 13 kWAC. The maximum system efficiency of 63.3% based on the lower heating value (LHV) could be reached at a system power of 10.4 kWAC. This operating point was also analyzed regarding the temperature distribution in the stack and efficiency losses. In electrolysis mode, a maximum efficiency of 71.1% (LHV) was achieved with an electrical power input of −49.6 kWAC. At this operating point, about 11.7 Nm3 h−1 of hydrogen are generated. The stack temperature distribution and the efficiency losses are also analyzed for the electrolysis mode. Finally, the potential for the efficiency optimization through higher heat integration in this mode is experimentally evaluated and discussed.

Influence of hydrogen fuel cell temperature safety on bus driving characteristics and stack heating mode

Hydrogen fuel cells have developed rapidly in bus because of their cleanliness. The driving parameters of hydrogen fuel cell bus and the internal temperature distribution of hydrogen fuel stack are investigated in this study. The 1-D model of hydrogen fuel cell bus and the 2-D model of hydrogen fuel cell stack are established based on AMESim. In the cooling process, for the existing typical thermal management system, the influence of ambient temperature on the fuel cell bus driving parameters is analyzed by the 1-D model. In the heating process, the internal temperature distribution of the stack is analyzed under three heating modes by the 2-D model. As the ambient temperature increases, to ensure the safe operating temperature of the fuel cells, the bus top speed should be reduced, and the acceleration time should be extended. When the stack needs to be heated, the gas-liquid heating mode can supply a more uniform temperature distribution inside the stack. • The 1-D vehicle model and 2-D stack model of hydrogen fuel cell are established. • The effect of ambient temperature on driving parameters is analyzed. • The internal temperature distribution of the stack is analyzed under three heating modes. • Gas-liquid heating mode can give a more uniform stack temperature distribution.

Design of the PID temperature controller for an alkaline electrolysis system with time delays

Electrolysis systems use proportional–integral–derivative (PID) temperature controllers to maintain stack temperatures around set points. However, because of heat transfer delays in electrolysis systems, the manual tuning of PID temperature controllers is time-consuming, and temperature oscillations often occur. This paper focuses on the design of the PID temperature controller for an alkaline electrolysis system to achieve fast and stable temperature control. A thermal dynamic model of an electrolysis system is established in the frequency domain for controller designs. Based on this model, the temperature stability is analysed by the root distribution, and the PID parameters are optimized considering the temperature overshoot and the settling time. The performance of the optimal PID controllers is experimentally verified. Furthermore, the simulation results show that the before-stack temperature should be used as the feedback variable for small lab-scale systems to suppress stack temperature fluctuations, and the after-stack temperature should be used for larger systems to improve the economy. This study helps ensure the temperature stability and control of electrolysis systems.

Model-based decoupling control for the thermal management system of proton exchange membrane fuel cells

As one of the most promising sustainable energy technologies available today, proton exchange membrane fuel cell (PEMFC) engines are becoming more and more popular in various applications, especially in transportation vehicles. However, the complexity and the severity of the vehicle operating conditions present challenges to control the temperature distribution in single cells and stack, which is an important factor influencing the performance and durability of PEMFC engines. It has been found that regulating the input and output coolant water temperature can improve the temperature distribution. Therefore, the control objective in this paper is regulating the input and output temperature of coolant water at the same time. Firstly, a coupled model of the thermal management system is established based on the physical structure of PEMFC engines. Then, in order to realize the simultaneous control of the inlet and outlet cooling water temperature of the PEMFC stack, a decoupling controller is proposed and its closed-loop stability is proved. Finally, based on the actual PEMFC engine platform, the effectiveness, accuracy and reliability of the proposed decoupling controller are tested. The experimental results show that with the proposed decoupling controller, the inlet and outlet temperatures of the PEMFC stack cooling water can be accurately controlled on-line. The temperature error range is less than 0.2 °C even under the dynamic current load conditions.

Design of a novel self-breathing micro fuel cell stack with a silicon membrane based on the demand for portable charging power sources

This study proposes a portable mobile power supply with a self-breathing micro-proton exchange membrane fuel cells (µPEMFCs) stack as the core for the application of µPEMFCs in portable charging power supplies. A porous silicon membrane etched with hydrofluoric acid was used to conduct protons. A series array of single cells was formed on a single printed circuit board (PCB) board. Four PCB boards are connected by rotation to form a PEMFC stack, and the cavity enclosed by the PCB boards was used to store hydrogen. The effects of hydrogen pressure and ambient conditions on stack performance were analyzed, and the discharging time of the stack was investigated at standard charging powers of 5 V and 1 A. The results revealed that the heat dissipation method of the self-breathing stack limits the operating current density of the stack. To ensure that the stack temperature was within 60–80 °C, the current density should be controlled within 600–820 mA·cm−2. The mobile power supply designed in this study can satisfy the standard charging power requirements of 5 V and 1 A for electronic devices. When the hydrogen pressure was 3 atm, the discharging time of the mobile power supply was 420 s, and the discharging time could be extended by increasing the hydrogen pressure.

Fundamental study for heat pump stack length determination with complex flow paths in a compact thermoacoustic system

The miniaturization of thermoacoustic systems is expected to find practical applications in various fields. To achieve miniaturization, we focused on the relation of the sound wave in the tube and the length of the heat-pump stack. Accordingly, three types of metal meshes with different channel diameters were prepared, and thermoacoustic systems were designed considering ωτ, a dimensionless quantity. In the experiment, the stack length was varied and the stack temperature difference and heat flow were measured. Consequently, temperature difference and heat flow with respect to the stack length were studied. In addition, even when the stack was short, temperature difference at both ends of the stack was formed. The better stack length in terms of energy efficiency was also examined. Accordingly, we confirmed that the ratio of stack length to wavelength is in the range of 0.2%–0.6%.

Part-load performance prediction of a novel diluted ammonia-fueled solid oxide fuel cell and engine combined system with hydrogen regeneration via data-driven model

This paper proposes a novel diluted ammonia-fueled solid oxide fuel cell and X-type rotary engine (SOFC-XRE) combined system with hydrogen regeneration sub-system to realize high energy conversion efficiency. The combined system key design parameters and geometric parameters are optimized. The influences of SOFC inlet temperature, stack fuel utilization ratio, stack excess air ratio and pressure ratio across membrane for permeating hydrogen on system off-design performance are investigated. Meanwhile, the improved off-design performance of combined system is revealed by comparing with standalone SOFC. The data-driven neural network model (NNM) is established for predicting system part-load performance based on the off-design performance datasets. The optimal system operation parameter combinations and performances in a wide power load range are obtained by combining data-driven NNM with particle swarm optimization (PSO) algorithm. The results show that the cell stack temperature gradient decreases with decreasing fuel flow rate coefficient. The stack fuel utilization ratio has a larger effect on combined system energy efficiency than the fuel flow rate coefficient. The optimal membrane pressure ratio and fuel flow rate coefficient decrease with decreasing power load. The combined system energy efficiency is increased by 5.94% as the power load is decreased by 75%, while it is decreased by 2.1% as the power load is increased by 20%. The combined system energy efficiency is 10.9–12.2% higher than standalone SOFC in a wide combined system power load range of 120–25% without carbon emission.

Research on Cold Start of Proton-Exchange Membrane Fuel Cells Based on Model Predictive Control

The cold start of fuel cells limits their wide application. Since the water produced by fuel cells takes up more space when it freezes, it may affect the internal structure of the stack, causing collapse and densification of the pores inside the catalytic layer. This paper mainly analyzes the influence of different startup strategies on the stack cold start, focusing on the change in the stack temperature and the ice volume fraction of the catalytic layer. When designing a startup strategy, it is important to focus not only on the optimization of the startup time, but also on the principle of minimizing the damage to the stack. A lumped parameter cold-start model was constructed, which was experimentally verified to have a maximum error of 8.9%. On this basis, a model predictive control (MPC) algorithm was used to control the starting current. The MPC cold-start strategy reached the freezing point at 17 s when the startup temperature was -10 °C, which is faster than other startup strategies. Additionally, the time to ice production was controlled to about 20 s. Compared with the potentiostatic strategy and maximum power strategy, MPC is optimal and still has great potential for further optimization.

Design of collaborative control system for low temperature operation of air-cooled proton exchange membrane fuel cell

The air-cooled proton exchange membrane fuel cell has an open cathode, so the temperature, air volume and humidity of the air participating in the cathode reaction and the air used for heat dissipation will directly affect the performance of the fuel cell. In this paper, the relationship between the optimal working temperature of the air-cooled proton exchange membrane fuel cell and the ambient temperature, ambient humidity and working current is obtained through experiments, and a collaborative control system based on BP neural network fuzzy incremental PID stack temperature and working chamber temperature is designed. Experiments show that the control system can make the stack and working cabin reach the set temperature in low temperature environment.

Experimental and theoretical evaluation of a 60 kW PEM electrolysis system for flexible dynamic operation

Electrolysis systems based on low temperature PEM cells have fast ramp rate and load-following capability that makes them suitable to provide services to the power grid, whose reliability is hindered by the increasing deployment of dynamic and non-controllable renewable energy sources. Flexible operation of the complete electrolysis unit is influenced by system design and auxiliaries: this work provides an insight on the dynamics of integrated components, aiming at the enhancement of the flexible operation of an industrial-scale PEM electrolysis system. A numerical dynamic model of a complete electrolysis unit is implemented with the software Simulink, combining customized dynamic models of the main balance of plant components. Mass and energy balances are solved during variable load operation, while PI–type and on–off controllers are included for system operation control. The electrolysis stack performance depends on semi-empirical polarization curves, reflecting voltage dependance on temperature, pressure, and current density. The solved transients of the device provide original indications for increasing the dynamic performance of the system.A 60 kWe commercial unit is tested to validate the model, assessing its nominal and partial load performance. The experimental data confirm the capability of the system to work at partial load without relevant deviations in the stack temperature and pressure. Despite the lower specific consumption of the stack at partial load, the system shows an increase in the average net specific consumptions when the load decreases toward its minimum (from 67 kWhe/kgH2 above 1A/cm2 to 140kWhe/kgH2 at 0.3A/cm2).The simulation results and the experimental data are in good agreement, confirming the prediction capacities of the model with a good accuracy. Among the different investigated operation strategies, the strongest reduction in average net specific consumption at partial load is given by the control of water flowrate to the stack (110 kWhe/kgH2 at 0.3 A/cm2) or of the hydrogen dryer regeneration (70 kWhe/kgH2 at 0.3 A/cm2). These are the suggested points to be investigated for further improvements of fast-changing load systems.

Neural network and URED observer based fast terminal integral sliding mode control for energy efficient polymer electrolyte membrane fuel cell used in vehicular technologies

In this research work, a Neural Network (NN) and Uniform Robust Exact Differentiator (URED) observer-based Fast Terminal Integral Sliding Mode Control (FTISMC) has been proposed for Oxygen Excess Ratio (OER) regulation of a Polymer Electrolyte Membrane Fuel Cell (PEMFC) power systems for vehicular applications. The controller uses URED as an observer for supply manifold pressure estimation. NN is used to estimate the stack temperature which is unavailable. The suggested control method increased the PEMFC’s effectiveness and durability while demonstrating the finite-time convergence of system trajectories. By controlling the air-delivery system in the presence of uncertain current requirements and measurement noise, the approach ensures maximum power efficiency. The Lyapunov stability theorem has been used to confirm the stability of the presented algorithm. In addition, the suggested method eliminated the chattering phenomenon and improved power efficiency. Given these noteworthy characteristics, the research has the potential to decrease sensor dependence and production costs while also improving the transient and steady-state response in vehicular applications.

An extreme gradient boosting-based thermal management strategy for proton exchange membrane fuel cell stacks

The working temperature is one of the critical factors for proton exchange membrane fuel cell (PEMFC) stacks, as it directly influences the performance and working life of PEMFCs. Due to the inherent nonlinearity of the PEMFC stack thermal management system model, current thermal management strategies (TMSs) generally face with the drawbacks of lower accuracy and robustness. In this research, a novel machine learning (ML) algorithm, i.e. the extreme gradient boosting (Xgboost) algorithm is applied to the TMS of a PEMFC stack to control the inlet and outlet temperatures of the PEMFC stack, where the proton exchange membrane (PEM) water content is also considered and kept at a reasonable level. In order to evaluate the effectiveness of the proposed strategy, a fuel cell hybrid vehicle (FCHV) model from Autonomie software is selected and the proposed TMS is tested and compared to other strategies under a mixed driving cycle for the PEMFC stack. Results show that the proposed Xgboost-based TMS presents the best control performance, which reduces the maximum deviation of the PEMFC stack temperature and the variation range of the PEM water content compared to other ML-based TMSs and the fuzzy logic-based TMS.

Remaining useful life prediction of PEMFC based on the multi-input cycle reservoir with jump network

In recent years, fuel cells have attracted more and more attention in the energy field. However, accurate evaluation of durability is one of the key aspects of large-scale commercial application of fuel cells. The data-driven method has good fitting performance for predicting remaining useful life (RUL) at complex and nonlinear systems. In this paper, Cycle Reservoir with Jump with multi-inputs and multi-outputs (MIMO-CRJ) is proposed to further improve the fitting speed and reduce the complexity of the system which improved the structure of echo state network (ESN). Different from the previous method that only uses voltage to predict the voltage, this paper considers stack current, temperature and pressure, reactant flow rate in fuel cell operation as input variables, and extracts more degradation information to predict the voltage trend. Experimental results show that MIMO-CRJ has better performance than Cycle Reservoir with Jump with single -inputs and single-output (SISO-CRJ) and ESN networks under both static and quasi-dynamic current conditions.

A Bi-level Optimization-based Architecture for the Scheduling and Real-Time Control of Microgrids With Hydrogen Production System

This paper proposes a novel bi-level architecture for the scheduling and real-time control of a microgrid with a hydrogen production system. It consists of an economic optimization that performs the operational management of the microgrid at the higher level. At the lower level, a real-time controller, based on a Reference Governor (RG), is designed to control the stack's temperature of a proton exchange membrane (PEM) electrolyzer under the flexible operation imposed by the economic optimization. When switching from one power level to another, the stack's temperature is having a sudden change that might lead to a violation of the operating limits set by the manufacturer. With the RG-based controller added at the lower level, the operating limits of the temperature are respected, and better performances of the system are guaranteed. The effectiveness of the proposed bi-level architecture has been proved through a real case study, in which the sudden changes in the temperatures have significantly been reduced by up to 91% with respect to the optimal operational management of the overall microgrid.

A modeling study on water and thermal management and cold startup of unmanned aerial vehicle fuel cell system

The water and thermal management of hydrogen/air proton exchange membrane fuel cell (PEMFC) system in a variable environment and its fast and efficient cold start at low temperature are the key issues in the practical design and integration of unmanned aerial vehicle (UAV) fuel cell power system. Herein, a complete mathematical model of fuel cell stack and balance of plant (BOP) is established, then the nonlinear relationship between cooling water flow rate, coolant bypass valve opening, and stack temperature at different flight altitudes is studied for a typical UAV configuration, and the safe working range of stack is pointed out. In addition, a cold-start model supporting operation at the high current density is proposed. After analyzing the cold start-up process of the fuel cell, it is found that a reduction of active reaction area caused by icing is the main reason for voltage loss. The optimal self-start current densities of the fuel cell at different low temperatures are detected by the galvanostatic method, as a result, a successful self-startup of the fuel cell is achieved at a temperature of 263.15 K.

Enhancing grid-connected hybrid PV-Wind systems: A comprehensive review

The ability to convert energy from one form to another is essential for both survival and the advancement of society. Wind and solar energy represent the most abundant and widely accessible renewable energy sources globally. It offers significant potential for sustainable power generation and reducing dependence on fossil fuels. Because of the tremendous advancements in power electronic systems in recent years, the output of electricity from wind and photovoltaic energy sources has expanded dramatically. The nonlinear programming (NLP) optimization method was used to determine the maximum electrical efficiency of the grid-connected SOFC subject to the constraints of fuel utilization factor, stack temperature, and output active power based on the benchmark Solid Oxide Fuel Cell (SOFC) dynamic model for power system studies and the analysis of the SOFC operating conditions. By solving the NLP issue with the power required by the air compressor, the optimal operating conditions of the grid-connected SOFC were determined. With the optimal SOFC operating conditions for maximum efficiency operation obtained at different active power output levels, a hierarchical load tracking control scheme for the grid-connected SOFC was proposed to have maximum electrical efficiency operation while keeping the stack temperature bounded.