Search

The generated gas bubbles in microfluidic fuel cells (MFCs) cover the active surface area of the electrode and greatly limit the cell performance. In this study, a feasible approach is proposed to effectively accelerate bubbles removal and enhance cell performance by embedding a paper separator in the middle of the microchannel. Visualization of bubble behaviors and the corresponding electrochemical measurements are performed to investigate the effect of bubbles on the cell performance. Periodical process of the bubble growth and removal leads to the fluctuation in the current density of the MFC. The rapid gas removal and the low ohmic resistance are observed after embedding a layer of filter paper in the microchannel, causing improved fuel transfer and smaller ohmic loss. Although the current density fluctuates more frequently, the fluctuation amplitude decreases and the cell performance is enhanced. Compared with the MFC without paper separator, the maximum power density and the limiting current density of the MFC with paper separator is increased by 25.2% and 130%, respectively. Moreover, the cell performance is improved with increasing the flow rate of reactant solution and the peak power density achieves 25.9 mW cm−2 under flow rate of 1500 μL min−1. • A microfluidic fuel cell with paper separator is proposed to enhance gas removal. • Bubble behaviors as well as its effect on the performance are studied. • The paper separator can restrict bubble size and improve cell performance. • Effect of flow rate on the bubble behavior and cell performance is studied. • Embedding a paper separator is a good and effective way for gas management. [ABSTRACT FROM AUTHOR]

The hybridization of energy storage systems consisting of fuel cells, batteries, and ultracapacitors has tremendous potential in fuel cell hybrid electric vehicles. However, the utilization of ultracapacitors in existing energy management strategies is insufficient, preventing complete exploitation of the energy storage system's benefits. To strengthen the utilization of ultracapacitors, this paper proposes an energy management strategy for fuel cell vehicles with three energy sources, considering the ideal state of charge (SOC) of ultracapacitors. The proposed method disassembles the motor power demand, with the ultracapacitor primarily supplying the acceleration portion. It calculates the ideal SOC of the ultracapacitor, and controlling the actual SOC of the ultracapacitor to match the ideal SOC. Furthermore, a fuzzy logic controller is designed to modify the fuel cell's output power, reducing power fluctuation. Additionally, according to the variation of the SOC of the battery, the fuel cell output power is modified to enhance the overall performance of the composite energy storage system. Simulation and hardware-in-loop results demonstrate that, compared to the adaptive filtering strategy with fuel cell power constraints and dynamic programming strategy, the strategy proposed in this paper reduces operational costs by 14.56 % and 4.99 %, respectively. • An energy management strategy considering ideal SOC in ultracapacitor is proposed. • The ideal SOC of UC is defined, with adjusting based on the average vehicle speed. • A fuzzy logic controller is designed to adjust the limits of the FC output power. • The output power of FC is adjusted according to the changes in the SOC of BAT. • This strategy offers better overall costs compared with the AFS strategy. [ABSTRACT FROM AUTHOR]

Purging offers a straightforward and cost-effective means of eliminating inert gases that accumulate in high-temperature proton exchange membrane fuel cells (HT-PEMFC) operating in dead-end anode (DEA) mode. Nevertheless, earlier studies primarily concentrated on inert gases, overlooking the significant challenge posed by the strong adsorption of CO within HT-PEMFC, complicating the use of inert gas methods for CO purification. This paper presents an analytical model that was validated experimentally. Leveraging this model, the paper analyzes the crucial parameter of CO concentration within the anode and deduces theoretical extreme values. Subsequently, it proposes purging strategies tailored to different operational conditions. The findings indicate that the minimum CO concentration within HT-PEMFC is influenced by current, feed gas flow rate, and feed gas CO concentration. Additionally, the maximum CO concentration is affected by the off-duration of the purge valve in addition to these factors. Specifically, under the operating condition of 473.15 K, adopting a CO concentration threshold purge strategy is recommended, whereas under the operating condition of 448.15 K, voltage drop and CO concentration threshold purge strategies should be respectively implemented above and below the cutoff line based on the current. This research contributes to mitigating the issue of CO accumulation in DEA mode. • A CO purging model was built in published literature. • The maximum and minimum values of CO molar concentration in CO purge were derived. • Purging strategies for different temperatures and current densities are proposed. • The maximum CO mole fraction that can be purified is increased to 3 %. [ABSTRACT FROM AUTHOR]

The diffusion on large scale of the hydrogen-fueled technologies could be a strategy for boosting the expected decarbonisation of the civil sector. However, there are still a lot of unanswered questions about the real feasibility and the problems of the integration in a pre-existing building-plants system. Despite some conflicting opinions, the research about the application of the hydrogen system in the building sector must continue to make the technology more attractive and easily available at low cost. Therefore, first of all, the paper analyses advantages and issues through a broad overview. The main limits of the available studies regard both the approach and the technology integration. Indeed, there are few data about in-field performance analysis since the simulative/numerical investigations are usually based on in-lab prototypes. The most diffused systems referred to integrated energy solutions with proton exchange membrane fuel cell technology and in any case the integration in very low energy buildings such as the nearly zero energy (nZEB) ones is not discussed. Then basing on the discussed research gap, the paper focuses on the set-up of a new installation of micro-combined cooling, heating and power unit based on solid oxide fuel cell technology fueled by green hydrogen. The complexity in the design of the integration with other innovative plant configurations is discussed considering as real case study a nZEB in Mediterranean climate. Preliminarily experimental results show that the combination of fuel cell and photovoltaic system could bring to positive energy with surplus energy, during autumn season, of about 23.8 kWh. Moreover, it is also proposed the validation of a dynamic numerical model developed by means of TRNSYS (Transient System Simulation Tool) that will be used for further sensitivity analysis. [ABSTRACT FROM AUTHOR]

Large ships are more utilized than ever before to facilitate transporting heavy goods and oils overseas. However, they rely on fossil fuels and cause severe environmental impacts. Utilizing alternative fuels and implementing new engine designs are considered promising methods to reduce greenhouse gas emissions and improve engine performance. This paper compares three developed, hybridized marine engines operated using five fuel blends of hydrogen, methanol, dimethyl ether, ethanol, and methane. In addition, the paper presents a multi-objective particle swarm optimization to increase the engine power and its exergetic efficiency and reduce the cost and environmental impact. A marine engine (SRC-GT-SOFC) containing a steam Rankine cycle, a gas turbine, and a solid oxide fuel cell is considered to weigh less and perform better. It is found that the optimized SRC-GT-SOFC engine power is increased by about 10 %, corresponding to 16269 kW with an increased exergetic efficiency of 70.6 %, respectively. Its specific fuel and product exergetic costs are found to be 12 $/GJ and 15.67 $/GJ, and its specific fuel and product environmental impacts are 7.4 Pt/GJ and 9.3 Pt/GJ, respectively. This results in a significant reduction in the relative cost difference and the relative environmental impact difference of the optimized SRC-GT-SOFC of 30 % and 25.4 %, respectively. • Three hybridized marine engines are compared and optimized to raise performance. • Optimized SRC-GT-SOFC engine has 16.3 MW power and 70.6 % exergetic efficiency. • Specific exergetic cost reduced to 12 $/GJ for fuel and 16 $/GJ for product. • Specific exergetic environmental impact for fuel and product becomes 7.4 and 9.3 Pt/GJ. • New operation conditions of SRC-GT-SOFC have low price and environmental impact. [ABSTRACT FROM AUTHOR]

During the driving of the fuel cell vehicles, temperature fluctuations occur in the Proton Exchange Membrane Fuel Cell (PEMFC), whose output efficiency decreases and hydrogen consumption increases. Rule-based (RB) and proportional-integral-derivative (PID) controllers can roughly suppress the temperature fluctuation, but they cannot perform multi-objective optimization, which cannot reduce hydrogen consumption at the same time. To solve this problem, in the first step of this paper, the hydrogen consumption factor and the temperature fluctuation factor in the operating temperature control are introduced to design the DDQN controller. In the second step of this paper, the DDQN controller is trained offline and verified under simulation conditions. The simulation results show that compared with the RB controller and PID controller, the average temperature fluctuation of the DDQN controller is reduced by 27.27 % and 28.50 %, and the hydrogen consumption is saved by 1.78 % and 2.58 %, respectively. The research in this paper is innovative in that it reduces the hydrogen consumption of PEMFC and improves the driving range of fuel cell vehicles from a specific point of view. • Design a DDQN controller to achieve intelligent real-time control of the operating temperature of PEMFC. • It can effectively reduce temperature fluctuations of PEMFC under complex operating conditions. • Simultaneously considering controlling temperature fluctuations and reducing hydrogen consumption. [ABSTRACT FROM AUTHOR]

This study thoroughly analyses and quantifies the operating temperature characterization of the proton exchange membrane (PEM) fuel cell and develops an automated temperature calibration model to precisely identify the optimal operating temperature point corresponding to different current densities. The results show that the automated temperature calibration model integrated with the metaheuristic optimization algorithms and CSO-SVR model has the best overall predictive performance, with the R 2 of the predicted values obtained in the training phase and the test phase both exceeding 0.999 and the RMSE less than 2.29 × 10−3 V. In addition, the optimum operating temperature obtained by this model is basically consistent with the experiment value under different current densities, which indicates that the automatic calibration model of fuel cell temperature proposed in this paper has high accuracy and robustness. Therefore, a lot of time-consuming and high-cost experiments can be avoided by using the proposed automatic calibration model of fuel cell temperature. Furthermore, the model and analysis in this paper may provide theoretical support for the thermal control of the vehicle fuel cell system. • The temperature sensitivity analysis for the polarization process is performed. • The optimal operating temperature automated calibration model is developed. • The optimal operating temperature analysis based on the proposed model. [ABSTRACT FROM AUTHOR]

It is crucial to accurately calculate the cost function of the energy management strategy (EMS) of the hybrid powertrain to improve the hydrogen economy of the system. This paper proposes an EMS for fuel cell hybrid electric vehicles (FCHEV) based on improved dynamic programming (DP) and air supply optimization to improve economy and reliability. Taking the maximum net power output of the FC system as the target, the optimal oxygen excess ratio (OER) and cathode pressure of the FC system under different current densities are solved by using PSO. A velocity prediction method based on Bi-LSTM is developed to predict short-term velocity changes in real time. The DP algorithm is introduced and the EMS of the DP algorithm based on short-term velocity prediction is developed for real-time hybrid powertrain optimization and management. Based on the results of energy allocation and optimal gas supply conditions of FCs, the cost function of EMS is modified to reallocate the power of the FC system and battery. The results demonstrate that the proposed method achieves the lowest hydrogen consumption compared to the other two algorithms. Remarkably, it reduces the fuel cost by up to 8.85 % compared to the commonly used online DP algorithm. • An EMS for FCHEV is developed based on improved DP and air supply optimization. • Optimize the best working conditions of the air supply system under different current densities. • An EMS was designed based on DP optimization under short-term condition prediction. • The cost function of the real-time EMS is modified in real-time. [ABSTRACT FROM AUTHOR]

Abstract The world's first fuel cell described in the early 1800's was fueled with hydrogen. While hydrogen is still the most common fuel, hydrocarbon fuels offer several advantages including availability at a lower cost, higher storage density and existing infrastructure. This paper provides an overview for the significant potential benefits of using hydrocarbon fuels directly in a fuel cell system. Their use leads to a reduction in the capital cost due to the elimination of the fuel processor unit. The fundamentals, advantages, types of direct hydrocarbon fuel cells (DHFC), challenges and applications are discussed in this paper. The past and current status of research and development activities are addressed with emphasis on efficiency and exergy analyses. In spite of their high theoretical energy efficiency, technical challenges remain unsolved in DHFC systems. In high temperature hydrocarbon fueled operation, the deposition of carbon-based material leads to fuel cell degradation. In lower temperature fuel cells, electrode (mainly the anode) over-potentials and fuel crossover are still challenging. Therefore, the improvement and commercialization of these types of fuel cells will probably require the development of less or non-noble catalysts and reasonably functioning membranes. Highlights • The potential for improving energy efficiency using direct hydrocarbon fuel cells is analyzed. • The merit of using hydrocarbon fuels directly is discussed. • The fundamentals, history, advantages, types of hydrocarbon fuel cells are reviewed. • The challenges and recommended improvements in direct hydrocarbon fuel cells are presented. [ABSTRACT FROM AUTHOR]

Fuel cells have been regarded as promising power sources for cleaner energy production. Despite their high theoretical efficiency, fuel cells are still challenged with their durability issues that hinder their full commercialization. Recent work on fuel cells' prognostics provided multiple opportunities for predicting and monitoring the fuel cells' durability. Prognostic studies evaluate, predict and model complex fuel cell systems. This field witnessed an increase in application, accuracy, and depth recently. The application of several prognostic studies to predict the failure modes helped to improve the efficiency and estimate the remaining useful life (RUL) of the complex fuel cell system. This paper discusses the most recent prognostic and health monitoring studies of fuel cells systems that use hydrogen or hydrocarbon fuels. It suggests that prognostics are promising approaches toward evaluating the fuel cell system's useful life. The paper also provides an overview of the most recent developments in the types of the applied prognostic models. It appears that the main challenge is the development of online prognostic methods for the dynamic fuel cell systems. This work concludes that a proper prediction/monitoring approach requires the application of more than one prognostic method. • The application of fuel cells is usually limited by their low lifespan and durability. • Prognostics offer a promising approach for predicting the failure modes in fuel cells. • Prognostic models are classified into model-free methods or model-based methods. • More than one model is required to properly evaluate the fuel cells failure modes. [ABSTRACT FROM AUTHOR]

Eco-driving strategies increasingly emphasize enhancing multi-performance including safety, comfort, durability, and traffic efficiency under optimal energy-saving. This paper introduces a multi-performance enhanced eco-driving strategy for connected fuel cell hybrid electric buses (FCHEBs) based on the stein soft actor-3-critic (SSA3C) algorithm. By leveraging the correlation between the international roughness index (IRI) and weighted root mean square acceleration (WRMSA), energy management system (EMS) and adaptive cruise control (ACC) are integrated the to enhancing multi-performance including vertical comfort and energy efficiency. To address the insufficient action representation and overestimation of Q-values of traditional soft actor-critic (SAC) algorithm, improvements are made to the actor and critic networks using stein variational gradient descent (SVGD) and clipped triple Q-learning (3C), respectively. Composite cycles integrating the standard cycle and IRI are developed to enhance the strategy's generalization. Experimental results indicate that the proposed eco-driving strategy, while maintaining dynamics, safety, durability, and efficiency, efficiently achieves 98.1 % energy savings and equivalent comfort level as dynamic programming (DP). Ablation experiments on vertical comfort demonstrate improvements of 1.24 % in energy efficiency and 2.94 % in WRMSA. Finally, the effectiveness of the network improvements and the adaptability of the proposed strategy are demonstrated through comparative experiments. • An DRL-based eco-driving strategy is developed for connected FCHEVs. • The relationship between road roughness, vertical comfort, and velocity is studied. • Vertical comfort is incorporated to co-optimize eco-driving multi-performances. • A novel Stein Soft Actor-3-Critic with high expressivity and precision is proposed. • Composite driving cycles including IRI are constructed for strategy adaptability. [ABSTRACT FROM AUTHOR]

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 %. • A coupling model of the air supply and thermal management subsystem is established. • The research on coordinated optimization of airflow, pressure, and temperature is conducted. • The feedback-linearization decoupling of flow, pressure and temperature is proposed. • An ADRC based on the decoupled model is proposed to realize the tracking control. [ABSTRACT FROM AUTHOR]

Hybrid electricity-heat-hydrogen energy system with demand response (DR) is promising in enhancing flexibility and energy efficiency. However, the multi-energy coupling and source-load uncertainties makes it challenging to efficiently schedule energy flows of electricity generation, storage and DR. To this end, this paper proposes a continues deep reinforcement learning algorithm, specifically the deep deterministic policy gradient (DDPG), for the energy management optimization. Different Markov decision processes are firstly employed to analyze and compare two kinds of incentive-based electro-thermal DR contracts, i.e., load curtailment and load shifting. Simulation results exemplify the superiority of the proposed DDPG-based scheduling incorporating electro-thermal DR in terms of economy and sustainability, leading to a 16.02 % reduction in scheduling costs for contract load curtailment and an 8.52 % reduction for contract load shifting when compared to that without DR consideration. Furthermore, the robustness of DDPG-based scheduling is verified under 60 random source-load scenarios compared with different algorithms. Compared to results obtained by DDPG, DDPG-LC and DDPG-LS reduce the mean cost by 22.15 % and 12.84 %, while their error from the theoretical optimum is only around 5 %. The results demonstrate that the approximate optimality and rapid decision-making illustrate DDPG's efficient real-time scheduling capability, thereby enhancing the system's adaptability to uncertain environments. • A hybrid electricity-heat-hydrogen energy system is proposed integrating renewable energy, hydrogen and fuel cells. • The scheduling optimization problem is comprehensively formulated considering costs and the demand response (DR). • Double electro-thermal DR contracts are discussed and described in distinct Markov decision processes for training. • The robustness of DDPG is verified through the scheduling comparison of different algorithms under random scenarios. [ABSTRACT FROM AUTHOR]

Energy storage systems (ESS) address the uncertainty of renewable energy sources (RES) in renewable energy based isolated microgrids. One type of ESS is unable to provide 100 % supply reliability. Moreover, there are either excess energy or reliability issues due to a lack of energy management techniques. Therefore, this paper proposes hybrid RES (HRES) with multiple-ESS (MESS), including battery and hydrogen storage system (HSS). The techno-economic analysis and optimal design of HRES-based microgrids, which include wind turbine (WT), solar photovoltaic (PV), fuel cell (FC), electrolyzer, HSS, and battery energy storage (BES) are performed using HOMER. The design objectives are to minimize the net present cost (NPC) and cost of energy (COE) in the proposed HRES with MESS. The analysis is performed for a multi-energy storage system. The results show that WT/PV/FC/Electrolyzer/HSS/BES/converter is the optimal microgrid with the lowest NPC of $838832, $679605, and COE of 0.232 $/kWh, 0.189 $/kWh at capacity shortages of 0.0 % and 1.0 %, respectively. The excess energy and unmet loads are also the lowest for the system at the same capacity shortage. The sensitivity analysis shows the impact of uncertain techno-economical parameters on NPC and COE. • Optimal design of Microgrid with smaller dump load size and capital investment. • Analyze the impact of Multi Energy Storage System to minimize the cost of energy. • Techno-economic and sensitivity analys is identifying relationship to parameters. [ABSTRACT FROM AUTHOR]

The current limitations of hydrogen storage technology in submarines have prompted the need for alternative solutions. One promising option is the use of ammonia, a readily stored fuel. This paper presents an innovative, integrated system that runs on ammonia and does not rely on atmospheric air. The system combines Direct Ammonia Fuel Cell (DAFC) stack and an Internal Combustion Engine (ICE) technology to generate power, freshwater, and cooling. The system also recovers waste heat and utilizes it efficiently to produce these useful outputs. The study further aims to assess the system's performance using energy and exergy analysis methods and to conduct a parametric analysis to examine the impact of parameters and operating conditions on system efficiency. In the developed integrated system, the quantities of net power produced, cooling provided, and freshwater flow rate produced under specified conditions are 4069 kW, 589.5 kW, and 1.269 kg/s, respectively. Its energy and exergy efficiency were found to be 38.58% and 44.77%, respectively. The analysis study also obtains that increasing the ammonia flow rate supplied to the ICE and the steam flow rate provided to the turbine, as well as the reference temperature, could potentially improve both energetic and exergetic efficiencies. • Designing a new Air Independent Propulsion system with reduced emissions. • Development of a multiple generation system using ammonia as the sole fuel source. • Evaluating the integrated system performance through energy and exergy efficiencies. [ABSTRACT FROM AUTHOR]

Establishing a precise model for proton exchange membrane fuel cell (PEMFC) is vital to simulate, manage, control, and estimate the optimal parameters accurately. However, the process has some challenges due to the nonlinear nature of fuel cells (FCs) and the missing parameters in datasheet. Also, the reported approaches have some restrictions in their behaviors. This paper proposes a new methodology incorporated exponential distribution optimizer (EDO) to construct PEMFC's equivalent circuit through estimating their parameters with the aid of experimental data. The algorithm is characterized by exploration/exploitation balance that avoids falling in local solutions. Sum square error (SSE) between the measured and estimated terminal voltages is selected as the target to be mitigated. The analysis is performed on four different FCs which are Ballard Mark V 5 kW, BCS-500 W, SR-12 500 W, and NedStack PS6. The proposed EDO is compared to reported approaches of mayfly optimization algorithm (MOA), chaotic mayfly optimization algorithm (CMOA), modified Harris hawks optimizer (MHHO), fractional order modified HHO (FMHHO), hybrid vortex search algorithm and differential evolution (VSDE), and other programmed approaches of sine cosine algorithm (SCA), seagull optimization algorithm (SOA), tunicate swarm algorithm (TSA), and gold rush optimizer (GRO). The best scores are 0.852056, 1.16978E-02, 1.056628, and 2.079166 obtained through the proposed EDO for Ballard Mark V 5 kW, BCS-500 W, SR-12 500 W, and NedStack PS6 cells, respectively. The absolute error ratios of CMOA, MOA, FMHHO, MHHO, VSDE, SOA, SCA, GRO, and TSA attributed to the error obtained by the proposed EDO in case of BSC-500W are 0.17%, 3.49%, 0.62%, 15.50%, 3.78%, 29.12%, 86.30%, 0.90%, and 10.33%, respectively. Despite the nine approaches considered in comparative analysis having some issues with convergence speed, they converged the best solution. Moreover, the dynamic model of PEMFC is established in Simulink and its performance is assessed through applying step load disturbance. The fetched results demonstrated the superiority of the proposed EDO in establishing reliable models of various PEMFCs. • EDO is proposed to estimate the optimal parameters of different PEMFCs. • Comparison to MOA, CMOA, MHHO, FOHHO, VSDE, SCA, SOA, TSA, and GRO is conducted. • The dynamic behavior of the established model is assessed at different demands. • The obtained results demonstrated the efficacy and reliability of the proposed EDO. [ABSTRACT FROM AUTHOR]

As the world's largest market for vehicles, China is facing challenges related to energy security and urban air pollution. The development of electric vehicles has been determined to be the national strategy for solving these problems. By the end of 2015, China had become the world's largest electric vehicles market, but its core technologies are still less competitive in the global marketplace. A scientific national strategy for 2016 to 2020 is expected to play a critical role in China becoming the global leader in the electric vehicle industry. The research process for this strategy includes a review of the technologies for electric vehicles, market analyses, benchmarking of the top levels in the field, and expert interviews. By these approaches, the strengths and weaknesses of China's electric vehicle technologies and industry are assessed. Competitive and feasible quantitative goals for key components and powertrains are proposed by this paper, and a core issue has been determined to be the need to improve the safety of high-energy density traction batteries. Improving the power density of electric control units is expected to the core for electric vehicles' electronics and control systems. Key problems for the fuel cell stacks used in cars and buses have been identified by this paper to be, respectively, power density and durability. Long-range plug-in hybrid electric powertrains are the optimal candidate for Chinese plug-in hybrid electric vehicles. Lightweight material, intelligent driving technologies and special electric chassis are set to be the focus for improving the energy efficiency of battery electric vehicles. Comprehensive safety and recyclable electric vehicle technologies are set to become key issues in the future, and the Chinese government should research and develop these in advance. [ABSTRACT FROM AUTHOR]

A reliable energy optimization strategy incorporating vehicle connectivity is of great importance for the performance enhancement of fuel cell electric vehicles. In this paper, a multihorizon hierarchical model predictive control framework is proposed, which reduces energy consumption while incorporating fuel cell lifetime management though real-time speed preview. Specifically, the trajectories of battery state of charge are explored via convex optimization in the upper layer to provide a suboptimal reference for real-time optimization, and the concept of multihorizon is introduced into convex optimization for the first time. At the lower level, an equivalent consumption minimum strategy-based model predictive control is designed, which improves energy utilization efficiency and prolongs the lifetime of fuel cells. The main contribution of this paper is to use multihorizon optimization to solve the energy optimization and lifetime management of fuel cell electric vehicles over different timescales. Experimental results show that the proposed strategy has great potential in cost saving, which can reduce 10.10% to 16.95% of the total cost in real driving conditions compared with the rule-based strategy. • The multihorizon HMPC framework is applied to energy management of FCEVs. • The sub-optimal reference trajectories of battery SOC are planned. • The fuel cell lifetime is considered in energy management. • The method can realize a sub-optimal result compared with DP algorithm. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cell (PEMFC) is considered as a potential power source for future automotive applications and microporous layer (MPL) is one of the core components. The cracks on the surface of microporous layer formed during the deposition process change the overall transport capacity and effect the output performance of fuel cells by regulating water management. In this work, the image recognition and feature extraction of microporous layer surface crack is proposed and elaborated in detail. The simple, fast and accurate method based on image recognition is realized by programming a regional connectivity algorithm and calculating the geometric features of the circumscribed geometry of the cracks. The application of artificial intelligence image processing technology at the micron level provides convenience for data analysis of crack distribution and improvement of microstructure. In this paper, scanning electron microscope (SEM) images of different carbon paper samples are taken, and the samples are tested under the same conditions to verify the feasibility and accuracy of the crack identification method, and to study the effect of cracks on the performance of fuel cells. The results prove that the image recognition algorithm can quickly and accurately identify the characteristics of cracks after Gaussian filtering and grayscale thresholding. Besides, it is found that cracks have impact on fuel cells and surface slender-shaped cracks with large area can improve the performance of fuel cells at high current densities. The method proposed in this paper can be used to improve the existing PEMFC design and quality check in process production by analyzing the local crack characteristics of the same sample and statistical data. In addition, the results of this paper are instructive for further water management research on microporous layer in fuel cells and can be used as a reference for practical applications. • Image recognition method of cracks in MPL is proposed and validated. • Cracks are proved to have impact on PEMFC. • Slender-shaped cracks with large area can improve the performance of PEMFC. • Image recognition provides convenience for analysis and improvement of PEMFC. [ABSTRACT FROM AUTHOR]

Durability is one of the main reasons limiting the large-scale application of fuel cells. Accurate prediction of state of health can help improve fuel cells safety and lifetime. This paper performs accurate health state prediction based on fuel cell semi-empirical model and machine learning algorithm. Firstly, based on the semi-empirical model, multiple stochastic conditions are imported and the voltage outputs are obtained. Voltage is selected as a health indicator characterizing the state of health for the fuel cells and is input to the neural network for training. The state of health prediction model is initially obtained. The model is then further validated and adjusted using real output voltages based on endurance test data. In the training process using model data and endurance test data, not only the output voltages under different operating conditions are used, but also the effects of different training weights on the prediction results are explored. This enables the selection of the most suitable training time and training accuracy. In the end, the lowest root mean squared error using simulated data is 2.48 mV and using test data is 8.88 mV. • State of health prediction is essential to ensure safety and reliability of fuel cells. • A modified semi-empirical model is used to characterize the fuel cell system. • Voltage data based on the model at different operating conditions are simulated. • A novel state of health prediction strategy based on machine learning is proposed. • Prediction effects are verified using training sessions with different proportions. [ABSTRACT FROM AUTHOR]

This paper establishes a two-phase flow model of the real pore structure to explore the liquid water removal process during the shutdown purge. The Monte Carlo algorithm is used to randomly throw circles in the rectangular region to simulate the two-dimensional interface of the real GDL pore structures. The real pore structure with different porosity is established by change the number of random circles to explore the influence of porosity on the liquid water removal process. A solid mechanical model is established to explore the variation of GDL under the effect of assembly force. The equivalent model of the compressed real pore GDL is established to explore the effect of the assembly force on the removal of liquid water during the shutdown purge stage. The results show that the liquid water under the ribs is difficult to remove during the removal of liquid water, affecting the speed of liquid water removal. The reduction of GDL porosity is conducive to the diffusion of air and the displacement of liquid water, thus improving the removal speed of liquid water. The assembly force can reduce the porosity below the rib, and effectively promote the removal speed of liquid water below the rib. • Employing the Monte Carlo method to replicate real pore structures of GDL. • Establishing the two-phase flow model explores the liquid water removal process. • Exploring the influence of assembly force on the removal of liquid water. [ABSTRACT FROM AUTHOR]

The need to increase renewable energy sources deployment and to reduce consumption of fossil fuels has led to the diffusion of small-scale DG (Distributed Generation) systems, which may be effectively integrated in micro-grids. The role of control logic in defining microgrid performances and reliability is predominant and can be improved by using advanced control logics such as the ones based on MPC (Model Predictive Control). In a previous paper, a MPC logic, based on the use of weather forecasts to improve the performances, has been applied to the analysis of power management in a domestic micro-grid system composed by: PV (Photovoltaic panels), FC (Fuel Cells) and a battery pack; in that case, the system was not affected by real uncertainties. In this paper the same system has been considered for domestic microgrid applications. The system control logic has been implemented by assuming real weather forecast as input data. DMPC and SMPC (Deterministic and Stochastic Model Predictive Control) concepts have been applied to the system and results have been compared to both MPC and to a standard RBC (Rule Based Control) logic. The impact of forecast uncertainties has been evaluated showing the advantages of a stochastic approach. In that case, the SMPC showed encouraging performances compared to standard control logics, primarily in terms of primary energy savings and downsizing potential of the power-delivering sub-systems using programmable energy sources. [ABSTRACT FROM AUTHOR]

Due to the combination of aerodynamic and buoyant lift, a multi-lobes hybrid air vehicle (HAV) is very suitable for cargo transportation, communications and surveillance in the stratosphere. Solar cells can be used as an ideal energy source for the HAV because of its flat upper surface and sufficient solar radiation in the stratosphere. A hybrid energy system that combines solar cells, fuel cells and lithium batteries is considered to power the HAV in this paper. To better manage the various energy subsystems and achieve optimal flight performance, a power-following management strategy, which is a typical rule-based energy management strategy(EMS), is constructed for the hybrid energy system of the HAV. The EMS takes into account the high specific power of lithium batteries and the high specific energy of fuel cells to improve the endurance of HAV while ensuring the maneuverability during takeoff and landing. The simulation results show that output of each energy subsystem not only meets the power demand of the HAV, but also conforms to output characteristics of each subsystem. Furthermore, the simulation analysis is also carried out to investigate the impact of one of the energy failures on energy management and corresponding strategies are given. The results show the advantages of EMS proposed in this paper, which can provide a reference for the design of HAV. • A hybrid energy system with PV, PEFC and lithium cells are proposed based on conceptual design of the Hybrid Air Vehicle. • A power-following management strategy is constructed for the hybrid energy system of the Hybrid Air Vehicle. • Simulation of Energy Management Strategy under the specific missions is presented and the impact of one of the energy failures is analyzed. [ABSTRACT FROM AUTHOR]

In this paper, a μDMFC (micro direct methanol fuel cell) with high performance was fabricated by adopting aluminum alloys as a substrate for the first time. PEO (plasma electrolytic oxidation) was adopted to prepare an oxide coating on the substrate to make it electrically non-conductive and corrosion resistant. The PEO-treated substrate shows excellent corrosion resistance in the simulated DMFC environment. Based on the substrate, a μDMFC was fabricated by using stainless steel fiber felt as a cathode current collector that was supported by a perforate flow field on the end plate to reduce the contact resistance. The results show that the cell can provide a peak power density as high as 40.90 mW cm −2 at room temperature of ca. 298 K. The research work presented in this paper provides another way for the design and fabrication of μDMFC. [ABSTRACT FROM AUTHOR]

The high-speed flight can be achieved by high-performance aero-engines. A traditional turbojet engine with steam injection is hardly operated at Mach 3.5–5 owing to the low specific impulse. The performance of turbine-less jet engines is outstanding. No turbines exist in the engine and compressors are powered by fuel cells. Therefore, the limitation of the highest combustion temperature caused by turbines disappears. In addition, fuel cell exhaust can still be used to burn and expand to output propulsion power. In this paper, the turbine-less jet engines integrated with a SOFC and steam injection are proposed to achieve high-speed flight. In order to analyze the performance of the engine, the thermodynamic model is built. Effects of thermodynamic parameters such as water-air ratio are studied. The main results are as follows: the performance of the turbine-less engine is significantly improved by integrating with steam injection. The augment of specific impulse and specific thrust of the engine is 13.13% and 51% respectively compared with the pre-cooling turbojet engine at the water-air ratio of 0.013. The specific impulse of the engine is 1.15 times that of the pre-cooling turbojet engine at Mach 4. Meanwhile, the specific thrust is increased by 26%. • Steam injection makes it possible to operate the turbine-less engine at the high Mach number. • The augment of specific thrust of the engine with steam injection is 51%. • The specific impulse of the engine is 1.92 times that of the pre-cooling turbojet engine at Mach 5. • The performance of the engine increases with the increase of fuel-air equivalence ratio and pressure ratio. [ABSTRACT FROM AUTHOR]

In the intelligent network traffic scenario, the convenient acquisition of microscopic vehicle states and global traffic states can help solve the problems of vehicle driving and energy management in complex traffic environments. This paper proposes a new energy management method for the hydrogen fuel cell bus based on the double-layer deep deterministic policy gradient (DDPG). Combined with the SUMO simulation platform, a double-layer deep reinforcement learning (D-DRL) architecture based on DDPG is designed to improve control accuracy and training speed. In the upper D-DRL, the Agent handles the effects of complex traffic environments to control the reasonable speed of the vehicle and keep it running smoothly to reduce the loss of energy caused by speed changes, compared with the SUMO-IDM model, the maximum-minimum velocity difference was reduced by 21 % and the acceleration and acceleration change was reduced by 7.9 % and 19 %, After the Agent receives the output speed from the upper layer, it distributes the power between fuel cell and power cell. Compared with the DP algorithm, it keeps the SOC at a higher level, the hydrogen consumption level reaches 93.25 %, and the fluctuation amplitude decreases by 42.09 %, effectively improving fuel cell durability. • Speed control and energy management of fuel cell bus on the freeway are completed. • D-DRL is designed to efficiently optimize energy management and speed control. • Optimize action space to improve energy management effect. [ABSTRACT FROM AUTHOR]

Tubular solid oxide fuel cell (TSOFC) converts a great part of chemical energy within fuel into high-grade waste heat, leading to a decrease in overall fuel efficiency. In this paper, a steady-state hybrid system model based on TSOFC and two-stage annular thermoelectric generator (TATEG) is proposed. Considering various kinds of irreversible effects, mathematical model for the hybrid system is explicitly formulated and verified to guarantee the accuracy. Energetic and exergetic performance indexes of the hybrid system and subsystems are obtained, and the corresponding performance characteristics are revealed. Calculation results display that the power density, energetic and exergetic efficiencies of TSOFC-TATEG hybrid system can be 15.29 %, 11.25 % and 11.25 % higher than that of stand-alone TSOFC, respectively. The power density, energetic efficiency and exergetic efficiency of TSOFC-TATEG hybrid system are all increased by 5.65 % compared to that of TSOFC-ATEG hybrid system. Furthermore, this study reveals that enhancing the performance of the hybrid system is attainable through regulating variables, such as elevated operating temperatures, optimal quantities of thermoelectric elements, appropriate ratios between thermoelectric element numbers, well-considered thermocouple thickness, suitable thermocouple radial width, and the judicious selection of parameters like annular shape or height ratio. • A geometry-matching TATEG is proposed to recover the waste heat from TSOFC. • Performance indicators are mathematically deduced based on model formulation. • Hybridization of TATEG enables to improve the TSOFC power density by 15.29 %. • The proposed system is superior to the sole TSOFC in terms of performance. • Effects of some decisive design parameters and operation conditions are revealed. [ABSTRACT FROM AUTHOR]

This paper proposes three online real-time energy management strategies for fuel cell hybrid electric vehicles aimed at minimizing the total running cost over the vehicle's lifecycle. The cost comprises hydrogen consumption, battery electricity consumption, and the degradation of fuel cell and battery systems. The first strategy is a rule-based approach that controls fuel cell power at several fixed levels, with the battery state-of-charge serving as the shifting condition between these levels. The second strategy is an optimization-based approach that determines the fuel cell power by minimizing the instantaneous running cost. Two coefficients, dependent on the battery state-of-charge, are employed to adjust the cost weights of hydrogen consumption, battery electricity consumption, and fuel cell system degradation. The third strategy combines rules with optimization, incorporating the rules as constraints into the cost optimization model. To account for practical driving conditions throughout the vehicle's lifecycle, short distance driving cycles, long distance driving cycles, and commuter driving cycles are separately investigated. The rule and optimization combined energy management strategy outperforms the others in all driving conditions, benefiting from fewer fuel cell system start-stop cycles and increased opportunities for operating in high-efficiency regions. • An instantaneous cost optimization model adjusted by battery SOC is built. • Rules are embedded into cost optimization model to formulate a real-time ROCEMS. • Long distance and commuter driving conditions are investigated for evaluating the cost of lifecycles. [ABSTRACT FROM AUTHOR]

Fuel cell distributed drive electric tractor (FCDET) provided a novel pathway for the development of green agricultural machinery technology. However, FCDET faced problems such as low traction efficiency, short endurance, and high hydrogen consumption. Relevant studies have shown that reasonable and effective power allocation strategy can improve the efficiency and reduce the energy consumption of fuel cell systems. In this paper, a drive power allocation strategy based on adaptive multi-resolution analysis theory (AMRA) was proposed. The strategy could realize effective decoupling between various energy sources, reduce the problems of frequent start-stop and large power fluctuation for fuel cells. We started with an equivalent circuit model of the fuel cell system, a total efficiency solution model, an energy dissipation model, and a drive motor response model. The first-time reconstruction strategy was based on Tunable Q-factor Wavelet Transform (TQWT) to obtain a subsequence that responds to the oscillatory characteristics of the power signal. The second-time reconstruction strategy took the low-frequency subsequence through Variational Mode Decomposition (VMD) into a number of discrete sub-signals with special sparse properties. Meanwhile, the Sparrow Search Algorithm (SSA) was used to obtain the optimal combined values of the modal decomposition layers and the quadratic penalty factor in the VMD on real time. Finally, the second-time decomposed subsequences and sub-signals were reconstructed according to the frequency characteristics, and the reconstructed power signals were redistributed among the individual energy sources. In order to validate the proposed power allocation strategy, we used the ET504-H prototype as a research object. The power information of plowing and transportation operating conditions was collected in Mengjin test base of China YTO Group. The MATLAB/Simulink-PXI joint simulation platform was set up and the driving motor bench test was carried out in the New Energy Key Laboratory of Henan Province. The results show that the AMRA-based FCDET drive power allocation strategy can effectively improve the energy utilization of the fuel cell system and the economy of the whole tractor operating unit. Compared to the power-following strategy under plowing and transportation conditions, the average efficiency of the fuel cell system was improved by 8.3 % and 3.3 %, the equivalent hydrogen consumption was reduced by 35.6 % and 43.89 %, respectively. The average efficiency of the drive motor was improved by 2.06 % and 2.08 %, the total energy consumption was reduced by 3.73 % and 2.6 %, respectively. This study can provide a theoretical foundation and a novel technical approach for the development of FCDET control system. • The fuel cell system total efficiency solution model and the energy consumption model were established. • The AMRA based FCDET drive power allocation strategy is proposed. • A joint MATLAB/Simulink-PXI simulation platform was constructed for real-time validation. • The motor cooperative operation characteristics of the distributed drive system were tested. [ABSTRACT FROM AUTHOR]

The optimal energy management strategy of power sources plays a vital role in enhancing the efficiency and extending the driving range of fuel cell electric vehicles. However, current studies predominantly focus on optimizing energy management for standard driving cycles, which may differ significantly from actual driving cycles. This paper addresses this gap by developing a dynamic programming-based fuzzy logic strategy for optimizing energy management in a demonstration fuel cell hybrid bus. It begins by introducing the propulsion system of the fuel cell hybrid bus and the specific driving cycles used in the demonstration. Subsequently, a dynamic programming-based fuzzy logic strategy is derived by considering the relationship between fuel cell power, battery state of charge, and demand power from the optimal allocation strategy of dynamic programming, which is tailored to the unique characteristics of the demonstration driving cycles instead of a generic standard driving cycle. The effectiveness and real-time performance of the dynamic programming-based fuzzy logic strategy are then validated through offline simulation and hardware-in-the-loop simulation. The results highlight that the proposed method outperforms the original rule-based strategy regarding energy efficiency, particularly when the initial state of charge is low and the energy consumption per unit mileage of the driving cycle is high. [ABSTRACT FROM AUTHOR]

The main contribution of this study is to integrate energy source aging and running costs into the deep reinforcement learning (DRL) based EMS of fuel cell hybrid electric vehicles (FCHEV). For the FCHEV, a multi-objective energy management strategy (EMS) based on twin delayed deep deterministic policy gradient (TD3) is proposed, which aims to simultaneously reduce energy source degradation and lower running costs. To achieve this, the paper innovatively designs the reward function and it's comparative approach. Additionally, it verifies the superiority of the proposed EMS over other EMS based on continuous action space algorithm, including previous action guided deep deterministic policy gradient (PA-DDPG) and soft actor-critic (SAC). Lastly, the agent's action output is changed from fuel cell (FC) current to FC power ratio, and a comparative analysis on results generated by different action outputs is conducted. Simulation results show that the proposed EMS can reduce the running costs while extending the lifespan of battery and FC efficiently. This work holds significant practical significance in the energy distribution of automobiles. • An energy management strategy based on twin delayed deep deterministic policy gradient is proposed. • The results of the same reward function in different deep reinforcement learning algorithms with continuous action space are compared. • The effects of different reward functions and different action outputs on strategy results and agent exploration are compared. • Validation results denote that the proposed EMS effectively improves durability of battery and fuel cell, and saves the running costs. [ABSTRACT FROM AUTHOR]

For fuel cell/battery/ultracapacitor hybrid electric vehicles (FCHEV), the complex topology and terrain information pose challenges to fuel cell efficiency, power sources lifespan, and fuel economy. In this paper, a hierarchical energy management strategy (EMS) considering predicted terrain information is constructed to optimize the demand power allocation of FCHEV based on twin delayed deep deterministic policy gradient (TD3). Firstly, to improve the fuel economy of FCHEV, an adaptive fuzzy filter-based upper-level frequency separation is designed considering negative power. Secondly, to achieve the minimum fuel consumption, a TD3-based lower-level EMS is designed, where the terrain information is predicted through multi long short-term memory network and considered in the TD3 framework. The optimal state of charge obtained by dynamic programming (DP) based on the demand power and predicted terrain information, is considered in the reward function of TD3, as well as the optimal experience solved by DP based on historical road data is integrated into the designed hybrid experience replay. Finally, the effectiveness and optimality of the TD3-based EMS considering predicted terrain information are verified by a series of comprehensive simulations under different driving cycles. The simulation results under CYC_SC03 and test driving cycles indicate that compared to traditional TD3-based EMS, the fuel economy is increased by 33.3% and 27.5%, respectively, and can reach the benchmark level of 80% and 80.9% of DP, respectively. • Adaptive fuzzy filter allocates peak power and braking energy to ultracapacitors. • Considering predicted terrain information to extend lifespan of fuel cell and battery. • Considering dynamic programming to improve efficiency of offline network learning. • Minimizing equivalent fuel consumption while reducing state of charge fluctuation of battery. [ABSTRACT FROM AUTHOR]

Energy management strategies (EMSs) are crucial for the hydrogen economy and energy component lifetimes of fuel cell hybrid electric unmanned aerial vehicles (UAVs). Reinforcement learning (RL)-based schemes have been a hotspot for EMSs, but most of RL-based EMSs focus on the energy-saving performance and rarely consider energy component durability and safe exploration. This paper proposes a health-aware energy management strategy based on a safe RL framework to minimize the overall flight cost and achieve safe operation of UAVs. In this framework, a universal three-dimensional environment that integrates the UAV kinematics and dynamics model is developed. In addition, wind disturbances and random loading of the mission payload during flight are considered for robust training. The energy management problem is formulated as a constrained Markov decision process, where both hydrogen consumption and energy component degradation are incorporated in the multi-objective reward function. A safety optimizer is then designed to satisfy operation constraints by correcting the action through analytical optimization. The results indicate that the safety of the explored action is guaranteed, maintaining zero constraint violations in both training and real-time control scenarios. Compared with other RL-based methods, the proposed method had better convergence capability and reduced the training time. Furthermore, the simulation showed that the proposed method can reduce the total flight cost and fuel cell degradation by 14.6% and 15.3%, respectively, compared with the online benchmark method. • A multi-objective health-aware energy management strategy is developed. • A universal training environment for energy management of hybrid electric UAV is built. • Proposed strategy ensure safe exploration in an offline training scenario. • Safe reinforcement learning provides a more optimal control policy. [ABSTRACT FROM AUTHOR]

Large cargo that cannot be moved worldwide by air or rail transportation is typically conveyed via marine transportation, which relies on fossil fuels and contributes to global emissions. However, a more cost-effective solution is to utilize eco-friendly fuels to reduce carbon emissions. The design of a new hybridized powering system using environmentally friendly hydrogen-based fuel blends is investigated. This paper presents a new hybrid combined marine engine, which involves an internal combustion engine paired with a hybrid solid oxide fuel cell and gas turbine. The exergy, exergoeconomic, and exergoenvironmental analyses are conducted on this proposed engine. It is found that the average net power is about 27 MW with an increase of 30% and the energy and exergy efficiencies are 31% and 41%, respectively. In addition, the average exergetic efficiency based on fuel and product principle is 60%. This new marine engine has 228 $/h Levelized cost rate and 1109 mPt/h environmental rate. Finally, the average overall specific product exergy cost and environment are 64 $/GJ and 22 mPt/MJ, respectively. Also, carbon emissions are significantly reduced by 35%–61%, depending on the fuel blend. Comparing five fuel blends, the methane and hydrogen fuel blend is the most economical and has the least impact on the environment, and the second option is the ethanol blend. • Five alternative fuels are chosen with different concentrations. • Hybrid marine engine produces 27 MW overall power with 60% exergetic efficiency. • Hybrid marine engine has a total levelized cost rate of 228 $/h. • Total component-related environmental of hybrid marine engine is 1109 mPt/h. • Specific product cost and environment are 64 $/GJ and 22 mPt/MJ, respectively. [ABSTRACT FROM AUTHOR]

The air-conditioning system (ACS), as a high-power component on the fuel cell hybrid electric bus (FCHEB), has a significant impact on the whole vehicle economy while maintaining comfortable temperature. Achieving cabin comfort in a way that reduces the whole vehicle operating cost is a great challenge. This task requires excellent coordination between the ACS and the on-board energy sources. Given that, this paper proposes an energy management strategy (EMS) with on-board energy sources health awareness considering air-conditioning control. Specifically, firstly, cabin comfort is combined with fuel cell/battery durability control to minimize total vehicle operating cost while satisfying cabin comfort. Secondly, the state-of-the-art twin delayed deep deterministic policy gradient algorithm is adopted to improve the training efficiency and optimization capability of the EMS to achieve the best power allocation. Finally, comparative analysis is performed to verify the effectiveness of the proposed EMS, and the results show that the proposed strategy can enhance training efficiency by 56.7% and decrease total operating cost by 8.58% compared to the benchmark strategy based on deep deterministic policy gradient. • The TD3 algorithm is adopted to improve the optimization capability of the strategy. • An EMS with health awareness considering air-conditioning control is developed. • An accurate energy consumption model of the ACS is established. • The impact of ACS on overall vehicle operating costs is analyzed in detail. • The effectiveness of the proposed EMS is verified by comparative analysis. [ABSTRACT FROM AUTHOR]

Integrating a high power source, like a super capacitor (SCAP), and a lithium-ion battery (LIB) for electric vehicle (EV) applications yields achievement improvements, including maximum reliability, long lifetime (LT), small size, and competitive pricing for the overall source. A hybrid energy storage system (ESS) controlled by an intelligent energy management strategy (EMS) may be substantially included in multi-source EV design and development. Therefore, this paper proposes a hybrid chimp optimization algorithm (ChOA) and Levy walk technique to create an optimum EMS. The proposed technique reduces battery power (BP) stress and increases the LT, which is accomplished by using a hybrid ChOA-Levy walk (ChOA-LW) optimization algorithm with a rule-based approach in accordance with understanding the performance of LIB and SCAP. In order to optimize the rule-based EMS's control settings, the latter strategy is suggested. The control approach can be implemented online once the offline optimization procedure is finished. The presented technique is evaluated via simulation and on an experimental platform by means of a power emulator testbed of a LIB/SCAP hybrid ESS. In terms of BP stress and LT, the findings are compared with a conventional rule-based approach and a mono-source containing a regular high-power LIB. Results obtained demonstrate the effectiveness of the suggested technique, which enables the requested performance to be satisfied with better energy utilization. The assessment results also show notable LT improvements for the LIB, an improvement of up to 19% over the mono-source in reference to a conventional single cell LIB. • A hybrid ChOA and Levy walk technique to create an optimum energy management strategy. • Reduce battery power stress and increase the long lifetime of the battery. • Presenting an experimental platform by means of a power emulator testbed of a lithium-ion battery hybrid energy storage system. • An improvement of up to 19% over the mono-source in reference to a conventional single-cell lithium-ion battery. [ABSTRACT FROM AUTHOR]

Data-driven intelligent energy management strategy (EMS) helps to further improve the performance and efficiency of fuel cell hybrid electric bus (FCHEB). However, most deep reinforcement learning (DRL) algorithms suffer from disadvantages such as overestimation and poor training stability, which limit the optimization effectiveness of the strategy. In addition, DRL-based EMSs tend to achieve good control only for the set optimization objectives and cannot be generalized to optimization objectives beyond the reward function. To solve the above problems, a novel health-aware DRL energy management for FCHEB is proposed in this paper. Firstly, based on the actual collected city bus driving cycles, a large amount of high-quality learning experience containing health-aware information is obtained through an advanced model predictive control strategy. Secondly, the state-of-the-art Twin Delayed Deep Deterministic policy gradient (TD3) algorithm is combined with offline high-quality learning experience to address the inherent shortcomings during the "cold-start phase" and to enhance the generalization capability of the proposed strategy. Finally, validation results showed that the proposed EMS improves training efficiency by 61.85% and fuel economy by 7.45%, extends fuel cell life by 4% and battery life by 19.4% compared to the conventional TD3-based EMS. • A novel EMS for FCHEB based on TD3 algorithm is proposed. • An advanced MPC strategy is proposed to obtain high-quality learning experiences. • The TD3 algorithm effectively incorporates offline high-quality learning experience. • Enhanced the optimization and generalization capabilities of the proposed strategy. • The shortcomings of the "cold-start phase" in DRL-based EMS have been resolved. [ABSTRACT FROM AUTHOR]

This paper introduces a novel enhancement to the Transient Search Optimization (TSO) algorithm to estimate an accurate electrical model of the proton exchange membrane fuel cell (PEMFC). The PEMEFC model is a non-linear model that includes seven unknown variables which cannot be calculated analytically. The TSO is enhanced by inserting two new factors, the Levy function and the Weibull distribution function. The proposed enhanced Transient Search Optimization (ETSO) and TSO algorithms are applied to estimate the seven variables by minimizing the sum of the squared errors (SSEs) between the measured and calculated voltages. The error is defined as the difference between the measured and the calculated output voltage of the PEMFC. Three different commercial types of PEMFCs are modeled: i) Ballard, Mark V 5 kW, ii) Horizon H-12, and iii) 6 kW Nedstack PS6 stacks PEMFC. The estimated seven variables and the minimum SSE of electrical PEMFCs using ETSO and TSO algorithms are compared with the results obtained by using other optimization algorithms like whale optimization algorithm, genetic algorithm, neural network algorithm and others. The results obtained by the ETSO are better than that obtained by TSO by more than 10% and this percentage increases with other algorithms. The accuracy of the proposed PEMFC model is verified by comparing the estimated V – I and P – I characteristics with the measured data. The effectiveness of the proposed ETSO based model is verified by an investigation of sensitivity analysis for design variables and the robustness of the ETSO algorithm via the statistical analysis and the parametric t -test. • This paper presents a novel improved TSO algorithm to extract PEMFC model parameters. • Three commercial PEMFC models are implemented in this paper. • The parameters of proposed model are compared with other recent models. • The proposed model is verified by comparing its results with the measured results. • The sensitivity and robustness analyses of the proposed method are tested. [ABSTRACT FROM AUTHOR]

Abstract: This paper examines dynamic operation and control strategies for a microgrid hybrid wind–PV (photovoltaic)–FC (fuel cell) based power supply system. The system consists of the PV power, wind power, FC power, SVC (static var compensator) and an intelligent power controller. A simulation model for this hybrid energy system was developed using MATLAB/Simulink. An SVC was used to supply reactive power and regulate the voltage of the hybrid system. A GRNN (General Regression Neural Network) with an Improved PSO (Particle Swarm Optimization) algorithm, which has a non-linear characteristic, was applied to analyze the performance of the PV generation system. A high-performance on-line training RBFNSM (radial basis function network-sliding mode) algorithm was designed to derive the optimal turbine speed to extract maximum power from the wind. To achieve a fast and stable response for real power control, the intelligent controller consists of an RBFNSM and a GRNN for MPPT (maximum power point tracking) control. As a result, the validity of this paper was demonstrated through simulation of proposed algorithm. [Copyright &y& Elsevier]

Abstract: Refuse derive fuel and refuse plastic/paper fuel were evaluated in a direct carbon fuel cell (DCFC) system as energy sources, and two different grades of coals were also employed for comparison. The maximum power density of refuse fuels was reached up to 43–62% level comparing to that of coals, despite their significantly low carbon content. Significant properties such as thermal reactivity, nitrogen gas adsorption characteristics, and functional groups on the surface of the fuel were investigated using the TGA (thermogravimetric analysis), BET (Brunauer–Emmett–Teller test), and XPS (X-ray photoelectron spectroscopy) techniques, respectively. The correlation between fuel properties and electrochemical reactions was investigated, and it was found that the total carbon content, surface area, pore volume, and oxygen functional groups on the surface might have an influence on the reactions in the DCFC system. The effect of temperature increase from 973 K to 1023 K was restricted in the RDF (refuse derived fuel) because of its highly activated gasification phenomenon. The stirring effect could improve the performance of the RDF and the RPF only at 1023 K and 923 K, respectively, because of thermal characteristics and certain substances that affected the viscosity of the electrolyte. [Copyright &y& Elsevier]

Abstract: Recently, due to technology improvements, governmental incentives for the use of green energies and rising concerns about high cost of energy from fossil fuels, renewable energy sources (RESs) appears to be a promising approach for producing local, clean, and inexhaustible energy. This motivates the implementation of microgrids (MGs) introduced as a cluster of electrical and/or thermal loads and different RESs. Due to different uncertainties linked to electricity supply in renewable microgrids, probabilistic energy management techniques are going to be necessary to analyze the system. This paper proposes a probabilistic approach for the energy and operation management (EOM) of renewable MGs under uncertain environment. The proposed framework consists of 2m point estimate method for covering the existing uncertainties in the MGs and a self-adaptive optimization algorithm based on the gravitational search algorithm (GSA) to determine the optimal energy management of MGs. This paper considers uncertainties in load demand, market prices and the available electrical power of wind farms and photovoltaic systems. In this study, a self-adaptive mutation technique is offered to enhance the convergence characteristics of the original GSA and avoid being entrapped into local optima. The Weibull and normal distributions are employed to model the input random variables. Moreover, the Gram–Charlier expansion is used to find an accurate distribution of the total energy and operational cost of MGs for the next day-ahead. The effectiveness of the proposed method is validated on a typical grid-connected MG including energy storage and different power generating units. [Copyright &y& Elsevier]

Hydrogen release from hydrolysis of LiNH2BH3, NaNH2BH3, LiH-NH3BH3 and NaH-NH3BH3 was investigated respectively in this paper. Their applications on man-free submarine vehicles were discussed in this paper. It is shown experimentally that LiNH2BH3 and NaNH2BH3 hydrolysis can release 3 equivalents of hydrogen at room temperature. Hydrolysis of LiNH2BH3 or NaNH2BH3 exhibits greatly improved kinetics in comparison with neat NH3BH3 hydrolysis. The mechanism of LiNH2BH3 and NaNH2BH3 hydrolysis is the combination of H+ and OH− ions of water with the polar ions of LiNH2BH3 and NaNH2BH3. The process of LiH-NH3BH3 and NaH-NH3BH3 hydrolysis involves the thermohydrolysis of NH3BH3. Our results present a novel noncatalytic method for hydrogen release from NH3BH3 by co-hydrolyzing it with other high exothermic hydrides, and also show a novel strategy to improve hydrogen release kinetics of LiNH2BH3 and NaNH2BH3. The hydrolyzing materials presented in this context are promising for use on fuel cells’ based vehicle. [Copyright &y& Elsevier]

In the narrative of changing energy production environment, renewable sources of energy provide a fundamental basis for the growth of energy storage technologies. In particular, many settlements in Russia and other countries are located outside the centralized grids. With the goal of replacing diesel engines, a pilot project with solar panels produced 30 000 kWh. However, a support of energy storage systems is needed to ensure higher replacement percentage. The present paper introduces the development of a novel kW-scale power production unit that utilizes metal-hydride (MH) energy storage and 1 kW PEM fuel cell (FC). In the effort to enable the technology for autonomous applications, the novel concept of using FC exhaust air for hydrogen desorption process replacing an external heating agent was successfully proved. In the first two experiments, the limitations of the initial stage of the MH reactor were formulated. Warming up speed of the MH reactor was not sufficient to support the necessary hydrogen pressure level output. Third and fourth experiments provide possible solutions to this limitation: (i) higher load on the FC enable satisfactory warm-up speed of the MH reactor, (ii) higher initial pressure of MH reactor charge mitigates an initial pressure drop during the FC startup procedures. • A 1 kWh hydrogen storage system was designed, developed and tested. • The system utilizes exhaust heat from FC for the desorption process of MH reactor. • The system has 13 st. L/min H 2 consumption, 700 st. L storage capacity. • Higher initial pressure of MH reactor charge mitigates a pressure drop during startup. • Higher load on the FC enables satisfactory warm-up speed of the MH reactor. [ABSTRACT FROM AUTHOR]

Towards renewable energy systems, the coupling of multiple sectors is important and incorporates novel technologies where currently no models exist that correctly represent all transient effects. Therefore, we present a method that incorporates Hardware-in-the-Loop simulations where virtual components as models are coupled to real and experimental facilities in real time. By including experimental components, a higher validity can be obtained and the practical applicability of renewable energy scenario can be discussed more profoundly. In this paper, the considered energy system consists of an experimental biocatalytic methanation reactor, a real photovoltaic park, a regenerative fuel cell and short-term storage units to supply a residential district. A representative control sequence of the methanator is obtained by modeling the scenario as an optimal control problem. A first HIL simulation highlights that modifications of the instrumentation are required for a grid injection of the generated methane. The scientific approach can be applied to any energy system where some of the considered components are available as experimental or real facilities. Non-exisiting components are simply replaced by models. The presented approach helps to determine which parts or process parameters are crucial for the planed operation before the overall energy system is realized on a larger scale. [ABSTRACT FROM AUTHOR]

In this paper, a three-dimensional multiphase model of the polymer exchange membrane (PEM) fuel cell is simulated to study the effect of assembly pressure on the contact resistance between the gas diffusion layer (GDL) and bipolar plate (BP) interface. The results reveal that the increase of assembly pressure is associated with a decrease in the contact resistance between the GDL and BP interface, which results in reaching an ideal fuel cell performance. The performance improves until the assembly pressure of 4.5 MPa and it slightly drops with a clamping pressure of 5.5 MPa in the ohmic loss region of the polarization curve. Additionally, the variation of the electrical field in a cross-section of the channel length shows that the intrusion of GDL into the flow channel increases with increasing assembly pressure; consequently, the maximum electrical current will increase. The cell temperature rises at higher assembly pressure when considering the thermal contact resistance. This increase is higher on the cathode side because of the existence of the reaction heat source. Additionally, it is found that the distribution of electrical potential and oxygen concentration is more uniform at higher clamping pressure. This results in the development of the PEM fuel cell life cycle. • 3D Multiphase modeling of PEM fuel cell considering the thermal and electrical contact resistance. • Improve the overall performance of PEM fuel cell by increasing assembly pressure. • Increasing the temperature of PEM fuel cell by decreasing the electrical and thermal contact resistance. • Obtaining a suitable range of assembly pressure for ideal performance. [ABSTRACT FROM AUTHOR]

Abstract Fuel cell hybrid excavators (FCHEs) are an attractive long-term future option. This paper presents an approach for the energy management and economic analysis for an excavator powered by fuel cell and supercapacitor. The operating conditions and energy flows of a hydraulic excavator are analysed, the influence factors of the fuel cell stack (FCS) lifetime under the operating conditions are discussed. The marked load changes posed major challenges to the FCS performance, appropriate energy management strategies (EMSs) for FCHEs are indispensable. Three representative EMSs based on dynamic programming, Pontryagin's minimum principle, and model predictive control are developed, considering hydrogen consumption and FCS durability. Simulations performed in the MATLAB environment with cyclic loading of an excavator demonstrate the superiority of the proposed EMSs. With the introduction of restrictions on FCS power change, FCS durability can be improved. Economic analysis of the FCHE is proposed, which includes the effect of FCS and supercapacitor sizes on hydrogen consumption, and the use-cost at the present and in the future. It indicates that the sizes of FCS are the primary influence on the FCHE fuel economy. FCHEs will become increasingly attractive as costs fall. Highlights • We present energy management strategies for a fuel cell supercapacitor excavator. • Operating conditions and energy flows of the fuel cell hybrid excavator are analysed. • Influences on fuel cell stack lifetime oriented to excavator application are discussed. • The effect of fuel cell and supercapacitor sizes on hydrogen consumption is evaluated. • Present and future use-cost of the fuel cell hybrid excavator are evaluated. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a comparative study related to the impacts of the operating parameters. A structural design and modeling approach of the polymer electrolyte membrane (PEMFC) fuel cell is presented. The purpose of this study is to review many control and clarification strategies for fuel cell components that have been conducted by several researchers for improving the performance. The models that have been studied and compared to literature models appear clearly the dynamics of the PEM fuel cell. Consequently, the fuel cell performance is related to several parameters, which are (δ , D H 2 , D O 2 , I 0 , I lim , r). A simulation by MATLAB software is used to highlighting the powerful model compared to the mentioned models. The simulation results showed that many parameters change under different operating conditions such as, pressure, temperature and humidity. Finally, an accurate study shown that model 02 is the best model with an R2 value of 0.9994. This model has demonstrated a close agreement with the experimental data provided by a real fuel cell in the literature. Highlights • The models parameters of polymer electrolyte membrane fuel cells were studied. • Design and compare of fuel cells models. • The various parameters are depending on the temperature, humidity and pressure. • The cooling and the degradation of fuel cell have been reviewed. [ABSTRACT FROM AUTHOR]

Abstract: A carbon nanotube based gas diffusion layer (CNT-GDL) for direct methanol fuel cells (DMFCs) was prepared by sintering the wet carbon paper made of CNT, polyacrylonitrile-based carbon fibre (PCF) and PTFE, completely replacing the commercial carbon paper based GDL (Toray-GDL). The results of X-ray diffraction (XRD), scanning electron microscope (SEM) and mercury intrusion porosimeter (MIP) showed that CNT-GDL is of higher graphitization degree, more abundant pores in the pore diameter range of 1000–3000 nm and better mass transfer ability, compared with those of Toray-GDL. Electrochemical impedance spectroscopy (EIS) results display that the cell with CNT-GDL had better electrical conductivity and mass transfer ability than those of Toray-GDL. As a result, the limiting current density and peak power density of DMFC single cell with CNT-GDL are improved by 40% and 27%, respectively. [Copyright &y& Elsevier]

Abstract: This editorial and overview of a Special Issue dedicated to the 11th Conference Process Integration, Modelling and Optimisation for Energy Saving and Pollution Reduction – PRES 2008. Thirteen papers have been selected and after being peer-reviewed. Nine were accepted for publication covering important subjects of energy generation and usage. They are focusing on recent development of various features of heat integration which is an important methodology for increasing energy efficiency and saving. The complementary issues covered are emissions reduction and the security of energy supply. This issue of ENERGY is the fourth special journal issue dedicated to selected papers from PRES conferences. [Copyright &y& Elsevier]

Abstract: During the project Clean Urban Transport for Europe (CUTE), which ended in May 2006, 27 fuel cell buses were operated in nine European cities. In this paper key performance parameters from the operation of the fuel cell buses in the project are reported, the energy system of the bus is analysed and drive cycle tests in five cities are presented and analysed. The focus of the paper is on fuel consumption and optimisation potential but experiences of, and recommendations for, evaluation in large demonstration projects are also presented. The results show that although the total fuel cell system efficiency was found to be high (36–41%), the fuel consumption was higher for the fuel cell buses than for diesel buses. Since the CUTE buses were a pre-commercial generation of fuel cell buses, with standard auxiliaries and extensive reliability measures, large fuel consumption reduction is possible. Suggestions on how to increase the efficiency is presented in this paper. Minimising the reliability measures would decrease fuel consumption by about 20% and lowering the weight by 2 tonnes would decrease fuel consumption by another 10%. Hybridisation in combination with using electrical auxiliaries could save an additional 5–10% or more. [Copyright &y& Elsevier]