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This paper introduces an adaptive filtering algorithm based on the Laplacian continuous mixed-norm (LCMN) as a control methodology to improve both transient and dynamic wind energy systems (WESs) performances. The foundation of this wind system is a variable-speed wind turbine that powers a permanent magnet synchronous generator. The proposed LCMN algorithm automatically updates all interface circuits’ proportional-integral (PI) controllers gains. It has several advantages compared with other algorithms such as higher algorithm stability, lower fluctuations and steady-state errors. The efficacy of the LCMN-based PI control approach is validated by a fair comparison with other control methods such as the least mean square, robust mixed norm and continuous mixed p-norm when the WES is subjected to severe symmetrical and various unbalanced conditions. Furthermore, the applicability of the suggested technique is examined in typical operational circumstances utilizing actual wind speed measurements obtained from Hokkaido Island. The results indicate preferable performance of dynamic analyses even though the wind speed profile is intermittent.. The dynamic responses exhibit an actuating error of less than 2% across numerous profiles. Therefore, the LCMN-based PI control methodology is considered as an effective solution for online adjustment of controller gains in the course of system nonlinearities and uncertainties. A key advantage of this method is its independence from constructing system transfer functions and the avoidance of optimization methods. This can save substantial time and effort typically required for optimization processes.

Proton Exchange Membrane Fuel Cells (PEMFCs) play a crucial role in the advancement of clean hydrogen vehicles. Their ability to convert hydrogen into electricity makes them promising candidates to replace conventional engines. However, optimizing their performance and efficiency necessitates accurate modeling techniques capable of simulating their behavior. In this context, this paper proposes an advanced approach for precise parameter estimation in PEMFC models. Employing an Enhanced Walrus Optimization (EWO) algorithm integrated with Lévy flight exploration, the approach tackles the inherent nonlinearity of PEMFC systems. The technique aims to minimize the squared error between measured and simulated terminal voltage, thereby ensuring superior accuracy and robustness compared to established algorithms. The effectiveness of the proposed model is validated through comparisons between theoretical simulations and experimental measurements. The findings demonstrate the efficacy of the EWO algorithm, consistently outperforming previously published algorithms and achieving notably lower errors. Moreover, the incorporation of Lévy flights enhances the algorithm’s capabilities, leading to expedited convergence and more accurate parameter estimations. Beyond facilitating precise parameter estimation, this enhanced modeling strategy opens avenues for refining design and optimization strategies in fuel cell research and development. The major contributions of this paper include the enhancement of the WO algorithm, evaluation of theoretical model accuracy, and robustness assessment of the EWO in optimizing the PEMFC model. By furnishing accurate models validated through experimental evidence, this enhanced modeling strategy paves the way for refining design and optimization strategies in fuel cell research and development.

The iron–chromium redox flow battery (ICRFB) has a wide range of applications in the field of new energy storage due to its low cost and environmental protection. Graphite felt (GF) is often used as the electrode. However, the hydrophilicity and electrochemical activity of GF are poor, and its reaction reversibility to Cr3+/Cr2+ is worse than Fe2+/Fe3+, which leads to the hydrogen evolution side reaction of the negative electrode and affects the efficiency of the battery. In this study, the optimal composite modified GF (Bi-Bio-GF-O) electrode was prepared by using the optimal pomelo peel powder modified GF (Bio-GF-O) as the matrix and further introducing Bi3+. The electrochemical performance and material characterization of the modified electrode were analyzed. In addition, using Bio-GF-O as the positive electrode and Bi-Bio-GF-O as the negative electrode, the high efficiency of ICRFB is realized, and the capacity attenuation is minimal. When the current density is 100 mA·cm−2, after 100 cycles, the coulomb efficiency (CE), voltage efficiency (VE), and energy efficiency (EE) were 97.83%, 85.21%, and 83.36%, respectively. In this paper, the use of pomelo peel powder and Bi3+ composite modified GF not only promotes the electrochemical performance and reaction reversibility of the negative electrode but also improves the performance of ICRFB. Moreover, the cost of the method is controllable, and the process is simple. [ABSTRACT FROM AUTHOR]

This review discusses the progress of research on sulfonated poly(ether ether ketone) (SPEEK) and its composite membranes in proton exchange membrane fuel cells (PEMFCs). SPEEK is a promising material for replacing traditional perfluorosulfonic acid membranes due to its excellent thermal stability, mechanical property, and tunable proton conductivity. By adjusting the degree of sulfonation (DS) of SPEEK, the hydrophilicity and proton conductivity of the membrane can be controlled, while also balancing its mechanical, thermal, and chemical stability. Researchers have developed various composite membranes by combining SPEEK with a range of organic and inorganic materials, such as polybenzimidazole (PBI), fluoropolymers, and silica, to enhance the mechanical, chemical, and thermal stability of the membranes, while reducing fuel permeability and improving the overall performance of the fuel cell. Despite the significant potential of SPEEK and its composite membranes in PEMFCs, there are still challenges and room for improvement, including proton conductivity, chemical stability, cost-effectiveness, and environmental impact assessments. [ABSTRACT FROM AUTHOR]

Fuel cells, as clean and efficient energy conversion devices, hold great potential for applications in the fields of hydrogen-based transportation and stand-alone power systems. Due to their sensitivity to load parameters, environmental parameters, and gas supply, the performance monitoring and fault diagnosis of fuel cell systems have become crucial research areas. Electrochemical impedance spectroscopy (EIS) is a widely applied analytical method in fuel cell systems. that can provide rich information about dynamic system responses, internal impedance, and transmission characteristics. Currently, EIS detection is primarily implemented by using simple topologies such as boost circuits. However, the injection of excitation signals often results in significant power fluctuations, leading to issues such as uneven temperature distributions within the cell, unstable gas supply, and damage to the proton exchange membrane. To address this issue, this paper proposes a real-time EIS detection technique for a proton exchange membrane fuel cell (PEMFC) system that connects a lithium-ion battery and injects the load voltage perturbation through a triple active bridge (TAB) converter. By applying the small-signal model of the TAB converter and designing a system controller using a decoupling control method, the PEMFC power remains stable after the disturbance injection across the entire frequency range under tests. Furthermore, the lithium-ion battery can instantly track load changes during fluctuations. The proposed EIS detection method can acquire EIS data in real time to monitor the state of the PEMFC. Simulation results validate the effectiveness and accuracy of the proposed method for EIS detection. [ABSTRACT FROM AUTHOR]

AbstractAs a clean and renewable energy source, mine geothermal energy holds immense potential for application. To better utilize this resource, a buried tube ground-source heat pump system has been proposed for mine geothermal energy development. A mathematical model for the mine geothermal heat exchange system has been established. Firstly, the experiment of constant heat flow and heat transfer is conducted to verify the applicability of the mathematical model and to explore the temperature response law of single pipe fluid and rock and soil. Subsequently, the influence of ten single-tube factors on the heat transfer performance of the mine geothermal heat exchange system is studied using the single-factor analysis method. The sensitivity of the single tube factor is analyzed using the range analysis method to obtain six main factors. The impact of the group tube and time factors on the heat transfer performance is qualitatively analyzed. Finally, the orthogonal experiment and the non-linear regression method are employed to derive a prediction expression for the average output temperature of the mine geothermal heat exchange system by considering the single tube, group tube, and time factors. The research results enable the quantitative prediction of heat exchange performance and guide the design and optimization of mine geothermal heat exchange systems. [ABSTRACT FROM AUTHOR]

Proton-exchange membrane fuel cells (PEMFCs) play a crucial role in the transition to sustainable energy systems. Accurately estimating the state of health (SOH) of PEMFCs under dynamic operating conditions is essential for ensuring their reliability and longevity. This study designed dynamic operating conditions for fuel cells and conducted durability tests using both crack-free fuel cells and fuel cells with uniform cracks. Utilizing deep learning methods, we estimated the SOH of PEMFCs under dynamic operating conditions and investigated the performance of long short-term memory networks (LSTM), gated recurrent units (GRU), temporal convolutional networks (TCN), and transformer models for SOH estimation tasks. We also explored the impact of different sampling intervals and training set proportions on the predictive performance of these models. The results indicated that shorter sampling intervals and higher training set proportions significantly improve prediction accuracy. The study also highlighted the challenges posed by the presence of cracks. Cracks cause more frequent and intense voltage fluctuations, making it more difficult for the models to accurately capture the dynamic behavior of PEMFCs, thereby increasing prediction errors. However, under crack-free conditions, due to more stable voltage output, all models showed improved predictive performance. Finally, this study underscores the effectiveness of deep learning models in estimating the SOH of PEMFCs and provides insights into optimizing sampling and training strategies to enhance prediction accuracy. The findings make a significant contribution to the development of more reliable and efficient PEMFC systems for sustainable energy applications. [ABSTRACT FROM AUTHOR]

To improve the heat collection performance of flat plate solar collectors, a corrugated flat plate solar collector (CFPSC) with a triangular collector tube was first innovatively designed in this paper. The effect of various nanofluids that are used as working fluid on the heat collection performance of CFPSC was comprehensively analyzed based on the heat collection characteristics test system and numerical simulation model. The results indicate that when CuO and Al2O3 were used as nanoparticles, the heat collection stabilization time of the nanofluids for which ethylene glycol (EG) was used as the base fluid was 12.4~28.6% longer than that of the nanofluids for which water was used as the base fluid. Moreover, when the base fluid was EG, the temperature difference of CuO-EG nanofluid under different radiation intensities was about 5.8~19.2% higher than that of water. Furthermore, the heat collection performance of CuO nanofluids and Al2O3 nanofluids was superior to TiN nanofluids. Specifically, the heat collection of CuO-EG nanofluid was 2.9~4% higher than that of TiN-EG nanofluid at different radiation intensities. Therefore, using nanofluids as a working medium and designing a flat plate solar collector with triangular collector tubes can significantly improve the collector performance. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cells have attracted widespread attention due to their cleanliness and high energy density, but the performance degradation during operation greatly limits their commercialization. Therefore, the reliable degradation prediction of fuel cell performance is of great significance. The recovery phenomenon of the reversible voltage loss that occurs during the operation of fuel cells has posed great difficulties for model training and prediction. Moreover, the models may easily and erroneously learn the combined trends in the recovery of reversible voltage loss and performance degradation. To address this issue, this paper employs the Transformer model to predict the performance degradation of fuel cells. By utilizing the unique self-attention structure and masking mechanism of the Transformer model, the signal for the recovery of the reversible voltage loss is adopted as the input for the model to avoid interference from information before voltage recovery on subsequent predictions. Experimental results show that the model has the highest prediction accuracy at various prediction starting points. Meanwhile, it can predict the accelerated performance degradation of fuel cells, which has positive implications for health management. [ABSTRACT FROM AUTHOR]

Accurate and reliable mathematical modeling is essential for the optimal control and performance analysis of polymer electrolyte membrane fuel cell (PEMFC) systems, which are mainly implemented based on accurate parameter estimation. In this paper, a multi-strategy tuna swarm optimization (MS-TSO) is proposed to estimate the parameters of PEMFC voltage models and compare them with other optimizers such as differential evolution, the whale optimization approach, the salp swarm algorithm, particle swarm optimization, Harris hawk optimization and the slime mould algorithm. In the optimizing routine, the unidentified factors of the PEMFCs are used as the decision variables, which are optimized to minimize the sum of square errors between the estimated and measured data. The optimizers are examined based on three PEMFC datasets including BCS500W, NedStackPS6 and harizon500W as well as a set of experimental data which are measured using the Greenlight G20 platform with a 25 cm2 single cell at 353 K. It is confirmed that MS-TSO gives better performance in terms of convergence speed and accuracy than the competing algorithms. Furthermore, the results achieved by MS-TSO are compared with other reported approaches in the literature. The advantages of MS-TSO in ascertaining the optimum factors of various PEMFCs have been comprehensively demonstrated. [ABSTRACT FROM AUTHOR]

In this research, the performance of vanadium redox flow batteries (VRFBs) in grid-connected energy storage systems centering on frequency and power sharing using voltage source inverters was evaluated. VRFBs are increasingly promising due to their scalability and long lifespan. We explore the impact of voltage source inverters on the frequency of the power supply when there is a change in load and power sharing between the grid and VRFB through MATLAB simulations. The test system demonstrates a storage system at an 11-kV substation and considers a load profile commencing from residential and commercial consumers. The outcome of this study reveals that VRFBs provide the required power to maintain load demand. Due to fast response time, VRFBs also regulate the frequency efficiently during the change in load demand and maintaining current total harmonic distortions (THDs). This research confirms the design and operation of VRFB-based energy storage systems, contributing to resilient grid infrastructure and reliable power distribution systems. [ABSTRACT FROM AUTHOR]

Durability is a desired characteristic for all battery packs in Electric Vehicles. In this study, the service life of the EV battery pack under real-world operating conditions is projected using an Arrhenius mathematical simulation model. The model comprises a 39.2 kWh EV Lithium-Ion battery pack integrated with a three-phase inverter to convert the battery pack's Direct Current output to Alternating Current. In addition, the Alternating Current output is coupled to a 100 kW permanent magnet synchronous motor, which is regarded as the load. A field-oriented controller provides pulse width-modulated output signals that are supplied back to the inverter to generate the correct driving current. Variable conditions of charge rate (C-rate: 1.25C -- 4C), discharge rate (C-rate: 0.5C -- 4C), temperature (25°C-60°C), and depth of discharge (30%-90%) are evaluated to determine the battery pack's service life. Under a 4C charge rate/0.5C discharge rate and 50% depth of discharge, the modeling results indicate the battery pack has a service life of approximately 6,000 h at low temperatures (25°C) and roughly 3,000 h at high temperatures (60°C). The model has been validated by comparing the results with experimental data from the literature. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are essential for electrochemical energy storage, yet they undergo progressive aging during operational lifespan. Consequently, precise estimation of their state of health (SOH) is crucial for effective and safe operation of energy storage systems. This paper investigates the viability of ultrasound-Based methods for assessing the SOH of prismatic lithium-ion batteries. In the experimental framework, a designated prismatic lithium-ion battery was subjected to numerous charging and discharging cycles using a battery cycling system. Subsequently, ultrasonic detection experiments were conducted to record the waveforms of the transmitted and received signals. These signals were then processed through wavelet transforms to extract signal amplitude and time-of-flight data. To analyse these data, we applied four algorithms: linear regression, support vector machines, Gaussian process regression, and neural networks. The predictive performance of each algorithm was evaluated through extensive experimentation and analysis. The combination of ultrasonic signals with computational models has emerged as a robust technique for precise battery degradation assessment, suggesting its potential as a standard in battery health evaluation methods. [ABSTRACT FROM AUTHOR]

As the "blood vessels" and "nerves" of the power grid, cables are important carriers for transmitting electric energy, and their failures will affect the safe and stable operation of the power grid. In this paper, a digital twin model of thermal characteristics is constructed for 10 kV YJVx400 cluster cable and its joint model. Based on the multi-physical field coupling analysis method, the fluid field, temperature field and current-carrying capacity of the cluster cable are analyzed. The temperature rise and airflow distribution characteristics of thejoint and its external insulation when the defect of joint false connection occurs are analyzed. Based on the fuzzy analytic hierarchy process (FAHP), weights are assigned to the locations of multiple measurement points of the cable external insulation, and the temperature distribution of each measurement point is combined with the digital twin technology to calculate the hot spot temperature of the cable, so as to assess the defects of the joints of the cables. The digital twin platform is used for real-time monitoring of cable status and assessment of defects. The temperature rise experiments of cables under different carrying capacity and joint defects show that this method can accurately and efficiently calculate hot spot temperatures and assess defects of joints in cluster cables. [ABSTRACT FROM AUTHOR]