Showing papers in "International Journal of Energy Research in 2022"

A novel heat dissipation structure based on flat heat pipe for battery thermal management system

Abstract: Flying car is an effective transport to solve current traffic congestion. The power batteries in flying cars discharge at a high current rate in the takeoff and landing phase, evoking a severe thermal issue. Flat heat pipe (FHP) is a relatively new type of battery thermal management technology, which can effectively maintain the temperature uniformity of the battery pack. We have constructed a resistance‐based thermal model of the batteries considering the impact of the state of charge (SOC), battery temperature, and current on the battery heat generation. The FHP model is developed based on segmental heat conduction model, and integrated into the battery model to form the battery‐FHP‐coupled model for a battery module. Experiments are carried out to verify its accuracy. Then, the battery thermal performance is analyzed under the different discharging conditions including constant discharge rates and dynamic discharge rates for flying cars. Under the condition of the flying cars, the battery maximum temperature appears at the end of takeoff stage, while the maximum temperature difference appears during the forward flight segment. Moreover, different FHP heat dissipation structures are studied to further improve the battery thermal performance. The configuration with the best performance is adopted for the battery pack, and it can meet the heat dissipation requirements of the pack at a discharge rate of 3C or that of flying cars. Finally, the influence of inlet cooling air velocity and temperature on battery thermal performance is investigated. According to the research results, air velocity has little effect on the battery maximum temperature at the discharge rate of flying cars, but it can obviously affect the temperature decrease rate. Besides, the battery maximum temperature and its temperature difference develop linearly with the air temperature.

Enhanced optimization algorithm for the structural design of an air‐cooled battery pack considering battery lifespan and consistency

Abstract: Electric scooters are increasingly popular for short‐distance commuting. To improve the thermal safety, performance, and lifespan of their batteries, their heat needs to be managed. This study proposes a method for optimizing the air channels in a scooter battery pack. It includes an electro‐thermal‐degradation model for predicting the battery's electrical and thermal behaviors and capacity loss, a heat transfer model for predicting convective heat exchange between the battery and the air, and a genetic algorithm for structural optimization of an air‐cooled battery thermal management system (BTMS). Unlike conventional optimization of a BTMS, the proposed algorithm aims to improve the electrical consistency, lifespan, and thermal safety of the battery via rapid global optimization of its air ducts. The optimization algorithm was tested on a 3P4S air‐cooled battery pack from an electric scooter. It improved the pack's consistency of state of charge (SOC) and its lifespan by reducing its heat and temperature gradient. Under on‐design conditions, the optimized air ducts reduced the maximum pack temperature by 0.45°C and the difference between the average temperatures of the cells in a branch to 15.9% that of the original pack. Moreover, the optimized air ducts decrease the SOC difference by 81.1% and improved the state of health by 0.03%. Hence, the proposed air duct optimization method can improve the pack's thermal performance, SOC distribution, and lifespan under off‐design conditions.

Temperature prediction of lithium‐ion batteries based on electrochemical impedance spectrum: A review

Abstract: With the rapid development of global electric vehicles, artificial intelligence, and aerospace, lithium‐ion batteries (LIBs) have become more and more widely used due to their high property. More and more disasters are caused by battery combustion. Among them, the temperature prediction of LIBs is the key to prevent the occurrence of fire. At present, using surface temperature sensor to measure the temperature of LIBs is the main method. High‐capacity LIB packs used in electric vehicles and grid‐tied stationary energy storage system essentially consist of thousands of individual LIB cells. Therefore, installing a physical sensor at each cell, especially at the cell core, is not practically feasible from the solution cost, space, and weight point of view. So developing a new method for battery temperature prediction has become an urgent problem to be solved. Electrochemical impedance spectroscopy (EIS) is a widely applied non‐destructive method of characterization of LIBs. In recent years, methods of predicting LIBs temperature by EIS have been developed. The prediction of LIBs temperature based on EIS has the advantages of high real‐time performance and prediction accuracy, and the device is simple and practical. The proposed method has a good development prospect in electric vehicles and other fields and can effectively solve the current problems of LIBs temperature prediction. Therefore, it is urgent to summarize these works to promote the next development. This review summarizes the main methods of using EIS to predict the temperature of LIBs in recent years, including the methods based on the impedance, phase shift, and intercept frequency. The principle and application of various methods are reviewed. The advantages and disadvantages of different methods and the future development direction are discussed.

A novel reed‐leaves like aluminum‐doped manganese oxide presetting sodium‐ion constructed by coprecipitation method for high electrochemical performance sodium‐ion battery

Abstract: In this paper, Al‐doped manganese oxide with presetting sodium‐ion (NaxMnyAlzO2) is synthesized by a facile coprecipitation method and combines with Na3V2(PO4)3 cathode active materials for sodium‐ion battery in order to optimize the electrochemical performances. During the coprecipitation, a stable NaxMnyAlzO2 polynary metal oxide with Mn and Al molar ratio to 3:1 shows a special micromorphology similar to that of reed leaves, which makes it have a more ideal electrochemical specific surface area and ample mass transfer channels for rapid sodium‐ion insertion and desorption. Electrochemical test indicates that reed‐leaves like NaxMnyAlzO2 polynary metal oxides as active material for sodium‐ion capacitor exhibit a very excellent reversibility and low electrochemical (Rct = 434 Ω) and concentration polarization (DNa+ = 2.2728 × 10−11 cm2 s−1). Specific discharge capacity can achieve to 73.76 F g−1 at 100 mA g−1 current density corresponding to 368.8 m2 g−1 electrochemical specific surface area. After 200 cycles, the capacity retention rate can be maintained at 81.81%. In addition, reed‐leaves like NaxMnyAlzO2 polynary metal oxide playing a good depolarization role combined with Na3V2(PO4)3 active materials in the mass ratio to 7:3 can bring about the most excellent electrochemical performances.

Low‐complexity state of charge and anode potential prediction for lithium‐ion batteries using a simplified electrochemical model‐based observer under variable load condition

Abstract: The accurate knowledge of the physics‐based state of charge (SOC) and anode potential for lithium‐ion batteries (LIBs) plays an essential role in the driving range prediction and charge strategy optimization of electric vehicles (EVs). However, the SOC estimation based on empirical equivalent circuit models and the lack of anode potential information makes it challenging in developing advanced battery management systems for EVs. For this reason, this paper proposes a low‐complexity SOC and anode potential prediction method for LIBs using a simplified electrochemical model (SEM)‐based observer under variable load condition. First, based on the Padé approximation and volume average method, a reduced‐order SEM is proposed and verified. Then, a low‐complexity proportional‐integral‐differential observer framework incorporating the SEM is developed to obtain the physics‐based SOC and anode potential. Finally, the effectiveness of the proposed method under variable load conditions is assessed by combining data collected by experiment and COMOSL simulation. The results show that the maximum absolute errors of SOC estimation are basically maintained within 2% under HPPC test profiles and the root mean squared errors of anode potential can be kept at 4.31 mV under US06 test profiles, which achieves a good balance between accuracy and computation cost and provides a strong support on substantially ensuring safe operation of EVs.

Energy‐related clean and green framework for shipbuilding community towards zero‐emissions: A strategic analysis from concept to case study

Abstract: Shipping is looking to progressively develop into a zero‐emissions industry, in agreement with pledges made under the Initial IMO Strategy on reduction of greenhouse gas emissions from ships, and its vision to phasing out greenhouse gas emissions as soon as possible during this century. International regulations currently focus on the design and operational phases of the industry, which carries the largest life‐cycle climate impact of a ship. As the level of emissions from the operational phase is reduced, the inclusion of emissions from shipyards during construction, maintenance, and disposal becomes increasingly important. The main objective of this study is to improve energy efficiency and reduce air emissions in the shipbuilding industry through the development of an energy‐management framework. A systematic literature review was used to define criteria for clean and green shipyards and to develop a novel, holistic, systematic, and transdisciplinary framework for energy management. The energy‐management framework is aimed at creating a decision‐support mechanism for complex situations, using a multiple criteria decision‐making approach. The energy‐management framework was applied to case studies of a Bangladeshi and an Italian shipyard. The potential implementation of the framework in yards of different sizes, types and geographical location demonstrates that it has the potential to improve energy management and thereby energy efficiency while increasing productivity and profitability in shipyards.

Comparative evaluation of AI‐based intelligent GEP and ANFIS models in prediction of thermophysical properties of Fe3O4‐coated MWCNT hybrid nanofluids for potential application in energy systems

Abstract: Hybrid nanofluids are gaining popularity owing to the synergistic effects of nanoparticles, which provide them with better heat transfer capabilities than base fluids and normal nanofluids. The thermophysical characteristics of hybrid nanofluids are critical in shaping heat transmission properties. As a result, before using thermophysical qualities in industrial applications, an in‐depth investigation of thermophysical properties is required. In this paper, a metamodel framework is constructed to forecast the effect of nanofluid temperature and concentration on numerous thermophysical parameters of Fe3O4‐coated MWCNT hybrid nanofluids. Evolutionary gene expression programming (GEP) and an adaptive neural fuzzy inference system (ANFIS) were employed to develop the prediction models. The model was trained using 70% of the datasets, with the remaining 15% used for testing and validation. A variety of statistical measurements and Taylor's diagrams were used to assess the proposed models. The Pearson's correlation coefficient (R), coefficient of determination (R2) was used for the regression index, the error in the model was evaluated with root mean squared error (RMSE). The model's comprehensive assessment additionally includes modern model efficiency indices such as Kling‐Gupta efficiency (KGE) and Nash‐Sutcliffe efficiency (NSCE). The proposed models demonstrated impressive prediction capabilities. However, the GEP model (R > 0.9825, R2 > 0.9654, RMSE = 0.7929, KGE > 0.9188, and NSCE > 0.9566) outperformed the ANFIS model (R > 0.9601, R2 > 0.9218, RMSE = 1.495, KGE > 0.8015, and NSCE > 0.8745) for the majority of the findings. The generated metamodel was robust enough to replace the repetitive expensive lab procedures required to measure thermophysical properties.

Optimal sizing of hybrid renewable energy systems by considering power sharing and electric vehicles

Abstract: Currently, the ideal sizing of hybrid technologies is one of the vital aspects of power system design. In this article, the design and optimization of the sizing of hybrid renewable energy systems (HRESs) with power‐sharing capabilities in conjunction with electric vehicles (EVs) were proposed in two case studies. Two algorithms, namely, multi‐objective particle swarm optimization (MOPSO) and multi‐objective crow search (MOCS), have been formulated and were used to solve the problem being investigated. In case study 1 (CS1), four different HRESs are designed in the presence of EVs, meaning that for each HRES an EV and the power‐sharing capability is employed. And also, the stochastic behavior of the EV using Monte Carlo simulation (MCS) is modeled. In case study 2 (CS2), four HRESs are designed with power‐sharing capabilities, but in this case, for any of the HRESs, EV is not considered. This idea can be considered a novel breakthrough for the potential of power‐sharing has been incorporated with the integration of EVs and HRESs. This approach improves the life cycle cost and loss of power supply probability indices. In summary, both cases in the presence and absence of EVs were compared with the simulation results. The results show that the use of the proposed EV significantly reduces the total cost of the engineered system. Furthermore, two meta‐heuristic techniques were compared, and it was concluded that MOPSO had performed better than MOCS.

Online identification of battery model parameters and joint state of charge and state of health estimation using dual particle filter algorithms

Abstract: Aiming at the problems of time‐varying battery parameters and inaccurate estimations of state of charge (SOC) and state of health (SOH), a joint estimation algorithm of SOC and SOH is proposed. A particle filter algorithm is used to identify the parameters online on the basis of a second‐order equivalent circuit model. The algorithm feasibility is verified through the terminal voltage estimation accuracy. Considering that an accurate SOH is one of the foundations to achieve an accurate SOC estimation, a dual particle filter is used to jointly estimate SOC and SOH. Under different test conditions, the effect of different initial values (initial SOC and capacity), temperatures, operation conditions, particle number, and model parameters on the estimation accuracy and robustness is compared and analyzed. The effectiveness of the proposed algorithm is validated by experimental data under different operation conditions. Experimental results show that the online particle filter algorithm can well predict the dynamic battery model parameters. The proposed algorithm has high robustness and a good tracking effect when estimating SOC with a mean absolute error of less than 1.3%, a root mean square error of less than 1%, and a tracking terminal voltage.

Prediction of electric vehicle charging duration time using ensemble machine learning algorithm and Shapley additive explanations

Abstract: Electric vehicles (EVs) are the most important components of smart transportation systems. Limited driving range, prolonged charging times, and inadequate charging infrastructure are the key barriers to EV adoption. To address the problem of prolonged charging time, the simple approach of developing a new charging station to enhance the charging capacity may not work due to the limitation of physical space and strain on power grids. Prediction of precise EV charging time can assist the drivers in effective planning of their trips to alleviate range anxiety during trips. Therefore, this study employed four different ensemble machine learning (EML) algorithms: random forest, extreme gradient boosting (XGBoost), categorical boosting, and light gradient boosting machine, for predicting EVs' charging time. The prediction experiments were based on 2 years of real‐world charging event data from 500 EVs in Japan's private and commercial vehicles. The study emphasized predicting charging time for different charging modes, that is, normal and fast charging operations. The results indicate that EML models performed well under various scenarios, with the XGBoost model having the highest accuracy. Moreover, we also employ the newly developed Shapley additive explanation (SHAP) approach to tackle the non‐interpretability issues of the ML algorithm by interpreting the XGBoost model outputs. The obtained SHAP value plots demonstrated the nonlinear relationship between explanatory variables and EV charging time.

State of charge estimation of supercapacitors based on multi‐innovation unscented Kalman filter under a wide temperature range

Abstract: Supercapacitors are characterized by a long service lifetime and high power density, which can meet the instantaneous high‐power demand during the acceleration of electric vehicles. In this study, a fractional‐order model is developed to simulate the polarization effect and charging/discharging characteristics of supercapacitors, considering the precision of the electrochemical model and the amount of calculation of the equivalent circuit model and using the adaptive genetic algorithm to identify the parameters. The accurate prediction of the state of charge (SOC) can improve efficiency, prolong the service lifetime, and ensure the safety of supercapacitors. This study proposes a multi‐innovation unscented Kalman filter algorithm based on the fractional‐order model to improve the SOC estimation accuracy. The proposed algorithm is compared with other algorithms and analyzed under different temperatures and operating conditions to verify the accuracy and effectiveness of the proposed algorithm in estimating the SOC and tracking the terminal voltage. Experimental results show that the root mean squared error and mean absolute error of the proposed algorithm are less than those of the other algorithms. The proposed algorithm accurately estimates the SOC and tracks the terminal voltage. The maximum root mean squared error and mean absolute error of SOC estimation error are 1.8% and 1.78%, respectively.

A state‐of‐health estimation method considering capacity recovery of lithium batteries

Abstract: At present, the rapid development of new energy sources makes lithium‐ion batteries (LIBs) widely used, but LIBs will inevitably age during using. State of health (SOH) is a direct reflection to the aging of LIBs, so it is necessary to estimate the SOH. During the aging process of the LIBs, the phenomenon of capacity recovery will occur if the battery is standing for too long. Existing SOH estimation methods based on neural network do not propose countermeasures for the phenomenon, but in fact, capacity recovery is inevitable and it has a great impact on SOH estimation. According to this vacancy, this paper proposes a SOH estimation method based on double bi‐directional long short‐term memory (DBiLSTM) model, which can accurately estimate recovered capacity and improve accuracy of SOH estimation. First, the capacity of LIB is decomposed at multiple scales using wavelet analysis, and the smooth and fluctuating components are obtained. Then six features are proposed based on the changes in the battery after aging. The proposed features are decomposed into new features suitable for the two components. Finally, the smooth component and the fluctuation component are estimated synchronously, and the estimated results are reconstructed to obtain the final estimated SOH. The method proposed in this paper is verified in the NASA dataset and compared with the bi‐directional long short‐term memory (BiLSTM) model. Comparing with the direct estimation by BiLSTM, the root mean square error (RMSE) is reduced by at least 0.0084 and the mean absolute percentage error (MAPE) is reduced by at least 0.52% when the battery capacity fluctuates greatly. The experimental results show that the proposed method can significantly improve the accuracy of SOH estimation when the capacity fluctuates greatly.

A method for estimating the aging state of lithium‐ion batteries based on a multi‐linear integrated model

Abstract: The state of health (SOH) of the lithium‐ion battery (LIB) is a key parameter of the battery management system. Due to the complex internal electrochemical properties of LIBs and the uncertain external working environment, it is difficult to achieve accurate SOH determination. In this paper, we propose a new SOH estimation method using a directed acyclic graph (DAG) structure based on incremental capacity analysis and empirical mode decomposition (EMD), and finally with gated recurrent unit (GRU) for fitting. First, we combine IC curves and real features into the input feature map and use EMD to separate out high‐frequency capacity regeneration fluctuations. Then, the feature maps are input into the DAG‐GRU structure to fit multiple EMD decomposition functions and build SOH prediction models, which are compared with different neural network prediction models. The prediction method simplifies the prediction process, does not need to select complex health indicators as features, and has the ability to capture the fluctuations of capacity regeneration, and can fit the fluctuations in the capacity decay curve with high precision, this integrated multi‐linear model takes into account accuracy and computational efficiency, reduces manual subjective operations, and uses artificial intelligence to complete most of the work, which is one of the important directions in future SOH research. The experimental results show that using the method proposed in this paper, the minimum mean square error and mean absolute error of SOH are reduced to 0.65‰ and 1.61%, respectively, and it also possesses excellent generalization ability.

Strategy improvements to minimize the drawbacks of geothermal investments by using spherical fuzzy modelling

Abstract: This study aims to evaluate the significant risks in geothermal energy investments. For this purpose, a model is suggested by considering the Spherical fuzzy DEMATEL technique. With the help of the literature examination, eight important risks are identified for these projects. In the following step, their weights are calculated so that more significant risk issues can be understood. Another calculation is also performed by triangular fuzzy DEMATEL to check the consistency of the analysis results. The results of both Spherical and triangular fuzzy DEMATEL techniques are similar. The most essential risk issues are the same for both methods that demonstrate the validity of the findings. The results indicate that the risk of accidents because of incorrect design‐material use has the greatest importance since it has the highest weight. Moreover, the risks of decreased water resources and pollution of them also play a significant role in this regard. Great attention should be paid to the quality of the material to be used in geothermal energy facilities. Otherwise, there is a risk of explosion and fire in the facility. These problems threaten the lives of many people. In this process, states also have very important duties. Some standards should be determined for the quality of the material used in these facilities. In addition, the compliance of the quality of these materials with the regulations should be checked with the routine inspections. This situation will minimize the risk of accidents due to using poor‐quality materials.

Demand‐side management based optimal scheduling of distributed generators for clean and economic operation of a microgrid system

Abstract: In general, the load demand of a standard microgrid system, changes on an hourly basis. Keeping in line with the rise and fall of this load demand curve, utilities fix different prices at different hours, which is termed as time of usage‐based electricity pricing. Elastic and inelastic are the two types of load that comprise the hourly demand of the microgrid system. Demand‐side management (DSM) shifts the elastic loads from peak load hours to those hours when the utility charges less thereby, restructuring the entire demand model‐based on demand‐price elasticity. Considering that the elastic loads contribute about 5% to 20% of the total load consumed during an hour, this paper implements a novel hybrid CSAJAYA algorithm to minimize the overall cost of a microgrid system considering DSM strategy. The various cost components taken into consideration are fuel cost, penalized emission cost, the cost of operation and maintenance, the cost of depreciation, etc. Numerical results depict that 30% to 40% decrement in overall generation cost was realized when DSM‐based energy management microgrid system was performed using the novel hybrid algorithm when compared to those available in the literature. Measures of central tendencies analysis claim the superiority of the proposed optimization algorithm.

A perspective on development of fuel cell materials: Electrodes and electrolyte

Abstract: The declining reserves and environmental concerns related to the use of fossil fuels have made the global think tanks to pursue for the technologies considered to be renewable as well as sustainable. Fuel cell technology is emerging and a green way to produce electricity, provided sufficient amount of hydrogen (fuel) is available that directly convert chemical energy to electrical energy. Despite of being an efficient and eco‐friendly source of energy, commercialization of fuel cells is still difficult to attain. Techno‐economic challenges like high manufacturing and assembly costs, water management, system size, nondurability, and instability of the system need to be addressed. In order to resolve these issues, considerable research has been carried out for the development of novel and inexpensive materials for the fuel cells. In this article, we have reviewed the development of electrodes and electrolyte materials for different types of fuel cells. A detailed description of applications, challenges, and improvement of electrodes/electrolytes materials of different types of fuel cells (polymer electrolyte membrane fuel cells, direct methanol fuel cells and solid oxide fuel cells) have been reported in this review article. A brief review of the development of materials for electrode of molten carbonate fuel cells has also been presented in the review.

A strong tracking adaptive fading‐extended Kalman filter for the state of charge estimation of lithium‐ion batteries

Abstract: Lithium‐ion batteries are widely used as rechargeable energy and power storage system in smart devices and electric vehicles because of their high specific energy, high power densities, etc. The state of charge (SOC) serves as a vital feature that is monitored by the battery management system to optimize the performance, safety, and lifespan of lithium‐ion batteries. In this paper, a strong tracking adaptive fading‐extended Kalman filter (STAF‐EKF) based on the second‐order resistor–capacitor equivalent circuit model (2RC‐ECM) is proposed for accurate SOC estimation of lithium‐ion batteries under different working conditions and ambient temperatures. The characteristic parameters of the established 2RC‐ECM for the lithium‐ion battery are identified offline using the least‐squares curve fitting method with an average R‐squared value of 0.99881. Experimental data from the hybrid pulse power characterization (HPPC) is used for the estimation and verification of the proposed STAF‐EKF method under the complex Beijing bus dynamic stress test (BBDST) and the dynamic stress test (DST) working conditions at varying ambient temperatures. The results show that the established 2RC‐ECM tracks the actual voltage of the battery with a maximum error of 28.44 mV under the BBDST working condition. For the SOC estimation, the results show that the proposed STAF‐EKF has a maximum mean absolute error (MAE) and root mean square error (RMSE) values of 1.7159% and 1.8507%, while the EKF has 6.7358% and 7.2564%, respectively, at an ambient temperature of −10°C under the BBDST working condition. The proposed STAF‐EKF delivers optimal performance improvement compared to the EKF under different working conditions and ambient temperatures, serving as a basis for an accurate and robust SOC estimation method with quick convergence for the real‐time applications of lithium‐ion batteries.

First‐principles investigation of the influence of point defect on the electronic and optical properties of α‐Ga2O3

Abstract: The Ga2O3 is a promising semiconductor, which is used in electric vehicles and 5G. However, the role of point defect of α‐Ga2O3 is unknown. To solve the problem, here, the influence of vacancy on the electronic and optical properties of α‐Ga2O3 is studied by the first‐principles calculations. Two typical vacancies, O vacancy and Ga vacancy, were designed. The calculated results show that the α‐Ga2O3 prefers to form O vacancy in comparison to the Ga vacancy. Furthermore, the calculated band gap of α‐Ga2O3 is 2.970 eV. However, the calculated band gap of O vacancy and Ga vacancy is 3.556 and 3.201 eV, which is bigger than the perfect α‐Ga2O3. Essentially, the wide band gap is that the removed atom results in a band shift from the Fermi level to the high‐energy regions. The change of band gap of these oxides is affirmed by the dielectric function. Finally, it is found that the α‐Ga2O3 oxide shows ultraviolet properties, which are in good agreement with the Ping and Berhanuddin's result. However, the calculated optical adsorption coefficient shows that the O vacancy induces the movement from the ultraviolet region to the visible light. The O vacancy and Ga vacancy weaken the storage optical properties of α‐Ga2O3 based on the analysis of loss function functional.

Perspectives on Li‐ion battery categories for electric vehicle applications: A review of state of the art

Abstract: Lithium‐ion batteries are widely used in the market, and are continuously improving, given their numerous benefits. Choosing the best materials for the cathode is fundamental for optimal battery pack projects. Lithium batteries using nickel cobalt aluminum and nickel manganese cobalt have technology that is already well consolidated within the market. However, some state‐of‐the‐art research describes important technological advances in lithium‐ion stores with lithium iron phosphate oxide and lithium titanate oxide. These have numerous advantages that can improve electric vehicle performance. Different battery technologies were analyzed in this paper, providing a guideline for lithium‐ion battery manufacturers. Previous evaluations on niobium batteries are also presented, analyzing comparative perspectives with lithium‐ion batteries. Advances in cutting edge knowledge show that niobium is a promising metal for use in lithium‐ion batteries. Niobium‐doped batteries have shown good conductivity at low temperatures and high energy density compared with other lithium‐ion storage systems. This study encourages researchers to further develop research on lithium‐ion batteries using a comparative study.

Supercapacitor performance based on nitrogen and sulfur co‐doped hierarchically porous carbons: Superior rate capability and cycle stability

Abstract: Herein, the banana‐peel waste was carbonized followed by KOH activation to design novel N, S‐co‐doped hierarchically porous carbonaceous materials (NS‐AC). The synthesized sample (NS‐AC) exhibited an interconnected porosity and endowed with a high specific surface area (SSA ~ 2452 m2 g−1), total pore volume (Vtotal ~ 1.82 cm3 g−1), and moderate nitrogen (3.2 at%) and sulfur (0.6 at%) contents. Moreover, these carbons, when scrutinized as electrode materials, demonstrated a specific capacitance (220 F g−1 at 0.5 A g−1), which persists at 145 F g−1 even at a large current density of 6 A g−1, thereby demonstrating an efficient rate capability. Furthermore, a capacitance retention of ~78.2% over 15 000 cycles was also observed. All these characteristics reveal NS‐AC carbons a promising contender for energy storage. Finally, as‐prepared symmetric supercapacitor (NS‐AC/NS‐AC) exhibited remarkable energy density ~5.3 Wh kg−1 at a power density of 2690 W kg−1 with capacitance retention of 88% over 4000 charge/discharge cycles, which surpasses the working performance of the many reported carbon materials obtained from biomass sources. In conclusion, outstanding textural features and enhanced conductivity by KOH activation, in addition to the improved surface wettability posed by N‐ and S‐enriched surfaces, resulted in considerable supercapacitor performance.

Model‐based state of X estimation of lithium‐ion battery for electric vehicle applications

Abstract: In developing an efficient battery management system (BMS), an accurate and computationally efficient battery states estimation algorithm is always required. In this work, the highly accurate and computationally efficient model‐based state of X (SOX) estimation method is proposed to concurrently estimate the different battery states such as state of charge (SOC), state of energy (SOE), state of power (SOP), and state of health (SOH). First, the SOC and SOE estimation is performed using a new joint SOC and SOE estimation method, developed using a multi‐time scale dual extended Kalman filter (DEKF). Then, the SOP estimation using T‐method and 2RC battery model is performed to evaluate the non‐instantaneous peak power during charge/discharge. Finally, the battery current capacity estimation is performed using a simple coulomb counting method (CCM)‐based capacity estimation with a sliding window. The performance of the proposed SOX estimation method is compared and analyzed. The experimental results show that the estimated SOC and SOE error is less than 1% under considered dynamic load profile at three different temperatures. After the final convergence, the estimated capacity maximum value absolute error is ±0.08 Ah. In addition, the low value of evaluated mean execution time (MET) justifies the high computational efficiency of the proposed method.

An ultrahigh‐energy density and wide potential window aqueous electrolyte supercapacitor built by polypyrrole/aniline 2‐sulfonic acid modified carbon felt electrode

Abstract: In this study, excellent performance and wide operating voltage are obtained with an aqueous electrolyte. Aqueous electrolytes are attracted more attention than other electrolytes (such as organic solvents, ionic liquids) in supercapacitor applications because of their safety, low cost, nontoxicity. Polypyrrole (PPy), aniline 2‐sulfonic acid (ASA) modified electrodes are hydrothermal synthesized on carbon felt (CFt) for supercapacitor applications. We investigate the presence of the concentration of ASA on the polymerization of the PPy. Furthermore, a high operating voltage of 3.0 V asymmetric supercapacitor (ASC) with graphite plate is assembled 3 M KCI. CFt/PPy/ASA(0.02)//GP exhibits a high specific capacitance value of 902.9 F g−1 at 5 mV s−1 and after 5000 cycle‐life testing 93.6% capacitance retention at a scan rate of 1000 mV s−1. The assembled ASC has an ultrahigh‐energy density of 1005 Wh kg−1 while delivering a power density of 3000 W kg−1 at a current density of 2 A g−1. The effect of ASA concentration on the supercapacitor performance has been observed.

Structural, optical, thermal, and dielectric properties of carboxymethyl cellulose/sodium alginate blend/lithium titanium oxide nanoparticles: Biocomposites for lithium‐ion batteries applications

Abstract: Nanocomposites samples of carboxymethyl cellulose (CMC)/sodium alginate (SA) blend (70/30 wt%)‐loaded lithium titanium oxide nanoparticles (Li4Ti5O12 NPs) were prepared using the casting method. X‐ray diffraction (XRD) spectra showed a decrease of the crystallinity with increasing Li4Ti5O12 concentrations. Fourier‐transform infrared (FT‐IR) inferred the interaction and complexation between the blend components (CMC/SA), also showed the interaction between Li4Ti5O12 and the functional groups of CMC/SA in particular CHOCH2 stretching, COO− stretching, and OH stretching vibrations. The optical properties of samples enhanced in optical energy gap as the concentration of Li4Ti5O12 increased. The differential scanning calorimetry (DSC) measurement showed that the pure sample has a single glass transition temperature (Tg), which denoted that the blend components were miscible. Also, DSC showed an enhancement in the thermal stability of polymeric blend after the addition of Li4Ti5O12. The highest value of AC conductivity was noticed at the highest concentration of Li4Ti5O12, which was also increased with increasing temperature. The maximum electrical conductivity obtained at 0.40 wt% of Li4Ti5O12 in the polymeric matrix is 9.35 × 10−6 S/cm at room temperature at 107 Hz. The complex permittivity (ε*) was decreased with the increase of both Li4Ti5O12 content and the temperature degree. At high frequencies, the decreasing trend of permittivity was assigned to dipoles orientation. These results indicated that CMC/SA/Li4Ti5O12 nanocomposites would be promising for energy storage capacitors applications and alternative separator rechargeable lithium‐ions batteries industries.

Experimental evaluation over the effects of natural antioxidants on oxidation stability of binary biodiesel blend

Abstract: Prolonged storage of biodiesel leads to deterioration of fuel properties and its usage in engines results in drop‐in performance characteristics. The present study deals with the effect of natural antioxidants on the storage stability of binary biodiesel blend of Jatropha biodiesel and lemongrass oil, which are synthesized through transesterification and steam distillation process, respectively. Natural antioxidants such as sesame, horse gram, sweet basil, coffee, and peas were employed in the current study. Thermal analysis of the antioxidants was carried out utilizing differential scanning calorimetry and thermogravimetric analysis to study the effect of temperature on antioxidants. Ethanol extract of natural antioxidants was prepared at various concentrations such as 500, 1000, and 1500 ppm. Total phenol content in the antioxidant extracts was determined through the Folin‐Ciocalteu method in terms of gallic acid equivalent (GAE). The oxidation stability of fuel samples with the addition of antioxidant extracts was estimated by the Rancimat method in terms of the induction period (IP). As a result, the scavenging effect in percentage for sesame, horse gram, sweet basil, coffee, and peas were 88.76%, 86.51%, 75.28%, 74.15%, and 70.78% at 1500 ppm, respectively. Besides, the total phenol content (mg GAE/100 g) for sesame, horse gram, sweet basil, coffee, and peas observed are 97.37, 39.99, 35.07, 30.15, and 25.25, respectively. More interestingly, adding antioxidants extracted from sesame, horse gram, sweet basil, coffee, and peas could increase the IP of the Jatropha biodiesel‐lemongrass oil binary blend to 9.92, 8.10, 7.74, 7.10, and 4.10 hours, respectively compared to the case of binary blend without antioxidant (only 2.06 hours). In general, the inclusion of natural antioxidants additives increased the oxidation stability of biodiesel, promoting its prolonged storage, and the overall effectiveness of selected natural antioxidants was found to be in the order of sesame > horse gram > sweet basil > coffee > peas.

Accelerating the charging process in a shell and dual coil ice storage unit equipped with connecting plates

Abstract: Frequent power outage in developing countries has created many problems for the people living in these regions, one of the most important of which is food spoilage due to the rise of refrigerator temperature. Ice storage systems are one of the promising techniques for handling this difficulty. Computational simulations are done here to influence the effects of dimensionless parameters on the charging rate of a shell and dual coil ice storage unit equipped with connecting plates as heat transfer enhancers. The ice storage unit is intended to be used as a backup cooling source for refrigerators in these regions. The studied parameters include the helical pitch length/storage height ratio (α1), the helical coil distance/storage diameter ratio (α2), the helical coil diameter/storage diameter ratio (α3), the connecting plate length/storage height ratio (α4), the connecting plate thickness/tube diameter ratio (α5), the modified Stefan number of the refrigerant flow (Ste*), and refrigerant flow Reynolds number (Re). The results suggest that the geometrical optimization of the proposed ice storage with α1, α2, and α3 parameters can improve the charging process up to 16.69%, 7.25%, and 18.84%, respectively. Also, the presence of full‐length connecting plates can enhance the charging rate by up to 12%. While the influence of the Ste* on the charging rate is considerably high (25.56%), the Re does not exhibit a noticeable effect (0.95%). Moreover, the influence of natural convection on the process was considered, however, it was found that it does not have a considerable effect on the ice formation.

Process simulation and analysis of high‐pressure reverse osmosis (HPRO) in the treatment and utilization of desalination brine (saline wastewater)

Abstract: Reverse osmosis (RO) is nowadays considered to be the most dominant desalination technology. Nonetheless, due to osmotic pressure constraints, conventional RO cannot desalinate brine effluents (>70 g/L of total dissolved solids [TDS]). Thus, high‐pressure RO (HPRO), that is, RO operating at a pressure of more than 82 bar, has recently attracted the interest of the water and wastewater industry. To this aim, a process simulation for the HPRO process was implemented and several sensitivity analyses were conducted for the first time. The results showed that by increasing the recovery rate by 0.05, energy consumption decreased by 3.5%. An increase in the feed brine temperature from 5°C to 30°C increases the permeate flow rate (up to 0.929 m3/h), the permeate concentration (up to 468 mg/L TDS), and the recovery rate (up to 0.435). A 10‐bar pressure increases the permeate flow by approximately 9.8% and decreases the permeate concentration by approximately 6 mg/L TDS. Moreover, the use of an energy recovery device significantly reduces energy consumption by 26% (from 4.93‐5.10 to 3.65‐3.78 kWh/m3). Overall, HPRO is a promising technology for brine treatment and valorization in zero liquid discharge and minimal liquid discharge systems.

Enhancing the structural, thermal, and dielectric properties of the polymer nanocomposites based on polymer blend and barium titanate nanoparticles for application in energy storage

Abstract: Solution casting and ultrasonic‐assisted solution‐cast methods were used to create polymer nanocomposites films based on polyvinyl alcohol (PVA)/polyvinyl pyrrolidone (PVP) filled with varying concentrations of BaTiO3 nanoparticles. The X‐ray diffraction (XRD), Fourier‐transform infrared (FT‐IR), transmission electron microscope, and differential scanning calorimetry (DSC) were used to study the properties of the produced polymer nanocomposite samples. The properties of PVA/PVP‐BaTiO3 nanocomposites, such as ac conductivity, dielectric constant, and dielectric loss, were investigated as a function of BaTiO3 concentration. XRD measurements demonstrate that the pure polymer blend is semi‐crystalline and that the crystallinity degree (Xc) of the doped PVA/PVP mix films is lower than that of the pure blend. Significant variations in the FT‐IR spectra demonstrate the interaction between the BaTiO3 ions and the PVA/PVP matrix. The DSC analysis demonstrates that the PVA/PVP has a single glass transition temperature (Tg), showing that the two polymers are miscible. In addition, when the amount of BaTiO3 NP's increased, the Tg of the nanocomposite films decreased. The AC conductivity spectra of all samples obey Jonscher's power law. For a better understanding of charge storage characteristics and conductivity relaxation, dielectric constant and loss investigations have been carried out. The PVA/PVP mixed with 1.5 wt% BaTiO3 nanofiller achieves a maximum ionic conductivity of ~8.57 × 10−5 S/cm. In this investigation, which introduced a novel approach, the complex permittivity revealed that the real part value of the dielectric constant (ε′) for all samples was much bigger than the imaginary part (ε″) value. These results are predicted to have a significant influence on a variety of applications, including polymer organic semiconductors, energy storage, polymer solar cells, and nanoelectronics.

Structural, electronics, magnetic, optical, mechanical and hydrogen storage properties of Ga‐based hydride‐perovskites XGaH3 (X = K, Li)

Abstract: The present study investigates some physical properties of KGaH3 and LiGaH3 whose lattice parameters and band gap match well with a previous study involving Ga‐based hydride‐perovskites. Both the compounds are found to be stable in cubic form and have a metallic character with zero band gap. The TDOS and PDOS confirm the metallic behavior by showing maximum conductivity at the Fermi level. Both materials are found antiferromagnetic, anisotropic and hard in nature. KGaH3 is found brittle and LiGaH3 is found ductile according to Poisson's ratio v. The brittleness in KGaH3 and the ductility in LiGaH3 is also verified by the B/G ratio. The high value of Bulk modulus, young modulus, and mean shear modulus for LiGaH3 show that it is harder than KGaH3. A high optical conductivity and absorption is found in both materials in the lower energy regime. At 0 eV, LiGaH3 possesses high value of reflectivity and refractive index than KGaH3. The hydrogen storage properties show that both materials are capable for storing hydrogen, however, LiGaH3 is found a preferred material for hydrogen storage applications.

New insight into the structural and physical properties of AlH3

Abstract: AlH3 hydride is a typical hydrogen storage material due to the high storage, low density and low temperature decomposition temperature, but the structural feature of AlH3 remains controversial. In addition, the optical properties of AlH3 are unclear. In this work, the first‐principles method is used to study the structural, optical and electronic properties of AlH3 hydride. Two possible AlH3 hydrides, viz. rhombohedral and cubic structures are considered. The stability of the rhombohedral AlH3 is demonstrated based on the analysis of phonon dispersion. Moreover, the band gap of rhombohedral and cubic AlH3 is 2.170 and 0.656 eV, respectively. Naturally, the semiconductor feature of AlH3 is related to the band separation of H‐s state and Al‐3p state. The dielectric functional diagram demonstrates the semiconductor properties of AlH3 hydride. Furthermore, it is found that the AlH3 shows the ultraviolet response.

Enhanced electrocatalysis of WSe2 nanosheets by partial oxidation for hydrogen generation

Abstract: Pristine WSe2 based electrocatalyst shows poor reaction kinetics in electrochemical hydrogen evolution reaction due to a lack of active atomic sites and an inert basal plane. Herein, we report an active system of non‐metallic plasmonic WSe2/WO3−x, supported by highly conductive and eco‐friendly Ag/cellulose paper electrodes. An air thermal annealing technique is used to enrich the p‐WO3−x dopants in WSe2, which is significant for substantially improving electrocatalytic behaviour as well as photo responsive performance in the near‐infrared (NIR) region. The WSe2/WO3−x dramatically decreases the overpotential of HER from −330 to −150 mV (at 10 mA/cm2) owing to homo doping and generation of Se‐vacancies. Surface plasmons resonances of p‐WO3−x give the contributions to injection of photogenerated electrons and overpotential as low as −120 mV can be achieved under the illumination of NIR light. These results represent significant advances in light‐enhanced electrocatalysis and would provide potential applications for commercial hydrogen generation.