Energy Storage最新文献

The gravitational force (g-force) of the Earth is referred to as 1G. When pilots, astronauts, and race car drivers get under the effect of higher G-forces, their blood accumulates in the lower parts of their bodies, although their heart rates increase, and their cardiovascular systems supply less blood to their brains and lungs. When insufficient oxygenation of the brain causes loss of consciousness, incapacitated pilots or drivers may cause accidents resulting in serious injury or death. Pilots receive high-G training to prevent gravity-induced loss of consciousness (G-LOC). Wearing anti-G suits on flights may help to decrease the G-LOC. While the maximum G-Force tolerance of a normal person is approximately 4G, it increases to 9G when a pilot is with equipment. In the experiments at 46.2G, a pilot, John Stapp, managed to barely stay conscious; his retinas were severed, but he got his normal vision back after a day and he showed that position and safety precautions could help the human body withstand high amounts of crash forces. This research helped to develop several safety changes in automobiles. Indycar is America's top single-seater racing series. The crash recorders installed in their cars have shown that as long as safety systems are in place, the Indycar drivers can tolerate impacts over 100G without serious injuries. This study makes a thermodynamic assessment of the effects of the high G-force when the race car drivers and the jet pilots are tentatively subject to it but get out of that effect when their task is over.

A new vision to evaluate rate-capability and electrical properties of the cathode materials of a particular olivine structured family for Li-ion batteries is established. These evaluations obtain electrical conductivity, by noble approaches using DFT, which is related to intrinsic/extrinsic bands concepts and electrical rate-capability. Individual and alloyed transition metals-containing cathodes are investigated, namely, Li₂MSiO₄/Li₂M_0.5N_0.5SiO₄ (M, N = Mn, Fe Co, Ni). Our analysis focused on the electrical properties, including band-gap (BG) and rate-capability, utilizing the GGA(+U)/LSDA(+U) approximations. The electrical properties of the Li₂MSiO₄/Li₂M_0.5N_0.5SiO₄ materials were thoroughly examined by evaluating both the band-gap and electrical rate-capability. For band-gap assessment, we considered two types of band-gap (ILBG/ELBG); while, two criteria (Delta/CCTB) were employed to evaluate the rate-capability. The evaluation of band-gap indicated that all the considered materials exhibited low conductivity. Nonetheless, our findings highlight that the electrical rate-capability of a cell holds greater practical importance than the band-gap property. Our approaches provided reliable predictions for the rate-capability of the alloyed transition metals materials. The theoretically obtained results and conclusions are validated by available experimental data. We conclude that the rate capability approaches are more important than sole band gap. Also, the CCTB approach is more applicable for this electrode family than the Delta. This study can help understanding of behaviors of the alloyed electrode materials. Also, its methodology is worthy to use for other analogous materials.

The strategy of mixing adsorption skeleton to obtain composite phase change materials (CPCM) aiming to strengthen its thermal stability is confirmed to be simple but effective, and CPCM's thermal stability is directly proportional to the weight ratio of the adsorption skeleton. However, the processability and thermal storage density of which are inversely proportional to the content of adsorption backbone. To relieve the above contradiction, this paper proposed an in situ construction method for a phase-changeable adsorption backbone (PCPB). The in situ growth strategy avoided the processing difficulties caused by high stirring viscosity owing to the addition of large dosage of adsorption filler. Moreover, PCPB prepared via in situ polymerization of octadecyl methacrylate and 1,6-hexanediol diacrylate in PCM matrix presented obvious endothermic peak with latent heat of 89.5 J g⁻¹, which could undoubtedly alleviate the decay rate of CPCM's latent heat. In details, the maximum PCM loading percentage of PCPB could reach 50 wt%, and CPCM at this loading amount could reach latent heat as high as 149.7 J g⁻¹ and maintain form-stable without leakage even after thermal storage saturation. In addition, with the growth of PCPB in the phase change matrix, the 50% degradation temperature increased dramatically from 164.6°C to 350.0°C.

A numerical simulation was conducted to evaluate the performance of a structured compact rock-bed energy storage system. The study encompassed the analysis of various configurations of rock bed arrangements, including simple cubic, body-centred cubic, and face-centred cubic structures, along with their dimensions and the velocity of the heat transfer fluid (HTF) during the charging process. A transient three-dimensional reduced model incorporating symmetry planes was developed and subsequently validated. This approach has been shown to significantly minimize the extensive computational time typically required when the full model is adopted, while providing enhanced accuracy compared to commonly used models in existing literature, achieving an improvement in accuracy exceeding 5%. Moreover, the methodology that has been adopted enables a more comprehensive investigation of the storage system, thus facilitating the capture of local data. The findings indicated a pronounced effect of the arrangement of the rock bed, the rock dimensions, and the HTF velocity on the heat transfer within the thermocline rock bed thermal energy storage system. It was determined that the thermal energy storage was optimized when the rocks were arranged in a face-centered cubic configuration, which is associated with lower porosity. It was established that, at an HTF velocity of 3.84 × 10–4 m.s-1 and a rock diameter of 0.01 m, transitioning from a simple cubic arrangement to a face-centred cubic arrangement resulted in a 16.5% increase in capacity ratio and a 21.5% enhancement in exergy efficiency. Furthermore, it was determined that this transition also delayed the charging process by 39% (equivalent to 40 min). Moreover, a reduction in the rock diameter from 0.05 to 0.01 m resulted in a 44% increase in capacity ratio and a 54.5% rise in exergy efficiency for a simple cubic arrangement at the same HTF velocity, with a recorded 76% increase in charging duration. Furthermore, for a rock diameter of 0.03 m and a simple cubic arrangement, decreasing the HTF velocity from 9.233 × 10–4 m.s-1 to 2 × 10–4 m.s-1 resulted in a 26.5% increase in capacity ratio, a 28% increase in exergy efficiency, and a delay of 2.8 h in charging duration.

This work is concerned with the penetration of renewable energy-based distributed generation along with electric vehicle (EV) charging loads into power distribution systems. This presents a new optimization procedure integrating Particle Swarm Optimization and the Andean Condor Algorithm (PSO-ACA) into a high-performing route for system design. It analyzes the performance of the system in terms of minimizing the loss of power and maximizing reliability. The study evaluates reliability indices and power loss reductions in detail by utilizing benchmark 33-bus and 69-bus test systems. The findings indicate that for the 33-bus system, the active power loss reduction obtained is 64.3 KW, with real power loss showing a 68% reduction, whereas for the 69-bus system, real power loss decrease is 72% (62.8 KW). This led to a substantial reduction in reliability indices thus enhancing the overall system performance as hybrid optimization techniques improved the reliability of the system remarkably. These results show the immense potential that advanced hybrid optimization approaches combined with reliability analyses have for delivering the economically viable, sustainable renewable energy systems of the future. This collaboration is in line with the larger objectives of promoting sustainable energy solutions and establishing a more resilient and efficient energy framework.

Industry insiders who want to lower the greenhouse gas emissions linked to conventional fuel cars consider electric vehicles (EVs) as a practical alternative for mobility. EVs are a potential problem even though their performance is limited by their low battery power, long service charging times, and high resource costs. To improve the EV performance, this manuscript presents the hybrid technique for the optimal position of electric vehicles fast-charging stations (EVFCSs) in the distribution network. The proposed scheme is a joined execution of Wild Horse Optimizer (WHO) and Gradient Boosting Decision Tree (GBDT), which is commonly named the WHO-GBDT technique. The primary goal of the research is to decrease loss of power and voltage deviation. The optimal position for an electric vehicle charging station (EVCS) is determined using the WHO method. GDBT is used to predict the load demand. The proposed WHO-GDDT regulates the placement of EVCS, balancing their integration with distributed generation while enhancing the sustainability and reliability of distribution networks. The proposed WHO-GBDT algorithm is actualized in the MATLAB platform and compared their performance with various existing strategies like the Forensic Investigation Algorithm, Archimedean Optimization Algorithm (FBIAOA), Tunicate Swarm Algorithm (TSA), and Cuttlefish Algorithm (CA). The simulation findings of the proposed scheme are validated under three cases in the IEEE 33 bus system, like load 1, load 2 and load 3. From the result, the proposed method effectively reduced loss of power and voltage variation by 58.24% and 90.47%, respectively.

Lithium-ion cells have become an important part of our daily lives. They are used to power mobile phones, laptops and more recently electric vehicles (both two- and four-wheelers). The chemical behavior of the cells is rather complex and non-linear. For reliable and sustainable use of the cells for practical applications, it is imperative to predict the precise pace at which their capacity will degrade. More importantly, the lifetime of the cells must be predicted at an early stage, which would accelerate development and design optimization of the cells. However, most of the existing methods cannot predict the lifetime at an early stage, since there is a weak correlation between the cell capacity and lifetime. In this study for accurate forecasting of the battery lifetime, the patterns of the parameters such as cell current, voltage, temperature, charging time, internal resistance, and capacity were examined during charging and discharging cycle of the cell. Twelve manually crafted features were prepared from these parameters. The dataset for the features was created using the raw data of the first 100 cycles of 124 cells. Six ensemble and non-ensemble machine learning algorithms, namely, multiple linear regression (MLR), decision tree, support vector machine (SVM), gradient boosting machine (GBM), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost), were trained with the features for predicting the life-cycle of the cells. The R² and root mean squared error (RMSE) values of MLR, decision tree, SVM, GBM, LGBM, and XGBoost were found to be 0.72 and 201, 0.83 and 155, 0.85 and 146, 0.92 and 100, 0.9 and 112, and 0.94 and 95, respectively. The prediction accuracy of lithium-ion cell life-time was found to be the best with the XGBoost algorithm. This shows that only first 100 cycles are required foraccurately predicting the number of cycles the lithium-ion cell can work for. Lastly, the results of the study were compared with the available studies in the literature. Three studies were chosen, and the RMSE of the method proposed in this study was found to be higher than the three studies by 43, 17, and 20. Therefore, the proposed method is a suitable option for predicting the lifetime of lithium-ion cells during the early stages of its development.