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This study conducted numerical investigations on solitary wave attenuation by a vertical plate-type flexible breakwater constructed using hyperelastic neo-Hookean material. The wave attenuation performance and elastic behaviors of the flexible breakwater were discussed systematically by considering the effects of three prominent factors: mass coefficient, stiffness coefficient, and Poisson’s ratio. It is indicated that more compressible and flexible materials are beneficial for enhancing efficiency in mitigating solitary wave energy and protecting the structure from damage. In addition, the performance of the hyperelastic neo-Hookean material model was compared with that of a linear elastic isotropic material model coupled with linear and nonlinear geometry analysis (LGEOM and NLGEOM) by evaluating several key targets: wave reflection coefficient, transmission coefficient, horizontal tip displacement, and wave load. Our findings revealed that the hyperelastic neo-Hookean material model showed almost the same predictions as the linear elastic isotropic material model with NLGEOM, but significantly diverged from that with LGEOM. The linear elastic isotropic material model with LGEOM cannot capture the nonlinear variations in structural geometry and stress–strain relationship, resulting in the underestimation and overestimation of horizontal tip displacement under moderate and extreme wave loads, respectively. Moreover, it underestimates the damage inflicted by solitary waves due to inaccurately predicted wave reflection and transmission.

This paper proposes a long short-term memory (LSTM) network to predict the power degradation of proton exchange membrane fuel cells (PEMFCs), and in order to promote the performance of the LSTM network, the ant colony algorithm (ACO) is introduced to optimize the hyperparameters of the LSTM network. First, the degradation mechanism of PEMFCs is analyzed. Second, the ACO algorithm is used to set the learning rate and dropout probability of the LSTM network combined with partial aging data, which can show the characteristics of the dataset. After that, the aging prediction model is built by using the LSTM and ACO (ACO-LSTM) method. Moreover, the convergence of the method is verified with previous studies. Finally, the fuel cell aging data provided by the Xiangyang Da’an Automotive Testing Center are used for verification. The results show that, compared with the traditional LSTM network, ACO-LSTM can predict the aging process of PEMFCs more accurately, and its prediction accuracy is improved by about 35%, especially when the training data are less. At the same time, the performance of the model trained by ACO-LSTM is also excellent under other operating conditions of the same fuel cell, and it has strong versatility.

Electric vehicles (EVs) have considerable potential in promoting energy efficiency and carbon neutrality. State of health (SOH) estimations for battery systems can be effective for avoiding accidents involving EVs. However, existing methods have rarely been developed using real driving data. The complex working environments of EVs and their limited data acquisition capability increase the challenges for estimating SOH. In this study, a novel battery SOH definition for EVs was established by analyzing and extracting six potential SOH indicators from driving data. The definition proposed using the entropy weight method (EWM) described the degradation trend for different EV batteries. Combined with a denoising autoencoder, a novel long short-term memory neural network model was established for SOH prediction. It can learn robust features using noisy input data without being affected by different environments or driver behaviors. The network achieved a maximum mean absolute percentage error (MAPE) of 0.8827% and root mean square error (RMSE) of 0.9802%. The results have shown that the proposed method has a higher level of accuracy and is more robust than existing methods in the field.

Power distribution and battery thermal management are important technologies for improving the energy efficiency of plug-in hybrid electric vehicles (PHEVs). In response to the global optimization of integrated energy thermal management strategy (IETMS) for PHEVs, a dynamic programming algorithm based on adaptive grid optimization (AGO–DP) is proposed in this paper to improve optimization performance by reducing the optimization range of SOC and battery temperature, and adaptively adjusting the grid distribution of state variables according to the actual feasible region. The simulation results indicate that through AGO–DP optimization, the reduction ratio of the state feasible region is more than 30% under different driving conditions. Meanwhile, the algorithm can obtain better global optimal driving costs more rapidly and accurately than traditional dynamic programming algorithms (DP). The computation time is reduced by 33.29–84.67%, and the accuracy of the global optimal solution is improved by 0.94–16.85% compared to DP. The optimal control of the engine and air conditioning system is also more efficient and reasonable. Furthermore, AGO–DP is applied to explore IETMS energy-saving potential for PHEVs. It is found that the IETMS energy-saving potential range is 3.68–23.74% under various driving conditions, which increases the energy-saving potential by 0.55–3.26% compared to just doing the energy management.

Under the background of global warming, the frequency and intensity of extreme climate have increased, especially extreme high temperatures. In order to correctly predict the changes in the extreme high temperatures in summer in China in this century, it is urgent to deepen the understanding of the characteristics and physical mechanisms of the extreme high temperatures in summer on the centennial timescale. Many researchers have explored the mechanism of the influences of the variability of the solar cycle on climate change, while the mechanism of the influences of the centennial variation of solar activity on climate change remains elusive. Here, we use the outputs from the Control (CTRL) experiment, Total solar irradiance and Orbital (TSI_ORB) experiment, and Orbital (ORB) experiment from Nanjing Normal University-Holocene (NNU-Hol) experiments to study the extreme high temperatures in summer in China during the Holocene. On the basis of verifying the consistency of the centennial period between the TSI (TSI_ORB minus ORB plus CTRL) experiment and the reconstructed data, we compared the centennial variation characteristics of the summer extreme high temperature in the CTRL experiment and the TSI experiment. It shows that under the modulation of total solar irradiance, the centennial spatial pattern of the summer extreme high temperatures changed from dipole mode to uniform mode, with 300-year and 500-year periodicity, compared to the influence of only internal variability. On the centennial time scale, the greatest difference is located in northeast China. The subsidence movement and the reduction of cloud cover caused by the anticyclone under the control of high-pressure lead to the increase of downward solar radiation, thus making a positive center is showed in northeast China on the impacts of total solar irradiance. Furthermore, the center of the Rossby wave train in the barotropic structure of the upper circulation related to the summer extreme high temperature significantly moves northward. This barotropic structure is composed of continuous pressure ridges from Eurasia to North America and the North Atlantic, which is conducive to the increase of the summer extreme high temperatures. Furthermore, we investigated the underlying physical mechanisms. Under the influence of total solar irradiance, the Pacific Decadal Oscillation (PDO) with the same centennial cycle as extreme high temperatures lead to obvious subsidence movement and increase of radiation flux, causing an increase in extreme high temperatures over northeast China.

Durability is the key issue for the proton exchange membrane fuel cell application and its commercialization. Current research usually uses the accelerated stress test to decrease the experiment time, whereas the performance evolution—especially the internal state evolution—under real use may be different from that under the accelerated stress test. In addition, studies rarely report this kind of durability in real decay scenarios. This paper investigates the seldom-reported impact of dry–wet cycles on durability in terms of open circuit voltage (OCV), inner resistance, and hydrogen crossover current at the condition of 20,000 cycles or the equivalent 400 h, while simultaneously running the test for the same time interval in the control experiment. The mechanical and chemical test is independent. Frequent dry–wet cycles make the OCV decay over 14% compared to 6.9% under the normal decay. Meanwhile, the dry–wet cycle helps to alleviate deterioration in terms of the inner resistance decline (61% vs. 37%) and in terms of the hydrogen crossover current increase (−64% vs. 15%). The inner state evolution is irregular and against common sense. The relationship between the crack, platinum transfer, and the moisture which heals the crack is the potential reason for the above-mentioned phenomena. These findings are beneficial to navigating fuel cell storage.

The development of connected and automated vehicles (CAV) technology not only helps to reduce traffic accidents and improve traffic efficiency, but also has significant potential for energy saving and emission reduction. Using the dynamic traffic flow information around the vehicle to optimize the vehicle trajectory is conducive to improving the energy efficiency of the vehicle. Therefore, an energy-efficient driving method for CAVs based on reinforcement learning is proposed in this paper. Firstly, a set of vehicle trajectory prediction models based on long and short-term memory (LSTM) neural networks are developed, which integrate driving intention prediction and lane change time prediction to improve the prediction accuracy of surrounding vehicle trajectories. Secondly, an energy-efficient driving model is built based on Proximity Policy Optimization (PPO) reinforcement learning. The model takes the current states and predicted trajectories of surrounding vehicles as input information, and outputs energy-saving control variables while taking into account various constraints, such as safety, comfort, and travel efficiency. Finally, the method is tested by simulation on the NGSIM dataset, and the results show that the proposed method can save energy consumption by 9–22%.

As one of the most appealing options for large-scale energy storage systems, the commercialization of aqueous zinc-ion batteries (AZIBs) has received considerable attention due to their cost effectiveness and inherent safety. A potential cathode material for the commercialization of AZIBs is the manganese-based cathode, but it suffers from poor cycle stability, owing to the Jahn–Teller effect, which leads to the dissolution of Mn in the electrolyte, as well as low electron/ion conductivity. In order to solve these problems, various strategies have been adopted to improve the stability of manganese-based cathode materials. Among those, the doping strategy has become popular, where the dopant is inserted into the intrinsic crystal structures of electrode materials, which would stabilize them and tune the electronic state of the redox center to realize high ion/electron transport. Herein, we summarize the ion doping strategy from the following aspects: (1) synthesis strategy of doped manganese-based oxides; (2) valence-dependent dopant ions in manganese-based oxides; (3) optimization mechanism of ion doping in zinc-manganese battery. Lastly, an in-depth understanding and future perspectives of ion doping strategy in electrode materials are provided for the commercialization of manganese-based zinc-ion batteries.

With the popularity of electric vehicles (EV), the charging technology has become one of the bottleneck problems that limit the large-scale deployment of EVs. In this paper, a charging method using multi-stage constant current based on SOC (MCCS) is proposed, and then the charging time, charging capacity and temperature increase of the battery are optimized by multi-objective particle swarm optimization (MOPSO) algorithm. The influence of the number of charging stages, the cut-off voltage, the combination of different target weight factors and the ambient temperature on the charging strategy is further compared and discussed. Finally, according to the ambient temperature and users’ requirements of charging time, a charging strategy suitable for the specific situation is obtained by adjusting the weight factors, and the results are analyzed and justified on the basis of the experiments. The results show that the proposed strategy can intelligently make more reasonable adjustments according to the ambient temperature on the basis of meeting the charging demands of users.

In this study, the role of AD 1258 Samalas mega volcanic eruption in the summer hydroclimate change over Europe and the corresponding mechanisms are investigated through multi-member ensemble climate simulation experiments based on the Community Earth System Model (CESM). The results show that the CESM simulations are consistent with the reconstructed Palmer Drought Severity Index (PDSI) and the historical records of European climate. Europe experiences significant summer cooling in the first three years after the Samalas mega volcanic eruption, peaking at −3.61 °C, −4.02 °C, and −3.21 °C in year 1 over the whole Europe, Southern Europe, and Northern Europe, respectively. The summer surface air temperature (SAT, °C) changes over the European continent are mainly due to the direct weakening of shortwave solar radiation induced by volcanic aerosol. The summer precipitation over the European continent shows an obvious dipole distribution characteristic of north-south reverse phase. The precipitation increases up to 0.42 mm/d in year 1 over Southern Europe, while it decreases by −0.28 mm/d in year 1 over Northern Europe, respectively. Both simulations and reconstructions show that the centers with the strongest increase in precipitation have always been located in the Balkans and Apennine peninsulas along the Mediterranean coast over Southern Europe, and the centers with the strongest precipitation reduction are mainly located in the British Isles and Scandinavia over northwestern Europe. The negative response of North Atlantic Oscillation (NAO) with significant positive sea level pressure (SLP) anomaly in the north and negative SLP anomaly in the south is excited in summer. The low tropospheric wind anomaly caused by the negative phase of NAO in summer affects the water vapor transport to Europe, resulting in the distribution pattern of summer precipitation in Europe, which is drying in the north and wetting in the south. The knowledge gained from this study is crucial to better understand and predict the potential impacts of single mega volcanic eruption on the future summer hydroclimate change in Europe.

A low temperature environment will lead to the decrease of chemistry reaction rate and increase of the internal resistance of the lithium battery. In addition, the excessive charging current will cause the lithium to separate out and even the permanent attenuation of battery capacity. In order to solve these problems, this paper proposes a low-temperature charging heating combined control strategy, which takes the temperature acceptable charging current of the battery at low temperature as the charging current constraint and the maximum output power of the system as the power constraint. Firstly, a scheme of combined charging and heating control system is put forward. Secondly, the low temperature charging control strategy based on adaptive fuzzy control is established and then the model is simulated and analyzed in MATLAB software. At last, a Chroma 72,001 charge and discharge tester is used to conduct a low temperature test on 18,650 lithium iron phosphate battery monomers. The results show that the low-temperature charging control strategy proposed in this paper has a more stable temperature control effect on the battery, the constant current charging time of the battery is reduced by 14% compared with the traditional threshold control method, and the overall charging energy consumption is reduced by 5.6%.

In this study, the influences on persistent droughts over Eastern China from tropical volcanic eruptions with three categories of magnitudes, i.e., 25 Tg, 50 Tg, and 100 Tg, were investigated through three groups of volcanic sensitivity experiments based on the Community Earth System Model (CESM). The results showed that, the 25 Tg tropical volcanic eruptions are too weak to significantly influence the regional precipitation changes over Eastern China, while the 50 Tg tropical volcanic eruptions can strongly intensify droughts and prolong the drought conditions for about five years. Both the extension and intensification of the drought conditions induced by 100 Tg tropical volcanic eruption are the largest among the three sensitivity experiments. These drought conditions are mainly caused by the weakened East Asia Summer Monsoon (EASM), and their extension and intensification depend on the strength of the volcanic eruptions. The intensities of weakened EASMs after volcanic eruptions are associated with the distinct ocean−land thermal contrast after eruptions. The ocean−land thermal contrast is the largest after the 100 Tg tropical volcanic eruptions, while it is much weaker after the 25 Tg volcanic eruptions. The durations of drought extensions are determined by the recovery rates of the West Pacific Subtropical High (WPSH), which are associated with the magnitudes of the volcanic eruptions.

Pyrolusite leaching slag is a Fe-containing slag generated from pyrolusite leaching process with SO2. Recovery of iron from the slag not only has economic benefit, but also prevents the secondary pollution to the environment. A novel lab-scale cyclic process for recovering iron from pyrolusite leaching slag was introduced. The process contains two steps: (1) iron was leached with oxalic acid and [Fe(C2O4)n](3−2n)+ solution was generated; (2) the [Fe(C2O4)n](3−2n)+ solution was irradiated by ultraviolet and ferrous oxalate precipitation were obtained. The effect of operation parameter on leaching and irradiation process were studied separately. In the leaching process, the optimal solid/liquid ratio, oxalic acid concentration, leaching temperature, stirring rate, and leaching time are 1:50, 0.40 mol/L, 95 °C, 300 r/min, and 3 h, respectively. In the irradiation process, the best irradiation wavelength, Fe/oxalic acid molar ratio and irradiation time are 254 nm, 1:4, and 30 min. Besides, a test of 9 continuous cycles was carried out and the performance and material balance of the combined process were investigated. The results showed that the cyclic process is entirely feasible and prove to be stable producing, and ferrous oxalate of 99.32% purity. Material balance indicated that 95.17% of iron was recovered in the form of FeC2O4·2H2O, and the recovery efficiency of oxalic acid was 58.52%.

Charging performance affects the commercial application of electric vehicles (EVs) significantly. This paper presents an optimal charging strategy for Li-ion batteries based on the voltage-based multistage constant current (VMCC) charging strategy. In order to satisfy the different charging demands of the EV users for charging time, charged capacity and energy loss, the multi-objective particle swarm optimization (MOPSO) algorithm is employed and the influences of charging stage number, charging cut-off voltage and weight factors of different charging goals are analyzed. Comparison experiments of the proposed charging strategy and the traditional normal and fast charging strategies are carried out. The experimental results demonstrate that the traditional normal and fast charging strategies can only satisfy a small range of EV users’ charging demand well while the proposed charging strategy can satisfy the whole range of the charging demand well. The relative increase in charging performance of the proposed charging strategy can reach more than 80% when compared to the normal and fast charging dependently.