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The series-parallel architecture of the plug-in hybrid electric powertrain has attracted wide attentions in recent years for its flexible and highly efficient operating modes. However, despite the improvement of the vehicle fuel economy, it has been gradually facing more challenges on the design of the optimal controller due to the complex structure and the fast depletion of the battery. In this paper, a two-step optimal energy management strategy is proposed for a novel single-shaft series-parallel powertrain. In the first step, an equivalent method is adopted, in which two motors are equivalently regarded as one. After detailed analysis of the operating modes, various objective functions are established to pre-optimize the power split between engine and motor or two motors. In the second step, a stochastic dynamic programming (SDP) is adopted to optimize the power split between the engine and the equivalent motor, and the optimal combination of the operating modes. Then coupled with the pre-optimized results of the first step, the optimal power split among the engine and two motors could be obtained and then constructed as simple lookup-tables, which have great potential for practical applications. Finally, the preliminary test about the real-time performance of the optimal results is developed on the hardware-in-the-loop (HIL) system. [ABSTRACT FROM AUTHOR]

Grassland degradation is a significant ecological challenge, it changes the grassland environment, including the structure of soil microbial communities. Grazing is a critical factor in this process, yet the impact of different grazing intensities on soil bacterial communities, particularly in unique environments like the Qinghai-Tibet Plateau, remains poorly understood. This study utilized a full-length 16 S rRNA gene sequencing technology combined with environmental factors to assess the impacts of the Holba sheep grazing on soil bacterial community diversity in Zhongba County, Shigatse City, Qinghai-Tibet Plateau. The specific aim was to explore the changes in soil bacterial community diversity and stability under contrasting grazing intensities: prohibited grazing, light grazing, moderate grazing, and heavy grazing. The results showed that grazing activities significantly altered the diversity of bacterial communities, particularly under light and heavy grazing. The spatial dissimilarity in soil bacterial communities was mainly driven by species turnover (i.e., species replacement), which was more pronounced under heavy grazing. The number of keystone species increased with the grazing intensity of the bacterial co-occurrence network. Under heavy grazing, bacterial communities showed the highest number of keystone species and the community stability. Environmental factors such as exchangeable magnesium (EM), soil available kalium (SAK), and soil pH were the most important environmental variables impacting changes in bacterial community structure by influencing community α-diversity and stability. This study enhances our understanding of the response mechanisms of soil microbial communities to grazing intensities and other environmental stresses in grassland ecosystems. In addition, this study also provides important references and guidance for microbial diversity research in extreme environments. These findings are crucial for promoting the sustainable management and conservation of grassland ecosystems in climatically harsh areas such as the Qinghai-Tibet Plateau. [Display omitted] [ABSTRACT FROM AUTHOR]

Considering the complicated characteristics of traffic flow in city bus route and the nonlinear vehicle dynamics, optimal energy management integrated with clustering and recognition of driving conditions in plug-in hybrid electric bus is still a challenging problem. Motivated by this issue, this paper presents an innovative energy optimization control framework based on the cloud computing for plug-in hybrid electric bus. This framework, which includes offline part and online part, can realize the driving conditions clustering in offline part, and the energy management in online part. In offline part, utilizing the operating data transferred from a bus to the remote monitoring center, K-means algorithm is adopted to cluster the driving conditions, and then Markov probability transfer matrixes are generated to predict the possible operating demand of the bus driver. Next in online part, the current driving condition is real-time identified by a well-trained support vector machine, and Markov chains-based driving behaviors are accordingly selected. With the stochastic inputs, stochastic receding horizon control method is adopted to obtain the optimized energy management of hybrid powertrain. Simulations and hardware-in-loop test are carried out with the real-world city bus route, and the results show that the presented strategy could greatly improve the vehicle fuel economy, and as the traffic flow data feedback increases, the fuel consumption of every plug-in hybrid electric bus running in a specific bus route tends to be a stable minimum. [ABSTRACT FROM AUTHOR]

As one of the main working modes, the energy recovered with regenerative braking system provides an effective approach so as to greatly improve fuel economy of hybrid electric bus. However, it is still a challenging issue to ensure braking stability while maximizing braking energy recovery. To solve this problem, an efficient energy recovery control strategy is proposed based on the modified nonlinear model predictive control method. Firstly, combined with the characteristics of the compound braking process of single-shaft parallel hybrid electric bus, a 7 degrees of freedom model of the vehicle longitudinal dynamics is built. Secondly, considering nonlinear characteristic of the vehicle model and the efficiency of regenerative braking system, the particle swarm optimization algorithm within the modified nonlinear model predictive control is adopted to optimize the torque distribution between regenerative braking system and pneumatic braking system at the wheels. So as to reduce the computational time of modified nonlinear model predictive control, a nearest point method is employed during the braking process. Finally, the simulation and hardware-in-loop test are carried out on road conditions with different tire–road adhesion coefficients, and the proposed control strategy is verified by comparing it with the conventional control method employed in the baseline vehicle controller. The simulation and hardware-in-loop test results show that the proposed strategy can ensure vehicle safety during emergency braking situation and improve the recovery energy almost 17% compared with the conventional rule-based strategy in the general braking situation. Therefore, the proposed control strategy might offer a theoretical reference for the design of the actual braking controller in engineering practice. [ABSTRACT FROM AUTHOR]

Driving cycles of a city bus is statistically characterized by some repetitive features, which makes the predictive energy management strategy very desirable to obtain approximate optimal fuel economy of a plug-in hybrid electric bus. But dealing with the complicated traffic conditions and finding an approximated global optimal strategy which is applicable to the plug-in hybrid electric bus still remains a challenging technique. To solve this problem, a novel driving-behavior-aware modified stochastic model predictive control method is proposed for the plug-in hybrid electric bus. Firstly, the K -means is employed to classify driving behaviors, and the driver models based on Markov chains is obtained under different kinds of driving behaviors. While the obtained driver behaviors are regarded as stochastic disturbance inputs, the local minimum fuel consumption might be obtained with a traditional stochastic model predictive control at each step, taking tracking the reference battery state of charge trajectory into consideration in the finite predictive horizons. However, this technique is still accompanied by some working points with reduced/worsened fuel economy. Thus, the stochastic model predictive control is modified with the equivalent consumption minimization strategy to eliminate these undesirable working points. The results in real-world city bus routines show that the proposed energy management strategy could greatly improve the fuel economy of a plug-in hybrid electric bus in whole driving cycles, compared with the popular charge depleting–charge sustaining strategy and it may offer some useful insights for realizing the approximate global optimal energy management for the plug-in hybrid electric vehicles. [ABSTRACT FROM AUTHOR]

[Display omitted] • Magnetic-single-molecular material is used for uranium adsorption for the first time. • Fully exposure of functional group guarantees high uranium adsorption capacity. • The magnetism of the adsorbent endows it with high recyclability and reusability. • High adsorption capacity of 84.31 mg/g is achieved in simulated nuclear wastewater. • Highest uranium adsorption speed is achieved in simulated nuclear wastewater. Highly efficient treatment of uranium contamination is of great importance for the sustainable development of the environment. The recovery of uranium by adsorbents is a feasible strategy for treating uranium contamination. However, due to insufficient exposure of the contact interfaces, currently available adsorbents usually take extremely long time for uranium adsorption. Herein, a magnetic-single-molecular amidoxime adsorbent M−AO−Fc is fabricated for highly efficient recovery of uranium from nuclear wastewater by endowing the amidoximated cyanoferrocene with magnetism. The adsorbent M−AO−Fc shows a maximum uranium adsorption capacity of 510.60 mg/g in uranium solution. In an acidic simulated nuclear wastewater containing a large number of interfering ions, M−AO−Fc also shows a high uranium adsorption capacity of 84.31 mg/g within a short equilibrium time of 60 min. The full dispersion of M−AO−Fc and full exposure of functional groups are the critical reasons for the excellent uranium adsorption performance. Attributing to the magnetism of M−AO−Fc, this adsorbent can be efficiently recovered by magnetic recovery and shows high reusability. This study proves that the magnetic-single-molecule would be a promising adsorbent for uranium recovery and opens up a new direction for designing a uranium adsorbent. [ABSTRACT FROM AUTHOR]

• A new double-network hydrogel is explored for uranium recovery from seawater. • U-adsorption capacity of the hydrogel is greatly enhanced by added nanoclay. • Mechanical property of the hydrogel is largely improved by th double-network. • Used materials are lowcost and the hydrogel owns good U-adsorption reusability. Massive uranium recovery from seawater is a key project to provide the long-time consumption of global nuclear power, while it is still greatly difficult to achieve high-efficient adsorbents with high mechanical property. In this work, a new nanoclay-poly(amidoxime) (NC-PAO) high-strength double-network (DN) composite hydrogel adsorbent with high-efficient and fast uranium extraction capacity has been explored. The uranium adsorbing performance can be significantly enhanced by the small content of added super-hydrophilic NC (Laponite RDS sheets), because of the largely improved hydrophilicity of the hydrogel. Compared with the blank PAO DN hydrogel without nanoclay, the uranium adsorbing capacity of this NC-PAO DN hydrogel can largely increase 73.4% from 4.97 mg/g to 8.62 mg/g, after in seawater only 25 days. The uranium adsorption rate of this hydrogel from natural seawater, can reached 0.345 mg/(g∙day), which is excellent among existing uranium adsorbents. Furthermore, this hydrogel has high mechanical performance with 2.42 MPa of tensile strength and 16.13 MPa of compressive strength, which can be used in seawater for relatively longer time than traditional low-strength single-network hydrogel, owing to its double network (DN) structure (the weakly crosslinked gelatin soft network and the strongly crosslinked cellulose nanofiber (CNF) hard network). This NC-PAO DN hydrogel adsorbent will be an excellent candidate for fast uranium recovery from seawater in largescale. Most importantly, this work will inspire the development of new hydrogel-based adsorbents and even other type of adsorbents for high-efficient uranium extraction, through compositing various low-cost and super-hydrophilic nanomaterials to significantly improve the hydrophilicity of the adsorbent and designing double-network structure to largely enhance the mechanical property. [ABSTRACT FROM AUTHOR]

[Display omitted] • A charge balanced adsorbent is fabricated for uranium extraction from seawater. • The adsorbent can resistant both biofouling and abiotic fouling. • The uranium extraction capacity is highly enhanced. • The uranium adsorption ability and anti-adhesive ability are highly reusable. The extraction of uranium from seawater is vital to meet the increase demands of uranium resource for the nuclear energy industry, while it is severe hazarded by the marine fouling, including both the biofouling and abiotic fouling. Previous reports only focused on the control of biofouling and rare attention is paid to the abiotic fouling. However, the abiotic fouling is the fundamental of the biofouling. Herein, a charged balanced hydrogel adsorbent is fabricated by introducing the charge-positive tetraethylenepentamine (TEPA) into the charge-negative polyacrylamidoxime (PAO). The fabricated PAO-CB hydrogel membrane shows high anti-adhesive ability to the marine fouling, including both the biofouling and abiotic fouling, which is due to the decrease of the charge attraction between the adsorbent and the charged contaminants. Benefitting from the reduce of marine fouling, the PAO-CB hydrogel membrane shows a high uranium extraction capacity of 8.59 mg g−1, which is higher than that of PAO hydrogel membrane by 44.59%. The finding of this study indicates that the construction of charge balanced adsorbent would be an effective strategy to control the complicated marine fouling during uranium extraction from seawater. [ABSTRACT FROM AUTHOR]

[Display omitted] • A charge balanced adsorbent is fabricated for uranium extraction from seawater. • The adsorbent can resistant both biofouling and abiotic fouling. • The uranium extraction capacity is highly enhanced. • The uranium adsorption ability and anti-adhesive ability are highly reusable. The extraction of uranium from seawater is vital to meet the increase demands of uranium resource for the nuclear energy industry, while it is severe hazarded by the marine fouling, including both the biofouling and abiotic fouling. Previous reports only focused on the control of biofouling and rare attention is paid to the abiotic fouling. However, the abiotic fouling is the fundamental of the biofouling. Herein, a charged balanced hydrogel adsorbent is fabricated by introducing the charge-positive tetraethylenepentamine (TEPA) into the charge-negative polyacrylamidoxime (PAO). The fabricated PAO-CB hydrogel membrane shows high anti-adhesive ability to the marine fouling, including both the biofouling and abiotic fouling, which is due to the decrease of the charge attraction between the adsorbent and the charged contaminants. Benefitting from the reduce of marine fouling, the PAO-CB hydrogel membrane shows a high uranium extraction capacity of 8.59 mg g−1, which is higher than that of PAO hydrogel membrane by 44.59%. The finding of this study indicates that the construction of charge balanced adsorbent would be an effective strategy to control the complicated marine fouling during uranium extraction from seawater. [ABSTRACT FROM AUTHOR]