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Water flooding occurs at the output region of the anode side, which is one of the main reasons for performance degradation of automotive fuel cell systems. If liquid water saturation ratio in gas diffusion layers can be observed and regulated within a suitable range, water flooding in anode channels can be avoided. For this purpose, a zero dimensional dynamic model which focuses on the internal states (such as liquid water saturation ratio in gas diffusion layer) for the anode side before water flooding is presented. A nonlinear state observer for the anode side is designed using the unscented Kalman filter algorithm. Influences of the relative humidity sensor and initial values of internal states on the observation results are analyzed. The results show (1) four key internal states (e.g., water vapor pressure, hydrogen and nitrogen pressure, average liquid water saturation ratio) can be observed using this method, and the time constant of the sensor has a smaller influence on the observer than the signal noise. (2) With a suitable sensor (noise power

Variable inter-vehicle distances influence significantly the wind resistance of platoon vehicles due to the sheltering of airflow. Accurate air drag estimation is extremely important for platoons in scenarios like energy-oriented driving and high-precision tracking. Most aerodynamic researchers have performed qualitative analysis of wind resistance for equally inter-spaced platoons, while the quantitative description of wind resistance variation with coupled inter-vehicle distances is rare. In addition, data measured through offline wind tunnel experiments, Computational Fluid Dynamics (CFD) simulations, or road tests via fuel consumption calibration for a period of time is unsynchronized, which may be unconvincing for real air drag estimation on road. Aiming at the quick and accurate approximation of platoon wind resistance, this paper proposes a novel and universal modeling strategy combining offline CFD simulation, online air drag observation, and real-time parameter identification. The variation characteristics of air drag with distance of a longitudinal platoon consisting of three homogeneous C-class Notchback cars are analyzed by CFD simulation. With appropriate data processing, a well-designed basis function is summarized. Then a novel wind resistance separation method combining Back-Propagation Neural Network (BPNN) and Extended State Observer (ESO) is proposed. Using the observed data stored in experience memory, the hybrid optimization method via particle swarm optimization (PSO) and gradient descent with momentum (GDM) is employed to identify the model parameters toward high accuracy and global optimality. Results of Hardware-In-the-Loop (HIL) experiment show that the proposed modeling strategy realizes effective real-time observation and accuracy description; the developed approximation model can describe the platoon wind resistance with continuous and coupled inter-vehicle distances, with the RMSE less than 11.4%.

The independently controlled electric wheels of distributed drive vehicles provide faster and more accurate actuators for vehicle slip ratio control. Meanwhile, the estimation of the slip ratio of electric wheels has been of vital importance for the dynamics control of distributed drive electric vehicles. However, the conventional slip ratio estimation method is hard to accurately estimate the slip ratio under steering conditions without multiple observations, increasing the cost and introducing errors. Considering that the output torque and motor rotation rate of electric wheels can be accurately collected, the novel slip ratio estimation method takes advantage of the signals of the electric wheels and requires fewer vehicle sensors. Based on the torsional vibration model of electric wheel, the slip ratio estimation method was proposed and validated by simulations and experiments. With the drum dynamometer, the slip ratio estimation method was applied to a single electric wheel for testing, proving the feasibility and accuracy of the proposed method. The slip ratio estimation was finally applied to a fuel cell heavy truck for road tests, of which the results show that the error index is reduced from 0.0152 to 0.0064 compared to the conventional slip ratio estimation method, confirming the good estimation performance achievable via the proposed method.

To realize the high-power density of the proton exchange membrane fuel cell (PEMFC), it is important to explain the influence of liquid water on cell voltage quantitatively, which is still not fully understood yet. This paper proposes a study on polarization decomposing of PEMFC considering effect of liquid water accumulation. The voltage model starts from oxygen transport resistance, which is described as the summation of molecular diffusion resistance, Knudsen diffusion resistance, and transport resistance in the ionomer of the catalyst layer. Then the concentration polarization on the cathodic side of the MEA is described using oxygen transport resistance and liquid water saturation ratio based on Fick’s law. Equations for the influences of liquid water saturation ratio on activation and concentration polarization are deduced mathematically. Based on the model and experiments, a comprehensive polarization decomposition method is proposed. Using this method, cell polarization can be decomposed in detail, and the liquid water saturation ratio under various operation conditions can also be estimated. This work can also be applied to the improvement of fuel cell performance and the evaluation of water content within fuel cells.

Secondary growth is a key characteristic of trees, which requires the coordination of multiple regulatory mechanisms including transcriptional regulators and microRNAs (miRNAs). However, the roles of microRNAs in the regulation of secondary growth need to be explored in depth. Here, the role of miR319a and its target, PtoTCP20, in the secondary growth of Populus tomentosa stem was investigated using genetic and molecular analyses. The expression level of miR319a gradually decreased from primary to secondary growth in P. tomentosa, while that of PtoTCP20 gradually increased. MiR319a overexpression in seedlings resulted in delayed secondary growth and decreased xylem production, while miR319a knockdown and PtoTCP20 overexpression promoted secondary growth and increased xylem production. Further analysis showed that PtoTCP20 interacted with PtoWOX4a and activated PtoWND6 transcription in vitro and in vivo. Our data show that PtoTCP20 controls vascular cambium proliferation by binding to PtoWOX4a, and promotes secondary xylem differentiation by activating PtoWND6 transcription, thereby regulating secondary growth in P. tomentosa. Our findings provide insight into the molecular mechanisms underlying secondary growth in trees.

Seawater pumped storage systems have bright prospect for energy storage in the coming years. The operational conditions of seawater pumped storage system are complex and harsh, where metal materials suffer from severe general and local corrosion. The corrosion behavior of Q235B carbon steel in simulated seawater pumped storage system under operational conditions was studied by potentiodynamic polarization, cyclic potentiodynamic polarization, and scanning electron microscope (SEM). The results confirm that the working pressure affected the corrosion resistance of Q235B carbon steel during the whole immersion period. The pressure promoted the electrochemical reaction of corrosion process and the corrosion rate increased with pressure at the initial immersion period. However, the stable rust layer formed after longtime immersion at different pressures increased the corrosion resistance of carbon steel, and decreased the corrosion degree of carbon steel. Meanwhile, the working pressure affected the pitting corrosion behavior of Q235B carbon steel during the whole immersion period. The pitting corrosion potential was more negative and the tendency of pitting corrosion was higher at 4 MPa during the whole immersion period. However, pressure also accelerated the formation rate of protective rust layer on the steel surface. Q235B carbon steel has higher susceptibility to pitting corrosion at 4 MPa in the static seawater.

The thickness of the surface layer on both poly(methyl methacrylate) (PMMA) and polystyrene (PS) films was investigated by monitoring the surface reorganization of fluorinated tracer-labeled PMMA a...

Trichomes are specialized epidermal cells that contribute to plant resistance against herbivores. Their formation is controlled precisely by multiple genetic and environmental signals. Previous studies have shown that microRNA319 (miR319) and gibberellin (GA) signaling are involved in trichome development in Arabidopsis, but little is known about their interaction between these factors. Here we reported that the miR319a/TEOSINTE BRANCHED/CYCLOIDEA/PCF (TCP) module participates in trichome initiation synergistically with GA signaling in Populus tomentosa. We demonstrated that overexpression of miR319a decreased transcription levels of its targeted TCPs and significantly elevated leaf trichome density in transgenic poplar, resulting in decreasing insect herbivory. Conversely, repressing miR319a by short tandem target mimics (STTM) elevated TCP expression levels and decreased trichome density in transgenic plants. The trichome phenotype of 35S:miR319a plants could be abolished by introducing a miR319a-resistant form of TCP19. Furthermore, the miR319a-targeted TCP19 interacted directly with REPRESSOR OF ga1-3 (RGA), a downstream repressor of GA signaling. TCP19 and RGA synergistically inhibited the GLABROUS1 (GL1)-induced expression of trichome marker gene GLABRA2 (GL2), thereby repressing leaf trichome initiation. Our results provide an insight into the molecular mechanism by which miR319/TCP19 module and GA signaling coordinated regulating trichome initiation in P. tomentosa.

Ferulate 5-hydroxylase (F5H) is a limiting enzyme involved in biosynthesizing sinapyl (S) monolignol in angiosperms. Genetic regulation of F5H can influence S monolignol synthesis and therefore improve saccharification efficiency and biofuel production. To date, little is known about whether F5H is post-transcriptionally regulated by endogenous microRNAs (miRNAs) in woody plants. Here, we report that a microRNA, miR6443, specifically regulates S lignin biosynthesis during stem development in Populus tomentosa. In situ hybridization showed that miR6443 is preferentially expressed in vascular tissues. We further identified that F5H2 is the direct target of miR6443. Overexpression of miR6443 decreased the transcript level of F5H2 in transgenic plants, resulting in a significant reduction in S lignin content. Conversely, reduced miR6443 expression by short tandem target mimics (STTM) elevated F5H2 transcripts, therefore increasing S lignin composition. Introduction of a miR6443-resistant form of F5H2 into miR6443-overexpression plants restored lignin ectopic composition, supporting that miR6443 specifically regulated S lignin biosynthesis by repressing F5H2 in P. tomentosa. Furthermore, saccharification assays revealed decreased hexose yields by 7.5-24.5% in miR6443-overexpression plants compared with the wild-type control, and increased hexoses yields by 13.2-14.6% in STTM6443-overexpression plants. Collectively, we demonstrate that miR6443 modulates S lignin biosynthesis by specially regulating F5H2 in P. tomentosa.

Self-humidification with anode/cathode recirculation was found to be an alternative in the place of external humidifier according to the previous studies in the last decade, however, there is still a lack of quantitative experimental and theoretical comparison between them. In this study, the dynamic PEM fuel cell system model with anodic and cathodic exhaust gas recirculation is proposed and related model calibration and verification are carried out by measured output voltage, gaseous pressure and mole concentration during the dynamic process. The model simulation shows quite good agreement with the experimental result and source of error is discussed. Based on developed model, it is simulated that the humidification effect is much better for cathode recirculation compared with anode recirculation, resulting in performance improvement, especially under the condition of low inlet RH. However, this improved humidification effect is weakened, considering the structure limit of recirculation pump and gas channel in this case. Besides, the power consumption of cathode recirculation pump is found to be much higher than that of anode recirculation. As a result, the anode recirculation is found to be a better solution considering the energy efficiency and mechanical structure limit. In contrast, cathode recirculation conducts better humidification effect, which is more suitable for the short-time low-inlet-humidity working conditions.

Lithium-ion (Li-ion) cells degrade after repeated cycling and the cell capacity fades while its resistance increases. Degradation of Li-ion cells is caused by a variety of physical and chemical mechanisms and it is strongly influenced by factors including the electrode materials used, the working conditions and the battery temperature. At present, charging voltage curve analysis methods are widely used in studies of battery characteristics and the constant current charging voltage curves can be used to analyze battery aging mechanisms and estimate a battery’s state of health (SOH) via methods such as incremental capacity (IC) analysis. In this paper, a method to fit and analyze the charging voltage curve based on a neural network is proposed and is compared to the existing point counting method and the polynomial curve fitting method. The neuron parameters of the trained neural network model are used to analyze the battery capacity relative to the phase change reactions that occur inside the batteries. This method is suitable for different types of batteries and could be used in battery management systems for online battery modeling, analysis and diagnosis.

High-power charging (HPC) has been associated with a great potential to shorten the charging time, relative to increasing the all-electric range (AER) of battery electric cars (BECs). Such promise of applicability is however restrained by setbacks attributed to the high-voltage system of BECs, its negative influence on the battery performance, and a higher requirement of charging infrastructures. More importantly, the core issue is the functionality of the battery, as research has found that HPC may cause a high charging rate on the battery cell that consequently promotes its heat generation and eventually results in a significant loss of functionality and safety degradation. Considering these elements, the thermal reaction of a battery under HPC is seemingly the key toward the design of an effective thermal management system. Thus, we conducted the present study with a focal objective of examining the thermal reaction of a traction battery to the HPC, to evaluate the gap between existing battery technologies and HPC to find the suitable condition. We investigated the heat generation characteristics of Nickel–Cobalt–Manganese (NCM) and lithium iron phosphate (LFP) batteries by HPC testing in non-adiabatic and adiabatic conditions, respectively, for different charging rates from 0.5 to 5.0C. We analyzed and incorporated the test results to establish a model of battery heat generation, which could predict temperature rise under specified conditions. Moreover, we proposed an HPC operating boundary by considering the present battery technologies. The findings herein indicate that the increased charging rate leading to higher heat generation and lower charge efficiency can be effectively handled through specific thermal management, which, with the boundary, provides a limit for the optimal operation region to avoid safety risk. More practically, a good thermal management system is required to control the boundary setting and extend the operation region of battery charging.