Abstract

The power control strategy (PCS) is a key technology of series hybrid electric vehicle (SHEV) owing to the ability to coordinate the power flow between multi-energy sources to reduce fuel consumption. When power allocation command of PCS fluctuates sharply, engine-generator set (EGS) might not output the power allocated by the PCS in time, due to the dynamic response lag of engine. In this case, it would cause the lack of vehicle driving power. Therefore, how to ensure smooth power output of EGS and obtain the optimal fuel economy is a challenge. To solve this problem, a predictive and coordinated power control strategy for SHEV is proposed in this study. A predictive adaptive equivalent consumption minimization strategy (PA-ECMS) is proposed to optimize power flow in real time, in which long short-term memory network is applied to predict future demand power to adjust the equivalent factor. In order to reduce the fluctuation of power allocation command, an adaptive fuzzy controller is designed to modify the working points of engine according to the optimizing results of PA-ECMS. The results show that the power command of PCS can be smoothed, and the fuel economy can be improved by 4.91% over conventional ECMS.

Abstract

As an alternative, polymer flooding has been widely promoted and tested in the heterogeneous reservoir. In our earlier studies, we have recognized that oil recovery increases with the viscosity of the polymer as the displaced phase by observing the Hele-Shaw model.
Using visualization techniques, we explore the effects of water flooding and polymer flooding on fluid flow in a vertical heterogeneous reservoir filled with porous media. Based on macro-micro experimental image recognition, this study obtains the characteristics of the saturation distribution and production performance on water flooding and polymer flooding. The performance evaluation of planar sweep efficiency and vertical flow profile adjustment of polymer flooding has been systematically investigated to clarify the mechanism of enhanced oil recovery by polymer flooding. Compared with primary water flooding, the oil recovery of subsequent water flooding increases demonstrates that polymer flooding achieved better planar sweep performance based on macroscopic images. The work also qualitatively analyzed the changes of various types of residual oil during polymer flooding based on microscopic images.
Through literature and experimental research, we realized that a plethora of factors in polymer flooding may affect the development of drainage zones in heterogeneous reservoirs, including but not limited to polymer-oil mobility ratio, the timing of injection, velocity of injection, and polymer-rock surface adsorption. After optimizing the injected polymer parameters in this study, we tested the percolation condition of polymer in reservoirs with different permeability. The results displayed that the polymer easily entered the high-permeability layer and weakened its fingering phenomenon of preferential flooding channel. By the method of controlling the amount of polymer solution and observed preferential flooding channels interactions to the type of residual oil, we were able to design more comprehensive and appropriate evaluation methods for improving the performance of vertical heterogeneous reservoirs.

Abstract

To address the problem of the accuracy decrease of state of charge estimation caused by sudden high current impact, this paper proposes a lithium-ion battery SOC estimation method based on optimized deep convolutional neural network. Firstly, the 18650 battery was tested under actual driving conditions to obtain experimental data, and the experimental data was preprocessed by moving window to fit the two dimensional convolutional neural networks. Secondly, the proposed method was trained and tested, and the model parameters were further optimized. Thirdly, the proposed method is compared with sequence-to-sequence methods such as long short term memory and gated recurrent unit, and the results verify the superiority of the proposed method. This article provides a method to the battery SOC estimation, which is more conducive to practical applications.

Abstract

In this study, a hybrid renewable energy system consisting of photovoltaics (PVs), wind turbines, bioenergy, and pumped hydropower energy storage (PHES) is proposed. This study aims to optimise the size of the hybrid system components based on peak periods to increase the reliability of the overall system performance. The non-dominated sorting genetic algorithm (NSGA2) optimization model is developed to minimise two main objectives: loss of power supply (LPSP) and loss of renewable energy (LORE). The input variables are the number of wind turbines, the number of solar modules, the number of pumps/generators, and the capacity of the upper reservoir of PHES. The novelty of this study is to develop a multi objectives optimisation model focusing on peak load period for long time assessment taking advantage of hybridisation dispatchable and non-dispatchable renewable sources to size renewable energy components accurately. The results reveal that increasing the number of PV modules and wind turbines improves the LORE while reducing the LPSP, as it covers more demand at a given period. The best solutions obtained for the LPSP and LORE are 5% and 5%, respectively. The entire system dispatchability might be improved by increasing bioenergy power plant and PHES capacity.

Abstract

Wind power and photovoltaic power generation occupies an important position in the development of new energy. Wind power and photovoltaic power generation have made great contributions to the protection of the environment and the conservation of non-renewable resources such as coal and oil. The proportion of national wind power and photovoltaic power generation in the total electricity consumption of the whole society is continuously increasing. National policies also strongly support the development of wind power and photovoltaic power generation. This paper compares the application of two clean energy power generation methods and gives a perspective description on their development.

Abstract

With the fast development of the economy and industry, the demand for oil is increasing and the oil trade is becoming more and more frequent. And the oil price tends to be closely related to economic developments. So grasping the direction and trend of oil price is of great significance to the development of the world economy. In this paper, we selected the LSTM and Prophet algorithm to do the oil price’s prediction, to reach good results. RMSE and MAE are selected to represent the prediction’s precision. The RMSE and MAE values of LSTM are the smallest when setting the time_step as 10, which are 3.741 and 3.109 respectively. Values of Prophet are 3.212 and 2.471 respectively, which indicates that the prediction effect of Prophet is better.

Abstract

Lithium-ion batteries (LIB) are vital components of modern electric vehicles and load management in smart grids due to their relatively high energy density, power density, and efficiency. It is important that onboard battery management systems in battery electric vehicles have accurate battery state of charge (SOC) information to gauge the remaining vehicle range and minimize battery degradation through power management. However, owing to highly dynamic vehicle driving habits and the nonlinear nature of SOC relative to other battery parameters such as current, voltage, and temperature, SOC is unable to be measured directly and is difficult to be accurately estimated in real-time. This article proposes a novel Li-ion battery SOC estimation method through a Deep Feedforward Neural Network Multimode Ensemble (DNN-ME). K-means clustering is used to separate the training data into differentiable data subsets, which are then each fed into N deep feedforward neural network (DNN) base learners. The final ensemble output through weighted averaging is less susceptible to error from weight initialization variation than single models, ensuring greater prediction accuracy.

Abstract

The tar-rich coal is not only coal, but also a kind of coal-based oil and gas resources. If the pyrolysis technology and scale application are broken through, the abundant oil resource derived from tar-rich coal can be obtained. The underground in-situ pyrolysis technology of tar-rich coal has been proposed lately. Through this technology, the oil-gas resources with in tar-rich coal are preferentially extracted without environmental pollution and excessive semi-coke production. Here, the pyrolysis and mild oxidation characteristics of tar-rich coal were firstly evaluated using thermogravimetric analysis.

Abstract

Modeling techniques have shown that fossil generation can be complemented with renewables and resources capable of providing flexibility in the operation of the system, according to the rules of the electricity market. In order to involve flexible demand, a market scheme is proposed that encourages it and that its adequacy allows the conditions to increase renewable participation to contribute to the reduction of CO2 emissions. The model tested indicates that by shifting 10.49% from the peak to the off-peak block of the curve, an additional 5% increase in renewable penetration is obtained, and a reduction in the cost of supply of 8.4%. Considering a variation of emissions cost from 0 to 10 $/Ton of CO2, the cost of supply increases to 16.03%, with a renewable participation of 12%. And the cost of supply improves by 7.27% when the penetration of renewables reaches 17%. According to the verified results, this tool is useful to evaluate the system operation programs and their effects on the market, contributing to decision-making for selecting the best operational scenarios, especially when subsidy and quality service problems are underlying.

Abstract

To obtain the corrosion behavior of a widely-used boiler steel (15CrMo) exposing to saline gas and saline water with sodium sulfate (Na2SO4), the experiments were designed and implemented. The samples were analyzed by scanning electron microscopy/energy disperse spectrum (SEM/EDS) and X-ray diffraction (XRD). The results show the different corrosion behavior between liquid phase and gas phase. The particles among corrosion products in gas phase are larger than that in liquid phase corrosion. The crystal structures of Fe3O4 could fully form in gas phase. The corrosion products in liquid phase are complicated, including Fe, Fe2O3, Fe3O4. The corrosion behavior of 15CrMo in saline circumstances could deeply influence the efficiency and safety of the boilers.