The shift towards green aviation necessitates the adoption of all‐electric aircraft, marking an essential trend in the aviation industry. Distributed propulsion systems enhance aircraft flexibility and reliability, synchronously reducing performance demands and costs of single propulsion units compared to centralized propulsion system. The high reliability and lightweight characteristics of brushless DC motors have made them increasingly popular in distributed propulsion systems. Despite its advantage which helps to build efficient aircraft aerodynamic layout, there are more cables in the distributed propulsion system, which accounts for most of the weight of the aircraft. To address this, all‐electric aircraft utilizes high‐voltage power supply to reduce the current, so it uses SiC MOSFETs as power devices to improve its efficiency and heat dissipation. The speed controller in all‐electric aircraft determines the efficiency and reliability of the propulsion unit. However, the traditional high‐voltage speed controller poses challenges for aircraft maintenance because of high cost. Therefore, this paper emphasizes the significance of designing a low‐cost, high‐performance speed controller for all‐electric aircraft. Challenges such as circuit interference, battery voltage fluctuation, and sampling noise affecting the high‐voltage controller during operation are solved, respectively. First, to address the likelihood of mutual interference in the bridge arm, a self‐bootstrapping voltage reduction drive mode is designed to turn off SiC MOSFET and enhance circuit anti‐interference. Second, to solve the challenge of wide battery voltage fluctuation range and inconsistent controller output, an adaptive pulse width modulation (PWM) regulation mode for battery voltage is proposed, ensuring stable controller output. Finally, to counteract the impact of the comparator threshold on zero‐crossing sampling, an electrical angle delay method based on delay compensation is designed to improve the efficiency of the brushless DC motor during operation. Experimental results show that the three solutions proposed in this paper are helpful to improve the anti‐interference, stability, and efficiency of the controller in the high‐voltage distributed electric propulsion system and contribute to reduce the cost and weight in the propulsion unit.
Integrated energy systems (IESs) provide support for improving energy utilization and have become an inevitable choice for the energy revolution. However, because the dynamic characteristics of different energy conversion devices vary considerably, the behavior of output power change actions should be studied in finer timescales. This paper proposes an optimization dispatch strategy based on dynamic modeling and double-loop optimization framework for IESs to realize the collaborative dispatch of multiple energy devices and reduce the source-load energy deviation during system operation. The proposed strategy first characterizes dynamic response capacity, time-varying efficiency and nonlinear behavior of the energy conversion devices in an IES. Next, the multi-energy flow relationship is analyzed and a dynamic optimization dispatch model is established. Then, dynamic constraints are developed via optimization period decomposition, nonlinear time-series modeling, and power time-domain integration to optimize the device output in real time according to the system operating conditions, ensuring that the devices remain under the optimal operating condition while maintaining energy balance and stability. Finally, a case study is conducted to perform system modeling and optimization analysis, the simulation results showed that the optimized strategy reduced the daily operation cost by 10.09% and carbon emission by 6.41%, increased the source-load matching by 3.47%, and the effectiveness of the proposed strategy and established model are verified.
To improve the optimization performance of Gauss pseudospectral method for bang-bang optimal control problems, a two-stage mesh refinement Gauss pseudospectral method based on the sensitivity is proposed. In the first stage, the state and control vector are approximated by global interpolation polynomials. The differential equations are approximated by orthogonal polynomials and the optimal control problem is then transformed into an NLP problem. In the second stage, the relative sensitivity is calculated and the mesh is refined by merging and subdividing collocation points. Finally a new NLP problem is obtained with refined Gauss collocation points. Simulation tests are carried out on two classical bang-bang optimal control problems. The test results show that this method is better than the traditional Gauss pseudo-spectral method in both accuracy and time.
The automotive electronic throttle (AET) control system has been widely applied in modern automotive engines, and accurate control of AET can improve engine performance as well as reduce pollution emissions. However, the noise in the sensor circuit and the variation in automotive driving conditions seriously affect the control performance of the AET system, making controller designing challenging. This paper proposes a self-tuning backstepping control with a Kalman-like filter (SBCKLF) strategy. First, the noise affecting the position sensor measurement is verified to be non-Gaussian by acquiring and processing the noise signal. To eliminate its influence on control precision, a Kalman-like filter is introduced to estimate the real position of the valve. Then, a self-tuning backstepping controller is designed to automatically adapt to the variation in vehicle driving conditions. A self-tuning algorithm based on fuzzy control is used to tune the parameters of the backstepping controller online, so as to optimize the controller performance. Finally, an experimental platform based on dSPACE for the AET control system is built to perform the controller comprehensive test in a real-time environment. Experimental results and performance analysis demonstrate the effectiveness of the proposed SBCKLF strategy. Compared to the best results of other methods, the proposed method reduces the maximum and root mean square tracking errors by 21.65% and the average error by 12.89%. The steady-state and tracking error bounds are controlled to 0.9° and 2.3°, respectively. It also shows that the SBCKLF strategy has the strongest robustness as well as the best response speed.
Abstract A unique workflow and methodology enabled analysis of production data using reservoir simulation to help understand the shale gas production mechanism and the effectiveness of stimulation treatments along the lateral of horizontal wells. Starting from early 2008, we have analyzed production data from more than 30 horizontal wells in the Haynesville Shale using this methodology. This paper presents case studies demonstrating results of this new technique in several different areas of the Haynesville Shale. After integration of all available data, we built simulation models for the wells stimulated with multistage hydraulic fracture treatments. This modeling work investigates factors and parameters relating to short- and long-term well performance including 1) pore pressure, 2) matrix rock quality, 3) natural fractures, 4) hydraulic fractures, and 5) complex fracture networks. By historymatching the observed production, we have identified the primary factors for creating good early well performance. The Haynesville study has provided a better understanding of the gas production mechanism and effectiveness of stimulation along the laterals. After calibration of the simulation model, effective well drainage area and reserve potential can be calculated with more confidence. The Haynesville Shale is a very tight source rock. The shale matrix quality correlates with production performance when stimulation treatments are consistent along the lateral. A complex fracture network created during the stimulation treatment is the key to generating superior early well performance in the Haynesville Shale. Knowing how to effectively create more surface area during treatment and preserve the surface area after treatment are critical factors for making better wells in the Haynesville. Operators can use this information to determine where and how to spend resources to produce better wells. It also helps refine expectations for well performance and minimizes the uncertainties of developing these properties. The workflow and methodology have also been successful in other shale plays.
Combined cooling, heating, and power (CCHP) systems are a promising energy-efficient and environment-friendly technology. However, their performance in terms of energy, economy, and environment factors depends on the operation strategy. This paper proposes a multi-energy complementary CCHP system integrating renewable energy sources and schedulable heating, cooling, and electrical loads. The system uses schedulable loads instead of energy storage, at the same time, a collaborative optimization scheduling strategy, which integrates energy supply and load demand into a unified optimization framework to achieve the optimal system performance, is presented. Schedulable cooling and heating load models are formulated using the relationship between indoor and outdoor house temperatures. A genetic algorithm is employed to optimize the overall performance of energy, economy, and environment factors and obtain optimal day-ahead scheduling scheme. Case studies are conducted to verify the efficiency of the proposed method. Compared with a system involving thermal energy storage and demand response (DR), the proposed method exhibits a higher primary energy saving rate, greenhouse gas emission reduction rate, and operation costs saving rate of 7.44%, 6.59%, and 4.73%, respectively, for a typical summer day, thereby demonstrating the feasibility and superiority of the proposed approach.
Aiming at the noticeable problems relating to coagulating agent in the drinking water treatment, The paper reviewed briefly the development and application of drinking water treatment of coagulating agent worldwide. It was suggested, as to coagulating agent research and development in the future.
As the key position in the road transport, passenger depot is the enterprise of high energy consumption.Passenger depot should set the energy conservation standard, take effective measures to reduce the energy consumption.In the paper, factors related with energy conservation are extracted by analyzing the survey data and the weight of each factor can be calculated by GAHP.It provides the theoretic foundation for confirming energy-saving evaluation indicators of highway transport passenger station.
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