To obtain a deep understanding of the pyrolysis mechanism of oil shale kerogen, critical organic intermediate——thermal bitumen was firstly prepared by heating Fushun oil shale up to the critical temperature point (450℃), and then extracted by IL(BmimCl) which was mixed with NMP. Scanning electron microscope, X-ray diffraction, Fourier transform infrared spectrophotometry and flash pyrolysis-gas chromatography-mass spectrometry were employed to investigate the physical and chemical transformation in oil shale pyrolysis and extraction processes. Results showed that thermal bitumen was mainly composed of aliphatic structures. In the thermolysis and extraction courses, aliphatic structures have poor stability by comparison of peak intensities. Thermal bitumen mainly comprised short and middle-chain alkanes/alkenes in the range from C4 to C26.
To improve the cold-starting performance of a polymer electrolyte fuel cell (PEFC), we devised a microporous layer (MPL) with a planar wettability distribution. Hydrophilic and hydrophobic strips in the MPL were arrayed in alternating rows in the in-plane direction. Due to the exclusion of liquid water from the hydrophobic regions, the water moved towards the hydrophilic areas and was absorbed. Since frozen water near the interface between the MPL and catalyst layer (CL) was thought to inhibit the continuation of power generation, minimizing the amount of water on the CL was important. We extended the continuous operating temperature range to encompass lower temperatures and improved the operating time of the PEFC at subfreezing temperatures.
In order to reduce CO2 emissions, PV & BEC railway system which is combined with Photovoltaic Power Generation (hereinafter referred to as PV) and Battery-powered Electric Railway Car (hereinafter referred to as BEC) has been proposed. However, the air-conditioning energy consumption is about 30% of the whole of railway car energy consumption. Therefore, it is important to improve the energy efficiency of the air-conditioner for railway cars. In this paper, a novel railway system which is integrated BEC, air-conditioners and PV has been proposed. In this system, Air-conditioner Integrated Electric Vehicle (hereinafter referred to as AI-EV) system which is combined air-conditioner with a power generator driven by a small engine is applied as an air-conditioning system for BEC. The effects of the novel railway system have been evaluated based on a theoretical simulation when a novel system is applied to the non-electrified sections of the Kibi line in Okayama prefecture.
Operation strategy is critical to the economic performance of combining cooling, heat and power (CCHP) microgrid. This work took different costs of operation, maintenance, startup and shut down, storage aging, and different nonlinear characteristics of tariff electricity, storage loss and COP, etc. into consideration. To tackle the uncertainties of renewable energy and load demands, a chance-constrained operation strategy was established. PSO algorithm was applied to solve the problem. Simulation results show that uncertainty of CCHP deteriorates system economic performance compared with deterministic economic operation strategy. And following electrical load and following thermal load are inefficient for economic operation of CCHP microgrid.
SF6 gas is the main arc-extinguishing medium for high-voltage circuit breakers. It has excellent characteristics such as electronegativity and high medium recovery strength. But as a kind of greenhouse gas, it has a very high greenhouse effect. Finding alternative gas for SF6 and ensuring reliable arcextinguishing have gradually become a hot spot in the field of high-voltage switches. Basing on the magnetohydrodynamic equations, a model of SF6 circuit breaker nozzle is simplified in this paper. Under the condition of local thermodynamic equilibrium, the process of arc formation in air, nitrogen, argon and SF6 under the same breaking condition are simulated in this paper. The influence of breaking current on arc characteristics is also analyzed. It provides a reference for the research work of SF6 substitute gas.
The subject addressed is the fault diagnosis of the circuit breaker based on the coil current in the operating circuit. The characteristics of the coil current in the operating circuit are analyzed at length by extracting eight features. The discretization of continuous variables based on the matrix decomposition is applied to change the eight continuous features into discrete variables. Followed by that, the Bayesian algorithm is used to achieve the fault diagnosis based on the discrete variables. Finally, the accuracy of the improved algorithm is verified by the simulation results.
A dual-motor coupling propulsion system with multi-speed transmission offers the possibility of comprehensive improvement of the vehicle, with an increased difficulty and time cost of design though. This paper takes an electric city bus as research object to design a matching dual-motor propulsion system with two-speed transmission. For convenience and rapidity, a bi-level programming method for parameter matching and energy management of the propulsion system is established. The inner level seeks for the optimal control rules concluding gearshift schedule and torque-allocation proportion for instantaneous minimum power loss, while the outer level leverages the particle swarm optimization algorithm (PSO) to seek the optimal propulsion system parameters within reasonable limits. The objective function of the whole loop takes into account the whole power loss of the entire C-WTVC condition. It indicates that the proposed design and energy management strategy provide a significant improvement of the powertrain efficiency and great reduction of the design cost.
Energy management strategy is important for improving fuel economic of hybrid electric vehicles. We present a deep neuroevolution based energy management strategy for hybrid electric vehicles, which learns optimal energy split strategies through evolution of its deep neural networks structure. We define the optimization objective of the deep neural networks by the fuel consumption and properties of target HEV. The deep neural networks controller is learnt through a parallel and evolution way. The simulation results on a standard driving cycles show that the proposed deep neuroevolution method outperforms the DRL based model, and achieves comparative performance to global–‐optimal method–‐dynamic programming.