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Energy management strategy (EMS), as a core technology in fuel cell vehicles (FCVs), profoundly influences the lifespan of fuel cells and the economy of the vehicle. Aiming at the problem of the EMS of FCVs based on a global optimization algorithm not being applicable in real-time, a rule extraction-based EMS is proposed for fuel cell commercial vehicles. Based on the results of the dynamic programming (DP) algorithm in the CLTC-C cycle, the deep learning approach is employed to extract output power rules for fuel cell, leading to the establishment of a rule library. Using this library, a real-time applicable rule-based EMS is designed. The simulated driving platform is built in a CARLA, SUMO, and MATLAB/Simulink joint simulation environment. Simulation results indicate that the proposed strategy yields savings ranging from 3.64% to 8.96% in total costs when compared to the state machine-based strategy. [ABSTRACT FROM AUTHOR]

Nano-clean fracturing fluids have broad application potential in coalbed methane reservoir fracturing owing to their high stability, good temperature resistance, low filtration loss, and strong frictional resistance reduction. However, the sand-carrying regularity of nano-clean fracturing fluids in coalbed methane reservoirs is unclear, especially for complex fractures with variable directions. This study established a sand transport model that considers proppant collision, wall friction blocking, fracture fluid filtration loss, and the fracture branching angle to study the sand-carrying law of nano-clean fracturing fluids and its influencing factors in complex fractures. The degrees of influence on equilibrium height and placement rate from high to low were the proppant particle size, proppant density, fracturing fluid properties, sand ratio, and pumping discharge volume, and the correlation degrees obtained by grey correlation analysis are 0.862, 0.861, 0.855, 0.854, and 0.832, respectively. As the complexity of the fractures deepens and the resistance increases, the flow rate of the fracturing fluid is reduced, making it difficult for the proppant to enter the branching joints. The sand-carrying performance of a nano-clean fracturing fluid is better than that of a common clear-water fracturing fluid. The fluid-structure coupling model of a nano-clean fracturing fluid can accurately characterize the sand-carrying law of nano-clean fracturing fluids, providing a research basis for optimizing high efficiency sand-carrying fracturing fluid parameters in coalbed methane reservoir fracturing construction. [ABSTRACT FROM AUTHOR]

The powertrain of a fuel cell vehicle typically consists of two energy sources: a proton electrolyte membrane fuel cell (PEMFC) stack and a battery package. In this paper, multi-dimensional dynamic programming (MDDP) is used to solve the energy management strategy (EMS) of fuel cell hybrid powertrain. This study built a fuel cell hybrid powertrain model, in which the battery model is built based on the Thevenin equivalent circuit. In order to improve the calculating efficiency and maintain the accuracy of the algorithm, the state variables in each stage are divided into primary and secondary. In the reverse solution process, the corresponding relationship between the multi state variables grid and the optimal cumulative function has been changed from three-dimensional to two-dimensional. The EMS based on MDDP is applied to component sizing of a commercial vehicle. Simulations were conducted using MATLAB under the C-WTVC working condition. By analyzing the fuel economy and system durability, the optimal component combination of comprehensive performance is obtained. Compared with the EMS based on dynamic programming (DP), the proposed method effectively improves the calculation accuracy: the hydrogen consumption can be reduced by 3.10%, and the durability of the fuel cell and battery can be improved by 1.08% and 0.13%, respectively. [ABSTRACT FROM AUTHOR]

Drop-and-pull transportation can repeatedly utilize tractors with different trailers and reduce costs, carbon emissions, and the number of tractors to purchase and use. Fuel-cell electric vehicles (FCEV) possess high power and long drive endurance. These performance characteristics complement the performance requirements of drop-and-pull transportation of heavy loads and long mileage. This paper proposes a novel fuel-cell electric articulated vehicle featuring three power sources: fuel cell, power battery, and ultracapacitor. Then, based on the proposed vehicle, we expound on a highly efficient and flexible transport system. To compare economics and durability of fuel-cell electric trailers with two energy sources (i.e., fuel-cell-battery) and three energy sources, we developed and simulated a rule-based energy management strategy under driving conditions. The results indicate that, although similar levels of fuel economy and capacity degradation of the fuel cell occur for the proposed vehicle and its two-energy-source counterpart, the ampere–hour throughput of three-energy-source vehicles was 64% lower than that of two-energy-source vehicles, which indicates the introduction of the ultracapacitor in fuel-cell-battery electric articulated vehicles can offer significant protection to the power battery. This result shows that the three energy sources increase the service life of the energy system. [ABSTRACT FROM AUTHOR]

The powertrain model of the series-parallel plug-in hybrid electric vehicles (PHEVs) is more complicated, compared with series PHEVs and parallel PHEVs. Using the traditional dynamic programming (DP) algorithm or Pontryagin minimum principle (PMP) algorithm to solve the global-optimization-based energy management strategies of the series-parallel PHEVs is not ideal, as the solution time is too long or even impossible to solve. Chief engineers of hybrid system urgently require a handy tool to quickly solve global-optimization-based energy management strategies. Therefore, this paper proposed to use the Radau pseudospectral knotting method (RPKM) to solve the global-optimization-based energy management strategy of the series-parallel PHEVs to improve computational efficiency. Simulation results showed that compared with the DP algorithm, the global-optimization-based energy management strategy based on the RPKM improves the computational efficiency by 1806 times with a relative error of only 0.12%. On this basis, a bi-level nested component-sizing method combining the genetic algorithm and RPKM was developed. By applying the global-optimization-based energy management strategy based on RPKM to the actual development, the feasibility and superiority of RPKM applied to the global-optimization-based energy management strategy of the series-parallel PHEVs were further verified. [ABSTRACT FROM AUTHOR]