The optimization of energy control strategy is one of the key technologies of plug-in hybrid vehicles (PHEVs) to improve the capabilities of energy saving and emission reduction. In order to improve fuel economy of PHEV, adaptive equivalent minimum fuel consumption strategy (A-ECMS) is proposed. Firstly, optimization methods of different energy control strategies are analyzed, and the Pontryagin’s Minimum Principle (PMP) and the equivalent fuel consumption theory are selected to optimize energy control strategy of the PHEV. Secondly, the configuration of PHEV and research objectives of the power control system are determined. Thirdly, the energy control problem is analyzed by the PMP theory, and the improvement measures for the energy control problem are put forward by the equivalent minimum fuel consumption strategy (ECMS). Fourthly, after analyzing the relationship between the equivalent factor and reference SOC, adaptive equivalent minimum fuel consumption strategy (A-ECMS) model is established by MATLAB/Simulink. Finally, combined with Cruise software, the PHEV simulation model is simulated, and the simulation results are analyzed. The results show that compared with the CD/CS energy control strategy, the A-ECMS energy control strategy reduced the 100 km fuel consumption of the vehicle by 7% under three times WLTC driving condition.
In this Letter, we report on pattern engineering in superconducting microstrips to achieve a fast and efficient detection of a single-photon over a large detection area. The proposed detector is composed of hole-patterned superconductor microstrips fabricated with a 5-nm-thick amorphous molybdenum silicide film. It exhibits a saturated internal detection efficiency at 1550 nm with a negligible dark count rate and a recovery time of 9 ns with a large detection area (50 × 50 μm2). The simulation reveals that the holes patterned in the superconductor microstrip stimulate the nucleation of the vortex, which constitutes a major key to achieving the efficient detection of photons. This work paves the way for the facile and prolonged regulation of vortex nucleation in superconductors, which shows promise for developing high-performance large-area superconductor single-photon detectors.
Under the dual−carbon goal, the research on energy conservation and emission reduction of new energy vehicles has once again become a current hotspot, and plug−in hybrid electric vehicles (PHEVs) are the first to bear the brunt. In order to improve the fuel economy of PHEV, an adaptive energy management strategy is designed on the basis of the intelligent prediction of driving cycles. Firstly, according to the vehicle dynamics model, the optimal control objective function of PHEV is established, and the relationship between vehicle fuel consumption and driving cycle is analyzed. Secondly, the initial weights and threshold of the backpropagation (BP) neural network are optimized using the particle swarm optimization (PSO) algorithm, and a PSO−BP neural network vehicle velocity prediction controller is established. Thirdly, combined with the approximate equivalent consumption minimization strategy (ECMS) algorithm to calculate the optimal initial equivalent factor in the prediction time domain, the fast−planning SOC and PI control are introduced to determine the optimal equivalent factor sequence, and the optimal torque distribution ratio of the engine and motor is calculated. Lastly, three different energy management strategies are simulated and verified under six China light−duty vehicle test cycle−passenger car (6*CLTC−P) driving cycles. Simulation results show that the established velocity prediction model has good prediction accuracy, and the proposed adaptive energy management strategy based on prediction is 9.85% higher than the rule−based strategy in terms of fuel saving rate and 5.30% higher than the ECMS strategy without prediction, which further improves the fuel saving potential of PHEV.
Olaparib (Lynparza) is a potent, highly selective inhibitor of poly(ADP-ribose)polymerase enzymes, approved by the U.S. FDA and EMA for the treatment of ovarian cancer. Herein, we report a practical, economical, and scalable process for the synthesis of 2-fluoro-5-((4-oxo-3,4-dihydrophthalazin-1-yl)methyl)benzoic acid, a key intermediate for olaparib. The lowcost industrial byproduct phthalhydrazide was used as the starting material to construct the phthalazinone moiety, which allowed access to the key intermediate by the Negishi coupling reaction. Optimization of each step has enabled the development of an environmentally benign and robust process with effective control of impurities.
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