Considering the frequent acceleration and deceleration of bus vehicles, the working conditions are complex, efficiency-oriented power-split hybrid electric bus (PSHEB) typically require frequent shifting to stay in hi...
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Considering the frequent acceleration and deceleration of bus vehicles, the working conditions are complex, efficiency-oriented power-split hybrid electric bus (PSHEB) typically require frequent shifting to stay in high-efficiency areas, driving comfort and fuel economy may be affected. Therefore, to achieve a good balance be-tween overall efficiency and shifting stability, the study proposes a Real time Multi-objective optimization Guided-MPC strategy (RMGMPC) for PSHEB based on velocity prediction. Firstly, considering the different driving habits of drivers, combining with multi-source data fusion technology, a vehicle speed prediction controller is established;secondly, based on global optimization algorithm and multi-source data fusion tech-nology, a SOC reference generator is designed, which will determine the SOC guidance at predicted vehicle speed time domain online;then, to coordinate fuel efficiency, shifting stability and online optimization control real-time, the novel RMGMPC based on the direct multiple shooting method and sequential quadratic program-ming algorithm for PSHEB is proposed;finally, to avoid experience value of uncertain weight coefficient affecting the MPC, a weighted method of objective function with orientation is proposed. To verify the effectiveness of RMGMPC, the fuel economy reaches 98.41% of the global optimum;the shifting times are improved by 12.5%;Compared with MPC-DP, the calculation time is improved by 93.97%;And HIL test was carried out to further verify the real-time performance of the algorithm. The results manifest the excellent performance of the proposed RMGMPC.
For networks with fixed network topology, when the total coupling strength between nodes is limited and the coupling strength between nodes is saturated, the global optimization algorithms including genetic algorithm ...
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For networks with fixed network topology, when the total coupling strength between nodes is limited and the coupling strength between nodes is saturated, the global optimization algorithms including genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are used to adjust the coupling strength between nodes to improve the synchronizability of the network, respectively. Simulation results show that in WS small-world network, when the edge betweenness centrality of the edge is large, the coupling strength of the edge after optimization is greater. Furthermore, compared with GA, PSO has better performance.
Because of the fragility and vulnerability of the satellite navigation system, it is unable to provide continuous and reliable positioning navigation for UAVs in complex regions such as indoor and canyons. This paper ...
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ISBN:
(纸本)9781450372640
Because of the fragility and vulnerability of the satellite navigation system, it is unable to provide continuous and reliable positioning navigation for UAVs in complex regions such as indoor and canyons. This paper presents a combined navigation method based on visual optical flow and inertial navigation. This method uses ORB to realize the factor extraction of images, and improve Lucas-Kanade method by using the whole optimization method. Combining optical flow and inertial navigation based on extending the Kalman filter. The result of simulation experience shows that the evaluated error of the arithmetic presented in this paper is 0.08m/s, which can satisfice the Indoor integrated navigation of unmanned aerial vehicle (UAV).
In order to acquire more accurate crop yield information,the global optimization algorithm SCE-UA was used to integrate leaf area index derived from remote sensing with crop growth model EPIC to simulate regional summ...
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In order to acquire more accurate crop yield information,the global optimization algorithm SCE-UA was used to integrate leaf area index derived from remote sensing with crop growth model EPIC to simulate regional summer maize yield and field management information in Huanghuaihai Plain in China. The results showed that the mean relative error of estimated summer maize yield was 4.37% and RMSE was 0.44t/ha. Compared with the actual field observation data,the mean relative error of simulated sowing date,plant density and net nitrogen fertilization application rate was 1.85%,-7.78% and -10.60% respectively. These above simulated results could meet need of accuracy of crop growth simulation and yield estimation at regional scale. It was proved that integrating remotely sensed LAI with EPIC model based on SCE-UA for simulating regional summer maize yield and field management information was feasible and reliable.
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