Abstract
In recent years, due to the strengthening of our country’s comprehensive strength, the rapid development of science and technology and artificial intelligence has also attracted people’s attention. Artificial intelligence is a highly applicable subject, which has very good applications in power systems. In the experiment, the open circuit voltage method and the ampere-hour integration method are used to estimate the SOC of the lithium battery and the particle swarm energy management algorithm is used to allocate the output power of the fuel cell and the lithium battery. The particle swarm algorithm module calls the dual source hybrid power system module through the sim function to convert the actual value input in the system into a fuzzy quantity suitable for fuzzy control. The energy management strategy based on particle swarm optimization and fuzzy control was tested based on working conditions under the comprehensive test bench. Finally, the matching of the hybrid system is analyzed from the structure, component parameters, control strategy, and driving cycle of the vehicle. The experimental data show that the total fuel consumption of the three sets of experiments is averaged to get a fuel consumption rate of 26.3 m3/100 km for the hybrid city bus under the optimized energy management strategy. The results show that the real-time energy management strategy based on particle swarm algorithm can significantly improve the real-time performance of traditional instantaneous energy management strategies while reducing fuel consumption.
Highlights
Due to the increasingly serious problems of energy shortage and environmental pollution, modern urban transportation needs a new type of transportation with energy saving and low pollution
Bizon proposed a new energy management strategy for hybrid power systems based on proton exchange membrane fuel cell systems as backup energy sources to reduce hydrogen consumption
Optimization of Energy Management Strategies. e particle swarm optimization algorithm is written in .m file of MATLAB, and the dual source hybrid system model is built in MATLAB/Simulink environment. e particle swarm optimization (PSO) module calls the dual source hybrid system module through sim function and uses newfis, writefis, and other functions to create fuzzy controller and write parameters
Summary
Due to the increasingly serious problems of energy shortage and environmental pollution, modern urban transportation needs a new type of transportation with energy saving and low pollution. Bizon proposed a new energy management strategy for hybrid power systems based on proton exchange membrane fuel cell systems as backup energy sources to reduce hydrogen consumption He uses the load demand on the DC bus to follow the control loop and optimize the control loop to improve fuel economy based on the global extreme value search algorithm applied to the air flow rate. He optimized and adjusted the different adjustable parameters considered in order to suppress the frequency and power of the IHPS model due to changes in load demand through the quasialignment harmony search (QOHS) algorithm He conducted robust and nonlinear research on the configuration of the studied IHPS model based on the SF-FLC-PID controller. According to the identified road surface, the corresponding electric braking force distribution method is proposed, which makes the vehicle recover more braking energy on the high adhesion road and less on the low adhesion road on the premise of ensuring safety and improves the overall braking energy recovery rate of the vehicle
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