Research On Energy Management Strategy Of Fuel Cell City Bus Based On SA-PSO

As the environmental pollution crisis caused by traditional cars becomes more and more serious,people pay more and more attention to pure electric vehicles and renewable energy.Accordingly,proton exchange membrane fuel cell(PEMFC)has great potential in the development of new energy vehicles due to its high energy conversion rate(30%～60%),low operating noise and environmental friendliness,and high energy density.By introducing the power battery pack,the shortcomings of the fuel cell electric vehicle such as the inability to recover braking energy,slow starting speed and soft output characteristics can be made up.Dual power sources can make the fuel cell hybrid electric vehicle play a better power performance,but how to make the power distribution of the power source more reasonable and improve the economy is a research difficulty.Aiming at the limitation of traditional fuzzy control strategy in optimizing power distribution and improving fuel economy of fuel cell hybrid city bus,a fuzzy energy management strategy combined with condition identification is proposed.The influence of driving conditions on vehicle performance was analyzed and studied.Appropriate feature parameters were extracted and samples were divided for three typical working conditions.Least squares support vector machines(LSSVM)optimized based on simulated anneal-particle swarm optimization(SA-PSO)was used for condition identification.The corresponding fuzzy control strategy was developed under three typical operating conditions,and the influence of the instantaneous change of operating conditions on the performance and life of the fuel cell was studied and analyzed,so as to improve the control strategy.The membership function of fuzzy control was optimized by using sa-pso to minimize the hydrogen consumption of the vehicle,and the optimized fuzzy control strategy was obtained under various working conditions.The adaptive switching of fuzzy control strategy under different working conditions is realized through condition recognition.The simulation results show that the fuzzy control strategy has more efficient power distribution and better fuel economy.