Research On Grouped Hybrid Power System Matching And Energy Management Strategy

With the rapid development of automotive industry,new energy vehicle technology has been listed as strategic emerging industries.Electric vehicle technology has been fully developed in the past decade.As a key part of the electric vehicle,the power battery currently has unsatisfactory performance in terms of specific energy,specific power and lifetime.In contrast,the introduction of ultracapacitors into energy storage systems which taks full advantages of power battery and ultracapacitors,is a viable solution.The paper aims at the structure,matching,energy management strategy formulation and optimization of the hybrid power system used in electric vehicles and conducts relative researches.(1)Based on existing experimental conditions,experiments are conducted on the key parts of the hybrid power system--Iron phosphate-lithium power battery,ultracapacitors and bidirectional DC/DC converte,in order to fully study the characteristics of its cycle,internal resistance and efficiency,and provide the basis for matching and strategy optimization in later chapters.(2)A hybrid power system component model is established and verified.The current hybrid power system structure is analyzed and a packetized hybrid power system structure is proposed.By analyzing the requirements of the hybrid power system,the parameter matching method is used based on dynamic programming.(3)Based on the established model,the dual-Kalman filter is used to estimate the battery health(SOH),and the Sage-Husa adaptive algorithm and fuzzy controller are used to suppress the state noise covariance and the observed noise covariance,to improve the accuracy of the strategy.(4)The basic energy management strategy of the hybrid power system is proposed,and the strategy is optimized according to the dynamic programming results.Finally,a packetized hybrid power system and the experimental platform are set up for experimental verification.(5)A grouped hybrid power system and experimental platform were set up,proposed strategy was verified through comparative experiments that the optimization strategy based on dynamic programming results can make full use of ultracapacitors and reduce system energy consumption.