Study On Energy Management Strategy Of Hybrid Energy Storage System For Electric Vehicles

Electric vehicle(EV)is one of the main development directions of automobile industry in the future,but the battery as its power source encounters the problems of comparatively high energy density yet low power density,low cycle life,which limits the popularity of EVs.As a novel type of energy storage system,supercapacitor(SC)has the advantages of high power density and fast charging/discharging performance,which is suitable service as auxiliary energy.Therefore,the combination of SC and battery can be used in EVs to meet the dual demand of energy and power of on-board power supply system.This paper takes the hybrid energy storage system(HESS)of EVs as the research object,and carries out research about parameter matching,energy management strategy(EMS)formulation and optimization:Firstly,the working characteristics of battery,SC and bi-directional DC/DC converter in the HESS are analyzed.On this basis,the topology of the HESS is confirmed,and the HESS simulation model is established based on the ADVISOR software.From the perspective of satisfying the energy and power demands of EV under various typical driving cycles,the parameter range of the HESS is preliminarily determined,and the optimization goal is to minimize the total cost and quality of the HESS.Based on the linear weighting method,the multi-objective optimization function is solved and the optimal parameter configuration is obtained.Secondly,In order to solve the problem of poor adaptability of EMS under different driving patterns,an adaptive wavelet transform-fuzzy control EMS based on driving pattern recognition(DPR)is proposed.Firstly,K-means clustering algorithm was used to classify driving patterns into 3 categories,and pattern recognition was used to identify the types of driving patterns in real-time.Then,considering the reception of transient power of battery and the difference of working characteristic between battery and SC,an adaptive wavelet transform is adopted to decompose the power demand with different levels based on the DPR results,the low frequency and high frequency components of demand power are allocated to battery and SC respectively.The fuzzy logic control is introduced to maintain the soc of SC within appropriate range,which aims to take fully advantage of the SC.The results show that compared with the traditional EMS,the adaptive wavelet transform-fuzzy control based on the DPR can effectively improve the efficiency of the HESS,and reduce the output current,voltage fluctuation and battery temperature rise of the battery significantly,and improve the battery cycle life by about 9.22%.Finally,aiming at the problem that driving speed and road slope will affect the power demand of the vehicle,a EMS optimization based on traffic information fusion is proposed.This paper analyzes the influence of driving speed and road slope on EMS,and designs a method to estimate the speed change trend in short time based on the combination of traffic congestion information and driving speed type.A fuzzy controller which can automatically correct the output power of the SC based on the traffic information is designed with the combination of the road slope and speed change trend.On the premise that traffic information can be obtained,the control strategy is optimized with the EMS given in this paper service as the priority and the fuzzy controller which can automatically correct the output power of the supercapacitor as auxiliary.The results show that the EMS based on traffic information fusion optimization can further exploit the advantage of SC and improve the battery cycle life by about 2.65% compared with that before optimization.