Online Estimation Of State-of-Charge,State-of-Health And State-of-Power Of Lithum-Ion Batteries

With the rapid increase of automobiles in the world,the traditional fuel cars,because of its emission pollution and dependence on fossil fuels which cause serious environmental pollution and energy crisis,are becoming hard to adapt to the development needs of the world and the new energy vehicles are the trend of the global automobile industry.The new energy vehicles mainly includes electric vehicles(EVs),hybrid electric vehicles(HEVs),and fuel cell electric vehicles(FVEVs).In the storage components of EVs and HEVs,the battery management systems(BMS)are the core components.However,the accurate and rapid estimation of state of charge(SOC),state of health(SOH)and state of power(SOP)is the basis of the efficient management of BMSs.Firstly,the research background and significance of new energy vehicles,battery management system and the estimation of battery states,including SOC,SOH and SOP,are introduced,and the research status of battery modeling,battery parameters identification,SOC estimation,SOH estimation and SOP estimation is summarized.Secondly,based on the first-order resistance-capacity(RC)equivalent circuit model,two state-space battery models are built;then using the dual extended Kalman filtering(DEKF),the battery states and parameters are estimated and identified,thus realizing the estimation of SOC and SOH.Thirdly,the methods of peak power/SOP estimation are divided into two categories including the method based on peak current and the method based on peak state,and the two categories are introduced respectively.The fact that the battery power can reach the maximum/minimum only in peak state is proved,and an improved rapid-calculating peak power/SOP estimation method with current,voltage,SOC and designed power multiple constraints is proposed.Fourthly,three simplified versions of the Federal Urban Driving Schedule(SFUDS)with inserted pulse experiments are conducted to verify the effectiveness and accuracy of the battery model,and the proposed online SOC,OCV and SOH estimation method.Finally,the accuracy of the six methods in the two categories for peak power/SOP is compared respectively under three different prediction time and in seven different areas,thus verifying the accuracy of the proposed peak power/SOP estimation method.The calculating efficiency of the six methods under three different prediction time is compared,thus verifying the effectiveness of the proposed method in the paper.In addition,in the battery operating process,the actual constraint that the battery is under is analyzed specifically.