Reserch On SOC And Internal-surface Temperature Estimation Of Lithium-ion Battery For Electric Vehicles Considering The Influence Of Ambient Temperature

In rencent years,as global reserves of diesel,gasoline and other fossil fuels are rapidly decreasing,and the climat e problem is getting wors e,electric vehicles(EVs)have become more widely used in transportation and urban transportation.Because the high capacity and relatively large series-parallel number of lithium-ion batteries(LIBs)for EVs,and the lithium-ion battery must be operated in a safe and reliable operating area,A suitable temperature and voltage are required,so an efficient battery management system is needed.It is very important for the development of an efficient and practical battery management system(BMS)that can accurately estimate the battery terminal voltage,state of charge and internal and surface temperature.To that end,this paper has carried out the related research on the estimation methods of battery SOC and the internal and external temperature considering the influence of ambient temperature,which includes the following aspects.(1)Analyze the impact of ambient temperature on lithium batteriesFirst,we build up an improved li-ion battery cell dual polarization(DP)model whose internal parameters are assumed to be dependent ambient temperature.Next,the key parameters of this proposed DP model are identified by using forgetting factor least square(FFLS)approach.Finally,with this model,the influence of the ambient temperature on the terminal voltage and SOC estimation of the LIBs is analyzed.(2)An enhanced temperature-dependent model for a LIB cellFirst,the new battery model is elaborated,including a newly integrated resistance-capacitor structure,a static hysteresis voltage and a temperature compensation voltage term.The forgetting factor least square approach is utilized to realize the parameter identification.Next,the proposed battery model is employed to estimate battery SOC by incorporating the extended Kalman filter algorithm.Finally,the simulation results are provided to demonstrate the superior performances of the proposed battery model in comparison with the common first-order Thevenin temperature model;(3)Construction of an improved electro-thermal coupling model for lithium battery for internal and surface temperature predictionBased on the classic electro-thermal coupling model,an improved model is constructed by considering the difference in discharge capacity of LIBs under different ambient temperatures.Next,the forgetting factor least square approach is utilized to realize the parameter identification.Finally,the estimation of battery internal and surface temperature is achieved by means of EKF under different ambient temperatures.(4)Li-ion battery performance prediction software developmentAccording to the performance requirements of power LIBs,a composite software framework for LIBs performance prediction is designed based on single interface software technology.Based on the classic Thevenin and DP models and by using MATLAB GUI technology,a comprehensive performance prediction software is developed with including the four functional modules as:?OCV-SOC fitting,?Parameter identification,?Terminal voltage estimation,?battery SOC estimation with EKF algorithm.This designed LIBs comprehensive performance prediction software can be used to calculate the basic performances of various kinds of LIBs and to provide an effective verification method for the development and implementation of advanced BMS.