The Estimation Of State Of Charge And State Of Power For Lithium-ion Batteries

With the intensification of environmental pollution and the shortage of fossil energy,the development of electric or hybrid vehicles has become an inevitable trend.Among them,battery as the only power source of pure electric vehicle,the development of battery-related technology determines the development of electric vehicle industry.In order to ensure the safe and reliable operation of the power battery of pure electric vehicle,it is necessary to control and manage the battery,so the battery management system(BMS)has been widely studied.BMS mainly contains the estimation of state of charge(SOC),the estimation of state of power(SOP),the estimation of state of health(SOH)and the equalization.Accurate SOC estimation extends battery life,increases single fully charged mileage,and prevents battery fire and explosion.Accurate SOP estimation helps optimize battery power distribution,improve the efficiency of battery packs,and prevent battery over-charging or over-discharge.Therefore,this paper takes the SOC estimation and SOP of lithium-ion battery as the research goal,and carries out the following research:(1)In this paper,the types of lithium-ion batteries and the basic principles of electrochemical reactions are introduced,and the experimental platform of lithium-ion battery testing is established.Based on the established experimental platform,a series of charging and discharging experiments are carried out,and the internal resistance,polarization effect,the relationship between open-circuit voltage and SOC of lithium-ion battery are fully studied.(2)Based on the research results of charging and discharging characteristics of lithium-ion batteries,a fractional second-order resistor capacitor(RC)model is established,which is defined by the GL for digital expression.In addition,the parameters of fractional order model at different temperatures are obtained by using the data of pulse discharge condition experiment at different temperature and genetic algorithm.Finally,the parameters of the identified fractional order model are verified,and the results show that the fractional order model has higher accuracy at different temperatures,which has more advantages in describing the dynamic characteristics of the battery,and lays a foundation for the accurate estimation of the SOC.(3)The disadvantages of fractional order extended Kalman filter and Extended Kalman filter are analyzed,which shows that the information of system noise and measurement noise are assumed to be known in the process of estimating SOC by using them.However,this information is not available in practice,so an adaptive algorithm is introduced.The variance of system noise and measurement noise are updated in each step of the recursive process by using historical data.Finally,the fractional order adaptive Extended Kalman filter is established to estimate the SOC of lithium-ion battery.Through two dynamic working conditions test,the algorithm can get closer to the real value faster in the case of inaccurate initial SOC,the average estimation error of SOC is less than 1.4%,and the maximum estimation error is less than 3.8%.(4)In this paper,the peak current and continuous peak current in the charging and discharging process of lithium-ion battery are determined by three conditions: open circuit voltage,SOC and current design limit.The continuous peak current is brought into the battery model to obtain the end voltage,and the estimation method of the continuous peak power of lithium battery based on multi-constraint condition is established.Combined with the SOC estimation method proposed in this paper,the SOC-SOP Joint estimation algorithm is finally established.Then,the estimation method is verified by two dynamic working conditions.In the discharge process,the average error estimated by SOP is less than 5W,the maximum error is within 20 W range.In the charging process,the average error estimated by SOP is less than 1.3W,and the maximum error is less than 16 W.