Research On State Estimation Of Li-ion Battery Based On Improved Kalman Algorithm

The battery management system is the key to ensuring the safe and stable operation of lithium batteries.Therefore,in order to better optimize the battery management system,ensure the safety and reliability of battery use,and determine the charging and discharging power according to the needs of electric vehicles,and extend the power battery as much as possible.The service life target is very necessary.One of the difficulties of the battery management system is the estimation of internal state variables,including the estimation of the state of charge(SOC),the state of health(SOH)and the state of power(SOP).The accurate estimation of the state estimation value will be beneficial to the safety management,decision-making and control of the system.However,these variables cannot be directly measured by experimental equipment,and the power battery has strong nonlinearity,and is easily affected by other factors such as temperature and operating conditions.It is difficult to estimate accurately,which restricts the development of battery management systems.To this end,this paper takes the SOC,SOH and SOP of lithium batteries as the research objects,and mainly completes the following work:(1)Introduce the research background and significance of the subject,then introduce the definitions of SOC,SOH and SOP,and focus on analyzing the current estimation strategies of SOC,SOH and SOP.(2)First,the working principle of lithium battery is introduced;based on the charging and discharging characteristics of lithium battery,a second-order RC equivalent circuit model suitable for state estimation is established,and the voltage characteristics of lithium battery are obtained by static method;The forgetting factor recursive least squares method realizes the online identification of lithium battery model parameters,and it is verified that the second-order RC equivalent circuit model has good accuracy under UDDS conditions,and can be used as the basic model for subsequent internal state estimation.(3)Firstly,the extended Kalman filter is introduced and analyzed,and it is pointed out that the estimation error caused by the linearization will cause the filter to diverge.Therefore,in order to reduce the error and improve the estimation accuracy of SOC,especially in the low SOC range,this paper adds the ampere-hour integration method as an auxiliary algorithm of the extended Kalman filter algorithm,and the two algorithms are fused by weighting.After the simulation verification of the dynamic working condition experiment,it is proved that the SOC estimated by the weighting algorithm is closer to the real value.Even when the SOC is low,the maximum absolute error is only 1.85%,and the average absolute error and root mean square error are both less than 0.5%.The error precision is small.(4)In order to realize the joint estimation of SOC/SOH,the unscented Kalman filter is firstly proposed.Then an adaptive algorithm is introduced,and finally a multi-innovation adaptive unscented Kalman filter algorithm is established to estimate the SOC of lithium batteries.Then,the SOH of the lithium battery is estimated by adding the extended Kalman filter,so that the SOC and the SOH are jointly estimated.The joint algorithm has been verified under working conditions,the SOC errors are all within 0.01,the error of the maximum available capacity is always less than 0.15 Ah,and the error of SOH is always less than 0.5%.(5)In order to achieve accurate estimation of SOP,a peak power estimation method under multiple constraints based on SOC,terminal voltage and current limit of lithium battery itself is proposed.Finally,the estimation method is verified under UDDS conditions: in the process of charging and discharging,the maximum error under different durations(30s,2min and 5min)does not exceed 10 W.It shows that the established estimation method can accurately predict the SOP of lithium battery.