State Estimation Of Lithium Batteries Based On Second-order RC Equivalent Model

Building a battery management system(BMS)that can accurately monitor the real-time status of the power battery is extremely important to ensure the safe operation of new energy vehicles and improve their endurance.The state of charge(SOC)and peak power(SOP)of the battery are the main state parameters of the BMS system.Based on the second-order RC equivalent circuit model,this paper takes the single ternary lithium battery as the experimental object,and aims to improve the accuracy,convergence speed and robustness of lithium battery state estimation.It mainly includes the following contents:1)In this study,the characteristics of lithium battery are analyzed through constant current pulse discharge experiment,and the dynamic data of the two driving conditions(US06)and the Federal Urban Driving Schedule(FUDS)are obtained through the cyclic dynamic condition discharge experiment;By analyzing the characteristics of lithium battery and the characteristics of various battery models,taking into account the calculation amount and accuracy,the second-order RC equivalent model is established,and the recursive least squares(RLS)method is used to complete the off-line parameter identification of the model,and the accuracy and applicability of the model are verified in the constant current pulse discharge experiment and two dynamic conditions respectively.2)In view of the divergence of results when Kalman Filter(KF)is applied to the state estimation of nonlinear systems such as lithium batteries,this study uses Particle Filter(PF)to estimate the state of lithium batteries;In order to ensure the accuracy of the lithium battery SOC estimation and improve the convergence speed of the estimation algorithm,this study improved the UPF algorithm based on Schmidt orthogonal transformation and established the unscented particle filter(SOUPF)algorithm based on Schmidt orthogonal transformation,aiming at solving the problem of particle degradation in traditional PF algorithm and the problem of excessive computational complexity and slow convergence in traditional unscented particle filter(UPF)algorithm;Aiming at the time-varying characteristics of lithium battery parameters,the extended Kalman filter(EKF)is introduced to identify the parameters and capacity of the battery model online and estimate the SOH value of the lithium battery based on its capacity,the SOUPF-EKF algorithm is established to estimate the SOC of lithium battery in real time,and the convergence effect of PF,UPF,SOUPF and SOUPF-EKF algorithm is compared and verified in two dynamic conditions.The experimental results show that the average error of SOUPF for SOC estimation is not more than 0.6%,the convergence speed and estimation accuracy are better than PF and UPF algorithms,and the average error of SOUPF-EKF algorithm for SOC estimation is kept within 0.3%,which can be applied to various conditions and has good accuracy and robustness.3)In view of the inaccuracy of the SOP estimation results of lithium battery under single constraint,this study uses the multi-constraint method to estimate the peak power of single lithium battery under different durations based on the SOC value obtained by the SOUPF-EKF algorithm,and realizes the joint estimation of SOC and SOP,and carries out verification analysis in the FUDS dynamic condition.The experimental results show that the estimation accuracy of continuous peak charge and discharge power by this method is high.The maximum error of SOP estimation during discharge is only 3.9074 W,and the maximum error during charging is 12.6437 W.Figure[43] Table[10] Reference[83]...