Multi-Time Scale Lithium Battery Parameter Identification And SOC And SOH Estimation

In today’s rapid rise of new energy and new energy vehicles,new energy vehicles are gradually becoming the first choice for people’s daily travel means of transportation.Lithium batteries are widely used in electric vehicle power batteries because of their advantages such as high energy density,long cycle life and environmental protection.The state of charge(SOC)and state of health(SOH)of a battery are important indicators of battery range and life.The analysis of its real-time accurate estimation is an important part of lithium battery charge and discharge management and the optimal management of battery systems for electric vehicles.To solve the problem of poor accuracy of battery SOC estimation due to capacity decay variation and the problem of time-varying model parameters and inconsistent time scales of relevant state and covariance changes.In this paper,we build a lithium battery test platform,take 18650 ternary lithium battery as the research object,and select the equivalent circuit model with second-order RC circuit by comparing and analyzing the current common equivalent circuit models.The accuracy and precision of the established battery model are verified by two working conditions,FUDS and DST.Firstly,the extended Kalman filter(EKF)algorithm and the traceless Kalman filter(UKF)algorithm are used to achieve the estimation of the SOC of the lithium battery.The experimental results show that the UKF algorithm has higher SOC estimation accuracy and convergence compared with the EKF algorithm.Based on this,the joint multi-timescale double extended Kalman filter algorithm(DEKF)and the joint multi-timescale UKF-EKF algorithm are proposed.The proposed multi-timescale algorithm is used to estimate the lithium battery parameters and capacity identification with slow timescale,and SOC with fast timescale.The online parameter identification results of the joint multi-timescale algorithm at the slow timescale are verified to be consistent with the discharge characteristics of the Li-ion battery by the DST condition and the FUDS condition.The state of health(SOH)of the battery is expressed from the available capacity,and the fast time-scale SOC and slow time-scale SOH are estimated for Li-ion batteries.The joint multi-timescale UKF-EKF algorithm has better SOC and SOH estimation accuracy and better robustness,and the estimated capacity closely follows the real capacity,as verified by the FUDS and DST working conditions.This study mainly solves the problem of poor accuracy of battery SOC estimation due to capacity decay variation and inconsistent time scale of model parameters and related state change parameters by the joint multi-timescale algorithm.By dividing the fast and slow time scales of the model parameters and the battery state,the computational power of the system is reduced.The online identification of battery model parameters and the joint estimation of SOC and SOH with dual time scales are realized,which provides a more accurate method for estimating the SOC of lithium batteries in electric vehicle battery management systems(BMS).