Estimation Of State-of-Charge For Lithium-Ion Batteries Based On Kalman Filter Algorithm

With the process of global energy structure transformation,electric vehicles,and energy storage devices using lithium-ion batteries play an important role in the new energy structure due to the advantages of energy conservation and environmental protection,and have received considerable attention.Lithium-ion batteries are widely used in various energy storage devices due to the advantages of high energy density,long life,and excellent safety performance.Accurate knowledge of the states of lithium-ion batteries,such as State-ofCharge(SOC)and State-of-Health(SOH),is important for the safe operation,rational utilization,and maximization performance of the batteries.Inaccurate battery state estimation may affect the decision of the battery management system,causing overcharge and overdischarge of the battery,and even causing accidents such as fire and explosion,endangering personal and equipment safety.In this dissertation,the causes of the degradation of battery state estimation accuracy are investigated from the aspects of battery modeling and parameter identification.Aiming at the key technical problems of high-temperature and low-temperature adaptability of lithium-ion battery state estimation,the cylindrical lithiumion batteries are selected for research,a SOC estimation method considering the temperature effect and a co-estimation method of temperature and SOC are proposed,and the effectiveness of the proposed methods are verified with experimental results.First,aiming at the problem that the estimation accuracy of the battery equivalent circuit model decreases when the temperature changes,a temperature-adaptive dual-polarization equivalent circuit model is constructed.The model parameters are adaptive and can be updated to obtain accurate parameters when the battery temperature changes.Aiming at the problem that the accuracy of model parameter identification is decreased due to the influence of temperature,a parameter identification method by combining radial basis neural network and forgetting factor recursive least squares method is proposed,which considers the characteristics of lithium-ion batteries at different temperatures and different SOCs.Furthermore,the identified battery parameters are constructed as three-dimensional look-up tables for temperature and SOC,providing data support for updating the model parameters when the temperature changes.Secondly,aiming at the problem that the extended Kalman filter(EKF)algorithm is not efficient for SOC estimation when the temperature changes,an adaptive dual extended Kalman filter(DEKF)algorithm based on the adaptive dual-polarization equivalent circuit model is proposed.The DEKF contains two EKFs for model parameter estimation and SOC estimation respectively,and the EKFs can interact with each other to update the algorithm parameters with temperature changes during the estimation process,which ensures estimation accuracy even when the temperature changes.The effectiveness of the proposed algorithm is verified by comparative experiments at low,normal,and high temperature operating conditions.Finally,aiming at the problem that the estimation precision of lithium-ion battery SOC is reduced due to the neglect of battery heat generation,an electro-thermal coupled model is constructed,and a co-estimation method of temperature and SOC is proposed.The EKFs for battery heat production estimation and SOC estimation are designed based on the thermal submodel and the electrical submodel,respectively,and the two EKFs are coupled into a DEKF algorithm for co-estimation.To improve the estimation precision of temperature and SOC during the estimation process,the DEKF algorithm updates the algorithm parameters according to temperature and SOC using the information interaction between the temperature estimator and SOC estimator.The results show that the co-estimation method is effective in estimating the battery temperature and SOC accurately using the information interaction property of the electro-thermal coupled model.