Research On The State Of Charge And Health Estimation Of Vehicle Power Lithium Battery

In recent years,with the excessive exploitation and consumption of energy,the world’s energy shortage and environmental pollution have become more and more serious.In order to cope with this problem,countries are actively carrying out research on pure electric vehicles.As the best core energy storage power source for pure electric vehicles,the development of key technologies is far from meeting the needs of pure electric vehicles,which seriously restricts the development of pure electric vehicles.Among them,two factors affect the energy management system of electric vehicles.There are two main indicators,one is the state of charge(SOC)of the power lithium battery,and the other is the state of health(SOH).Accurate estimation of SOC and SOH can improve the safety and The service life also makes electric vehicles get better performance.Therefore,this topic takes the vehicle power lithium battery as the research object,and conducts research on the estimation method of SOC and SOH of the lithium battery.First,the model and parameter identification of power lithium battery are established.After comparing the existing battery models,a second-order RC equivalent circuit model was selected as the lithium battery model,and then the model parameters were identified,including the relationship between open circuit voltage and SOC,internal resistance of the battery,and RC network parameters.According to the comparison of experimental data,the validity of the model is verified.Secondly,the SOC estimation design and simulation verification based on the equivalent circuit model.In order to reduce the error of the power battery model,improve the anti-interference ability of the SOC estimation algorithm and the accuracy of the SOC estimation,a H∞ filtering algorithm is added on the basis of the sliding mode observer,and an SOC estimation algorithm based on the improved sliding mode observer method is designed.The SOC estimation error covariance matrix is updated in real time,and then the observation gain matrix is adjusted to further improve the estimation accuracy.Then,the SOH estimation design and simulation verification based on the capacity decline model of lithium battery are performed.In order to solve the problem that the estimation accuracy is reduced due to the uncertainty of the model and improve the calculation speed of the estimation algorithm,based on the particle filter algorithm,the particle weights are decomposed using wavelet transform ideas,and then duplicates and irrelevant particles are removed.This method can ensure the estimation Accuracy while increasing the speed of the estimation algorithm.Finally,the SOC and SOH joint estimation design and simulation verification based on the Extended Kalman Filter(EKF)are performed.Because the single estimation ignores the relationship between SOC and SOH,the problem of joint online estimation of SOC and SOH for lithium batteries results in inaccurate SOC values that will affect the estimation of SOH.Therefore,this paper proposes a SOC based on joint estimation And SOH estimation method,this method is not affected by the initial value of SOC,thereby improving the estimation accuracy.