Research On State Of Health Estimation Of Electric Vehicle Power Battery Based On Improved Unscented Kalman Algorithm

At present,the rapid increase of traditional car ownership aggravates the environmental pollution and resource shortage.Electric vehicles are driven by lithium batteries,and there is no exhaust pollution,which is in line with the era background of energy saving and emission reduction,and has been vigorously promoted.Battery management system on electric vehicles can intelligently manage and maintain battery cells,and realize on-line monitoring and status estimation of power batteries.Although the power lithium battery has a long life,it has aging problems such as capacity attenuation after repeated use,which seriously affects the working performance of the battery.State of Health can quantify the degree of battery aging.As the core function of BMS,it is of great significance for battery aging warning and improving driving safety.At present,most of the methods to estimate the SOH of battery are used in laboratory condition,and the SOH is deduced by recording the number of charge discharge cycles of battery,which is not suitable for the actual working conditions.In daily life,people test the current capacity of the battery through the complete discharge method,but it is timeconsuming and difficult to meet the requirements of real-time monitoring of electric vehicles.Therefore,in this paper,by building the battery model,the improved unscented Kalman filter is used to estimate the SOH of the battery,which can be combined with the actual conditions to achieve online detection.Firstly,according to the dynamic characteristics of the battery and the characteristics of various battery models,the second-order equivalent circuit model is selected to simulate the working state of the battery.Through the pulse test of the battery at different temperatures,the temperature factor is introduced to identify the size of the component parameters in the model,and the dynamic battery model is established.The accuracy of the model is verified by different working conditions.The results show that the battery model can meet the requirements of battery state estimation.Secondly,when unscented Kalman filter is used to estimate the battery state,the convergence speed of the estimation results is slow,and it is easy to be interfered by noise,which affects the accuracy of the results.The improved unscented transformation algorithm and adaptive filtering algorithm are integrated into UKF algorithm,and the unscented transformation and noise error are optimized respectively to form IUKF algorithm,which is conducive to improving the convergence speed and algorithm accuracy.By establishing three algorithm filters,the state of charge,ohmic internal resistance and capacity of the battery are estimated as state variables,which can update the model parameters in real time and improve the estimation accuracy of SOH.Finally,simulation in Matlab environment to verify the convergence of the algorithm and the estimation accuracy in complex conditions.Simulation results show that,compared with UKF algorithm,IUKF algorithm has better convergence speed and higher estimation accuracy,which can be maintained at about 2%.In order to verify the performance of the algorithm in practical application,a battery test platform is built to test the cycle charge and discharge of the battery.The results show that IUKF algorithm can maintain high accuracy and stability in the actual conditions,and has a certain practical value.