Study On SOC Estimation Of Lithium Battery For Electric Vehicles

In recent years, with the global population increasing, the number of cars in theworld has a sharp rise, causing a growing demand for energy and the increasinglyserious of environmental pollution. In order to reduce the pressure of energy andenvironment, governments have strengthened the research and development ofelectric vehicles. As the power source for electric vehicles, battery has become a toppriority in the study of electric vehicles. The state of charge of the battery is the mostimportant performance parameters during the battery operating, accurate estimation ofstate of charge not only can increase the battery life but also can improve theendurance of electric vehicles, So it is very important for the development of electricvehicles. This paper focuses on the research of lithium iron phosphate battery SOCestimation.On the basis of analyzing the existing SOC estimation methods and in view ofthe research status of SOC estimation routes, this paper put forward an idea ofcombining ampere-hour integral method, open circuit voltage method and extendedkalman filtering method to estimate SOC. Paper had an analysis and research on thedefinition of SOC, frequently used estimation methods and its influence factors, andon the basis of that, static models of the affecting factors was established, completedthe correction of ampere-hour integral method. Based on the analysis of the hystereticcharacteristic curve, this paper proposed a correct open circuit voltage SOCestimation method which can estimate SOC under the condition of charge ordischarge respectively at any time, and the accuracy of the correction algorithm wasverified by the experiments. After comprehensive comparison of various batterymodel, this paper selected PNGV model, and the model parameters were identifiedthrough HPPC experiments, at last paper established the model of battery voltage inthe Matlab/Simulink to verify the accuracy of battery model.Based on the principle of kalman filter and extended kalman filter,this papercompleted recursive estimation of SOC by using extended kalman filtering algorithmon the basis of state space model, and achieved the goal which combined ampere-hourintegral method, open circuit voltage method and extended kalman filtering method toestimate SOC; Matlab simulation results show that this SOC estimation method hasgood accuracy.