Estimation Of SOC And Its Application In New Energy Vehicle Battery Management System Application

Along with the growing energy crisis and environmental pollution problems inan emergency, at the same time people’s living standard is improving, more andmore families choose to purchase car, potentially promoting the development of theglobal automotive industry. Pure electric vehicles get more and more attention andbattery management system comes out consequently. Battery managementtechnology provide optimized use of battery (group) through real-time detection ofbattery state.It can make sure the safety and long life of use; to improve the efficiency ofvehicle operation, driving comfort, battery capacity and energy use efficiency.This paper comes from the Ministry of National Electronic IndustryDevelopment Fund, with support of the project, the research applies to lithium ironphosphate battery management system and the SOC estimation method.This paper first introduces the research background to clarify the significanceof the topic and research of this topic. At the same time, recalled the history of thedevelopment of electric vehicles and status quo of battery power. And analyze thecurrent research status of the battery management system home and abroad,pointing out the importance of estimation of remaining battery charge in theelectric vehicle battery management system. Review the main contents of thisarticle, including the function of the battery management system, analysis of thecharacteristics of lithium iron phosphate, as well as the research of SOCestimation method.We focus on lithium iron phosphate battery’s voltage,temperature, efficiencyand aging characteristics in chapter2and3. Design SOC estimation method andmake the optimization method to better estimate the remaining capacity of thebattery pack according to its characteristics. Iron phosphate lithium battery chargeand discharge voltage characteristics showing a plateau in chapter4.We adopteunscented Kalman filter algorithm by comparing the extended Kalman filteralgorithm and the unscented Kalman filter algorithm finally. Deal with the SOCof battery and make model for it at this time. We analyze the algorithm ofhardware and software platforms in chapter5. Based on iron phosphate lithiumbattery’s unique characteristics and distinguishing feature, we obtain the estimation error and estimation accuracy of SOC estimation method,Ah methodand Kalman filtering method by experiment in this article. The experimental resultsdemonstrate the feasibility, effectiveness and accuracy of this method. In thelast,we sum up the full text.