The Research About Methods To Forecast Lithium Battery’ SOC

The forecast for the battery’s SOC (state of charge) is the most important and basic parts in thelithium battery management system, and correct forecast can provide reliable operating information for theequipments powered by lithium cell, and also be helpful for the battery management system to manage thecharging-discharging and energy equilibrium precisely and efficiently, which makes the forecastingresearch of SOC quite important.We studied the lithium iron phosphate battery, and did further research on its SOC’s forecastingmethod basing on the Kalman Filter algorithm. By analyzing lithium iron phosphate battery’s workingprinciple, we know its charge-discharge features. And on that basis, the influences on the SOC’s forecastcaused by aging degree, charge-discharge rate, coulomb efficiency and temperature were offered.According to the battery’s working condition current, voltage and temperature, data acquisition hardwarecircuit was established and perfected, which provide reliable real date for SOC’s forecast. Contraposing thelithium iron phosphate battery’s character, the first order RC equivalent circuit model was established, andsolving methods for model parameter was also provided. By analyzing and comparing some existingprediction methods to estimate, basing on equivalent model, one more valid method for SOC estimationwas presented, which has thoroughly considered coulomb efficiency’s impact on SOC, based on theimproved ampere-hour integral method, established equivalent battery model and combined the opencircuit voltage method, the forecast of lithium iron phosphate battery’s SOC can be predicted effectively byusing Expanded Kalman Filter.In order to verify the validity and advantages of this method, we finally present a trial whichmakes lithium iron phosphate battery as experimental objects for actual charge-discharge test. Threedifferent circumstances will be given in the experiment: the normal battery charge and discharge, join noiseinto the current which is to be measured, the third one is under the condition that there is a large error in theSOC’s initial value. Through the experiment we can conclude that this method can effectively improve thecorrect rate about the forecast of SOC, and not only can restrain the noise in the current but also has a very good correction function to the initial state of SOC.