Research on State of Charge Estimation of Lithium Iron Phosphate Batteries in Electrochemical Energy Storage Systems
The conventional observation equation for estimating the state of charge of lithium iron phosphate batteries is set as a problem point measurement,and its estimation range is limited,resulting in a large steady-state error in the final state of charge estimation.Therefore,a study on estimating the state of charge of lithium iron phosphate batteries in electrochemical energy storage systems is proposed.Based on the current demand for state of charge estimation,first calculate the compensation coefficient for SOC estimation,and then use a multi-step approach to break the limitation of estimation range and establish a multi-step observation equation.Based on this,a deep neural network model for estimating the state of charge of lithium iron phosphate batteries is designed,and OCV verification correction is used to achieve state estimation.After four testing cycles of analysis,under three discharge time backgrounds of 0.1 s,0.3 s,and 0.5 s,the steady-state error of the estimated state of charge of lithium iron phosphate batteries was controlled below 0.4,and the generalization ability was significantly improved,indicating that this method is more efficient,specific,and practical.
electrochemistryenergy storage systemslithium iron phosphate batteriesbattery chargeSOC
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