SOC estimation method and system of lithium battery system

Abstract

The invention provides an SOC estimation method and system for a lithium battery system, and the method comprises the steps: dividing a standard charging and discharging curve into a plurality of standard curve sections according to the time point voltage difference between every two time points at a preset time interval of each single battery, obtaining the charge-discharge capacity and the charge-discharge calibration formula of each standard curve partition; combining each monomer voltage curve with a standard curve partition and a charging and discharging calibration formula correspondingto the standard curve partition to calculate a monomer SOH estimated value; obtaining a system SOH estimated value of the lithium battery system according to the single SOH estimated value of each single battery; performing integral processing on the charging and discharging current and the charging and discharging time of the lithium battery system to obtain a charging and discharging capacity estimated value; acquiring a system SOC estimated value according to the system SOH estimated value and the charging and discharging capacity estimated value; and performing calibration operation on theSOC estimated value of the system in combination with the corresponding system working condition. The method has the beneficial effect that the SOC is accurately estimated by accurately estimating the SOH.

Description

Technical field[0001]The present invention relates to the technical field of battery management, and in particular to a method and system for estimating SOC of a lithium battery system.Background technique[0002]The current SOC estimation algorithm of battery system usually adopts open circuit voltage method, ampere-hour integration method, neural network method, battery equivalent circuit model method and Kalman filter method; at the same time, the current battery control system of lithium ion battery system uses battery provide.[0003]However, the current neural network method, battery equivalent circuit model method, and Kalman filter method all require a large amount of data to extract models. Therefore, the current battery system SOC estimation mainly uses the open circuit voltage method and the ampere-hour integration method. At the same time, the current control system of the lithium-ion battery system uses electricity as the battery side.[0004]In the prior art, the estimation ...

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Problems solved by technology

[0004] In the prior art, the estimated SOH is mainly estimated by the method of linear relationship between cycle and capacity decay, but the life decay of lithium-ion batteries is not positively correlated with cycle, so the error of SOH continues to increase with the increase of the number of cycles; due to lithium iron phosphate When the battery is in the 10%-90% SOC state, the platform is relatively stable, and the relationship between SOC and voltage has no obvious relationship. Using the open circuit voltage method will cause the SOC state display to drop rapidly from 20% SOC to 5% SOC, and at 10%-90% The SOC state estimation error is very large; the SOC state is estimated by using the ampere-hour integral method, and SOH is cited as the denominator. Due to the large error in SOH estimation by the current method, the SOC estimation error is large; and the system SOC estimation method in the prior art Cannot be compatible with different working conditions and different types of batteries

The battery system powering the battery control system will also affect the accuracy of the system SOC estimation

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