A method for estimating the charging capacity of lithium-ion batteries using extended Kalman filter

Abstract

The invention discloses a method for estimating the charging quantity of a lithium-ion battery by using an extended Kalman filter, comprising the following steps: initializing, inputting the factory information of the lithium-ion battery; measuring the internal resistance of the lithium-ion battery, and calculating the internal resistance value as an extended Kalman filter; Measure the open-circuit voltage of the lithium-ion battery, calculate the SOC value before the charging start, and use it as the initial SOC value of the extended Kalman filter; start the constant current and constant voltage charging process, and monitor the voltage and current data at regular intervals, and use the voltage and current to pass through Extended Kalman filter to calculate the SOC value; turn off the water light after charging is completed, so that the displayed SOC value becomes 1; record the data of this battery charging process, which is used for battery standard capacity calibration, and improves the SOC estimation accuracy of the next charge. The SOC estimation effect of the present invention is more accurate than that of the ampere-hour integral, and solves the problem of accumulated error of the ampere-hour integral. The internal resistance model of the lithium-ion battery is simple, and the calculation amount of the algorithm is small, which can realize popularization.

Description

technical field [0001] The invention relates to the field of battery charging management, in particular to a method for estimating the charging quantity of a lithium-ion battery by using an extended Kalman filter. Background technique [0002] After the invention of lithium-ion batteries, they have been widely used in mobile energy storage units of modern equipment, and the estimation of SOC for charging lithium-ion batteries has become a research hotspot for many scholars. There are open-circuit voltage method, ampere-hour integration method, Kalman filter correlation method, neural network method, etc., but most of these methods have some problems, such as the calculation structure accuracy of ampere-hour integration method is not high, and there is a cumulative error problem. [0003] There is a close relationship between the internal resistance of lithium-ion batteries and SOC. Accurately estimating the internal resistance of lithium-ion batteries can greatly improve the...

AI Technical Summary

Problems solved by technology

However, the similarity of the two adjacent charging data is high, and the data of the last successful charging process can become the calibration data of the next charging process

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