Cell charging multiplying power estimation method of cell management system

Abstract

The invention discloses a cell charging multiplying power estimation method of a cell management system. The method is characterized by collecting real-time data of a cell in a circulation and timing mode and extracting an ohm inner resistance of a single cell, an output voltage of the single cell, a charging-and-discharging-current electric quantity and a cell temperature from the real-time data of the cell; combining a rated parameter of the cell and the collected real-time data of the cell so as to estimate cell charging multiplying power respectively; and correcting the estimated cell charging multiplying power through a historical data method, and comprehensively calculating a calculation charging multiplying power coefficient model, wherein the calculation charging multiplying power coefficient model can be used for each time of subsequent charging multiplying power analysis calculation. The cell charging multiplying power estimation method is suitable for storage batteries under various environments, has high adaptability and possesses a self-calibration function. An estimated value is close to a true value and estimation precision is high. A real health state of the cell can be accurately reflected.

Description

technical field [0001] The invention belongs to the technical field of battery life protection, and in particular relates to a battery charging rate estimation method of a battery management system. Background technique [0002] The battery management system of the existing energy storage power station usually only monitors and manages the voltage, internal resistance, and temperature of the battery, while the charging rate of the battery is usually a neglected quantity. The main reason is that the aging mechanism of the battery is not clear, and the battery’s The use process is not controlled, and the calculation methods and algorithms used have not been studied clearly. Second, use the same algorithm for the battery uncertainty. Therefore, the error of the diagnosed battery health status is relatively large. The most important thing is poor adaptability. Adjusting the parameters on the test battery pack according to the sample test data, the estimated accuracy is not bad...

AI Technical Summary

Problems solved by technology

The most important thing is the poor adaptability. Adjust the parameters on the test battery pack according to the sample test data. The estimated accuracy is not bad. Once the test sample changes and the working conditions change, the parameters are not applicable, so the error becomes larger and the accuracy is poor.

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