Battery SOH (section of health) estimation method of energy storage power station BMS (battery management system)

Abstract

The present invention relates to a battery SOH estimation method of an energy storage power station BMS. The method comprises the following steps: the step S1: electric operation on a system; the step S2: circle and timing collection of battery real time data and extraction of a single voltage, a single internal resistance, a battery temperature, a charge-discharge current and a charge-discharge electric quantity from the real time data; the step S3: respective estimation of a battery SOH through an open-circuit voltage method, an ampere-hour integral method, a Kalman filtering method and a resistance method via the combination of nominal parameter of the battery and collected real time data of the battery; and the step S4: comprehensive calculation of the battery SOH obtained by estimation through a historical data amendment method to work out a model for calculating SOH coefficients, wherein the model for calculating SOH coefficients may be used in subsequent each SOH analytical calculation. The estimation method described herein is strong adaptable to storage batteries under various environments, and has a self-calibration function and high estimation accuracy, wherein the estimated value is approximate to the real value, thereby the real health status of the battery may be relatively accurately reflected.

Description

technical field [0001] The invention relates to a battery life estimation method, in particular to a battery SOH estimation method for an energy storage power station BMS. Background technique [0002] The existing energy storage system BMS usually only monitors and manages the voltage, internal resistance, and temperature of the battery, and some estimates SOC, SOH (section of health, battery health) is usually a negligible quantity, that is, some manufacturers estimate SOH, the error It is also very large, and some errors exceed 50%. The main reasons are that the aging mechanism of the battery is not clear, the use process of the battery is not well grasped, and the calculation methods and algorithms used have not been studied clearly. Second, use the same algorithm for the battery uncertainty. [0003] At present, there are many BMS systems for lithium batteries, lead-acid batteries, and nickel-metal hydride batteries on the market. Some products also analyze and estimat...

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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