Battery charge state estimation method with improved noise estimator

Abstract

The invention discloses a battery charge state estimation method with an improved noise estimator. The method comprises the following steps of: according to an equivalent circuit model of a battery, establishing a spatial state equation of the battery, and acquiring a noise estimation value as shown in the specification at a moment K by using the improved noise estimator; furthermore, by taking the noise estimation value as noise counting information of a self-adaptive unscented Kalman filter method, with the combination of the spatial state equation of the battery, performing battery charge state estimation by using the self-adaptive unscented Kalman filter method so as to obtain a middle state amount as shown in the specification at the moment k, and by taking the middle state amount as the input amount of the improved noise estimator at the moment k+1, performing circulation recursion, thereby obtaining a battery charge state estimation value. The battery charge state estimation method with the improved noise estimator, which is disclosed by the invention, is relatively high in estimation precision and relatively good in robustness when compared with a standard unscented Kalman filter method.

Description

technical field [0001] The invention belongs to the technical field of design and control of a MW-level battery energy storage system in a smart grid, and relates to a method for estimating a state of charge of a battery. Background technique [0002] With the rapid development of wind power and photovoltaic power generation, battery energy storage systems have also been greatly developed. The battery is the main carrier of energy storage and release in the battery energy storage system. Accurately determining the power of the battery directly determines whether the battery energy storage system can be effectively operated and controlled. However, the charging and discharging process of the battery is a complex electrochemical reaction process, and the power contained in it is difficult to obtain directly. Usually, the battery state of charge (State of Charge, SOC) is used to characterize the battery power. [0003] At present, the commonly used SOC estimation methods mainl...

AI Technical Summary

Problems solved by technology

The shortcomings of the current method: (1) The accuracy of the ampere-hour method is not high due to the shortcomings of accumulation of errors and obtaining a clear initial value of SOC; (2) The open-circuit voltage method is not suitable for online estimation and is time-consuming; (3) Impedance method has the disadvantages of complex algorithm and inconvenient actual operation; (4) Neural network and fuzzy logic method need to obtain a large amount of experimental data, which is difficult to obtain during the actual operation of the battery, and its actual accuracy is not high; (5) ) Extended Kalman filter method (EKF) has the disadvantages of calculating the Jacobian matrix and ignoring high-order terms, and its estimation accuracy is not high. (6) The standard unscented Kalman filter (UKF) has the advantages of not needing to calculate the Jacobian matrix, and the calculation amount is small, but in the actual application process, the statistical information in the standard unscented Kalman filter (UKF) (such as system noise, measurement noise, etc.) are not constant, even unknown or unclear, resulting in low estimation accuracy and poor robustness, such as a power battery charge estimation method disclosed in the document (CN103675706A)

In the process of realizing the present invention, the inventors found that the existing methods at least have problems such as low precision, poor real-time performance, large amount of calculation, and poor robustness.

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