Method for estimating lithium battery charge state

Abstract

The invention discloses a method for estimating a lithium battery charge state. The method comprises steps of estimating the charge state in accordance with the system state initial value and the state equation; calculating the residual error of the measured value and the estimated value and then calculating the fading factor; calculating the time-varying fading factor; calculating and obtaining the adjusted value of the fading factor in accordance with the residual error change and the current of the actual station; obtaining the new value of the fading factor, and obtaining the gain matrix; updating the charge estimation state; and estimating and measuring the noise covariance matrix through the self-adapting filtering algorithm. Compared with the prior art, by the aid of the strong tracking filter algorithm and the modification of the strong tracking filter algorithm, the noise and the fading factor can be measured and updated in accordance with actual condition, the estimation accuracy of the lithium battery charge state can be improved effectively compared with the traditional Kalman filter algorithm, and the tracking and the self adaption of the algorithm can be improved.

Description

technical field [0001] The technology for predicting the state of charge of a lithium battery in the present invention particularly relates to a method for estimating the state of charge of a lithium battery during practical application. Background technique [0002] Due to the pressure of energy and the environment, lithium batteries have become the most potential energy storage devices due to their advantages such as high energy density, long service life, and environmental protection. The state of charge estimation of the lithium battery is the premise and key to the effective management of the battery. At present, the common battery state of charge estimation methods are: open circuit voltage method, ampere-hour measurement method, neural network, Kalman filter method and extended Kalman filter method. [0003] Among them, the Kalman filter algorithm is the most commonly used state of charge estimation method, but its robustness is poor when the model parameters are unc...

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Problems solved by technology

[0005] In order to overcome the deficiencies of the prior art, the present invention proposes a method for estimating the state of charge of a lithium battery. In view of the uncertainty of the lithium battery model and the unknown statistical characteristics of the measurement noise, the strong tracking filter algorithm and the adaptive filtering algorithm are combined. Combining, applying Field Progra

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