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Lithium battery charge state assessment method based on finite difference expansion Kalman algorithm

A technology of extending Kalman and state of charge, applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., can solve problems such as increasing computational complexity and errors

Active Publication Date: 2013-05-22
TIANJIN UNIV
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Problems solved by technology

However, in order to obtain the propagation of the estimated error variance through the nonlinear function, the extended Kalman filter method needs to expand and linearize the nonlinear function with Taylor series, which increases the computational complexity on the one hand, and on the other hand, in the process Neglecting higher-order terms will inevitably lead to errors

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  • Lithium battery charge state assessment method based on finite difference expansion Kalman algorithm
  • Lithium battery charge state assessment method based on finite difference expansion Kalman algorithm
  • Lithium battery charge state assessment method based on finite difference expansion Kalman algorithm

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Embodiment Construction

[0058] Specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. If there are exemplary contents in these embodiments, they should not be construed as limiting the present invention.

[0059] The battery model of a lithium battery is a nonlinear system, in which the open circuit voltage of the battery and the state of charge of the battery have a nonlinear relationship. Based on the Kalman filter algorithm, the extended Kalman filter algorithm can linearize the nonlinear equation and can be applied to nonlinear systems. When the model parameters match the process parameters exactly or basically, as long as the initial value is selected properly, the filtering process can converge gradually and obtain an approximate unbiased estimate of the state. However, when the model parameters do not match or there is a large deviation, the estimation accuracy of the extended Kalman filter algorithm will be greatly reduce...

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Abstract

The invention discloses a lithium battery charge state assessment method. The method includes the first step of setting an initial value and carrying out Cholesky decomposition on each covariance, the second step of state one-step prediction, the third step of covariance one-step prediction, the fourth step of gain filtering, the fifth step of updating the optimized value of a state, and the sixth step of updating filtering covariance. Compared with the prior art, the precision of the method is higher than that of first-order spreading of the Taylor series, effective error information caused by model linearization is fully made use of, and strong robustness for model parameter changes is achieved.

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] Batteries have been widely used as power sources in communications, power systems, military equipment, electric vehicles and other fields. With the increasingly popular concept of environmental protection, more and more systems begin to use batteries as the main power supply. In these systems, the working status of the power battery is directly related to the operational reliability of the entire system. In order to ensure the good performance of the power battery pack and prolong the service life of the battery pack, it is necessary to know the operating status of the battery in a timely and accurate manner, and manage and control the battery reasonably and effectively. The accurate estimation of the state of...

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Application Information

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IPC IPC(8): G01R31/36
Inventor 程泽刘艳莉张玉晖戴胜张秋艳
Owner TIANJIN UNIV
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