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Method for estimating lithium battery SOC based on self-adaptability fuzzy Kalman filter

A Kalman filtering and self-adaptive fuzzy technology, which is applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., can solve problems such as divergence and inaccurate estimation results

Active Publication Date: 2017-10-24
HEFEI UNIV OF TECH
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AI Technical Summary

Problems solved by technology

However, Kalman filtering requires preset noise initial value information, and an inappropriate noise initial value will make the estimation result inaccurate or even divergent

Method used

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  • Method for estimating lithium battery SOC based on self-adaptability fuzzy Kalman filter
  • Method for estimating lithium battery SOC based on self-adaptability fuzzy Kalman filter
  • Method for estimating lithium battery SOC based on self-adaptability fuzzy Kalman filter

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Embodiment Construction

[0084] Such as figure 1 As shown, a lithium battery SOC estimation method based on adaptive fuzzy Kalman filter includes the following steps:

[0085] (1) Establish the first-order RC model of the battery to determine the state equation and observation equation, and determine the Kalman filter discrete state space model of the battery model and the state variables and observation variables;

[0086] (2) according to the Kalman filtering discrete state-space model determined in step (1), calculate the estimated value of the state estimation update value of this model and noise covariance matrix;

[0087] (3) Taking the mean value of the terminal voltage residual and the matching degree of the terminal voltage residual at a certain moment as the two inputs of the Sugeno type fuzzy inference system, the output system noise variance matrix adjustment coefficient and measurement noise variance matrix adjustment coefficient;

[0088] (4) Calculate the discrete state covariance and ...

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Abstract

The invention discloses a method for estimating lithium battery SOC based on self-adaptability fuzzy Kalman filter. For different lithium batteries, after Kalman filter discrete state model is determined, the SOC is estimated by the utilization of the method of self-adaptability fuzzy Kalman filter, wherein the residual variance matching degree and residual error mean of terminal voltage in a lithium battery equivalent model serve as input of a fuzzy control system, so that adjustment factors of a system noise variance and a measurement noise variance are obtained, and then the two variances are adjusted; the adjusted system noise variance and measurement noise variance are substituted into a Kalman filter algorithm to estimate SOC values at all moments. With the method, the power lithium battery SOC can be accurately estimated, the problems that according to an existing estimation method, the requirement for on-line estimation can not be satisfied, has large accumulated errors, diverges and is prone to being affected by noise can be solved, and the estimation accuracy is high.

Description

technical field [0001] The invention relates to the technical field of charge prediction of lithium-ion batteries, in particular to a lithium battery SOC estimation method based on adaptive fuzzy Kalman filter. Background technique [0002] As the main means of transportation in the future, electric vehicles have certain requirements for their starting, accelerating, climbing performance and cruising range. These performances largely depend on the performance of the power battery. The battery state of charge (SOC) is an important parameter that reflects the energy state of the battery. Only by accurately estimating the battery SOC can we effectively improve the utilization rate of power vehicles, optimize driving, and prolong the service life of batteries. The SOC is an implicit state quantity in the power battery, which is difficult to measure and estimate directly. Factors affecting SOC mainly include operating voltage, current, temperature, etc. Therefore, only by esta...

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Application Information

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IPC IPC(8): G01R31/36
CPCG01R31/367
Inventor 刘征宇黎盼春汤伟武银行王雪松
Owner HEFEI UNIV OF TECH
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