Method for battery SOC estimation based on small model error criterion expanding Kalman filter

A technique for extending Kalman and model errors, applied in the direction of measuring electricity, measuring electrical variables, instruments, etc., can solve problems such as filter divergence, battery terminal voltage jump, SOC estimation fluctuation, etc.

Active Publication Date: 2014-07-23
SHANDONG UNIV
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Then, in the filtering, based on the neural network online prediction model error, the state estimation is measured and updated only when the predicted error is small, thus overcoming the problem of filtering divergence caused by the uncertain stati

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  • Method for battery SOC estimation based on small model error criterion expanding Kalman filter
  • Method for battery SOC estimation based on small model error criterion expanding Kalman filter
  • Method for battery SOC estimation based on small model error criterion expanding Kalman filter

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[0082] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0083] Estimating battery SOC using the extended Kalman filter requires an accurate battery model. Building a battery model refers to applying mathematical theory to describe the response characteristics and internal characteristics of the actual battery as comprehensively as possible. The so-called response characteristic refers to the corresponding relationship between the terminal voltage of the battery and the load current; the internal characteristic refers to the relationship between the internal variable ohmic internal resistance, polarization internal resistance and polarization voltage of the battery, SOC and temperature.

[0084] 1. Variable order RC model

[0085] Such as figure 1 Shown is the variable-order RC equivalent circuit model based on the AIC criterion proposed by the present invention, including a running time circuit and an I-V ...

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Abstract

The invention discloses a method for battery SOC estimation based on small model error criterion expanding Kalman filter. The method comprises the steps of first establishing a variable-order RC model based on an AIC criterion and laying a good foundation for SOC precise estimation; obtaining data such as battery terminal voltage, current and corresponding model errors in an off-line mode under different working conditions and establishing a model error prediction model based on a fuzzy neural network; predicting model errors in an on-line mode based on the neural network in filtering, and performing measurement and updating on state estimation only when predicted errors are small so that the problem of filtering divergence caused by the model errors and system noise statistical characteristic uncertainty and the problem of SOC estimation fluctuation caused by battery terminal voltage jump can be solved. The method can effectively eliminate filtering estimation errors caused by the model errors and can be suitable for the dynamic process of a battery under various complex working conditions.

Description

technical field [0001] The invention relates to a battery SOC estimation method based on the small model error criterion extended Kalman filter. Background technique [0002] As a key component of electric vehicles, vehicle-mounted power batteries are crucial to the power, economy and safety of the vehicle, and are a key factor restricting the scale development of electric vehicles. In order to maximize the performance of the power battery and prolong the service life of the battery, it is very important to manage the battery effectively, and accurately obtaining the state of charge (SOC) of the battery is the core technology of battery management. Battery SOC estimation is an important basis for judging whether the battery is overcharged or overdischarged, and whether it needs to be balanced or replace a single battery. Therefore, improving the accuracy of battery SOC estimation is of great significance for improving battery efficiency, prolonging battery cycle life, and e...

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Application Information

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IPC IPC(8): G01R31/36
Inventor 张承慧商云龙崔纳新
Owner SHANDONG UNIV
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