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Lithium battery estimation method based on UKF with fading factor under maximum likelihood

A technique of fading factor and maximum likelihood, applied in the field of SOC estimation of UKF lithium battery based on the fading factor based on the maximum likelihood criterion, can solve the problems of divergence of filtering results, unknown statistical characteristics of noise, decrease in accuracy, etc., to achieve prediction. high precision effect

Inactive Publication Date: 2017-12-22
中华人民共和国成都出入境检验检疫局
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Problems solved by technology

[0005] In order to solve the problems in the prior art, the present invention provides a UKF lithium battery SOC estimation method with fading factor based on the maximum likelihood criterion, which solves the problem of unknown statistical characteristics of noise in the prior art, which leads to divergence or accuracy of filtering results falling problem

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  • Lithium battery estimation method based on UKF with fading factor under maximum likelihood
  • Lithium battery estimation method based on UKF with fading factor under maximum likelihood
  • Lithium battery estimation method based on UKF with fading factor under maximum likelihood

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Embodiment Construction

[0020] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0021] Acronyms and key term definitions

[0022] UKF Unscented Kalman Filter lossless Kalman filter or unscented Kalman filter

[0023] SOC state of charge The percentage of remaining battery power

[0024] A UKF lithium battery estimation method based on the maximum likelihood band fading factor, including the following steps: (A) establish a lithium battery composite empirical formula model to simulate the nonlinear characteristics of lithium batteries; (B) establish system equations; (C) Online SOC estimation is carried out by the UKF lithium battery SOC estimation method with fading factor under the maximum likelihood criterion.

[0025] In described step (B), determine the parameter value in empirical formula by constant current discharge experiment and least square method, then in setting up system equation.

[0026] In the step (C), ...

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Abstract

The invention relates to the field of data processing, and provides a lithium battery SOC estimation method based on UKF with a fading factor under the maximum likelihood criterion. The method comprises the steps that (A) a lithium battery composite empirical formula model is established, and nonlinear characteristics of a lithium battery are simulated; (B) a system equation is established; and (C) the lithium battery soc estimation method based on UKF with the fading factor under the maximum likelihood criterion is used for online soc estimation. The method provided by the invention has the advantages that the prediction precision of the residual battery capacity of the battery is higher; the algorithm adaptability is improved; and the problem of filtering divergence can be solved.

Description

【Technical field】 [0001] The invention relates to the field of data processing, which relates to a method for estimating the SOC of a lithium battery based on the fading factor UKF under the maximum likelihood criterion. 【Background technique】 [0002] Lithium battery is an energy storage device, its reliability, stability and safety have become the most concerned issues. At present, the performance parameters of lithium batteries include battery current, voltage, temperature, remaining battery capacity (soc) and battery health (soh). However, because SOC cannot be directly measured and is affected by many other factors, it is very difficult to estimate it. At the same time, the SOC estimation algorithm is also a research hotspot at present. [0003] Currently commonly used SOC estimation algorithms include current integration method, neural network method and Kalman filter method. Among them, the current integration method uses the method of real-time current integration ...

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Application Information

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IPC IPC(8): G01R31/36
CPCG01R31/367
Inventor 王世繁董亮罗杨梁平李国良
Owner 中华人民共和国成都出入境检验检疫局
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