Lithium ion battery service life prediction method based on DAUPF

A lithium-ion battery, life prediction technology, applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve problems such as UPF algorithm noise and reference distribution influence

Active Publication Date: 2019-06-21
广西聚邦能源有限公司
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Problems solved by technology

[0010] The purpose of the present invention is to, in view of the above problems, propose a lithium-ion battery life prediction method based on D

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  • Lithium ion battery service life prediction method based on DAUPF
  • Lithium ion battery service life prediction method based on DAUPF
  • Lithium ion battery service life prediction method based on DAUPF

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specific Embodiment

[0079] This experiment uses matlab for simulation, based on the lithium battery experiment data of the University of Maryland Advanced Life Cycle Engineering Center, the experiment selects No.03, 05, 08 and 12 as the experimental data, and the four sets of lithium-ion battery experimental data are as follows: image 3As shown, the experiment was carried out under the same working conditions using the same type of batteries of the same brand with different capacity degradation rates. The charging and discharging test method of the lithium battery is: use the ArbinBT2000 battery test system to conduct the charging and discharging test at room temperature, and complete a charging or discharging process when the charging or discharging voltage reaches the cut-off voltage specified by the manufacturer. The rated capacity of the battery is 0.9Ah, and the discharge current is 0.4Ah.

[0080] The initial values ​​a, b, c, and d below are the values ​​obtained after fitting No.03, 05, ...

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Abstract

The invention provides a lithium ion battery service life prediction method based on DAUPF. The method comprises the following steps of in a sampling portion, adding a double-adaptive factor based ona UKF algorithm, and using a state value and a covariance obtained after one-step prediction of a Sigma point set as a guide, carrying out another UT conversion to get a new Sigma point set, bringinginto an observation equation and acquiring a new observed quantity so as to obtain a first cycle sample mean value and the covariance; in an improved UKF algorithm portion, updating an adaptive factorafter primary circulation is completed, and then carrying out next improved UKF algorithm cycle; after sampling is completed, entering into a PF process, and after one output prediction value is obtained, updating another adaptive factor to complete a DAUPF process; and finally predicting test data. In the invention, a UPF algorithm sampling portion is improved, through adding the double-adaptivefactor, the algorithm has higher robustness, through two-step UT conversion, the adaptive factor can be better integrated into the algorithm, and a algorithm prediction effect is accurate.

Description

technical field [0001] The invention relates to a lithium ion battery life prediction method based on DAUPF (dual adaptive sampling infinite Kalman particle filter algorithm), which belongs to the technical field of lithium battery health management. Background technique [0002] Lithium-ion batteries have been successfully used in many consumer electronics products (such as mobile phones, notebook computers and electric vehicles), and gradually expanded to military communications, navigation, aviation, aerospace and other fields. The safety of lithium-ion batteries has been paid more and more attention by people. The battery life is defined by the number of charge and discharge cycles or service life. The chemical substances in the battery will gradually age with the increase of the battery working time, and the failure of the battery will cause serious consequences. The California Fire Department said that a Tesla Model S car spontaneously ignited in a parking lot, and i...

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Application Information

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IPC IPC(8): G01R31/367G01R31/392
Inventor 袁慧梅朱骏谭天雄吴立锋宋宇
Owner 广西聚邦能源有限公司
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