Lithium ion battery state-of-charge estimation method based on EMD-GRU

A lithium-ion battery, state of charge technology, applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., can solve the problem of missing current time series, etc., and achieve the effect of dynamic estimation and high estimation accuracy

Active Publication Date: 2021-11-26
NORTHWEST BRANCH OF STATE GRID POWER GRID CO +1
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Problems solved by technology

[0005] The purpose of the present invention is to provide a method for estimating the state of charge of lithium-ion batteries based on EMD-GRU, which solves the problem of the loss of medium and long-term current time series in the cyclic neural network lithium-ion battery SOC estimation in the prior art

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  • Lithium ion battery state-of-charge estimation method based on EMD-GRU
  • Lithium ion battery state-of-charge estimation method based on EMD-GRU
  • Lithium ion battery state-of-charge estimation method based on EMD-GRU

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Embodiment

[0074] The present invention adopts the public data set in the data warehouse of the Advanced Life Cycle Engineering Center of the University of Maryland in the United States. In the test, the battery A123 was placed in a temperature chamber, and the temperature of the battery was measured. current distribution and measurement voltage as Figure 5 shown. Obviously, the dynamic stress test DST dataset ( Figure 5 top) with the Federal Urban Driving Schedule FUDS dataset ( Figure 5 Middle) and US06 dataset ( Figure 5 bottom) are different. US06 and FUDS are significantly different from DST in discharge current and voltage. Therefore, using DST and FUDS data sets as training data sets and US06 and DST as test data sets can well verify the generalization ability of the GRU network. The temperature of the DST profile is 0°C, 10°C, 25°C, 30°C, 40°C, 50°C, the same as the FUDS and US06 profiles.

[0075] The GRU model is adopted, the input dimension is 8, the output dimension...

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Abstract

The invention discloses a lithium ion battery state-of-charge estimation method based on EMD-GRU, and the method specifically comprises the steps: 1, carrying out the pulse current discharge of a lithium ion battery, stopping the discharge until the voltage of the battery is reduced to a discharge cut-off voltage, and collecting the discharge current, the voltage of the battery, the temperature, and an SOC (t) time sequence; 2, decomposing the collected discharge current by adopting an empirical mode decomposition (EMD) algorithm, and decomposing a current time sequence into a sub-current set time sequence and a residual current time sequence with different frequencies; 3, performing normalization processing on the sub-current set time sequence, the residual current time sequence, the battery voltage and the battery temperature; and 4, establishing an SOC estimation model of the sub-current set, the voltage and the temperature time sequence based on a gate control cycle unit (GRU), and performing SOC estimation of the lithium ion battery. The method solves the problem that in the prior art, the SOC estimation of the lithium ion battery of the recurrent neural network loses a medium-and-long-term current time sequence.

Description

technical field [0001] The invention belongs to the technical field of lithium battery state estimation methods, and relates to an EMD-GRU-based lithium ion battery charge state estimation method. Background technique [0002] Due to its high energy density, low self-discharge rate, and no memory effect, lithium-ion batteries are widely used in electric vehicles and various fields, and gradually become the key and support for many important fields in the future. However, as the core technology of electric vehicles, lithium-ion batteries still face many bottlenecks, and the cruising range and safety performance have become the focus of users' attention. In order to improve the mileage and safety performance of electric vehicles, universities, companies and scientific research institutions at home and abroad all regard battery management system (Battery Management System, BMS) technology as an important development direction. [0003] Accurate evaluation and prediction of the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/367
CPCG01R31/367Y02E60/10
Inventor 马晓伟李欣王康平王智伟江国琪张小东刘鑫程林张小奇李宁何复兴
Owner NORTHWEST BRANCH OF STATE GRID POWER GRID CO
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