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Estimation of state of charge of lithium ion battery based on XGBoost model

A lithium-ion battery, state-of-charge technology, applied in the measurement of electricity, measurement of electrical variables, measurement devices, etc., can solve the problems of extreme sensitivity to temperature and aging, and low prediction accuracy

Inactive Publication Date: 2020-10-23
GUANGXI NORMAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved in the present invention is to provide a method for predicting the state of charge of lithium-ion batteries based on the XGBoost (eXtreme GradientBoosting) model, which not only solves the nonlinearity and time-varying nature of lithium-ion batteries in the prediction process, but also is extremely sensitive to temperature and aging and other problems, and also solve the problem of low prediction accuracy. Compared with the above method, XGBoost adds a regular term in each round of iteration of the objective function, so as to control the complexity of the model and prevent overfitting

Method used

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  • Estimation of state of charge of lithium ion battery based on XGBoost model
  • Estimation of state of charge of lithium ion battery based on XGBoost model
  • Estimation of state of charge of lithium ion battery based on XGBoost model

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Experimental program
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Effect test

Embodiment 1

[0065] Such as figure 1 As shown, the state of charge estimation of lithium-ion batteries based on the XGboost model includes the following steps.

[0066] Step A, data preprocessing: Divide the steady-state lithium-ion battery discharge data of NASA Ames Research Center into a training data set and a test data set. The discharge data sets of the first group to the 14th group, the test data set is the discharge data set of the 15th group in the lithium-ion battery B0006 data set under the discharge rate of 1C;

[0067] Get the real SOC of the training data set:

[0068]

[0069] Among them, the remaining capacity is the lithium-ion battery B0006 data set. After the 9th to 14th groups in the 1.5A constant current mode reached a voltage of 4.2, they continued to charge at a constant voltage level of 4.2 until the current dropped to 2A. Discharge to the capacity when the voltage drops to 2.7V; the rated capacity is after the 9th to 14th groups in the lithium-ion battery B000...

Embodiment 2

[0138] Such as figure 1 As shown, the state of charge estimation of lithium-ion batteries based on the XGboost model includes the following steps.

[0139] Step A, data preprocessing: Divide the steady-state lithium-ion battery discharge data from NASA Ames Research Center into a training data set and a test data set. The training data set is the 8th in the lithium-ion battery B0029 data set at a 2C discharge rate The discharge data sets of the group to the 12th group; the test data set is the discharge data set of the 13th group in the lithium ion battery B0029 data set under the 2C discharge rate;

[0140] Get the real SOC of the training data set:

[0141]

[0142] Among them, the remaining capacity is the lithium-ion battery B0029 data set under the 2C discharge rate of the lithium-ion battery. After the 8th to 12th groups in the 1.5A constant current mode reach a voltage of 4.2, they continue to charge at a constant voltage level of 4.2 until the current drops. To 2A...

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Abstract

The invention relates to the field of lithium ion battery prediction, and relates to estimation of the state of charge of a lithium ion battery based on an XGBoost model, wherein the estimation comprises the following steps: dividing discharge data of the lithium ion battery into a training data set and a test data set, and taking voltage, current and temperature in the data set of the training data set as features and inputting into the XGBoost model; setting parameters of the XGBoost model; training the training data set by using the XGBoost model; judging an error between a predicted SOC ofthe training data set and a real SOC of the training data set, and if the error is minimum, taking the set parameters of the XGBoost model as optimal parameters; taking the voltage, current and temperature in the test data set as features and inputting into the XGBoost model, and predicting the test data set by using the obtained optimal parameters of the XGBoost model to obtain a predicted SOC of the test data; and the estimation precision and robustness are improved.

Description

technical field [0001] The invention relates to the field of lithium ion battery prediction, more specifically, relates to the state of charge estimation of the lithium ion battery based on the XGboost model. Background technique [0002] Lithium-ion batteries are widely used in new energy electric vehicles due to their high efficiency, long life, large capacity, no memory effect, and environmental friendliness. Not only that, lithium-ion batteries are also widely used in high-tech products such as mobile phones and various portable information processing terminals. However, the service life of lithium-ion batteries is closely related to the use and promotion of lithium-ion batteries. The state of charge (SOC) is a vital indicator of a Li-ion battery, which indicates the remaining capacity of the battery in its current state. The SOC estimation of high-precision lithium-ion batteries can avoid overcharging, over-discharging and overheating of batteries, thereby prolonging t...

Claims

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Application Information

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IPC IPC(8): G01R31/367G01R31/388
CPCG01R31/367G01R31/388
Inventor 宋树祥潘凯费陈夏海英
Owner GUANGXI NORMAL UNIV
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