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Prediction method of lithium-ion battery cut-off voltage based on ari algorithm

A lithium-ion battery and cut-off voltage technology, which is applied in computing, electrical digital data processing, special data processing applications, etc., can solve the problem of low prediction accuracy and achieve the effect of improving prediction accuracy

Active Publication Date: 2016-03-16
HARBIN INST OF TECH
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  • Application Information

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Problems solved by technology

[0003] The present invention is to solve the problem of low prediction accuracy when the existing ARI model is a linear model and the cut-off voltage of the lithium-ion battery is accelerated in the later stage. The present invention provides a method for predicting the cut-off voltage of the lithium-ion battery based on the ARI algorithm

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  • Prediction method of lithium-ion battery cut-off voltage based on ari algorithm
  • Prediction method of lithium-ion battery cut-off voltage based on ari algorithm
  • Prediction method of lithium-ion battery cut-off voltage based on ari algorithm

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specific Embodiment approach 1

[0021] Specific implementation mode one: see figure 1 Illustrate this embodiment, the prediction method of the cut-off voltage of lithium-ion battery based on ARI algorithm described in this embodiment, its concrete steps are:

[0022] Step 1: Extract the original data of the cut-off voltage in each charge and discharge cycle of the lithium-ion battery, save the original data of the cut-off voltage of the lithium-ion battery and preprocess the original data of the cut-off voltage of the lithium-ion battery to obtain the processed data;

[0023] Step 2: Using the processed data as the input value of the ARI prediction model to determine the parameters of the ARI prediction model to obtain the ARI model;

[0024] Step 3: Introduce the acceleration factor of the prediction step into the ARI model obtained in step 2 for fitting, obtain the ND-ARI prediction model, and realize the prediction of the lithium-ion battery cut-off voltage by using the ND-ARI prediction model;

[0025] ...

specific Embodiment approach 2

[0031] Specific implementation mode two: see figure 1 Describe this embodiment, the difference between this embodiment and the method for predicting the cut-off voltage of a lithium-ion battery based on the ARI algorithm described in the first embodiment is that the cut-off voltage in each charge-discharge cycle of the lithium-ion battery is extracted in the first step Raw data, saving the original data of the lithium-ion battery cut-off voltage and preprocessing the original data of the lithium-ion battery cut-off voltage, the specific steps to obtain the processed data are:

[0032] Step 11: Measure the raw data of the cut-off voltage of the lithium-ion battery in each charge-discharge cycle several times, and extract the cut-off voltage at the end of each cycle of the lithium-ion battery from the raw data of the cut-off voltage of the lithium-ion battery to obtain the lithium-ion battery The raw data set of battery cut-off voltage {V k′}, where V k′ Indicates the cut-off ...

specific Embodiment approach 3

[0036] Specific implementation mode three: see figure 1 Describe this embodiment, the difference between this embodiment and the method for predicting the cut-off voltage of a lithium-ion battery based on the ARI algorithm described in Embodiment 1 or 2 is that the specific form of the ND-ARI prediction model is:

[0037] x k ={(1-B)[φ 1 x k-1 +φ 2 x k-2 +…+φ n x k-n ]+a k} / K T (5-9),

[0038] where x k is the lithium-ion battery cut-off voltage prediction value of the ND-ARI prediction model at time k, B is the backward shift operator, x k-n is the true value of the lithium-ion battery cut-off voltage at time k-n.

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Abstract

The invention discloses an ARI (Auto-Regulation Index) algorithm-based lithium ion battery cut-off voltage prediction method, which belongs to the field of cut-off voltage prediction of a lithium ion battery and aims to solve the problem that the prediction accuracy is low during accelerating change of cut-off voltage at the later stage of a lithium ion battery since the conventional ARI model is a linear model. The method comprises the following steps: firstly extracting the cut-off voltage original data in each charging and discharging period of the lithium ion battery, saving the cut-off voltage original data of the lithium ion battery and preprocessing the cut-off voltage original data of the lithium ion battery to obtain processed data; secondly determining the parameter of the ARI prediction model by taking the processed data as an input value of the ARI prediction model to obtain an ARI model; and then introducing an accelerator of a predictive step length into the ARI model obtained by the step 2 for fitting to obtain an ND-ARI predictive model and predicting the cut-off voltage of the lithium ion battery by using the ND-ARI predictive model. The ARI algorithm-based lithium ion battery cut-off voltage prediction method is mainly suitable for predicting the cut-off voltage of the lithium ion battery.

Description

technical field [0001] The invention belongs to the field of cut-off voltage prediction of lithium ion batteries. Background technique [0002] Since ARI is still a linear model, the lithium-ion battery cut-off voltage prediction process is to continuously use the prediction values ​​of the previous steps to estimate the current state. The early part of the prediction data is in good agreement with the real data. When the change is accelerated, the real degradation trend cannot be predicted through the previous forecast data, and the ARI model is still difficult to achieve a good prediction effect, and the error is gradually expanding. Contents of the invention [0003] The present invention aims to solve the problem of low prediction accuracy when the existing ARI model is a linear model and the cut-off voltage of the lithium-ion battery accelerates to change in the later stage. The present invention provides a prediction method for the cut-off voltage of the lithium-ion ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F19/00
Inventor 刘大同李君宝郭力萌庞景月彭宇彭喜元
Owner HARBIN INST OF TECH
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