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Lithium iron phosphate cell lifetime prediction method based on MIV and SVM model

A lithium iron phosphate battery, life prediction technology, applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve problems such as reducing model accuracy, and achieve the effect of improving prediction efficiency and prediction accuracy

Inactive Publication Date: 2018-09-18
TAIYUAN UNIV OF TECH
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AI Technical Summary

Problems solved by technology

Based on the algorithm, Patil et al. used a combination of classification and regression to estimate the SOH of lithium-ion batteries in real time, but this method may introduce irrelevant variables, resulting in reduced model accuracy.

Method used

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  • Lithium iron phosphate cell lifetime prediction method based on MIV and SVM model
  • Lithium iron phosphate cell lifetime prediction method based on MIV and SVM model
  • Lithium iron phosphate cell lifetime prediction method based on MIV and SVM model

Examples

Experimental program
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Effect test

Embodiment 1

[0071] The experimental data used in this example comes from an electronics company in Shenzhen. The relevant rated data of the lithium iron phosphate battery of this experimental model are as follows: the rated single capacity is 120Ah, the rated charge cut-off voltage is 3.65V, and the rated discharge cut-off voltage is 2.5V. The batteries are connected in series to form a battery pack. The input parameters are the total voltage at the end of discharge, the total voltage at the end of charge, the number of cycles and the internal resistance, and the output parameter is the available capacity of the battery pack.

[0072] When the MIV algorithm is not used to optimize the four variables, the SVM prediction model is trained using the complete training set. The comparison results of the model's prediction of the test set and the actual situation are as follows:

[0073] figure 2 It is the prediction comparison chart of the dischargeable capacity of the battery pack, image 3...

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Abstract

The invention discloses a lithium iron phosphate cell lifetime prediction method based on MIV and an SVM model. The method comprises steps of by applying the MIV algorithm, obtaining the influence importance degree of an input variable to an output variable; then, screening out the most important variable to serve as the input variable; preventing unimportant independent variables to be introducedinto training and test processes of the prediction model; after variable optimization, obtaining a new training set and a new test set only including the optimal variables; and by use of the optimaltraining set and the SVM, carrying out training to obtain the prediction model. According to the invention, the structural risk minimization is used as the optimal criterion in the SVM, so the globaloptimal solution can be acquired; and by combining the prediction model which is subjected to the optimization, only includes variables of circulation times, resistance and the like and is obtained through training, prediction efficiency and prediction precision can be effectively improved.

Description

technical field [0001] The invention relates to the field of life prediction of lithium iron phosphate batteries, in particular to a method for life prediction of lithium iron phosphate batteries based on MIV and SVM models. Background technique [0002] Lithium iron phosphate battery has been widely used in many occasions because of its many advantages, replacing traditional lead storage, nickel-cadmium and other batteries, and is used in many fields such as agriculture, communication industry, industry, etc. Inseparable. However, lithium iron phosphate batteries also suffer from life problems. Many factors affect its charge and discharge capabilities, such as the loss of internal materials of the battery, unreasonable use methods, etc. These will gradually degrade the state of health (SOH) of the battery. SOH is used to represent the storage capacity of the battery and is a parameter used to describe the performance state of the battery. If the degradation process is ig...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/36
Inventor 刘晓峰赵哲峰王宁尚奥李伟郭丽芳李廷鱼刘帆柴晶陈泽华
Owner TAIYUAN UNIV OF TECH
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