A Fusion Algorithm Based Lithium-ion Battery Remaining Life Prediction Method

A lithium-ion battery, life prediction technology, applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve the problem of not mentioning the error compensation of the filtering model, and achieve high accuracy, improve prediction accuracy, and good stability. Effect

Active Publication Date: 2021-02-09
CHONGQING UNIV
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Problems solved by technology

In the first category, the use of data-driven methods to infer or compensate physical models, the use of data-driven methods to estimate predictive measurements of model-based methods, the use of data-driven methods to estimate or adjust the parameters of physical methods, the use of filtering methods These four methods estimate / adjust the parameters of the data-driven method; but none of them mentions the use of data-driven algorithms to compensate the filtering model for error compensation to achieve lithium-ion battery RUL prediction

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  • A Fusion Algorithm Based Lithium-ion Battery Remaining Life Prediction Method
  • A Fusion Algorithm Based Lithium-ion Battery Remaining Life Prediction Method
  • A Fusion Algorithm Based Lithium-ion Battery Remaining Life Prediction Method

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Embodiment Construction

[0037] Embodiments of the present invention are described below through specific examples.

[0038] figure 1 It is the overall flowchart of the method of the present invention, as shown in the figure, in this embodiment, the following method steps are adopted:

[0039] Step S1: Obtain open-source battery capacity decay data from the Advanced Life Cycle Engineering Center of the University of Maryland in the United States, select the capacity degradation model (CDM), and determine the RUL prediction model based on one of the optimal control algorithms—particle filter algorithm (PF) parameter;

[0040] Step S2: Utilize the superiority of PF in the process of nonlinear and non-Gaussian capacity degradation, fit the training set data (number of cycles k<160), and iteratively output PF algorithm model parameter filter estimates and battery capacity attenuation filter estimates value, the estimated value is filtered through the model parameters, and the initial RUL prediction valu...

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Abstract

The invention relates to a method for predicting the remaining life of a lithium-ion battery based on a fusion algorithm, and belongs to the technical field of battery management. The method includes the following steps: S1: Acquire battery capacity decay data, and determine model parameters based on optimal control algorithm RUL prediction. S2: Fit the training set data, iteratively output the optimal control algorithm model parameter filtering estimated value and the battery capacity attenuation data filtering estimated value, and obtain the initial RUL predicted value through the model parameter filtering estimated value. S3: Based on the difference between the estimated filtering value of the optimal control algorithm and the experimental data, the original error sequence is established and used as the input of the neural network algorithm, the error sequence is continuously iteratively trained, and the prediction result of the error sequence is output. S4: After the training set data is used, the initial prediction value of the optimal control algorithm and the error sequence prediction result of the neural network algorithm are integrated to obtain the final lithium-ion battery RUL prediction result.

Description

technical field [0001] The invention belongs to the technical field of battery management and relates to a method for predicting the remaining life of a lithium-ion battery based on a fusion algorithm. Background technique [0002] In recent years, due to its wide application in new energy vehicles and other technological fields, lithium-ion batteries have gradually become a research hotspot in the field of new energy vehicle power batteries due to their high energy density and long cycle life. However, as the battery capacity gradually decays with the increase of the number of cycles, the performance of the battery will deteriorate, and even cause economic losses and personal injuries. Therefore, battery health monitoring based on the capacity decay of lithium-ion batteries has become an urgent problem to be solved. The remaining battery life can be defined as the number of charge and discharge cycles before the attenuation performance index of the battery reaches the spec...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R31/392G01R31/367
CPCG01R31/367G01R31/392
Inventor 冯飞胡晓松杨鑫刘波李可心李云隆李佳承谢翌杨亚联
Owner CHONGQING UNIV
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