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A direct prediction method for remaining life of long-life space lithium-ion batteries

A lithium-ion battery and prediction method technology, which is applied in the field of direct prediction of remaining life and lithium-ion battery life prediction, can solve the problems of life prediction model mismatch, difficulty in obtaining accurate and stable prediction results, etc., to reduce measurement noise, Good forecasting effect and high accuracy of forecasting results

Active Publication Date: 2021-06-22
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

For the life prediction problem, such a long-term prediction scenario will directly lead to the mismatch of the traditional life prediction model based on the prediction of degradation characteristics, and it is difficult to obtain accurate and stable prediction results

Method used

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  • A direct prediction method for remaining life of long-life space lithium-ion batteries
  • A direct prediction method for remaining life of long-life space lithium-ion batteries
  • A direct prediction method for remaining life of long-life space lithium-ion batteries

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specific Embodiment approach 1

[0014] Specific implementation mode one: refer to figure 1 and figure 2 This embodiment is specifically described. The method for directly predicting the remaining life of a long-life space lithium-ion battery described in this embodiment includes the following steps:

[0015] Step 1. Collect the battery capacity data of each lithium-ion battery in each service cycle, construct a data set,

[0016] According to the set maximum life of each battery and the number of cycles, the remaining life of each battery under different cycles is obtained;

[0017] Step 2. Use the data set in step 1 as the input data of the training data, use the remaining life of the battery under the corresponding cycle as the output data of the training data, and bring the input data and output data into the relevant vector machine model for training , to obtain the mapping model of the trained capacity sequence and remaining life;

[0018] Step 3, using the capacity of the battery to be predicted un...

Embodiment 1

[0022] The remaining life prediction results of the three groups of batteries are shown in the table below, and their specific evaluation indicators are shown in Table 1.

[0023] Table 1:

[0024]

[0025] Step 1. Use the battery data set of the University of Maryland for testing. The battery data set contains CX2-26, CX2-37, and CX2-38 battery data. Construct a data set with the data of the battery capacity of each battery in each cycle, which is recorded as Cap i (k), where k is the number of cycles, and i is the battery number; set the maximum value of battery life in section i For the number of cycles corresponding to when the battery capacity degrades to 80% of the rated capacity, calculate the remaining life RUL of the battery corresponding to the kth cycle according to the formula (1) i (k);

[0026]

[0027] Step 2. Use the data of the two batteries of CX2-37 and CX2-38 as the training data to construct the training set [xtrain,ytrain] of the RVM model:

[0...

specific Embodiment approach 2

[0053] Embodiment 2: This embodiment is to further explain the method for directly predicting the remaining life of a long-life space lithium-ion battery described in Embodiment 1. In this embodiment, in step 1, the data set is recorded as: Cap i(k), wherein, k is the cycle number, and i is the battery number, which contains the data of 1 battery altogether;

[0054] The remaining life of each battery RUL under different cycles i (k) is:

[0055]

[0056] In the formula, is the maximum battery life of the i section.

[0057] In this embodiment, The cycle in is the corresponding number of cycles when the battery capacity degrades to 80% of the rated capacity.

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Abstract

The invention discloses a method for directly predicting the remaining life of a long-life space lithium-ion battery, and relates to the technical field of life-span prediction methods for lithium-ion batteries. The purpose of the present invention is to solve the problem that the traditional residual life prediction method based on degradation trajectory modeling is difficult to apply to such application scenarios with long prediction level and slow degradation for space lithium-ion batteries with a cycle life of 5 to 8 years. question. Collect the battery capacity data of each lithium-ion battery in each cycle to construct a data set, and obtain the remaining life of each battery in different cycles according to the maximum life of each battery and the number of cycles; the data set is input as training data, and the The remaining life of the battery is used as the output data, and the input and output data are brought into the relevant vector machine model to obtain the mapping model of the trained capacity sequence and the remaining life; the battery capacity to be predicted under each cycle is input into the mapping model, and the battery to be predicted is obtained. Estimates of remaining life. Used to predict the remaining life of lithium-ion batteries.

Description

technical field [0001] The invention relates to a method for directly predicting the remaining life of a long-life space lithium-ion battery. The invention belongs to the technical field of lithium ion battery life prediction method. Background technique [0002] Lithium-ion batteries have become the third generation of space energy storage batteries and are widely used in various spacecraft. With the operation of the spacecraft in orbit, the lithium-ion battery is continuously charged and discharged, and a series of irreversible electrochemical reactions will occur inside it, which will lead to the degradation of the performance of the battery. Accurately predicting the remaining life of lithium-ion batteries is one of the prerequisites for ensuring the safe and stable operation of spacecraft, and it is also a prerequisite for realizing flexible and autonomous mission planning of spacecraft clusters. However, the traditional lithium-ion battery life prediction methods foc...

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 彭宇宋宇晨刘大同彭喜元
Owner HARBIN INST OF TECH
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