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Indirect prediction method for remaining life of power battery

A power battery and prediction method technology, applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve the problem of difficult extraction of battery degradation characteristics, achieve the effect of low pressure on computing resources, improve accuracy, and solve the problem of fluctuations in curves

Pending Publication Date: 2019-08-09
JIANGSU UNIV
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Problems solved by technology

[0005] The present invention aims at the problem that it is difficult to extract the battery degradation characteristics under the working condition of the power battery during the actual operation of the automobile, and proposes an indirect prediction method for the remaining life of the power battery, which is simple in feature extraction and easy to realize

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  • Indirect prediction method for remaining life of power battery
  • Indirect prediction method for remaining life of power battery
  • Indirect prediction method for remaining life of power battery

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Embodiment Construction

[0034] The technical solution of the present invention will be further described in more detail below in conjunction with specific embodiments.

[0035] The present invention provides an indirect prediction method for the remaining life of a power battery, the realization process is as follows:

[0036] The battery data used in this example comes from the battery life test data numbered 5#, 6#, and 7# from the NASA Ames Center for Excellence Prediction. Battery specific parameters: 18650 battery, rated capacity 2000mAh, upper cut-off voltage 4.2V, lower cut-off voltage 2.75V.

[0037] Step 1: Perform a charge-discharge cycle life test on the power battery, and record the voltage change with time during each charge and discharge process, until the maximum discharge capacity of the power battery drops to 70% of the rated capacity, stop the experiment.

[0038] The specific steps of the battery cycle life test are:

[0039] ①Constant current charging: all three batteries are charged with ...

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Abstract

The invention discloses an indirect prediction method for the remaining life of a power battery. With equal charge voltage rise time and equal discharge voltage drop time as characteristic health factors, a time sequence for characteristic health factors and cycle times is built; global degradation and local fluctuation are decoupled by empirical mode decomposition, particle filtering and polynomial regression are combined to predict the remaining life of the battery, wherein the particle filtering is used for tracking a local fluctuation phenomenon, and the polynomial regression is used for fitting a global degradation trend. Experiment results show that the errors between the prediction result in the method and lithium battery cycle life experiment data from an NASA Ames superior prediction center are within 4%, and good accuracy and good practicability are achieved.

Description

Technical field [0001] The invention belongs to the technical field of battery management system health prediction and diagnosis, and more specifically, relates to an indirect prediction method for the remaining life of a power battery. Background technique [0002] In recent years, new energy vehicles have developed rapidly. Lithium-ion batteries have gradually become the main source of power due to their small size, long life, high working voltage, high specific energy density, and no memory effect. Its accurate battery state estimation and lifespan Prediction is very important for the improvement of vehicle performance and the practical application of lithium batteries. However, the battery life test cycle is particularly long and the implementation is difficult, making battery health assessment (SOH) and life prediction (RUL) research relatively few. Therefore, through a small sample of battery characteristic data, accurate life prediction is not only safe for the system, but...

Claims

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Application Information

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IPC IPC(8): G01R31/392G01R31/367G01R31/385G01R31/389
CPCG01R31/367G01R31/385G01R31/389G01R31/392
Inventor 何志刚魏涛盘朝奉周洪剑李尧太
Owner JIANGSU UNIV
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