Application analysis method for carrying out RUL prediction on automobile battery based on big data machine learning

A prediction method and data technology, applied in machine learning, data processing applications, special data processing applications, etc., can solve problems such as difficult measurement of internal parameters and battery capacity decay

Inactive Publication Date: 2019-06-25
常伟
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  • Abstract
  • Description
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Problems solved by technology

Power battery is the power source of electric vehicles. With the increase of charging and discharging times and driving mileage, the capacity of the battery continues to decline. This reaction is a ...

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  • Application analysis method for carrying out RUL prediction on automobile battery based on big data machine learning
  • Application analysis method for carrying out RUL prediction on automobile battery based on big data machine learning
  • Application analysis method for carrying out RUL prediction on automobile battery based on big data machine learning

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[0021] The specific implementation of this patent will be described in detail in conjunction with the following figures. It should be pointed out that this specific implementation is only an example of the preferred technical solution of the present invention, and should not be understood as limiting the protection scope of the present invention.

[0022] Figure 1-4 The steps of an electric vehicle battery RUL prediction in the specific implementation manner of this patent are shown. in:

[0023] Step S001 is a data preparation step, acquiring data related to the use of electric vehicle batteries.

[0024] In this step, the data of the electric vehicle battery includes the monitoring data of the electric vehicle, and the monitoring data is collected every ten seconds, and will be generated in different vehicle states of the electric vehicle, such as driving and charging. The monitoring data of the battery includes the battery's own data and the vehicle status data related to ...

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Abstract

The invention relates to a method for predicting an electric vehicle battery RUL (Remote Uplink) based on big data machine learning. The method is composed of a corresponding application architecture,a corresponding flow and a corresponding calculation model. The method comprises the following steps: firstly, acquiring battery real-time data in the operation process of an electric vehicle battery; and other operation data of the electric vehicle; performing data consolidation and cleaning, characteristic processing on the data; a model and a training verification algorithm are established through big data machine learning; wherein the modeling mainly uses a nonlinear hybrid algorithm model and a survival model, and the result is evaluated and optimized at different angles, so that a modelfor predicting the RUL of the electric vehicle battery is established, the maintenance and replacement of the battery are optimized, the safety index of a vehicle owner is improved, and the balance of system performance and economic benefits is achieved.

Description

technical field [0001] The invention relates to an application analysis method for predicting the RUL of an automobile battery based on big data machine learning. The field of application is the prediction of the decommissioning time of an electric vehicle battery and the value evaluation of a battery pack. Background technique [0002] With the promotion of electric vehicles in China and the application of Internet of Vehicles technology, more and more electric vehicles have entered the consumer market and collected driving data in real time according to the national standard (GBT32960). Power battery is the power source of electric vehicles. With the increase of charging and discharging times and driving mileage, the capacity of the battery continues to decline. This reaction is a typical dynamic nonlinear electrochemical system. It is difficult to measure internal parameters in online applications. Significant challenges remain in degraded state identification and state e...

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/00G06F16/215G06N20/00G01R31/367G01R31/392
Inventor 常伟叶磊李舰毛樑
Owner 常伟
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