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Method for predicting SOE of rail traffic lithium battery through large data

A lithium battery and big data technology, applied in the direction of measuring electricity, measuring electrical variables, testing electrical devices in transportation, etc., can solve problems such as difficult measurement of internal parameters and battery capacity attenuation

Pending Publication Date: 2019-12-20
GUANGZHOU DIQING ELECTRONICS TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, it is known that with the increase of charge and discharge times and 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. The degradation state identification and state It is estimated that there are still huge challenges

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  • Method for predicting SOE of rail traffic lithium battery through large data
  • Method for predicting SOE of rail traffic lithium battery through large data
  • Method for predicting SOE of rail traffic lithium battery through large data

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Embodiment Construction

[0053] The specific embodiment of the present invention will be further described below in conjunction with accompanying drawing:

[0054] Such as Figure 1-4 Shown:

[0055] A method for predicting the SOE of a rail transit lithium battery by big data, comprising the following steps:

[0056] S001 data preparation step, obtaining data related to the use of rail transit batteries.

[0057] In this step, the data of the rail transit battery includes the monitoring data of the rail transit, and the monitoring data is collected once every ten seconds (possibly other acquisition frequencies according to the actual situation). , charging process, will be generated. The monitoring data of the battery includes the battery's own data and rail transit status data related to the battery during normal use, and there are more than 200 data variables in total.

[0058] The usage data of the battery is based on time-series streaming data, including current, voltage, temperature, remaini...

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Abstract

The invention relates to the technical field of maintenance of rail traffic, in particular to a method for predicting SOE of a rail traffic lithium battery through large data. The method comprises thesteps of a data preparation step, a data arrangement step, a data characterization step, a target determination step, a data calculation step, a training verification step and an algorithm evaluationstep; hidden noise data is found through special cleaning means, so that the effects of good cleaning effect, high accuracy and the like are realized; in addition, model training and evaluation are carried out, different algorithms are selected for matching verification and are issued by using different models of machine learning through data import, so that a structured product is formed, and the prediction accuracy of the models is constantly improved along with time accumulation and data enrichment.

Description

technical field [0001] The invention relates to the technical field of rail transit maintenance, in particular to a method for predicting the SOE of a rail transit lithium battery through big data. Background technique [0002] The state of remaining energy of the battery (SOE, State of Energy), defined as the percentage of the remaining energy of the battery in the total available energy, refers to the actual performance of some directly measurable or indirectly calculated performance parameters after the battery has been used for a period of time under certain conditions. The ratio of the value to the nominal value is used to judge the health and usage of the battery. SOE is not only related to the electrochemical system of the battery itself and the battery manufacturing process, but also related to the driving conditions of the vehicle and the working environment inside the battery pack. [0003] In traditional rail transit, it is usually monitored by sensors to measure...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/367G01R31/382G01R31/392G01R31/00
CPCG01R31/367G01R31/382G01R31/392G01R31/008Y02T10/40
Inventor 常伟余捷全
Owner GUANGZHOU DIQING ELECTRONICS TECH
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