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Battery life prediction method based on combination of data driving and battery characteristics

A technology of battery life and battery characteristics, applied in the field of battery life prediction, can solve the problems of large battery error, lack of experimental data, difficult to establish physical models, etc., to achieve the effect of improving accuracy and wide application range

Active Publication Date: 2020-03-10
蓝谷智慧(北京)能源科技有限公司
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AI Technical Summary

Problems solved by technology

Due to the complexity of the failure mechanism of lithium batteries and the difficulty in establishing physical models, most of the existing research focuses on the establishment of data-driven models, such as autoregressive (AR) models, Kalman filters, neural networks, etc., but due to the lack of experimental data, resulting in a large error in the later stage of battery operation

Method used

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Examples

Experimental program
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Effect test

Embodiment 1

[0027] A battery life prediction method based on the combination of data-driven and battery characteristics, specifically comprising the following steps:

[0028] Step 1: Battery performance test and impact factor research, test the aging law of the battery under different environmental conditions, obtain different temperature conditions, different charge and discharge ratios, different initial SOC values ​​​​of battery charge and discharge, and different charging methods, etc. The attenuation trend of battery life under certain conditions, and analyze the influence of battery parameters on battery aging.

[0029] Step 2: Data selection, download the operating data of different battery-equipped models, including data of different mileage, different users, and different regions. For example, according to the domestic vehicle sales ranking, select a model with better sales, and then query and download 100 models with the same user type, the same region, and a mileage of about 30...

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Abstract

The invention discloses a battery life prediction method based on combination of data driving and battery characteristics. The battery life prediction method comprises the steps of battery aging and residual life factor test analysis and weight comparison, battery and user portrait algorithm establishment, data cleaning, extraction and analysis, SOH algorithm design and the like. The invention hasthe advantages that the method not only can be applied to estimation and prediction of vehicles or battery products with historical use data, but also can be applied to estimation of the battery SOHwithout historical operation or test data but with only the battery portrait and the user portrait, the application range is wide, the algorithm design has self-learning performance, and the estimation accuracy can be continuously improved along with increase of the data volume.

Description

technical field [0001] The invention relates to a battery life prediction (RUL) method, in particular to a battery life prediction method based on the combination of data driving and battery characteristics, and belongs to the field of battery life prediction (RUL) methods. Background technique [0002] As the application fields of lithium batteries become more and more extensive, its design capacity gradually increases, and the inconsistency of battery cells and different operating conditions make the service life of batteries vary greatly, and there are many factors for battery performance attenuation. The internal chemical reaction mechanism is relatively complex, making battery life prediction difficult to achieve. [0003] Electric vehicles use car batteries as power sources, and centrally manage and control the operation of car batteries through BMS (Battery Management System, battery management system). In order to ensure the normal operation of the BMS system, it is...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/367G01R31/392G01R31/3842G01R31/388
CPCG01R31/367G01R31/3842G01R31/388G01R31/392
Inventor 车晓刚赵彬董海书陈丽贝张津伟由勇李玉军李晓峰
Owner 蓝谷智慧(北京)能源科技有限公司
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