ARIMA Based Research on Cell Inconsistency Prediction of Power Battery

Cell inconsistency affects battery life and driving safety. In order to solve the accuracy problem of cell inconsistency prediction of power battery, a health indicator(HI) was constructed to reflect the degradation trends of cell inconsistency in the time dimension based on massive historical data...

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Main Authors: SONG Chao, XIONG Gang, XIE Yongbo, WANG Wenming
Format: Article
Language:zho
Published: Editorial Office of Control and Information Technology 2019-01-01
Series:Kongzhi Yu Xinxi Jishu
Subjects:
Online Access:http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2019.05.018
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author SONG Chao
XIONG Gang
XIE Yongbo
WANG Wenming
author_facet SONG Chao
XIONG Gang
XIE Yongbo
WANG Wenming
author_sort SONG Chao
collection DOAJ
description Cell inconsistency affects battery life and driving safety. In order to solve the accuracy problem of cell inconsistency prediction of power battery, a health indicator(HI) was constructed to reflect the degradation trends of cell inconsistency in the time dimension based on massive historical data of the vehicle network platform, and health indicator time series were extracted based on the segmentation interval of SOC. Considering the fluctuation of the health indicator after a long time, a ARIMA model was introduced for small sample prediction, only using the health indicator time series in recent several discharge conditions. The prediction results show that the method requires less training samples and less hardware resources, and the overall prediction accuracy is not less than 90%, which can meet the practical requirements.
format Article
id doaj-art-3d8dc3bcc2144df4bcecc5d6cbabb2ec
institution Kabale University
issn 2096-5427
language zho
publishDate 2019-01-01
publisher Editorial Office of Control and Information Technology
record_format Article
series Kongzhi Yu Xinxi Jishu
spelling doaj-art-3d8dc3bcc2144df4bcecc5d6cbabb2ec2025-08-25T06:54:25ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272019-01-0136828782327741ARIMA Based Research on Cell Inconsistency Prediction of Power BatterySONG ChaoXIONG GangXIE YongboWANG WenmingCell inconsistency affects battery life and driving safety. In order to solve the accuracy problem of cell inconsistency prediction of power battery, a health indicator(HI) was constructed to reflect the degradation trends of cell inconsistency in the time dimension based on massive historical data of the vehicle network platform, and health indicator time series were extracted based on the segmentation interval of SOC. Considering the fluctuation of the health indicator after a long time, a ARIMA model was introduced for small sample prediction, only using the health indicator time series in recent several discharge conditions. The prediction results show that the method requires less training samples and less hardware resources, and the overall prediction accuracy is not less than 90%, which can meet the practical requirements.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2019.05.018power batterycell inconsistencyARIMA modelhealth factor time seriesbattery managementvehicle network
spellingShingle SONG Chao
XIONG Gang
XIE Yongbo
WANG Wenming
ARIMA Based Research on Cell Inconsistency Prediction of Power Battery
Kongzhi Yu Xinxi Jishu
power battery
cell inconsistency
ARIMA model
health factor time series
battery management
vehicle network
title ARIMA Based Research on Cell Inconsistency Prediction of Power Battery
title_full ARIMA Based Research on Cell Inconsistency Prediction of Power Battery
title_fullStr ARIMA Based Research on Cell Inconsistency Prediction of Power Battery
title_full_unstemmed ARIMA Based Research on Cell Inconsistency Prediction of Power Battery
title_short ARIMA Based Research on Cell Inconsistency Prediction of Power Battery
title_sort arima based research on cell inconsistency prediction of power battery
topic power battery
cell inconsistency
ARIMA model
health factor time series
battery management
vehicle network
url http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2019.05.018
work_keys_str_mv AT songchao arimabasedresearchoncellinconsistencypredictionofpowerbattery
AT xionggang arimabasedresearchoncellinconsistencypredictionofpowerbattery
AT xieyongbo arimabasedresearchoncellinconsistencypredictionofpowerbattery
AT wangwenming arimabasedresearchoncellinconsistencypredictionofpowerbattery