Fault detection for Li-ion batteries of electric vehicles with segmented regression method

Abstract Electric vehicles are increasingly popular for their environmental benefits and cost savings, but the reliability and safety of their lithium-ion batteries are critical concerns. Current regression methods for battery fault detection often analyze charging and discharging as a single contin...

Full description

Saved in:
Bibliographic Details
Main Authors: Muaaz Bin Kaleem, Yun Zhou, Fu Jiang, Zhijun Liu, Heng Li
Format: Article
Language:English
Published: Nature Portfolio 2024-12-01
Series:Scientific Reports
Subjects:
Online Access:https://doi.org/10.1038/s41598-024-82960-0
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1841559420320874496
author Muaaz Bin Kaleem
Yun Zhou
Fu Jiang
Zhijun Liu
Heng Li
author_facet Muaaz Bin Kaleem
Yun Zhou
Fu Jiang
Zhijun Liu
Heng Li
author_sort Muaaz Bin Kaleem
collection DOAJ
description Abstract Electric vehicles are increasingly popular for their environmental benefits and cost savings, but the reliability and safety of their lithium-ion batteries are critical concerns. Current regression methods for battery fault detection often analyze charging and discharging as a single continuous process, missing important phase differences. This paper proposes segmented regression to better capture these distinct characteristics for accurate fault detection. The focus is on detecting voltage deviations caused by internal short circuits, external short circuits, and capacity degradation, which are primary indicators of battery faults. Firstly, data from real electric vehicles, operating under normal and faulty conditions, is collected over a period of 18 months. Secondly, the segmented regression method is utilized to segment the data based on the charging and discharging cycles and capture potential dependencies in battery behavior within each cycle. Thirdly, an optimized gated recurrent unit network is developed and integrated with the segmented regression to enable accurate cell voltage estimation. Lastly, an adaptive threshold algorithm is proposed to integrate driving behavior and environmental factors into a Gaussian process regression model. The integrated model dynamically estimates the normal fluctuation range of battery cell voltages for fault detection. The effectiveness of the proposed method is validated on a comprehensive dataset, achieving superior accuracy with values of 99.803% and 99.507% during the charging and discharging phases, respectively.
format Article
id doaj-art-3505a49b5e004587a86e666322f28d0f
institution Kabale University
issn 2045-2322
language English
publishDate 2024-12-01
publisher Nature Portfolio
record_format Article
series Scientific Reports
spelling doaj-art-3505a49b5e004587a86e666322f28d0f2025-01-05T12:29:27ZengNature PortfolioScientific Reports2045-23222024-12-0114111910.1038/s41598-024-82960-0Fault detection for Li-ion batteries of electric vehicles with segmented regression methodMuaaz Bin Kaleem0Yun Zhou1Fu Jiang2Zhijun Liu3Heng Li4School of Electronic Information, Central South UniversitySchool of Information Technology and Management, Hunan University of Finance and EconomicsSchool of Electronic Information, Central South UniversitySchool of Computer Science and Engineering, Central South UniversitySchool of Electronic Information, Central South UniversityAbstract Electric vehicles are increasingly popular for their environmental benefits and cost savings, but the reliability and safety of their lithium-ion batteries are critical concerns. Current regression methods for battery fault detection often analyze charging and discharging as a single continuous process, missing important phase differences. This paper proposes segmented regression to better capture these distinct characteristics for accurate fault detection. The focus is on detecting voltage deviations caused by internal short circuits, external short circuits, and capacity degradation, which are primary indicators of battery faults. Firstly, data from real electric vehicles, operating under normal and faulty conditions, is collected over a period of 18 months. Secondly, the segmented regression method is utilized to segment the data based on the charging and discharging cycles and capture potential dependencies in battery behavior within each cycle. Thirdly, an optimized gated recurrent unit network is developed and integrated with the segmented regression to enable accurate cell voltage estimation. Lastly, an adaptive threshold algorithm is proposed to integrate driving behavior and environmental factors into a Gaussian process regression model. The integrated model dynamically estimates the normal fluctuation range of battery cell voltages for fault detection. The effectiveness of the proposed method is validated on a comprehensive dataset, achieving superior accuracy with values of 99.803% and 99.507% during the charging and discharging phases, respectively.https://doi.org/10.1038/s41598-024-82960-0Adaptive thresholdBattery safetyElectric vehiclesFault detectionLithium-ion batteries
spellingShingle Muaaz Bin Kaleem
Yun Zhou
Fu Jiang
Zhijun Liu
Heng Li
Fault detection for Li-ion batteries of electric vehicles with segmented regression method
Scientific Reports
Adaptive threshold
Battery safety
Electric vehicles
Fault detection
Lithium-ion batteries
title Fault detection for Li-ion batteries of electric vehicles with segmented regression method
title_full Fault detection for Li-ion batteries of electric vehicles with segmented regression method
title_fullStr Fault detection for Li-ion batteries of electric vehicles with segmented regression method
title_full_unstemmed Fault detection for Li-ion batteries of electric vehicles with segmented regression method
title_short Fault detection for Li-ion batteries of electric vehicles with segmented regression method
title_sort fault detection for li ion batteries of electric vehicles with segmented regression method
topic Adaptive threshold
Battery safety
Electric vehicles
Fault detection
Lithium-ion batteries
url https://doi.org/10.1038/s41598-024-82960-0
work_keys_str_mv AT muaazbinkaleem faultdetectionforliionbatteriesofelectricvehicleswithsegmentedregressionmethod
AT yunzhou faultdetectionforliionbatteriesofelectricvehicleswithsegmentedregressionmethod
AT fujiang faultdetectionforliionbatteriesofelectricvehicleswithsegmentedregressionmethod
AT zhijunliu faultdetectionforliionbatteriesofelectricvehicleswithsegmentedregressionmethod
AT hengli faultdetectionforliionbatteriesofelectricvehicleswithsegmentedregressionmethod