Estimation of Battery Capacity Fade using Real-World Vehicle Data for Diagnosis of Abnormal Capacity Loss

Zirun Jia; Zekun Zhang; Zhenyu Sun; Peng Liu; Zhenpo Wang; Zhaosheng Zhang

doi:10.1109/ECCE53617.2023.10362083

Estimation of Battery Capacity Fade using Real-World Vehicle Data for Diagnosis of Abnormal Capacity Loss

Zirun Jia^*, Zekun Zhang, Zhenyu Sun, Peng Liu, Zhenpo Wang, Zhaosheng Zhang

^*Corresponding author for this work

School of Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Accurate estimation of battery capacity and diagnosis of its degradation state are essential for safe battery management. This paper presents an advanced method for accurate capacity estimation and abnormal capacity degradation diagnosis of electric vehicle battery systems. Base on the real-world electric vehicles (EVs) data, the reference capacity of the battery system can be calculated by integrates incremental Capacity (IC) curves and Coulomb counting method. Main factors, such as mileage, temperature, charging current, and depth of discharge, affecting the battery performance and life were discussed. And then, a fusion model developed by combining the XGBoost and LightGBM algorithms is used to estimate capacity. The results show that the proposed model outperforms the single model with a mean absolute percentage error (MAPE) of 2.45%, and has a better ability to follow the abnormal capacity degradation, which can evaluate the battery capacity and ensure safety.

Original language	English
Title of host publication	2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1480-1486
Number of pages	7
ISBN (Electronic)	9798350316445
DOIs	https://doi.org/10.1109/ECCE53617.2023.10362083
Publication status	Published - 2023
Event	2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023 - Nashville, United States Duration: 29 Oct 2023 → 2 Nov 2023

Publication series

Name	2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

Conference

Conference	2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023
Country/Territory	United States
City	Nashville
Period	29/10/23 → 2/11/23

Keywords

Abnormal capacity loss diagnosis
Battery systems
Capacity estimation
Electric Vehicle
Real-world data

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ECCE53617.2023.10362083

Cite this

Jia, Z., Zhang, Z., Sun, Z., Liu, P., Wang, Z., & Zhang, Z. (2023). Estimation of Battery Capacity Fade using Real-World Vehicle Data for Diagnosis of Abnormal Capacity Loss. In 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023 (pp. 1480-1486). (2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ECCE53617.2023.10362083

@inproceedings{5e3943aec58d4c7baa3f086743dc5b07,

title = "Estimation of Battery Capacity Fade using Real-World Vehicle Data for Diagnosis of Abnormal Capacity Loss",

abstract = "Accurate estimation of battery capacity and diagnosis of its degradation state are essential for safe battery management. This paper presents an advanced method for accurate capacity estimation and abnormal capacity degradation diagnosis of electric vehicle battery systems. Base on the real-world electric vehicles (EVs) data, the reference capacity of the battery system can be calculated by integrates incremental Capacity (IC) curves and Coulomb counting method. Main factors, such as mileage, temperature, charging current, and depth of discharge, affecting the battery performance and life were discussed. And then, a fusion model developed by combining the XGBoost and LightGBM algorithms is used to estimate capacity. The results show that the proposed model outperforms the single model with a mean absolute percentage error (MAPE) of 2.45%, and has a better ability to follow the abnormal capacity degradation, which can evaluate the battery capacity and ensure safety.",

keywords = "Abnormal capacity loss diagnosis, Battery systems, Capacity estimation, Electric Vehicle, Real-world data",

author = "Zirun Jia and Zekun Zhang and Zhenyu Sun and Peng Liu and Zhenpo Wang and Zhaosheng Zhang",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023 ; Conference date: 29-10-2023 Through 02-11-2023",

year = "2023",

doi = "10.1109/ECCE53617.2023.10362083",

language = "English",

series = "2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1480--1486",

booktitle = "2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023",

address = "United States",

}

Jia, Z, Zhang, Z, Sun, Z, Liu, P, Wang, Z & Zhang, Z 2023, Estimation of Battery Capacity Fade using Real-World Vehicle Data for Diagnosis of Abnormal Capacity Loss. in 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023. 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023, Institute of Electrical and Electronics Engineers Inc., pp. 1480-1486, 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023, Nashville, United States, 29/10/23. https://doi.org/10.1109/ECCE53617.2023.10362083

Estimation of Battery Capacity Fade using Real-World Vehicle Data for Diagnosis of Abnormal Capacity Loss. / Jia, Zirun; Zhang, Zekun; Sun, Zhenyu et al.
2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1480-1486 (2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Estimation of Battery Capacity Fade using Real-World Vehicle Data for Diagnosis of Abnormal Capacity Loss

AU - Jia, Zirun

AU - Zhang, Zekun

AU - Sun, Zhenyu

AU - Liu, Peng

AU - Wang, Zhenpo

AU - Zhang, Zhaosheng

PY - 2023

Y1 - 2023

N2 - Accurate estimation of battery capacity and diagnosis of its degradation state are essential for safe battery management. This paper presents an advanced method for accurate capacity estimation and abnormal capacity degradation diagnosis of electric vehicle battery systems. Base on the real-world electric vehicles (EVs) data, the reference capacity of the battery system can be calculated by integrates incremental Capacity (IC) curves and Coulomb counting method. Main factors, such as mileage, temperature, charging current, and depth of discharge, affecting the battery performance and life were discussed. And then, a fusion model developed by combining the XGBoost and LightGBM algorithms is used to estimate capacity. The results show that the proposed model outperforms the single model with a mean absolute percentage error (MAPE) of 2.45%, and has a better ability to follow the abnormal capacity degradation, which can evaluate the battery capacity and ensure safety.

AB - Accurate estimation of battery capacity and diagnosis of its degradation state are essential for safe battery management. This paper presents an advanced method for accurate capacity estimation and abnormal capacity degradation diagnosis of electric vehicle battery systems. Base on the real-world electric vehicles (EVs) data, the reference capacity of the battery system can be calculated by integrates incremental Capacity (IC) curves and Coulomb counting method. Main factors, such as mileage, temperature, charging current, and depth of discharge, affecting the battery performance and life were discussed. And then, a fusion model developed by combining the XGBoost and LightGBM algorithms is used to estimate capacity. The results show that the proposed model outperforms the single model with a mean absolute percentage error (MAPE) of 2.45%, and has a better ability to follow the abnormal capacity degradation, which can evaluate the battery capacity and ensure safety.

KW - Abnormal capacity loss diagnosis

KW - Battery systems

KW - Capacity estimation

KW - Electric Vehicle

KW - Real-world data

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U2 - 10.1109/ECCE53617.2023.10362083

DO - 10.1109/ECCE53617.2023.10362083

M3 - Conference contribution

AN - SCOPUS:85182938388

T3 - 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

SP - 1480

EP - 1486

BT - 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

Y2 - 29 October 2023 through 2 November 2023

ER -

Jia Z, Zhang Z, Sun Z, Liu P, Wang Z , Zhang Z. Estimation of Battery Capacity Fade using Real-World Vehicle Data for Diagnosis of Abnormal Capacity Loss. In 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1480-1486. (2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023). doi: 10.1109/ECCE53617.2023.10362083

Estimation of Battery Capacity Fade using Real-World Vehicle Data for Diagnosis of Abnormal Capacity Loss

Abstract

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Keywords

UN SDGs

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