Battery Diagnosis: A Lifelong Learning Framework for Electric Vehicles

Jingyuan Zhao; Jinrui Nan; Junbin Wang; Heping Ling; Yubo Lian; Andrew Burke

doi:10.1109/VPPC55846.2022.10003378

Battery Diagnosis: A Lifelong Learning Framework for Electric Vehicles

Jingyuan Zhao, Jinrui Nan^*, Junbin Wang, Heping Ling, Yubo Lian, Andrew Burke^*

^*Corresponding author for this work

School of Mechanical Engineering

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

15 Citations (Scopus)

Abstract

Expending manufacturing capacity and development of high-energy batteries greatly stimulate the growth and applications of electric vehicles (EVs). However, battery diagnostics and prognostics related to capacity degradation (referred as state of health, SOH) and safety issues (referred as state of safety, SOS) in real-world applications is still a big deal. Due to the uncertainties in materials and manufacturing, dynamic operation conditions as well as a lack of plentiful, high-quality on-road data, accurate diagnosis of battery performance for 'real EVs' is very challenging. Considering the difficulty in accurately predicting battery behaviors in real-world applications, brand-new control area networks (CAN) and cloud-based solution could have considerable benefits. An AI-powered cloud-based framework integrating longitudinal electronic health records with real-world data enables continuous battery performance evaluation for EVs. This offers opportunities for combining data generation with data-driven approaches to predict the behavior of complex, time-varying electrochemical systems. It is hoped that this paper will be of reference value to the EV and battery industries for ameliorating some of the hurdles for battery diagnostics and prognostics under realistic EV conditions.

Original language	English
Title of host publication	2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665475877
DOIs	https://doi.org/10.1109/VPPC55846.2022.10003378
Publication status	Published - 2022
Event	2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022 - Merced, United States Duration: 1 Nov 2022 → 4 Nov 2022

Publication series

Name	2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022 - Proceedings

Conference

Conference	2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022
Country/Territory	United States
City	Merced
Period	1/11/22 → 4/11/22

Keywords

SOH
battery
cloud
data-driven
failure
framework
machine learning
safety

Access to Document

10.1109/VPPC55846.2022.10003378

Cite this

Zhao, J., Nan, J., Wang, J., Ling, H., Lian, Y., & Burke, A. (2022). Battery Diagnosis: A Lifelong Learning Framework for Electric Vehicles. In 2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022 - Proceedings (2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VPPC55846.2022.10003378

@inproceedings{bdcdea7ed5fc4c0aae3f065406a36c38,

title = "Battery Diagnosis: A Lifelong Learning Framework for Electric Vehicles",

abstract = "Expending manufacturing capacity and development of high-energy batteries greatly stimulate the growth and applications of electric vehicles (EVs). However, battery diagnostics and prognostics related to capacity degradation (referred as state of health, SOH) and safety issues (referred as state of safety, SOS) in real-world applications is still a big deal. Due to the uncertainties in materials and manufacturing, dynamic operation conditions as well as a lack of plentiful, high-quality on-road data, accurate diagnosis of battery performance for 'real EVs' is very challenging. Considering the difficulty in accurately predicting battery behaviors in real-world applications, brand-new control area networks (CAN) and cloud-based solution could have considerable benefits. An AI-powered cloud-based framework integrating longitudinal electronic health records with real-world data enables continuous battery performance evaluation for EVs. This offers opportunities for combining data generation with data-driven approaches to predict the behavior of complex, time-varying electrochemical systems. It is hoped that this paper will be of reference value to the EV and battery industries for ameliorating some of the hurdles for battery diagnostics and prognostics under realistic EV conditions.",

keywords = "SOH, battery, cloud, data-driven, failure, framework, machine learning, safety",

author = "Jingyuan Zhao and Jinrui Nan and Junbin Wang and Heping Ling and Yubo Lian and Andrew Burke",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022 ; Conference date: 01-11-2022 Through 04-11-2022",

year = "2022",

doi = "10.1109/VPPC55846.2022.10003378",

language = "English",

series = "2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022 - Proceedings",

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

booktitle = "2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022 - Proceedings",

address = "United States",

}

Zhao, J, Nan, J, Wang, J, Ling, H, Lian, Y & Burke, A 2022, Battery Diagnosis: A Lifelong Learning Framework for Electric Vehicles. in 2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022 - Proceedings. 2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022, Merced, United States, 1/11/22. https://doi.org/10.1109/VPPC55846.2022.10003378

Battery Diagnosis: A Lifelong Learning Framework for Electric Vehicles. / Zhao, Jingyuan; Nan, Jinrui; Wang, Junbin et al.
2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022 - Proceedings).

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

TY - GEN

T1 - Battery Diagnosis

T2 - 2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022

AU - Zhao, Jingyuan

AU - Nan, Jinrui

AU - Wang, Junbin

AU - Ling, Heping

AU - Lian, Yubo

AU - Burke, Andrew

PY - 2022

Y1 - 2022

N2 - Expending manufacturing capacity and development of high-energy batteries greatly stimulate the growth and applications of electric vehicles (EVs). However, battery diagnostics and prognostics related to capacity degradation (referred as state of health, SOH) and safety issues (referred as state of safety, SOS) in real-world applications is still a big deal. Due to the uncertainties in materials and manufacturing, dynamic operation conditions as well as a lack of plentiful, high-quality on-road data, accurate diagnosis of battery performance for 'real EVs' is very challenging. Considering the difficulty in accurately predicting battery behaviors in real-world applications, brand-new control area networks (CAN) and cloud-based solution could have considerable benefits. An AI-powered cloud-based framework integrating longitudinal electronic health records with real-world data enables continuous battery performance evaluation for EVs. This offers opportunities for combining data generation with data-driven approaches to predict the behavior of complex, time-varying electrochemical systems. It is hoped that this paper will be of reference value to the EV and battery industries for ameliorating some of the hurdles for battery diagnostics and prognostics under realistic EV conditions.

AB - Expending manufacturing capacity and development of high-energy batteries greatly stimulate the growth and applications of electric vehicles (EVs). However, battery diagnostics and prognostics related to capacity degradation (referred as state of health, SOH) and safety issues (referred as state of safety, SOS) in real-world applications is still a big deal. Due to the uncertainties in materials and manufacturing, dynamic operation conditions as well as a lack of plentiful, high-quality on-road data, accurate diagnosis of battery performance for 'real EVs' is very challenging. Considering the difficulty in accurately predicting battery behaviors in real-world applications, brand-new control area networks (CAN) and cloud-based solution could have considerable benefits. An AI-powered cloud-based framework integrating longitudinal electronic health records with real-world data enables continuous battery performance evaluation for EVs. This offers opportunities for combining data generation with data-driven approaches to predict the behavior of complex, time-varying electrochemical systems. It is hoped that this paper will be of reference value to the EV and battery industries for ameliorating some of the hurdles for battery diagnostics and prognostics under realistic EV conditions.

KW - SOH

KW - battery

KW - cloud

KW - data-driven

KW - failure

KW - framework

KW - machine learning

KW - safety

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DO - 10.1109/VPPC55846.2022.10003378

M3 - Conference contribution

AN - SCOPUS:85146690694

T3 - 2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022 - Proceedings

BT - 2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 1 November 2022 through 4 November 2022

ER -

Zhao J, Nan J, Wang J, Ling H, Lian Y, Burke A. Battery Diagnosis: A Lifelong Learning Framework for Electric Vehicles. In 2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 IEEE Vehicle Power and Propulsion Conference, VPPC 2022 - Proceedings). doi: 10.1109/VPPC55846.2022.10003378

Battery Diagnosis: A Lifelong Learning Framework for Electric Vehicles

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Keywords

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