Online prognostication of remaining useful life for random discharge lithium-ion batteries using a gamma process model

Zeyu Wu; Zili Wang; Cheng Qian; Bo Sun; Yi Ren; Qiang Feng; Dezhen Yang

doi:10.1109/EuroSimE.2019.8724567

Online prognostication of remaining useful life for random discharge lithium-ion batteries using a gamma process model

School of Reliability and Systems Engineering

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

9 Scopus citations

Abstract

The prediction of remaining useful life (RUL) of lithium-ion batteries is an essential part of the prognostics and health management (PHM) for electric vehicles (EVs). The conventional method to estimate the RUL of batteries based on offline laboratory experiment data may give rise to a considerable amount of error by ignoring the uncertainties occurred in random charge-discharge cycles under operation. To overcome this problem, an online prognostic method based on a gamma process model was presented, and verified by using the experimental data from a set of four batteries test with random discharge recorded by National Aeronautics and Space Administration (NASA). In addition, the probability density function (PDF) and the reliability curve of the batteries were established along with the 0.95 confidence interval to reveal the statistical profile of predicted RULs. Compared to the conventional RUL prediction methods, the proposed method merely requires a small quantity of training data to achieve accurate RUL prediction for randomized usage batteries on EVs.

Original language	English
Title of host publication	2019 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538680407
DOIs	https://doi.org/10.1109/EuroSimE.2019.8724567
State	Published - Mar 2019
Event	20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2019 - Hannover, Germany Duration: 24 Mar 2019 → 27 Mar 2019

Publication series

Name	2019 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2019

Conference

Conference	20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2019
Country/Territory	Germany
City	Hannover
Period	24/03/19 → 27/03/19

UN SDGs

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

SDG 7 Affordable and Clean Energy

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10.1109/EuroSimE.2019.8724567

Cite this

Wu, Z., Wang, Z., Qian, C., Sun, B., Ren, Y., Feng, Q., & Yang, D. (2019). Online prognostication of remaining useful life for random discharge lithium-ion batteries using a gamma process model. In 2019 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2019 Article 8724567 (2019 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EuroSimE.2019.8724567

Wu, Zeyu ; Wang, Zili ; Qian, Cheng et al. / Online prognostication of remaining useful life for random discharge lithium-ion batteries using a gamma process model. 2019 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2019).

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title = "Online prognostication of remaining useful life for random discharge lithium-ion batteries using a gamma process model",

abstract = "The prediction of remaining useful life (RUL) of lithium-ion batteries is an essential part of the prognostics and health management (PHM) for electric vehicles (EVs). The conventional method to estimate the RUL of batteries based on offline laboratory experiment data may give rise to a considerable amount of error by ignoring the uncertainties occurred in random charge-discharge cycles under operation. To overcome this problem, an online prognostic method based on a gamma process model was presented, and verified by using the experimental data from a set of four batteries test with random discharge recorded by National Aeronautics and Space Administration (NASA). In addition, the probability density function (PDF) and the reliability curve of the batteries were established along with the 0.95 confidence interval to reveal the statistical profile of predicted RULs. Compared to the conventional RUL prediction methods, the proposed method merely requires a small quantity of training data to achieve accurate RUL prediction for randomized usage batteries on EVs.",

author = "Zeyu Wu and Zili Wang and Cheng Qian and Bo Sun and Yi Ren and Qiang Feng and Dezhen Yang",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2019 ; Conference date: 24-03-2019 Through 27-03-2019",

year = "2019",

month = mar,

doi = "10.1109/EuroSimE.2019.8724567",

language = "英语",

series = "2019 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2019",

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Wu, Z , Wang, Z , Qian, C , Sun, B , Ren, Y , Feng, Q & Yang, D 2019, Online prognostication of remaining useful life for random discharge lithium-ion batteries using a gamma process model. in 2019 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2019., 8724567, 2019 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2019, Institute of Electrical and Electronics Engineers Inc., 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2019, Hannover, Germany, 24/03/19. https://doi.org/10.1109/EuroSimE.2019.8724567

Online prognostication of remaining useful life for random discharge lithium-ion batteries using a gamma process model. / Wu, Zeyu ; Wang, Zili ; Qian, Cheng et al.
2019 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8724567 (2019 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2019).

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

TY - GEN

T1 - Online prognostication of remaining useful life for random discharge lithium-ion batteries using a gamma process model

AU - Wu, Zeyu

AU - Wang, Zili

AU - Qian, Cheng

AU - Sun, Bo

AU - Ren, Yi

AU - Feng, Qiang

AU - Yang, Dezhen

PY - 2019/3

Y1 - 2019/3

N2 - The prediction of remaining useful life (RUL) of lithium-ion batteries is an essential part of the prognostics and health management (PHM) for electric vehicles (EVs). The conventional method to estimate the RUL of batteries based on offline laboratory experiment data may give rise to a considerable amount of error by ignoring the uncertainties occurred in random charge-discharge cycles under operation. To overcome this problem, an online prognostic method based on a gamma process model was presented, and verified by using the experimental data from a set of four batteries test with random discharge recorded by National Aeronautics and Space Administration (NASA). In addition, the probability density function (PDF) and the reliability curve of the batteries were established along with the 0.95 confidence interval to reveal the statistical profile of predicted RULs. Compared to the conventional RUL prediction methods, the proposed method merely requires a small quantity of training data to achieve accurate RUL prediction for randomized usage batteries on EVs.

AB - The prediction of remaining useful life (RUL) of lithium-ion batteries is an essential part of the prognostics and health management (PHM) for electric vehicles (EVs). The conventional method to estimate the RUL of batteries based on offline laboratory experiment data may give rise to a considerable amount of error by ignoring the uncertainties occurred in random charge-discharge cycles under operation. To overcome this problem, an online prognostic method based on a gamma process model was presented, and verified by using the experimental data from a set of four batteries test with random discharge recorded by National Aeronautics and Space Administration (NASA). In addition, the probability density function (PDF) and the reliability curve of the batteries were established along with the 0.95 confidence interval to reveal the statistical profile of predicted RULs. Compared to the conventional RUL prediction methods, the proposed method merely requires a small quantity of training data to achieve accurate RUL prediction for randomized usage batteries on EVs.

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M3 - 会议稿件

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BT - 2019 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2019

Y2 - 24 March 2019 through 27 March 2019

ER -

Wu Z , Wang Z , Qian C , Sun B , Ren Y , Feng Q et al. Online prognostication of remaining useful life for random discharge lithium-ion batteries using a gamma process model. In 2019 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8724567. (2019 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2019). doi: 10.1109/EuroSimE.2019.8724567

Online prognostication of remaining useful life for random discharge lithium-ion batteries using a gamma process model

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