A Comparative Study of Particle Filters and its Variants in Lithium-ion Battery SOH Estimation

Dawei Pan; Hengfeng Li; Yuchen Song

doi:10.1109/ICSMD50554.2020.9261654

A Comparative Study of Particle Filters and its Variants in Lithium-ion Battery SOH Estimation

Dawei Pan^*
, Hengfeng Li
, Yuchen Song

^*Corresponding author for this work

College of Information and Communication Engineering, Harbin Engineering University
School of Electronics and Information Engineering, Harbin Institute of Technology

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

10 Scopus citations

Abstract

With the wide application of lithium-ion battery in various fields, the State of Health (SOH) estimation has become a research hotspot for advanced battery management system (BMS). Accurate SOH estimation is helpful to ensure the safe operation of equipment or system in practical applications. Among various lithium-ion battery health diagnosis methods, particle filter and its variants are the mainstream with the significant advantages in non-linear and non-Gaussian system modeling. But in practical applications, the BMS always suffers from the limited power supplication and finite computing resources. Therefore, this paper implemented a comparative study on particle filter (PF) and typical variants, including extended Kalman particle filter (EPF), unscented particle filter (UPF), regularized particle filter (RPF). Through the NASA's battery degradation model, the performance of the above particle filter algorithms is compared and analyzed. The experimental results show that UPF has the highest estimation accuracy, and it is more suitable for the situation with higher prediction accuracy requirements. PF has the least time consumption and is more suitable for on-line health assessment.

Original language	English
Title of host publication	International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	198-203
Number of pages	6
ISBN (Electronic)	9781728192772
DOIs	https://doi.org/10.1109/ICSMD50554.2020.9261654
State	Published - 15 Oct 2020
Externally published	Yes
Event	1st International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Xi'an, China Duration: 15 Oct 2020 → 17 Oct 2020

Publication series

Name	International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings

Conference

Conference	1st International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020
Country/Territory	China
City	Xi'an
Period	15/10/20 → 17/10/20

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

extended Kalman particle filter
lithium-ion battery
particle filter
regularized particle filter
state of health
unscented particle filter

Access to Document

10.1109/ICSMD50554.2020.9261654

Cite this

Pan, D., Li, H., & Song, Y. (2020). A Comparative Study of Particle Filters and its Variants in Lithium-ion Battery SOH Estimation. In International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings (pp. 198-203). Article 9261654 (International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSMD50554.2020.9261654

Pan, Dawei ; Li, Hengfeng ; Song, Yuchen. / A Comparative Study of Particle Filters and its Variants in Lithium-ion Battery SOH Estimation. International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 198-203 (International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings).

@inproceedings{47d307d9cd6547fcbca0046533a92827,

title = "A Comparative Study of Particle Filters and its Variants in Lithium-ion Battery SOH Estimation",

abstract = "With the wide application of lithium-ion battery in various fields, the State of Health (SOH) estimation has become a research hotspot for advanced battery management system (BMS). Accurate SOH estimation is helpful to ensure the safe operation of equipment or system in practical applications. Among various lithium-ion battery health diagnosis methods, particle filter and its variants are the mainstream with the significant advantages in non-linear and non-Gaussian system modeling. But in practical applications, the BMS always suffers from the limited power supplication and finite computing resources. Therefore, this paper implemented a comparative study on particle filter (PF) and typical variants, including extended Kalman particle filter (EPF), unscented particle filter (UPF), regularized particle filter (RPF). Through the NASA's battery degradation model, the performance of the above particle filter algorithms is compared and analyzed. The experimental results show that UPF has the highest estimation accuracy, and it is more suitable for the situation with higher prediction accuracy requirements. PF has the least time consumption and is more suitable for on-line health assessment.",

keywords = "extended Kalman particle filter, lithium-ion battery, particle filter, regularized particle filter, state of health, unscented particle filter",

author = "Dawei Pan and Hengfeng Li and Yuchen Song",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 1st International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 ; Conference date: 15-10-2020 Through 17-10-2020",

year = "2020",

month = oct,

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doi = "10.1109/ICSMD50554.2020.9261654",

language = "英语",

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Pan, D, Li, H & Song, Y 2020, A Comparative Study of Particle Filters and its Variants in Lithium-ion Battery SOH Estimation. in International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings., 9261654, International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 198-203, 1st International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020, Xi'an, China, 15/10/20. https://doi.org/10.1109/ICSMD50554.2020.9261654

A Comparative Study of Particle Filters and its Variants in Lithium-ion Battery SOH Estimation. / Pan, Dawei; Li, Hengfeng; Song, Yuchen.
International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. p. 198-203 9261654 (International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings).

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

TY - GEN

T1 - A Comparative Study of Particle Filters and its Variants in Lithium-ion Battery SOH Estimation

AU - Pan, Dawei

AU - Li, Hengfeng

AU - Song, Yuchen

PY - 2020/10/15

Y1 - 2020/10/15

N2 - With the wide application of lithium-ion battery in various fields, the State of Health (SOH) estimation has become a research hotspot for advanced battery management system (BMS). Accurate SOH estimation is helpful to ensure the safe operation of equipment or system in practical applications. Among various lithium-ion battery health diagnosis methods, particle filter and its variants are the mainstream with the significant advantages in non-linear and non-Gaussian system modeling. But in practical applications, the BMS always suffers from the limited power supplication and finite computing resources. Therefore, this paper implemented a comparative study on particle filter (PF) and typical variants, including extended Kalman particle filter (EPF), unscented particle filter (UPF), regularized particle filter (RPF). Through the NASA's battery degradation model, the performance of the above particle filter algorithms is compared and analyzed. The experimental results show that UPF has the highest estimation accuracy, and it is more suitable for the situation with higher prediction accuracy requirements. PF has the least time consumption and is more suitable for on-line health assessment.

AB - With the wide application of lithium-ion battery in various fields, the State of Health (SOH) estimation has become a research hotspot for advanced battery management system (BMS). Accurate SOH estimation is helpful to ensure the safe operation of equipment or system in practical applications. Among various lithium-ion battery health diagnosis methods, particle filter and its variants are the mainstream with the significant advantages in non-linear and non-Gaussian system modeling. But in practical applications, the BMS always suffers from the limited power supplication and finite computing resources. Therefore, this paper implemented a comparative study on particle filter (PF) and typical variants, including extended Kalman particle filter (EPF), unscented particle filter (UPF), regularized particle filter (RPF). Through the NASA's battery degradation model, the performance of the above particle filter algorithms is compared and analyzed. The experimental results show that UPF has the highest estimation accuracy, and it is more suitable for the situation with higher prediction accuracy requirements. PF has the least time consumption and is more suitable for on-line health assessment.

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KW - lithium-ion battery

KW - particle filter

KW - regularized particle filter

KW - state of health

KW - unscented particle filter

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DO - 10.1109/ICSMD50554.2020.9261654

M3 - 会议稿件

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BT - International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 1st International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020

Y2 - 15 October 2020 through 17 October 2020

ER -

Pan D, Li H, Song Y. A Comparative Study of Particle Filters and its Variants in Lithium-ion Battery SOH Estimation. In International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. p. 198-203. 9261654. (International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings). doi: 10.1109/ICSMD50554.2020.9261654

A Comparative Study of Particle Filters and its Variants in Lithium-ion Battery SOH Estimation

Abstract

Publication series

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UN SDGs

Keywords

Access to Document

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