GRU Optimized by Beetle Antennae Search Algorithm for State of Health Estimation of Lithium-ion Battery

Xiaofei Wang; Quan Sun; Liang Chen; Di Mu; Rui Liu

doi:10.1109/ACEEE58657.2023.10239621

GRU Optimized by Beetle Antennae Search Algorithm for State of Health Estimation of Lithium-ion Battery

Xiaofei Wang
, Quan Sun
, Liang Chen
, Di Mu
, Rui Liu

Faculty of Electronic and Information Engineering

Xi'an Jiaotong University
Changzhou Power Supply Company
Beijing Smart Chip Microelectronics Technology Company Limited

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

Abstract

State of health (SOH) estimation of Lithium-ion battery plays a key role in battery management system, as it characterizes the battery's health state and ensures safe and stable operation. This paper develops a novel SOH estimation method combining the Gate Recurrent Unit (GRU) and the Beetle Antennae Search Algorithm (BASA). First, the GRU NN with the outstanding capability of the time series and simple structure is optimized by BASA for selecting the optimal hyper-parameters to improve the SOH estimation performance. Second, an efficient features extracted method, called local tangent space alignment (LTSA), is utilized for extracting the crucial features from the measurement data. Finally, several experiments are conducted on a benchmark dataset, and the results show that the proposed method can obtain accurate SOH estimation compared with other methods.

Original language	English
Title of host publication	2023 6th Asia Conference on Energy and Electrical Engineering, ACEEE 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	456-461
Number of pages	6
ISBN (Electronic)	9798350312690
DOIs	https://doi.org/10.1109/ACEEE58657.2023.10239621
State	Published - 2023
Event	6th Asia Conference on Energy and Electrical Engineering, ACEEE 2023 - Chengdu, China Duration: 21 Jul 2023 → 23 Jul 2023

Publication series

Name	2023 6th Asia Conference on Energy and Electrical Engineering, ACEEE 2023

Conference

Conference	6th Asia Conference on Energy and Electrical Engineering, ACEEE 2023
Country/Territory	China
City	Chengdu
Period	21/07/23 → 23/07/23

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

beetle antennae search algorithm
gate recurrent unit
local tangent space alignment
state of health

Access to Document

10.1109/ACEEE58657.2023.10239621

Cite this

Wang, X., Sun, Q., Chen, L., Mu, D., & Liu, R. (2023). GRU Optimized by Beetle Antennae Search Algorithm for State of Health Estimation of Lithium-ion Battery. In 2023 6th Asia Conference on Energy and Electrical Engineering, ACEEE 2023 (pp. 456-461). (2023 6th Asia Conference on Energy and Electrical Engineering, ACEEE 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACEEE58657.2023.10239621

Wang, Xiaofei ; Sun, Quan ; Chen, Liang et al. / GRU Optimized by Beetle Antennae Search Algorithm for State of Health Estimation of Lithium-ion Battery. 2023 6th Asia Conference on Energy and Electrical Engineering, ACEEE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 456-461 (2023 6th Asia Conference on Energy and Electrical Engineering, ACEEE 2023).

@inproceedings{87d9aa47f6084cc389342acf92592494,

title = "GRU Optimized by Beetle Antennae Search Algorithm for State of Health Estimation of Lithium-ion Battery",

abstract = "State of health (SOH) estimation of Lithium-ion battery plays a key role in battery management system, as it characterizes the battery's health state and ensures safe and stable operation. This paper develops a novel SOH estimation method combining the Gate Recurrent Unit (GRU) and the Beetle Antennae Search Algorithm (BASA). First, the GRU NN with the outstanding capability of the time series and simple structure is optimized by BASA for selecting the optimal hyper-parameters to improve the SOH estimation performance. Second, an efficient features extracted method, called local tangent space alignment (LTSA), is utilized for extracting the crucial features from the measurement data. Finally, several experiments are conducted on a benchmark dataset, and the results show that the proposed method can obtain accurate SOH estimation compared with other methods.",

keywords = "beetle antennae search algorithm, gate recurrent unit, local tangent space alignment, state of health",

author = "Xiaofei Wang and Quan Sun and Liang Chen and Di Mu and Rui Liu",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 6th Asia Conference on Energy and Electrical Engineering, ACEEE 2023 ; Conference date: 21-07-2023 Through 23-07-2023",

year = "2023",

doi = "10.1109/ACEEE58657.2023.10239621",

language = "英语",

series = "2023 6th Asia Conference on Energy and Electrical Engineering, ACEEE 2023",

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

pages = "456--461",

booktitle = "2023 6th Asia Conference on Energy and Electrical Engineering, ACEEE 2023",

}

Wang, X, Sun, Q, Chen, L, Mu, D & Liu, R 2023, GRU Optimized by Beetle Antennae Search Algorithm for State of Health Estimation of Lithium-ion Battery. in 2023 6th Asia Conference on Energy and Electrical Engineering, ACEEE 2023. 2023 6th Asia Conference on Energy and Electrical Engineering, ACEEE 2023, Institute of Electrical and Electronics Engineers Inc., pp. 456-461, 6th Asia Conference on Energy and Electrical Engineering, ACEEE 2023, Chengdu, China, 21/07/23. https://doi.org/10.1109/ACEEE58657.2023.10239621

GRU Optimized by Beetle Antennae Search Algorithm for State of Health Estimation of Lithium-ion Battery. / Wang, Xiaofei; Sun, Quan; Chen, Liang et al.
2023 6th Asia Conference on Energy and Electrical Engineering, ACEEE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 456-461 (2023 6th Asia Conference on Energy and Electrical Engineering, ACEEE 2023).

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

TY - GEN

T1 - GRU Optimized by Beetle Antennae Search Algorithm for State of Health Estimation of Lithium-ion Battery

AU - Wang, Xiaofei

AU - Sun, Quan

AU - Chen, Liang

AU - Mu, Di

AU - Liu, Rui

PY - 2023

Y1 - 2023

N2 - State of health (SOH) estimation of Lithium-ion battery plays a key role in battery management system, as it characterizes the battery's health state and ensures safe and stable operation. This paper develops a novel SOH estimation method combining the Gate Recurrent Unit (GRU) and the Beetle Antennae Search Algorithm (BASA). First, the GRU NN with the outstanding capability of the time series and simple structure is optimized by BASA for selecting the optimal hyper-parameters to improve the SOH estimation performance. Second, an efficient features extracted method, called local tangent space alignment (LTSA), is utilized for extracting the crucial features from the measurement data. Finally, several experiments are conducted on a benchmark dataset, and the results show that the proposed method can obtain accurate SOH estimation compared with other methods.

AB - State of health (SOH) estimation of Lithium-ion battery plays a key role in battery management system, as it characterizes the battery's health state and ensures safe and stable operation. This paper develops a novel SOH estimation method combining the Gate Recurrent Unit (GRU) and the Beetle Antennae Search Algorithm (BASA). First, the GRU NN with the outstanding capability of the time series and simple structure is optimized by BASA for selecting the optimal hyper-parameters to improve the SOH estimation performance. Second, an efficient features extracted method, called local tangent space alignment (LTSA), is utilized for extracting the crucial features from the measurement data. Finally, several experiments are conducted on a benchmark dataset, and the results show that the proposed method can obtain accurate SOH estimation compared with other methods.

KW - beetle antennae search algorithm

KW - gate recurrent unit

KW - local tangent space alignment

KW - state of health

UR - https://www.scopus.com/pages/publications/85173610299

U2 - 10.1109/ACEEE58657.2023.10239621

DO - 10.1109/ACEEE58657.2023.10239621

M3 - 会议稿件

AN - SCOPUS:85173610299

T3 - 2023 6th Asia Conference on Energy and Electrical Engineering, ACEEE 2023

SP - 456

EP - 461

BT - 2023 6th Asia Conference on Energy and Electrical Engineering, ACEEE 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th Asia Conference on Energy and Electrical Engineering, ACEEE 2023

Y2 - 21 July 2023 through 23 July 2023

ER -

Wang X, Sun Q, Chen L, Mu D, Liu R. GRU Optimized by Beetle Antennae Search Algorithm for State of Health Estimation of Lithium-ion Battery. In 2023 6th Asia Conference on Energy and Electrical Engineering, ACEEE 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 456-461. (2023 6th Asia Conference on Energy and Electrical Engineering, ACEEE 2023). doi: 10.1109/ACEEE58657.2023.10239621

GRU Optimized by Beetle Antennae Search Algorithm for State of Health Estimation of Lithium-ion Battery

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this