Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network

Yu Guo; Dongfang Yang; Yang Zhang; Licheng Wang; Kai Wang

doi:10.1186/s41601-022-00261-y

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Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network

Contents | 更新时间：2026-09-30

- Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network
- Protection and Control of Modern Power Systems Vol. 7, Issue 3, Pages: 1-17(2022)
- 作者机构：
  
  1. School of Electrical Engineering, Weihai Innovation Research Institute, Qingdao University,Qingdao,China
  2. Xi'an Traffic Engineering Institute,Xi'an,China
  3. Strategic Research Institute, State Power Investment Corporation,Beijing,China
  4. School of Information Engineering, Zhejiang University of Technology,Hangzhou,China
- 作者简介：
- 基金信息：
- DOI：10.1186/s41601-022-00261-y
  CLC：
- Published：01 July 2022
- 稿件说明：
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Yu Guo, Dongfang Yang, Yang Zhang, et al. Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network[J]. Protection and Control of Modern Power Systems, 2022, 7(3): 1-17.
DOI：

Yu Guo, Dongfang Yang, Yang Zhang, et al. Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network[J]. Protection and Control of Modern Power Systems, 2022, 7(3): 1-17. DOI： 10.1186/s41601-022-00261-y.

摘要

The estimation of state of health (SOH) of a lithium-ion battery (LIB) is of great significance to system safety and economic development. This paper proposes a SOH estimation method based on the SSA-Elman model for the first time. To improve the correlation rates between features and battery capacity

a method combining median absolute deviation filtering and Savitzky-Golay filtering is proposed to process the data. Based on the aging characteristics of the LIB

five features with correlation rates above 0.99 after data processing are then proposed. Addressing the defects of the Elman model

the sparrow search algorithm (SSA) is used to optimize the network parameters. In addition

a data incremental update mechanism is added to improve the generalization of the SSA-Elman model. Finally

the performance of the proposed model is verified based on NASA dataset

and the outputs of the Elman

LSTM and SSA-Elman models are compared. The results show that the proposed method can accurately estimate the SOH

with the root mean square error (RMSE) being as low as 0.0024 and the mean absolute percentage error (MAPE) being as low as 0.25%. In addition

RMSE does not exceed 0.0224 and MAPE does not exceed 2.21% in high temperature and low temperature verifications.

Abstract

a method combining median absolute deviation filtering and Savitzky–Golay filtering is proposed to process the data. Based on the aging characteristics of the LIB

five features with correlation rates above 0.99 after data processing are then proposed. Addressing the defects of the Elman model

the sparrow search algorithm (SSA) is used to optimize the network parameters. In addition

a data incremental update mechanism is added to improve the generalization of the SSA-Elman model. Finally

the performance of the proposed model is verified based on NASA dataset

and the outputs of the Elman

LSTM and SSA-Elman models are compared. The results show that the proposed method can accurately estimate the SOH

with the root mean square error (RMSE) being as low as 0.0024 and the mean absolute percentage error (MAPE) being as low as 0.25%. In addition

RMSE does not exceed 0.0224 and MAPE does not exceed 2.21% in high temperature and low temperature verifications.

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