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An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage-temperature variation.

Wang, Shunli; Takyi-Aninakwa, Paul; Jin, Siyu; Yu, Chunmei; Fernandez, Carlos; Stroe, Daniel-Ioan

Authors

Shunli Wang

Paul Takyi-Aninakwa

Siyu Jin

Chunmei Yu

Daniel-Ioan Stroe



Abstract

The whole-life-cycle state of charge (SOC) prediction plays a significant role in various applications of lithium-ion batteries, but with great difficulties due to their internal capacity, working temperature, and current-rate variations. In this paper, an improved feedforward-long short-term memory (FF-LSTM) modeling method is proposed to realize an accurate whole-life-cycle SOC prediction by effectively considering the current, voltage, and temperature variations. An optimized sliding balance window is constructed for the measured current filtering to establish a new three-dimensional vector as the input matrix for the filtered current and voltage. Then, an improved steady-state screening model is constructed for the predicted SOC redundancy reduction that is obtained by the Ampere-hour integral method and taken as a one-dimensional output vector. The long-term charging capacity decay tests are conducted on two batteries, C7 and C8. The results show that the battery charging capacity reduces significantly with increasing time, and the capacity decreases by 21.30% and 22.61%, respectively, after 200 cycles. The maximum whole-life-cycle SOC prediction error is 3.53% with RMSE, MAE, and MAPE values of 3.451%, 2.541%, and 0.074%, respectively, under the complex DST working condition. The improved FF-LSTM modeling method provides an effective reference for the whole-life-cycle SOC prediction in battery system applications.

Citation

WANG, S., TAKYI-ANINAKWA, P., JIN, S., YU, C., FERNANDEZ, C. and STROE, D.-I. [2022]. An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage-temperature variation. Energy [online], In press, article 124224. Available from: https://doi.org/10.1016/j.energy.2022.124224

Journal Article Type Article
Acceptance Date May 6, 2022
Online Publication Date May 11, 2022
Deposit Date May 13, 2022
Publicly Available Date May 12, 2023
Journal Energy
Print ISSN 0360-5442
Publisher Elsevier
Peer Reviewed Peer Reviewed
Article Number 124224
DOI https://doi.org/10.1016/j.energy.2022.124224
Keywords Whole-life-cycle state of charge; Lithium-ion battery; Capacity fading; Feedforward-long short-term memory; Sliding balance window; Steady-state screening model
Public URL https://rgu-repository.worktribe.com/output/1664517