Innovative multiscale fusion – Antinoise extended long short-term memory neural network modeling for high precision state of health estimation of lithium-ion batteries

Junjie Tao, Shunli Wang, Wen Cao, Yixiu Cui, Carlos Fernandez and Josep M. Guerrero

Energy, 2024, vol. 312, issue C

Abstract: An accurate assessment of lithium-ion (Li-ion) batteries' state of health (SOH) is essential for the safe operation of new energy systems and extended battery life. Health factors were extracted by studying the aging test data of Li-ion batteries to estimate the health state. A multi-scale data fusion and anti-noise extended long short-term memory (LSTM) neural network is proposed. The current, voltage, and other micro-scale data of Li-ion batteries were extracted by fast Fourier transform (FFT), and the main frequency characteristics were extracted by principal component analysis (PCA). The hidden layer structure of the LSTM neural network is extended to separate independent positive and negative correlation gating weight parameters to reduce the risk of overfitting. At the same time, a novel network weight updating algorithm combining an extended Kalman filter (EKF) and gradient descent (GD) is proposed, and the inherent noise suppression property of the EKF is utilized to improve the algorithm's robustness. The experimental results show that the accuracy of the MSDF-ANELSTM algorithm is improved by 66.66 %, stability by 83.84 %, and generalization performance by 72.54 % compared with the traditional neural network. This is conducive to promoting the industrial application of data-driven Li-ion battery management systems.

Keywords: Lithium-ion battery health state estimation; Fast Fourier transform; Principal component analysis; Multi-scale data fusion; Anti-noise extended LSTM neural network (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224033176
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:312:y:2024:i:c:s0360544224033176

DOI: 10.1016/j.energy.2024.133541

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().