Remaining Useful Life Prediction for Lithium-Ion Batteries With a Hybrid Model Based on TCN-GRU-DNN and Dual Attention Mechanism-Reference-Cited by-同舟云学术

Remaining Useful Life Prediction for Lithium-Ion Batteries With a Hybrid Model Based on TCN-GRU-DNN and Dual Attention Mechanism

Published:2023-09 Issue:3 Volume:9 Page:4726-4740
ISSN:2332-7782
Container-title:IEEE Transactions on Transportation Electrification
language:
Short-container-title:IEEE Trans. Transp. Electrific.

Author:

Li Lei¹^ORCID,Li Yuanjiang²^ORCID,Mao Runze²^ORCID,Li Li³,Hua Wenbo⁴^ORCID,Zhang Jinglin⁵^ORCID

Affiliation:

1. School of Control Science and Engineering, Shandong University, Jinan, China

2. School of Oceanography, Jiangsu University of Science and Technology, Zhenjiang, China

3. School of Automobile and Traffic Engineering, Jiangsu University of Technology, Changzhou, China

4. Zhenjiang MingRun Info. Tech. Company Ltd., Zhenjiang, China

5. Shandong University, Jinan, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Key Research and Development Program of Jiangsu Province

Taishan Scholars Program, Major Basic Research Projects of Shandong Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Transportation,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6687316/10251026/10049579.pdf?arnumber=10049579

Reference45 articles.

1. A novel hybrid physics-based and data-driven approach for degradation trajectory prediction in Li-ion batteries;xu;IEEE Trans Transport Electrific,2022

2. Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE

3. A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery

4. Clothing Sale Forecasting by a Composite GRU–Prophet Model With an Attention Mechanism

5. Remaining useful life prediction of lithium‐ion battery based on extended Kalman particle filter

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