State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network-Reference-Cited by-同舟云学术

State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network

Published:2020-05 Issue: Volume:459 Page:228069
ISSN:0378-7753
Container-title:Journal of Power Sources
language:en
Short-container-title:Journal of Power Sources

Author:

Li Penghua,Zhang Zijian,Xiong Qingyu,Ding Baocang,Hou Jie,Luo Dechao,Rong Yujun,Li Shuaiyong

Funder

Ministry of Education China Mobile Research Fund

Chongqing Basic Research and Frontier Exploration Project

Chongqing Artificial Intelligence Technology Innovation Major Theme Special Project

Chongqing Key Industries Common Key Technological Innovation Specialized

The Key Research Program of Chongqing Science & Technology Commission

National Natural Science Foundation Project of China

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Physical and Theoretical Chemistry,Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Reference37 articles.

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5. Remaining useful life prediction and state of health diagnosis for lithium-ion batteries using particle filter and support vector regression;Wei;IEEE Trans. Ind. Electron.,2018

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