Prognostics of lithium-ion batteries health state based on adaptive mode decomposition and long short-term memory neural network-Reference-Cited by-同舟云学术

Prognostics of lithium-ion batteries health state based on adaptive mode decomposition and long short-term memory neural network

Published:2024-01 Issue: Volume:127 Page:107317
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Guo Li,He Hongwei^ORCID,Ren Yiran,Li Runze,Jiang Bin^ORCID,Gong Jianye

Funder

Anhui Province Department of Education

Anhui Polytechnic University

National Natural Science Foundation of China

University Natural Science Research Project of Anhui Province

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

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3. Prognostics of lithium-ion batteries based on capacity regeneration analysis and long short-term memory network;Cui;IEEE Trans. Instrum. Meas.,2022

4. Algorithm to determine the knee point on capacity fade curves of lithium-ion cells;Diao;Energies,2019

5. Learning human-understandable models for the health assessment of Li-ion batteries via Multi-Objective Genetic Programming;Echevarría;Eng. Appl. Artif. Intell.,2019

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