State of health estimation of lithium-ion batteries based on multi-health features extraction and improved long short-term memory neural network-Reference-Cited by-同舟云学术

State of health estimation of lithium-ion batteries based on multi-health features extraction and improved long short-term memory neural network

Published:2023-11 Issue: Volume:282 Page:128956
ISSN:0360-5442
Container-title:Energy
language:en
Short-container-title:Energy

Author:

Peng Simin^ORCID,Sun Yunxiang,Liu Dandan,Yu Quanqing,Kan Jiarong,Pecht Michael^ORCID

Funder

Qinglan Project of Jiangsu Province of China

Yancheng Institute of Technology

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

General Energy,Pollution,Mechanical Engineering,Building and Construction,Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Civil and Structural Engineering

Reference41 articles.

1. OCV-SOC-temperature relationship construction and state of charge estimation for a series–parallel lithium-ion battery pack;Yu;IEEE Trans Intell Transport Syst,2023

2. State of charge estimation for lithium-ion batteries based on cross-domain transfer learning with feedback mechanism;Yang;J Energy Storage,2023

3. Method of site selection and capacity setting for battery energy storage system in distribution networks with renewable energy sources;Peng;Energies,2023

4. State of charge estimation of lithium-ion batteries based on double-coefficient tracking improved square-root unscented Kalman filter;Peng;Batteries,2023

5. Evaluation of the safety standards system of power batteries for electric vehicles in China;Yu;Appl Energy,2023

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