State of Charge Estimation for Lithium-ion Batteries using Extreme Learning Machine and Extended Kalman Filter-Reference-Cited by-同舟云学术

State of Charge Estimation for Lithium-ion Batteries using Extreme Learning Machine and Extended Kalman Filter

Published:2022 Issue:24 Volume:55 Page:197-202
ISSN:2405-8963
Container-title:IFAC-PapersOnLine
language:en
Short-container-title:IFAC-PapersOnLine

Author:

Ren Zhong,Du Changqing

Publisher

Elsevier BV

Subject

General Medicine

Reference17 articles.

1. Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression;Deng;Energy,2020

2. Extreme learning machine: algorithm, theory and applications;Ding;Artificial Intelligence Review,2015

3. A Lagrange multiplier and sigma point Kalman filter based fused methodology for online state of charge estimation of lithium-ion batteries;Farhaj;Journal of Energy Storage,2021

4. Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm;Hannan;IEEE Access,2018

5. State of charge prediction algorithm of lithium-ion battery based on PSO-SVR cross validation;Li;IEEE Access,2020

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3. SOC Estimation of Vanadium Redox Flow Batteries Based on the ISCSO-ELM Algorithm;ACS Omega;2023-11-22

4. An Improved Whale Optimization Algorithm Based on Nonlinear Convergence Factors and Differential Evolution;2023 42nd Chinese Control Conference (CCC);2023-07-24

5. Non-Linear Kalman Filter Based SOC Estimation of a Lithium-Ion Battery Pack Considering Cell Balancing;2023 5th International Conference on Energy, Power and Environment: Towards Flexible Green Energy Technologies (ICEPE);2023-06-15