Improved Fixed Range Forgetting Factor-Adaptive Extended Kalman Filtering (FRFF-AEKF) Algorithm for the State of Charge Estimation of High-Power Lithium-Ion Batteries

Bobobee, Etse Dablu; Wang, Shunli; Zou, Chuanyun; Appiah, Emmanuel; Zhou, Heng; Takyi-Aninakwa, Paul; Haque, Md Amdadul

Improved Fixed Range Forgetting Factor-Adaptive Extended Kalman Filtering (FRFF-AEKF) Algorithm for the State of Charge Estimation of High-Power Lithium-Ion Batteries

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posted on 2024-06-03, 02:41 authored by Etse Dablu Bobobee, Shunli Wang, Chuanyun Zou, Emmanuel Appiah, Heng Zhou, Paul Takyi-Aninakwa, Md Amdadul Haque

Improved Fixed Range Forgetting Factor-Adaptive Extended Kalman Filtering (FRFF-AEKF) Algorithm for the State of Charge Estimation of High-Power Lithium-Ion Batteries

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DOI - Is version of https://doi.org/10.20964/2022.11.46

Location

Amsterdam, The Netherlands

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Journal

International Journal of Electrochemical Science

Volume

17

Article number

221146

Pagination

1-19

ISSN

1452-3981

eISSN

1452-3981

Issue

11

Publisher

Elsevier

Publication URL

http://dx.doi.org/10.20964/2022.11.46

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Keywords

adaptive extended Kalman filtering algorithm CHALLENGES ELECTRIC VEHICLES Electrochemistry fixed range forgetting factor HEALTH ESTIMATION Lithium-ion batteries MODEL Physical Sciences Science & Technology SOC ESTIMATION state of charge Thevenin equivalent model

Improved Fixed Range Forgetting Factor-Adaptive Extended Kalman Filtering (FRFF-AEKF) Algorithm for the State of Charge Estimation of High-Power Lithium-Ion Batteries

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