State of charge estimation strategy based on fractional-order model

Daniel Ioan Stroe, Jun Qi, Lei Chen, Shunli Wang, Yangtao Wang, Yongcun Fan, Yuyang Liu

Publikation: Bidrag til bog/antologi/rapport/konference proceedingBidrag til bog/antologiForskningpeer review

2 Citationer (Scopus)

Abstract

This chapter designs an adaptive fractional-order extended Kalman filtering algorithm. The combination of this equivalent circuit model and the estimation algorithm achieves accurate prediction of SOC (state of charge) in real time. This chapter first introduces the baseline Kalman filter algorithm and then applies it to the state estimation of lithium-ion batteries; a fractional-order model with a fractional-order algorithm is then designed based on the extended Kalman filtering to achieve the estimation of the battery SOC. A fixed-length memory factor is used in the fractional-order algorithm to reduce the disadvantages of high computational complexity in the fractional-order process to improve the effective use of historical data in the calculation of fractional-order derivatives. Finally, the validation analysis of the model is completed.

OriginalsprogEngelsk
TitelState Estimation Strategies in Lithium-ion Battery Management Systems
RedaktørerShunli Wang, Kailong Liu, Yujie Wang, Daniel-Ioan Stroe, Carlos Fernandez, Josep M. Guerrero
Antal sider16
ForlagElsevier
Publikationsdato2023
Sider191-206
Kapitel10
ISBN (Trykt)9780443161612
ISBN (Elektronisk)9780443161605
DOI
StatusUdgivet - 2023

Bibliografisk note

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© 2023 Elsevier Inc. All rights reserved.

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