Improved adaptive feedback particle swarm optimization-multi-innovation singular decomposition unscented Kalman filtering for high accurate state of charge estimation of lithium-ion batteries in energy storage systems-Reference-Cited by-同舟云学术

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Improved adaptive feedback particle swarm optimization-multi-innovation singular decomposition unscented Kalman filtering for high accurate state of charge estimation of lithium-ion batteries in energy storage systems

Published:2024-07-10 Issue:9 Volume:30 Page:5411-5427
ISSN:0947-7047
Container-title:Ionics
language:en
Short-container-title:Ionics

Author:

Li Yang,Wang Shunli,Liu Donglei,Liu Chunmei,Fernandez Carlos,Wang Xiaotian

Funder

National Natural Science Foundation of China

Dazhou City School Cooperation Project

Technopole Talent Summit Project

Sichuan Science and Technology Program

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11581-024-05663-6.pdf

Reference43 articles.

1. Rekioua D (2023) Energy storage systems for photovoltaic and wind systems: a review. Energies 16(9):3893–3918

2. Wei P et al (2023) Progress in energy storage technologies and methods for renewable energy systems application. Applied Sci-Basel 13(9):5626–5649

3. Kumar RR et al (2023) Advances in batteries, battery modeling, battery management system, battery thermal management, SOC, SOH, and charge/discharge characteristics in EV applications. Ieee Access 11:105761–105809

4. Habib AKMA et al (2023) Lithium-ion battery management system for electric vehicles: constraints, challenges, and recommendations. Batteries-Basel 9(3):152–175

5. Liu Y et al (2021) State of charge estimation of lithium-ion batteries based on temporal convolutional network and transfer learning. Ieee Access 9:34177–34187

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