Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries-Reference-Cited by-同舟云学术

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Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries

Published:2019-10 Issue: Volume:113 Page:109233
ISSN:1364-0321
Container-title:Renewable and Sustainable Energy Reviews
language:en
Short-container-title:Renewable and Sustainable Energy Reviews

Author:

Shrivastava Prashant,Soon Tey Kok,Idris Mohd Yamani Idna Bin,Mekhilef Saad

Funder

US Office of Naval Research Global NICOP research

Publisher

Elsevier BV

Subject

Renewable Energy, Sustainability and the Environment

Reference234 articles.

1. Global EV outlook 2018,2018

2. The lithium-ion battery: state of the art and future perspectives;Zubi;Renew Sustain Energy Rev,2018

3. The environmental impact of Li-Ion batteries and the role of key parameters – a review;Peters;Renew Sustain Energy Rev,2017

4. Review of energy storage systems for electric vehicle applications: issues and challenges;Hannan;Renew Sustain Energy Rev,2017

5. A materials perspective on Li-ion batteries at extreme temperatures;Kalaga;Nat Energy,2017

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1. A hybrid model for state of charge estimation of lithium-ion batteries utilizing improved adaptive extended Kalman filter and long short-term memory neural network;Journal of Power Sources;2024-11

2. A novel preheating systems for columnar lithium batteries for below zero degrees celsius environment based on topology optimization;International Communications in Heat and Mass Transfer;2024-11

3. ProADD: Proactive battery anomaly dual detection leveraging denoising convolutional autoencoder and incremental voltage analysis;Applied Energy;2024-11

4. Target decomposition-led light-weighted offline training strategy-aided data-driven state-of-charge online estimation during constant current charging conditions over battery entire lifespan;Energy;2024-10

5. Lifecycle battery carbon footprint analysis for battery sustainability with energy digitalization and artificial intelligence;Applied Energy;2024-10

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