The optimization of state of charge and state of health estimation for lithium‐ions battery using combined deep learning and Kalman filter methods-Reference-Cited by-同舟云学术

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The optimization of state of charge and state of health estimation for lithium‐ions battery using combined deep learning and Kalman filter methods

Published:2021-03-08 Issue:7 Volume:45 Page:11206-11230
ISSN:0363-907X
Container-title:International Journal of Energy Research
language:en
Short-container-title:Int J Energy Res

Author:

Shi Yu¹,Ahmad Shakeel¹,Tong Qing²,Lim Tuti M.³,Wei Zhongbao⁴,Ji Dongxu⁵,Eze Chika M.¹^ORCID,Zhao Jiyun¹^ORCID

Affiliation:

1. Department of Mechanical Engineering City University of Hong Kong Kowloon Hong Kong

2. Institute of Energy, Environment, and Economy Tsinghua University Beijing China

3. School of Civil and Environmental Engineering Nanyang Technological University Singapore Singapore

4. National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering Beijing Institute of Technology Beijing China

5. School of Science and Engineering The Chinese University of Hong Kong (Shenzhen) Shenzhen China

Funder

Department of Science and Technology, Ministry of Science and Technology, India

Publisher

Wiley

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/er.6601

Reference67 articles.

1. Comparative study of methods for integrated model identification and state of charge estimation of lithium-ion battery

2. Modelling and experimental evaluation of parallel connected lithium ion cells for an electric vehicle battery system

3. A new method of modeling and state of charge estimation of the battery

4. Online parameters identification and state of charge estimation for lithium‐ion batteries using improved adaptive dual unscented Kalman filter

5. A fractional-order model-based state estimation approach for lithium-ion battery and ultra-capacitor hybrid power source system considering load trajectory

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1. Intelligent recognition of structural health state of EV lithium-ion Battery using transfer learning based on X-ray computed tomography;Reliability Engineering & System Safety;2024-11

2. Modeling liquid immersion-cooling battery thermal management system and optimization via machine learning;International Communications in Heat and Mass Transfer;2024-11

3. Data driven health and life prognosis management of supercapacitor and lithium-ion battery storage systems: Developments, implementation aspects, limitations, and future directions;Journal of Energy Storage;2024-09

4. Advances in battery state estimation of battery management system in electric vehicles;Journal of Power Sources;2024-08

5. Advanced State of Charge Estimation Using Deep Neural Network, Gated Recurrent Unit, and Long Short-Term Memory Models for Lithium-Ion Batteries under Aging and Temperature Conditions;Applied Sciences;2024-07-30

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