A novel collaborative multiscale weighting factor‐adaptive Kalman filtering method for the time‐varying whole‐life‐cycle state of charge estimation of lithium‐ion batteries-Reference-Cited by-同舟云学术

A novel collaborative multiscale weighting factor‐adaptive Kalman filtering method for the time‐varying whole‐life‐cycle state of charge estimation of lithium‐ion batteries

Published:2022-01-23 Issue:6 Volume:46 Page:7704-7721
ISSN:0363-907X
Container-title:International Journal of Energy Research
language:en
Short-container-title:Intl J of Energy Research

Author:

Wang Shunli¹^ORCID,Fan Yongcun¹,Yu Chunmei¹,Jin Siyu²^ORCID,Takyi‐Aninakwa Paul¹,Fernandez Carlos³,Stroe Daniel‐Ioan²

Affiliation:

1. School of Information Engineering & Robot Technology used for Special Environment Key Laboratory of Sichuan Province Southwest University of Science and Technology Mianyang China

2. Department of Energy Technology Aalborg University Aalborg East Denmark

3. School of Pharmacy and Life Sciences Robert Gordon University Aberdeen UK

Funder

National Natural Science Foundation of China

China Scholarship Council

Publisher

Hindawi Limited

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.7672

Reference55 articles.

1. A novel state of charge estimation method for lithium‐ion batteries based on bias compensation;Ouyang TC;Energy,2021

2. Combined State of Charge and State of Energy Estimation of Lithium-Ion Battery Using Dual Forgetting Factor-Based Adaptive Extended Kalman Filter for Electric Vehicle Applications

3. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models

4. Stateof charge estimation framework for lithium‐ion batteries based on square root cubature Kalman filter under wide operation temperature range

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

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