A novel practical state of charge estimation method: an adaptive improved ampere‐hour method based on composite correction factor-Reference-Cited by-同舟云学术

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A novel practical state of charge estimation method: an adaptive improved ampere‐hour method based on composite correction factor

Published:2020-08-20 Issue:14 Volume:44 Page:11385-11404
ISSN:0363-907X
Container-title:International Journal of Energy Research
language:en
Short-container-title:Int J Energy Res

Author:

Xiong Xin¹,Wang Shun‐Li¹^ORCID,Fernandez Carlos²,Yu Chun‐Mei¹,Zou Chuan‐Yun¹,Jiang Cong¹^ORCID

Affiliation:

1. School of Information Engineering Southwest University of Science and Technology Mianyang China

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

Funder

National Natural Science Foundation of China

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.5758

Reference48 articles.

1. Design and Experiment of Nonlinear Observer with Adaptive Gains for Battery State of Charge Estimation

2. Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems

3. SOC Estimation of Lithium-Ion Battery Pack Considering Balancing Current

4. Parameters Identification and Sensitive Characteristics Analysis for Lithium-Ion Batteries of Electric Vehicles

5. State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

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2. State of charge estimation for lithium-ion batteries with pre-set convergence time based on a comprehensive unobservable model;Electrochimica Acta;2024-11

3. An improved limited memory-Sage Husa-cubature Kalman filtering algorithm for the state of charge and state of energy co-estimation of lithium-ion batteries based on hysteresis effect-dual polarization model;Energy;2024-10

4. Efficient state of charge estimation of lithium-ion batteries in electric vehicles using evolutionary intelligence-assisted GLA–CNN–Bi-LSTM deep learning model;Heliyon;2024-08

5. Study on the estimation of the state of charge of lithium-ion battery;Electrochimica Acta;2024-07

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