A multi‐model real covariance‐based battery state‐of‐charge fusion estimation method for electric vehicles using ordered weighted averaging operator-Reference-Cited by-同舟云学术

A multi‐model real covariance‐based battery state‐of‐charge fusion estimation method for electric vehicles using ordered weighted averaging operator

Published:2022-07-13 Issue:12 Volume:46 Page:17273-17284
ISSN:0363-907X
Container-title:International Journal of Energy Research
language:en
Short-container-title:Intl J of Energy Research

Author:

Tang Aihua¹,Wang Chun²^ORCID,Zhang Dongyang³,Zhang Kaiqing⁴,Zhou Yapeng⁴,Zhang Zhigang¹

Affiliation:

1. School of Vehicle Engineering Chongqing University of Technology Chongqing China

2. School of Mechanical Engineering Sichuan University of Science and Engineering Zigong China

3. School of Mechanical and Vehicle Engineering Chongqing University Chongqing China

4. China Merchants Testing Vehicle Technology Research Institute Co., Ltd. Chongqing China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Chongqing

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.8392

Reference35 articles.

1. Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles

2. Online SOC Estimation Based on Simplified Electrochemical Model for Lithium-Ion Batteries Considering Current Bias

3. A set membership theory based parameter and state of charge co-estimation method for all-climate batteries

4. A Multi-Model Probability Based Two-Layer Fusion Modeling Approach of Supercapacitor for Electric Vehicles

5. A hybrid approach to lithium‐ion battery SOC estimation based on recurrent neural network with gated recurrent unit and Huber‐M robust Kalman filter;Li C;Trans China Electrotech Soc,2020

Cited by 13 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Battery state estimation methods and management system under vehicle–cloud collaboration: A Survey;Renewable and Sustainable Energy Reviews;2024-12

2. 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

3. An improved log-cosine variation slime mold - simplified gated recurrent neural network for the high-precision state of charge estimation of lithium-ion batteries;Journal of Energy Storage;2024-07

4. An improved Cauchy robust correction-sage Husa extended Kalman filtering algorithm for high-precision SOC estimation of Lithium-ion batteries in new energy vehicles;Journal of Energy Storage;2024-05

5. An Improved Self-Adaptive Flower Pollination-Backpropagation Neural Network for the State of Charge Estimation of Lithium-Ion Batteries with Output Sliding Average Window Strategy;Journal of The Electrochemical Society;2024-02-01