Estimation State of Charge for Lithium Battery Based on Adaptive Unscented Kalman Filter-Reference-Cited by-同舟云学术

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Estimation State of Charge for Lithium Battery Based on Adaptive Unscented Kalman Filter

Published:2024-05-07 Issue: Volume: Page:1-6
ISSN:
Container-title:2024 7th International Symposium on Autonomous Systems (ISAS)
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Author:

Dong Zhengqi¹,Xiao Lechun²,Xu Daxing³,Wang Hailun⁴

Affiliation:

1. Hangzhou Dianzi University,College of Automation,Hangzhou,China

2. Zhejiang CITIC Inspection Co., Ltd,Quzhou,China

3. QuZhou University Kerun Intelligent Control Co., Ltd.,School of Electrical and Information Engineering,Quzhou,China

4. QuZhou University,School of Electrical and Information Engineering,Quzhou,China

Funder

Natural Science Foundation of Zhejiang Province

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10552442/10552389/10552565.pdf?arnumber=10552565

Reference13 articles.

1. State of Charge Estimation Model for Lithium-ion battery Based on Deep Learning Neural Networks;Songbo;Engineering Letters,2024

2. Comparison of the SOC Estimation Method for Lithium Battery Based on Simulink

3. Joint SOH-SOC Estimation Model for Lithium-Ion Batteries Based on GWO-BP Neural Network

4. Improved BP neural network algorithm to SOC estimation of mine power supply

5. Study on SOC Estimation of Li-ion Battery Based on the Comparison of UKF Algorithm and AUKF Algorithm

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