An Adaptive Unscented Kalman Filtering Approach for Online Estimation of Model Parameters and State-of-Charge of Lithium-Ion Batteries for Autonomous Mobile Robots-Reference-Cited by-同舟云学术

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An Adaptive Unscented Kalman Filtering Approach for Online Estimation of Model Parameters and State-of-Charge of Lithium-Ion Batteries for Autonomous Mobile Robots

Published:2015-01 Issue:1 Volume:23 Page:357-363
ISSN:1063-6536
Container-title:IEEE Transactions on Control Systems Technology
language:
Short-container-title:IEEE Trans. Contr. Syst. Technol.

Author:

Partovibakhsh Maral,Guangjun Liu

Funder

Natural Sciences and Engineering Research Council of Canada

Canada Research Chair Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/87/6985698/06814843.pdf?arnumber=6814843

Cited by 234 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. Subspace-based noise covariance estimation for Kalman filter in virtual sensing applications;Mechanical Systems and Signal Processing;2025-01

2. A comparative study on state-of-charge estimation for lithium-rich manganese-based battery based on Bayesian filtering and machine learning methods;Energy;2024-10

3. Battery Identification With Cubic Spline and Moving Horizon Estimation for Mobile Robots;IEEE Transactions on Control Systems Technology;2024-09

4. State of Charge Estimation in Batteries for Electric Vehicle Based on Levenberg–Marquardt Algorithm and Kalman Filter;World Electric Vehicle Journal;2024-08-28

5. State of charge estimation of lithium-ion battery based on GA-LSTM and improved IAKF;Applied Energy;2024-08

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