Real-time battery SOC estimation under hybrid power conditions using fast-OCV curve with unscented Kalman filters
Author:
Affiliation:
1. Shanghai Jiao Tong University,Key Lab. for Power machinery and Engineering of M. O. E.,Shanghai,P R China
2. Universidad Carlos III de Madrid,Intelligent Systems Lab (LSI),Leganés,Madrid
3. Drone Hopper S.L.,Leganés,Madrid
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10189920/10189924/10189947.pdf?arnumber=10189947
Reference19 articles.
1. Comparison Study on the Battery SoC Estimation with EKF and UKF Algorithms
2. Lithium Battery SOC Estimation Based on Improved Unscented Kalman Filter
3. State of charge estimation of li-ion batteries based on the noise-adaptive interacting multiple model
4. SOC estimation of Lithium battery by UKF algorithm based on dynamic parameter model
5. Estimation algorithm research for lithium battery SOC in electric vehicles based on adaptive unscented Kalman filter
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2. A Low-Cost Electrochemical Impedance Spectroscopy-Based Sensor Node for Online Battery Cell Monitoring;2024 IEEE International Workshop on Metrology for Automotive (MetroAutomotive);2024-06-26
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