SOC Estimation Method of Lithium-Ion Battery Based on Multi-innovation Adaptive Robust Untraced Kalman Filter Algorithm
Author:
Affiliation:
1. Anhui University of technology,College of electrical and information engineering,Ma’anshan,China
2. Hohai University,College of energy and electrical engineering,Nanjing,China
Funder
Natural Science Foundation of Anhui Province
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10367197/10367198/10367585.pdf?arnumber=10367585
Reference15 articles.
1. A novel model-based state of charge estimation for lithium-ion battery using adaptive robust iterative cubature Kalman filter
2. Review on the Battery Model and SOC Estimation Method
3. Online SOC Estimation Based on Simplified Electrochemical Model for Lithium-Ion Batteries Considering Current Bias
4. Improved SOC estimation of lithium-ion batteries with novel SOC-OCV curve estimation method using equivalent circuit model
5. SOC Estimation for Lithium-Ion Batteries Based on EKF[J];LI;China Mechanical Engineering,2020
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1. Dynamic K-Decay Learning Rate Optimization for Deep Convolutional Neural Network to Estimate the State of Charge for Electric Vehicle Batteries;Energies;2024-08-06
2. Design of Battery Management System Based on FOMIAUKF Algorithm;2024 IEEE 2nd International Conference on Power Science and Technology (ICPST);2024-05-09
3. An Attentional Deep Learning Model Based on Improved Cp Optimizer for State of Health Prediction of Lithium-Ion Batteries;2024
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