Higher Order Sliding-Mode Observers for State-of-Charge and State-of-Health Estimation of Lithium-Ion Batteries
Author:
Affiliation:
1. LUSAC Laboratory, University of Caen, Caen, France
2. Intelligent Robotic and Energy Systems Research Group, Department of Electronics, Carleton University, Ottawa, ON, Canada
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering
Link
http://xplorestaging.ieee.org/ielx7/25/10104191/09970377.pdf?arnumber=9970377
Reference49 articles.
1. The Co-estimation of State of Charge, State of Health, and State of Function for Lithium-Ion Batteries in Electric Vehicles
2. A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges
3. State-of-Charge Estimation and State-of-Health Prediction of a Li-Ion Degraded Battery Based on an EKF Combined With a Per-Unit System
4. Robust Adaptive Sliding-Mode Observer Using RBF Neural Network for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles
5. A comprehensive review of on-board State-of-Available-Power prediction techniques for lithium-ion batteries in electric vehicles
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1. State of Charge Estimation in Batteries for Electric Vehicle Based on Levenberg–Marquardt Algorithm and Kalman Filter;World Electric Vehicle Journal;2024-08-28
2. State-of-charge estimation by extended sliding mode observer based on lithium-ion battery voltage dynamics;Journal of Power Sources;2024-08
3. Research on SOC estimation of lithium-ion batteries based on robust full order proportional integral observer;International Journal of Electrochemical Science;2024-08
4. An Equivalent Siding Mode Observer for Electric Vehicle Lithium Battery SOC Estimation;Journal of The Electrochemical Society;2024-06-03
5. Linear, Nonlinear, and Distributed-Parameter Observers Used for (Renewable) Energy Processes and Systems—An Overview;Energies;2024-06-02
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