A Technique for Estimating the State of Health of Lithium Batteries Through a Dual-Sliding-Mode Observer

By Il-Song Kim. IEEE transactions on power electronics, 2010

442 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c927de3647041719c7af89579770382c352bcdbe

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In Automotive Engineering

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Citations in the corpus

Advanced Machine Learning Approach for Lithium-Ion Battery State Estimation in Electric Vehicles (2016, methodology): “Several observers, such as slidingmode observer [18], [19], H-infinity observer [20], and adaptive Luenberger observer [21], have also been deployed to estimate the battery SOC.”
Estimation of State of Charge, Unknown Nonlinearities, and State of Health of a Lithium-Ion Battery Based on a Comprehensive Unobservable Model (2014, unknown): “He extended his approach to evaluate the lithium battery SoH by the adaptive estimation of the series capacitance and resistance as the indicators of battery SoH , as reported in [28].”
Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling (2016, background): “Key scenarios comprise dual/joint extended Kalman filtering (EKF) [28], dual sliding-mode observer [29],”
Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression (2018, unknown): “For instance, dual extended Kalman filtering (EKF) in [11]–[13], particle filtering (PF) in [14], dual slide mode observer in [15], and Lyapunovbased adaptive observer in [6] and [7].”
Battery State Estimation for a Single Particle Model With Electrolyte Dynamics (2017, background): “Over the past decade, a variety of ECM-based algorithms have been developed, including linear parameter varying observers [7], sliding-mode observers [8], polynomial chaos [9], neural networks [10], unscented Kalman filters (KFs) [11], adaptive KFs [12], and particle filters [13].”
Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression (2013, background): “There have been several studies which estimated battery SOH using battery impedance or battery capacity [21,22].”
State of Charge Estimation of Vanadium Redox Flow Battery Based on Sliding Mode Observer and Dynamic Model Including Capacity Fading Factor (2017, unknown): “Kim [13] proposed a dual sliding mode observer to estimate capacity fade and resistance deterioration for lithium batteries.”