Online Adaptive Parameter Identification and State-of-Charge Coestimation for Lithium-Polymer Battery Cells

By Habiballah Rahimi-Eichi, Federico Baronti, Mo-Yuen Chow. IEEE transactions on industrial electronics (1982. Print), 2014

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Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries (2019, methodology): “In the case of EECM, the bilinear transformation method was mostly employed by researchers [56,63,132,182,183].”
Power Approaches for Implantable Medical Devices (2015, background): “The recent advances in material research have led to a new class of Li-based batteries, namely flexible Li or Li polymer batteries [77,78].”
State-of-Charge Estimation for Lithium-Ion Batteries Using a Kalman Filter Based on Local Linearization (2015, background): “Furthermore, equivalent model parameter online identification algorithms [14,19] and LIB online state estimation algorithms, which aim to simplify the system structure and improve system operation efficiency, have been widely studied [20], and have also resulted in obvious progress on the basis of a”
Energy Sharing Control Scheme for State-of-Charge Balancing of Distributed Battery Energy Storage System (2015, methodology): “There is a variety of methods to estimate the SOC of a battery, including model-based methods as in [26-27] and impedance-based methods as in [2, 28-29].”
Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine (2016, methodology): “The moving window method can update the dataset for the training of the LSSVM in the iterative process, expressed as (7) [13], [37], [38].”