Estimation of State of Charge, Unknown Nonlinearities, and State of Health of a Lithium-Ion Battery Based on a Comprehensive Unobservable Model

By Mehdi Gholizadeh, Farzad Rajaei Salmasi. IEEE transactions on industrial electronics (1982. Print), 2014

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State-of-Charge Estimation for Lithium-Ion Batteries Using a Kalman Filter Based on Local Linearization (2015, methodology): “The optimal estimation algorithm, represented by a Kalman filter, represents an obvious progress in terms of the LIB state estimation [1,2].”
A brief review on key technologies in the battery management system of electric vehicles (2018, methodology): “Then various observers such as particle ﬁ lters [62,63], Kalman ﬁ lters [64 – 66] and sliding mode [67 – 69], are adopted to estimate the capacity and internal resistance during battery operations, thus the SOH can be obtained accordingly.”
A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches (2015, background): “(PMI) observers [68]–[70], adaptive observer [71]–[73], sliding mode observers [74], [75], and descriptor observers [76], [77] are usually utilized for fault estimation/reconstruction.”
Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries (2018, background): “Luenberger observer [6], [10], adaptive observer [6], [11], sliding mode observer [6], [12], [13], and Kalman filters (KFs) [14], [15].”